AER/0000755000176200001440000000000013616730105010663 5ustar liggesusersAER/NAMESPACE0000644000176200001440000000273113561127502012105 0ustar liggesusers## imports import("stats") import("Formula") import("zoo") importFrom("lmtest", "coeftest", "waldtest", "waldtest.default", "lrtest", "lrtest.default") importFrom("car", "linearHypothesis") importFrom("sandwich", "bread", "estfun") importFrom("survival", "Surv", "survreg", "survreg.distributions") ## exported functions of AER export( "dispersiontest", "tobit", "ivreg", "ivreg.fit" ) ## methods for class tobit S3method("print", "tobit") S3method("print", "summary.tobit") S3method("summary", "tobit") S3method("formula", "tobit") S3method("update", "tobit") S3method("model.frame", "tobit") S3method("waldtest", "tobit") S3method("lrtest", "tobit") S3method("linearHypothesis", "tobit") ## methods for class tobit that could also be ## inherited from survival >= 3.1-6 S3method("bread", "tobit") S3method("vcov", "tobit") S3method("fitted", "tobit") S3method("nobs", "tobit") S3method("weights", "tobit") ## methods for class ivreg S3method("print", "ivreg") S3method("print", "summary.ivreg") S3method("summary", "ivreg") S3method("vcov", "ivreg") S3method("bread", "ivreg") S3method("estfun", "ivreg") S3method("hatvalues", "ivreg") S3method("predict", "ivreg") S3method("anova", "ivreg") S3method("terms", "ivreg") S3method("model.matrix", "ivreg") S3method("update", "ivreg") ## methods for class survreg S3method("deviance", "survreg") ## methods for class multinom, polr, fitdistr S3method("coeftest", "multinom") S3method("coeftest", "polr") S3method("lrtest", "fitdistr") AER/demo/0000755000176200001440000000000013165152422011606 5ustar liggesusersAER/demo/Ch-LinearRegression.R0000644000176200001440000004652713463421674015563 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: data-journals ################################################### data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations summary(journals) ################################################### ### chunk number 3: linreg-plot eval=FALSE ################################################### ## plot(log(subs) ~ log(citeprice), data = journals) ## jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) ## abline(jour_lm) ################################################### ### chunk number 4: linreg-plot1 ################################################### plot(log(subs) ~ log(citeprice), data = journals) jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) abline(jour_lm) ################################################### ### chunk number 5: linreg-class ################################################### class(jour_lm) ################################################### ### chunk number 6: linreg-names ################################################### names(jour_lm) ################################################### ### chunk number 7: linreg-summary ################################################### summary(jour_lm) ################################################### ### chunk number 8: linreg-summary ################################################### jour_slm <- summary(jour_lm) class(jour_slm) names(jour_slm) ################################################### ### chunk number 9: linreg-coef ################################################### jour_slm$coefficients ################################################### ### chunk number 10: linreg-anova ################################################### anova(jour_lm) ################################################### ### chunk number 11: journals-coef ################################################### coef(jour_lm) ################################################### ### chunk number 12: journals-confint ################################################### confint(jour_lm, level = 0.95) ################################################### ### chunk number 13: journals-predict ################################################### predict(jour_lm, newdata = data.frame(citeprice = 2.11), interval = "confidence") predict(jour_lm, newdata = data.frame(citeprice = 2.11), interval = "prediction") ################################################### ### chunk number 14: predict-plot eval=FALSE ################################################### ## lciteprice <- seq(from = -6, to = 4, by = 0.25) ## jour_pred <- predict(jour_lm, interval = "prediction", ## newdata = data.frame(citeprice = exp(lciteprice))) ## plot(log(subs) ~ log(citeprice), data = journals) ## lines(jour_pred[, 1] ~ lciteprice, col = 1) ## lines(jour_pred[, 2] ~ lciteprice, col = 1, lty = 2) ## lines(jour_pred[, 3] ~ lciteprice, col = 1, lty = 2) ################################################### ### chunk number 15: predict-plot1 ################################################### lciteprice <- seq(from = -6, to = 4, by = 0.25) jour_pred <- predict(jour_lm, interval = "prediction", newdata = data.frame(citeprice = exp(lciteprice))) plot(log(subs) ~ log(citeprice), data = journals) lines(jour_pred[, 1] ~ lciteprice, col = 1) lines(jour_pred[, 2] ~ lciteprice, col = 1, lty = 2) lines(jour_pred[, 3] ~ lciteprice, col = 1, lty = 2) ################################################### ### chunk number 16: journals-plot eval=FALSE ################################################### ## par(mfrow = c(2, 2)) ## plot(jour_lm) ## par(mfrow = c(1, 1)) ################################################### ### chunk number 17: journals-plot1 ################################################### par(mfrow = c(2, 2)) plot(jour_lm) par(mfrow = c(1, 1)) ################################################### ### chunk number 18: journal-lht ################################################### linearHypothesis(jour_lm, "log(citeprice) = -0.5") ################################################### ### chunk number 19: CPS-data ################################################### data("CPS1988") summary(CPS1988) ################################################### ### chunk number 20: CPS-base ################################################### cps_lm <- lm(log(wage) ~ experience + I(experience^2) + education + ethnicity, data = CPS1988) ################################################### ### chunk number 21: CPS-visualization-unused eval=FALSE ################################################### ## ex <- 0:56 ## ed <- with(CPS1988, tapply(education, ## list(ethnicity, experience), mean))[, as.character(ex)] ## fm <- cps_lm ## wago <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) ## wagb <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "afam", education = as.numeric(ed["afam",]))) ## plot(log(wage) ~ experience, data = CPS1988, pch = ".", ## col = as.numeric(ethnicity)) ## lines(ex, wago) ## lines(ex, wagb, col = 2) ################################################### ### chunk number 22: CPS-summary ################################################### summary(cps_lm) ################################################### ### chunk number 23: CPS-noeth ################################################### cps_noeth <- lm(log(wage) ~ experience + I(experience^2) + education, data = CPS1988) anova(cps_noeth, cps_lm) ################################################### ### chunk number 24: CPS-anova ################################################### anova(cps_lm) ################################################### ### chunk number 25: CPS-noeth2 eval=FALSE ################################################### ## cps_noeth <- update(cps_lm, formula = . ~ . - ethnicity) ################################################### ### chunk number 26: CPS-waldtest ################################################### waldtest(cps_lm, . ~ . - ethnicity) ################################################### ### chunk number 27: CPS-spline ################################################### library("splines") cps_plm <- lm(log(wage) ~ bs(experience, df = 5) + education + ethnicity, data = CPS1988) ################################################### ### chunk number 28: CPS-spline-summary eval=FALSE ################################################### ## summary(cps_plm) ################################################### ### chunk number 29: CPS-BIC ################################################### cps_bs <- lapply(3:10, function(i) lm(log(wage) ~ bs(experience, df = i) + education + ethnicity, data = CPS1988)) structure(sapply(cps_bs, AIC, k = log(nrow(CPS1988))), .Names = 3:10) ################################################### ### chunk number 30: plm-plot eval=FALSE ################################################### ## cps <- data.frame(experience = -2:60, education = ## with(CPS1988, mean(education[ethnicity == "cauc"])), ## ethnicity = "cauc") ## cps$yhat1 <- predict(cps_lm, newdata = cps) ## cps$yhat2 <- predict(cps_plm, newdata = cps) ## ## plot(log(wage) ~ jitter(experience, factor = 3), pch = 19, ## col = rgb(0.5, 0.5, 0.5, alpha = 0.02), data = CPS1988) ## lines(yhat1 ~ experience, data = cps, lty = 2) ## lines(yhat2 ~ experience, data = cps) ## legend("topleft", c("quadratic", "spline"), lty = c(2,1), ## bty = "n") ################################################### ### chunk number 31: plm-plot1 ################################################### cps <- data.frame(experience = -2:60, education = with(CPS1988, mean(education[ethnicity == "cauc"])), ethnicity = "cauc") cps$yhat1 <- predict(cps_lm, newdata = cps) cps$yhat2 <- predict(cps_plm, newdata = cps) plot(log(wage) ~ jitter(experience, factor = 3), pch = 19, col = rgb(0.5, 0.5, 0.5, alpha = 0.02), data = CPS1988) lines(yhat1 ~ experience, data = cps, lty = 2) lines(yhat2 ~ experience, data = cps) legend("topleft", c("quadratic", "spline"), lty = c(2,1), bty = "n") ################################################### ### chunk number 32: CPS-int ################################################### cps_int <- lm(log(wage) ~ experience + I(experience^2) + education * ethnicity, data = CPS1988) coeftest(cps_int) ################################################### ### chunk number 33: CPS-int2 eval=FALSE ################################################### ## cps_int <- lm(log(wage) ~ experience + I(experience^2) + ## education + ethnicity + education:ethnicity, ## data = CPS1988) ################################################### ### chunk number 34: CPS-sep ################################################### cps_sep <- lm(log(wage) ~ ethnicity / (experience + I(experience^2) + education) - 1, data = CPS1988) ################################################### ### chunk number 35: CPS-sep-coef ################################################### cps_sep_cf <- matrix(coef(cps_sep), nrow = 2) rownames(cps_sep_cf) <- levels(CPS1988$ethnicity) colnames(cps_sep_cf) <- names(coef(cps_lm))[1:4] cps_sep_cf ################################################### ### chunk number 36: CPS-sep-anova ################################################### anova(cps_sep, cps_lm) ################################################### ### chunk number 37: CPS-sep-visualization-unused eval=FALSE ################################################### ## ex <- 0:56 ## ed <- with(CPS1988, tapply(education, list(ethnicity, ## experience), mean))[, as.character(ex)] ## fm <- cps_lm ## wago <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) ## wagb <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "afam", education = as.numeric(ed["afam",]))) ## plot(log(wage) ~ jitter(experience, factor = 2), ## data = CPS1988, pch = ".", col = as.numeric(ethnicity)) ## ## ## plot(log(wage) ~ as.factor(experience), data = CPS1988, ## pch = ".") ## lines(ex, wago, lwd = 2) ## lines(ex, wagb, col = 2, lwd = 2) ## fm <- cps_sep ## wago <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) ## wagb <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "afam", education = as.numeric(ed["afam",]))) ## lines(ex, wago, lty = 2, lwd = 2) ## lines(ex, wagb, col = 2, lty = 2, lwd = 2) ################################################### ### chunk number 38: CPS-region ################################################### CPS1988$region <- relevel(CPS1988$region, ref = "south") cps_region <- lm(log(wage) ~ ethnicity + education + experience + I(experience^2) + region, data = CPS1988) coef(cps_region) ################################################### ### chunk number 39: wls1 ################################################### jour_wls1 <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice^2) ################################################### ### chunk number 40: wls2 ################################################### jour_wls2 <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice) ################################################### ### chunk number 41: journals-wls1 eval=FALSE ################################################### ## plot(log(subs) ~ log(citeprice), data = journals) ## abline(jour_lm) ## abline(jour_wls1, lwd = 2, lty = 2) ## abline(jour_wls2, lwd = 2, lty = 3) ## legend("bottomleft", c("OLS", "WLS1", "WLS2"), ## lty = 1:3, lwd = 2, bty = "n") ################################################### ### chunk number 42: journals-wls11 ################################################### plot(log(subs) ~ log(citeprice), data = journals) abline(jour_lm) abline(jour_wls1, lwd = 2, lty = 2) abline(jour_wls2, lwd = 2, lty = 3) legend("bottomleft", c("OLS", "WLS1", "WLS2"), lty = 1:3, lwd = 2, bty = "n") ################################################### ### chunk number 43: fgls1 ################################################### auxreg <- lm(log(residuals(jour_lm)^2) ~ log(citeprice), data = journals) jour_fgls1 <- lm(log(subs) ~ log(citeprice), weights = 1/exp(fitted(auxreg)), data = journals) ################################################### ### chunk number 44: fgls2 ################################################### gamma2i <- coef(auxreg)[2] gamma2 <- 0 while(abs((gamma2i - gamma2)/gamma2) > 1e-7) { gamma2 <- gamma2i fglsi <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice^gamma2) gamma2i <- coef(lm(log(residuals(fglsi)^2) ~ log(citeprice), data = journals))[2] } jour_fgls2 <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice^gamma2) ################################################### ### chunk number 45: fgls2-coef ################################################### coef(jour_fgls2) ################################################### ### chunk number 46: journals-fgls ################################################### plot(log(subs) ~ log(citeprice), data = journals) abline(jour_lm) abline(jour_fgls2, lty = 2, lwd = 2) ################################################### ### chunk number 47: usmacro-plot eval=FALSE ################################################### ## data("USMacroG") ## plot(USMacroG[, c("dpi", "consumption")], lty = c(3, 1), ## plot.type = "single", ylab = "") ## legend("topleft", legend = c("income", "consumption"), ## lty = c(3, 1), bty = "n") ################################################### ### chunk number 48: usmacro-plot1 ################################################### data("USMacroG") plot(USMacroG[, c("dpi", "consumption")], lty = c(3, 1), plot.type = "single", ylab = "") legend("topleft", legend = c("income", "consumption"), lty = c(3, 1), bty = "n") ################################################### ### chunk number 49: usmacro-fit ################################################### library("dynlm") cons_lm1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG) cons_lm2 <- dynlm(consumption ~ dpi + L(consumption), data = USMacroG) ################################################### ### chunk number 50: usmacro-summary1 ################################################### summary(cons_lm1) ################################################### ### chunk number 51: usmacro-summary2 ################################################### summary(cons_lm2) ################################################### ### chunk number 52: dynlm-plot eval=FALSE ################################################### ## plot(merge(as.zoo(USMacroG[,"consumption"]), fitted(cons_lm1), ## fitted(cons_lm2), 0, residuals(cons_lm1), ## residuals(cons_lm2)), screens = rep(1:2, c(3, 3)), ## lty = rep(1:3, 2), ylab = c("Fitted values", "Residuals"), ## xlab = "Time", main = "") ## legend(0.05, 0.95, c("observed", "cons_lm1", "cons_lm2"), ## lty = 1:3, bty = "n") ################################################### ### chunk number 53: dynlm-plot1 ################################################### plot(merge(as.zoo(USMacroG[,"consumption"]), fitted(cons_lm1), fitted(cons_lm2), 0, residuals(cons_lm1), residuals(cons_lm2)), screens = rep(1:2, c(3, 3)), lty = rep(1:3, 2), ylab = c("Fitted values", "Residuals"), xlab = "Time", main = "") legend(0.05, 0.95, c("observed", "cons_lm1", "cons_lm2"), lty = 1:3, bty = "n") ################################################### ### chunk number 54: encompassing1 ################################################### cons_lmE <- dynlm(consumption ~ dpi + L(dpi) + L(consumption), data = USMacroG) ################################################### ### chunk number 55: encompassing2 ################################################### anova(cons_lm1, cons_lmE, cons_lm2) ################################################### ### chunk number 56: encompassing3 ################################################### encomptest(cons_lm1, cons_lm2) ################################################### ### chunk number 57: pdata.frame ################################################### data("Grunfeld", package = "AER") library("plm") gr <- subset(Grunfeld, firm %in% c("General Electric", "General Motors", "IBM")) pgr <- pdata.frame(gr, index = c("firm", "year")) ################################################### ### chunk number 58: plm-pool ################################################### gr_pool <- plm(invest ~ value + capital, data = pgr, model = "pooling") ################################################### ### chunk number 59: plm-FE ################################################### gr_fe <- plm(invest ~ value + capital, data = pgr, model = "within") summary(gr_fe) ################################################### ### chunk number 60: plm-pFtest ################################################### pFtest(gr_fe, gr_pool) ################################################### ### chunk number 61: plm-RE ################################################### gr_re <- plm(invest ~ value + capital, data = pgr, model = "random", random.method = "walhus") summary(gr_re) ################################################### ### chunk number 62: plm-plmtest ################################################### plmtest(gr_pool) ################################################### ### chunk number 63: plm-phtest ################################################### phtest(gr_re, gr_fe) ################################################### ### chunk number 64: EmplUK-data ################################################### data("EmplUK", package = "plm") ################################################### ### chunk number 65: plm-AB ################################################### empl_ab <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + lag(log(output), 0:1) | lag(log(emp), 2:99), data = EmplUK, index = c("firm", "year"), effect = "twoways", model = "twosteps") ################################################### ### chunk number 66: plm-AB-summary ################################################### summary(empl_ab) ################################################### ### chunk number 67: systemfit ################################################### library("systemfit") gr2 <- subset(Grunfeld, firm %in% c("Chrysler", "IBM")) pgr2 <- pdata.frame(gr2, c("firm", "year")) ################################################### ### chunk number 68: SUR ################################################### gr_sur <- systemfit(invest ~ value + capital, method = "SUR", data = pgr2) summary(gr_sur, residCov = FALSE, equations = FALSE) ################################################### ### chunk number 69: nlme eval=FALSE ################################################### ## library("nlme") ## g1 <- subset(Grunfeld, firm == "Westinghouse") ## gls(invest ~ value + capital, data = g1, correlation = corAR1()) AER/demo/Ch-Microeconometrics.R0000644000176200001440000003056413461527024015760 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: swisslabor-data ################################################### data("SwissLabor") swiss_probit <- glm(participation ~ . + I(age^2), data = SwissLabor, family = binomial(link = "probit")) summary(swiss_probit) ################################################### ### chunk number 3: swisslabor-plot eval=FALSE ################################################### ## plot(participation ~ age, data = SwissLabor, ylevels = 2:1) ################################################### ### chunk number 4: swisslabor-plot-refined ################################################### plot(participation ~ education, data = SwissLabor, ylevels = 2:1) fm <- glm(participation ~ education + I(education^2), data = SwissLabor, family = binomial) edu <- sort(unique(SwissLabor$education)) prop <- sapply(edu, function(x) mean(SwissLabor$education <= x)) lines(predict(fm, newdata = data.frame(education = edu), type = "response") ~ prop, col = 2) plot(participation ~ age, data = SwissLabor, ylevels = 2:1) fm <- glm(participation ~ age + I(age^2), data = SwissLabor, family = binomial) ag <- sort(unique(SwissLabor$age)) prop <- sapply(ag, function(x) mean(SwissLabor$age <= x)) lines(predict(fm, newdata = data.frame(age = ag), type = "response") ~ prop, col = 2) ################################################### ### chunk number 5: effects1 ################################################### fav <- mean(dnorm(predict(swiss_probit, type = "link"))) fav * coef(swiss_probit) ################################################### ### chunk number 6: effects2 ################################################### av <- colMeans(SwissLabor[, -c(1, 7)]) av <- data.frame(rbind(swiss = av, foreign = av), foreign = factor(c("no", "yes"))) av <- predict(swiss_probit, newdata = av, type = "link") av <- dnorm(av) av["swiss"] * coef(swiss_probit)[-7] ################################################### ### chunk number 7: effects3 ################################################### av["foreign"] * coef(swiss_probit)[-7] ################################################### ### chunk number 8: mcfadden ################################################### swiss_probit0 <- update(swiss_probit, formula = . ~ 1) 1 - as.vector(logLik(swiss_probit)/logLik(swiss_probit0)) ################################################### ### chunk number 9: confusion-matrix ################################################### table(true = SwissLabor$participation, pred = round(fitted(swiss_probit))) ################################################### ### chunk number 10: confusion-matrix1 ################################################### tab <- table(true = SwissLabor$participation, pred = round(fitted(swiss_probit))) tabp <- round(100 * c(tab[1,1] + tab[2,2], tab[2,1] + tab[1,2])/sum(tab), digits = 2) ################################################### ### chunk number 11: roc-plot eval=FALSE ################################################### ## library("ROCR") ## pred <- prediction(fitted(swiss_probit), ## SwissLabor$participation) ## plot(performance(pred, "acc")) ## plot(performance(pred, "tpr", "fpr")) ## abline(0, 1, lty = 2) ################################################### ### chunk number 12: roc-plot1 ################################################### library("ROCR") pred <- prediction(fitted(swiss_probit), SwissLabor$participation) plot(performance(pred, "acc")) plot(performance(pred, "tpr", "fpr")) abline(0, 1, lty = 2) ################################################### ### chunk number 13: rss ################################################### deviance(swiss_probit) sum(residuals(swiss_probit, type = "deviance")^2) sum(residuals(swiss_probit, type = "pearson")^2) ################################################### ### chunk number 14: coeftest eval=FALSE ################################################### ## coeftest(swiss_probit, vcov = sandwich) ################################################### ### chunk number 15: murder ################################################### data("MurderRates") murder_logit <- glm(I(executions > 0) ~ time + income + noncauc + lfp + southern, data = MurderRates, family = binomial) ################################################### ### chunk number 16: murder-coeftest ################################################### coeftest(murder_logit) ################################################### ### chunk number 17: murder2 ################################################### murder_logit2 <- glm(I(executions > 0) ~ time + income + noncauc + lfp + southern, data = MurderRates, family = binomial, control = list(epsilon = 1e-15, maxit = 50, trace = FALSE)) ################################################### ### chunk number 18: murder2-coeftest ################################################### coeftest(murder_logit2) ################################################### ### chunk number 19: separation ################################################### table(I(MurderRates$executions > 0), MurderRates$southern) ################################################### ### chunk number 20: countreg-pois ################################################### data("RecreationDemand") rd_pois <- glm(trips ~ ., data = RecreationDemand, family = poisson) ################################################### ### chunk number 21: countreg-pois-coeftest ################################################### coeftest(rd_pois) ################################################### ### chunk number 22: countreg-pois-logLik ################################################### logLik(rd_pois) ################################################### ### chunk number 23: countreg-odtest1 ################################################### dispersiontest(rd_pois) ################################################### ### chunk number 24: countreg-odtest2 ################################################### dispersiontest(rd_pois, trafo = 2) ################################################### ### chunk number 25: countreg-nbin ################################################### library("MASS") rd_nb <- glm.nb(trips ~ ., data = RecreationDemand) coeftest(rd_nb) logLik(rd_nb) ################################################### ### chunk number 26: countreg-se ################################################### round(sqrt(rbind(diag(vcov(rd_pois)), diag(sandwich(rd_pois)))), digits = 3) ################################################### ### chunk number 27: countreg-sandwich ################################################### coeftest(rd_pois, vcov = sandwich) ################################################### ### chunk number 28: countreg-OPG ################################################### round(sqrt(diag(vcovOPG(rd_pois))), 3) ################################################### ### chunk number 29: countreg-plot ################################################### plot(table(RecreationDemand$trips), ylab = "") ################################################### ### chunk number 30: countreg-zeros ################################################### rbind(obs = table(RecreationDemand$trips)[1:10], exp = round( sapply(0:9, function(x) sum(dpois(x, fitted(rd_pois)))))) ################################################### ### chunk number 31: countreg-pscl ################################################### library("pscl") ################################################### ### chunk number 32: countreg-zinb ################################################### rd_zinb <- zeroinfl(trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") ################################################### ### chunk number 33: countreg-zinb-summary ################################################### summary(rd_zinb) ################################################### ### chunk number 34: countreg-zinb-expected ################################################### round(colSums(predict(rd_zinb, type = "prob")[,1:10])) ################################################### ### chunk number 35: countreg-hurdle ################################################### rd_hurdle <- hurdle(trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") summary(rd_hurdle) ################################################### ### chunk number 36: countreg-hurdle-expected ################################################### round(colSums(predict(rd_hurdle, type = "prob")[,1:10])) ################################################### ### chunk number 37: tobit1 ################################################### data("Affairs") aff_tob <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) summary(aff_tob) ################################################### ### chunk number 38: tobit2 ################################################### aff_tob2 <- update(aff_tob, right = 4) summary(aff_tob2) ################################################### ### chunk number 39: tobit3 ################################################### linearHypothesis(aff_tob, c("age = 0", "occupation = 0"), vcov = sandwich) ################################################### ### chunk number 40: numeric-response ################################################### SwissLabor$partnum <- as.numeric(SwissLabor$participation) - 1 ################################################### ### chunk number 41: kleinspady eval=FALSE ################################################### ## library("np") ## swiss_bw <- npindexbw(partnum ~ income + age + education + ## youngkids + oldkids + foreign + I(age^2), data = SwissLabor, ## method = "kleinspady", nmulti = 5) ################################################### ### chunk number 42: kleinspady-bw eval=FALSE ################################################### ## summary(swiss_bw) ################################################### ### chunk number 43: kleinspady-summary eval=FALSE ################################################### ## swiss_ks <- npindex(bws = swiss_bw, gradients = TRUE) ## summary(swiss_ks) ################################################### ### chunk number 44: probit-confusion ################################################### table(Actual = SwissLabor$participation, Predicted = round(predict(swiss_probit, type = "response"))) ################################################### ### chunk number 45: bw-tab ################################################### data("BankWages") edcat <- factor(BankWages$education) levels(edcat)[3:10] <- rep(c("14-15", "16-18", "19-21"), c(2, 3, 3)) tab <- xtabs(~ edcat + job, data = BankWages) prop.table(tab, 1) ################################################### ### chunk number 46: bw-plot eval=FALSE ################################################### ## plot(job ~ edcat, data = BankWages, off = 0) ################################################### ### chunk number 47: bw-plot1 ################################################### plot(job ~ edcat, data = BankWages, off = 0) box() ################################################### ### chunk number 48: bw-multinom ################################################### library("nnet") bank_mnl <- multinom(job ~ education + minority, data = BankWages, subset = gender == "male", trace = FALSE) ################################################### ### chunk number 49: bw-multinom-coeftest ################################################### coeftest(bank_mnl) ################################################### ### chunk number 50: bw-polr ################################################### library("MASS") bank_polr <- polr(job ~ education + minority, data = BankWages, subset = gender == "male", Hess = TRUE) coeftest(bank_polr) ################################################### ### chunk number 51: bw-AIC ################################################### AIC(bank_mnl) AIC(bank_polr) AER/demo/Ch-Basics.R0000644000176200001440000005165013461527000013471 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: calc1 ################################################### 1 + 1 2^3 ################################################### ### chunk number 3: calc2 ################################################### log(exp(sin(pi/4)^2) * exp(cos(pi/4)^2)) ################################################### ### chunk number 4: vec1 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) ################################################### ### chunk number 5: length ################################################### length(x) ################################################### ### chunk number 6: vec2 ################################################### 2 * x + 3 5:1 * x + 1:5 ################################################### ### chunk number 7: vec3 ################################################### log(x) ################################################### ### chunk number 8: subset1 ################################################### x[c(1, 4)] ################################################### ### chunk number 9: subset2 ################################################### x[-c(2, 3, 5)] ################################################### ### chunk number 10: pattern1 ################################################### ones <- rep(1, 10) even <- seq(from = 2, to = 20, by = 2) trend <- 1981:2005 ################################################### ### chunk number 11: pattern2 ################################################### c(ones, even) ################################################### ### chunk number 12: matrix1 ################################################### A <- matrix(1:6, nrow = 2) ################################################### ### chunk number 13: matrix2 ################################################### t(A) ################################################### ### chunk number 14: matrix3 ################################################### dim(A) nrow(A) ncol(A) ################################################### ### chunk number 15: matrix-subset ################################################### A1 <- A[1:2, c(1, 3)] ################################################### ### chunk number 16: matrix4 ################################################### solve(A1) ################################################### ### chunk number 17: matrix-solve ################################################### A1 %*% solve(A1) ################################################### ### chunk number 18: diag ################################################### diag(4) ################################################### ### chunk number 19: matrix-combine1 ################################################### cbind(1, A1) ################################################### ### chunk number 20: matrix-combine2 ################################################### rbind(A1, diag(4, 2)) ################################################### ### chunk number 21: vector-mode ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) ################################################### ### chunk number 22: logical ################################################### x > 3.5 ################################################### ### chunk number 23: names ################################################### names(x) <- c("a", "b", "c", "d", "e") x ################################################### ### chunk number 24: subset-more ################################################### x[3:5] x[c("c", "d", "e")] x[x > 3.5] ################################################### ### chunk number 25: list1 ################################################### mylist <- list(sample = rnorm(5), family = "normal distribution", parameters = list(mean = 0, sd = 1)) mylist ################################################### ### chunk number 26: list2 ################################################### mylist[[1]] mylist[["sample"]] mylist$sample ################################################### ### chunk number 27: list3 ################################################### mylist[[3]]$sd ################################################### ### chunk number 28: logical2 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) x > 3 & x <= 4 ################################################### ### chunk number 29: logical3 ################################################### which(x > 3 & x <= 4) ################################################### ### chunk number 30: logical4 ################################################### all(x > 3) any(x > 3) ################################################### ### chunk number 31: logical5 ################################################### (1.5 - 0.5) == 1 (1.9 - 0.9) == 1 ################################################### ### chunk number 32: logical6 ################################################### all.equal(1.9 - 0.9, 1) ################################################### ### chunk number 33: logical7 ################################################### 7 + TRUE ################################################### ### chunk number 34: coercion1 ################################################### is.numeric(x) is.character(x) as.character(x) ################################################### ### chunk number 35: coercion2 ################################################### c(1, "a") ################################################### ### chunk number 36: rng1 ################################################### set.seed(123) rnorm(2) rnorm(2) set.seed(123) rnorm(2) ################################################### ### chunk number 37: rng2 ################################################### sample(1:5) sample(c("male", "female"), size = 5, replace = TRUE, prob = c(0.2, 0.8)) ################################################### ### chunk number 38: flow1 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) if(rnorm(1) > 0) sum(x) else mean(x) ################################################### ### chunk number 39: flow2 ################################################### ifelse(x > 4, sqrt(x), x^2) ################################################### ### chunk number 40: flow3 ################################################### for(i in 2:5) { x[i] <- x[i] - x[i-1] } x[-1] ################################################### ### chunk number 41: flow4 ################################################### while(sum(x) < 100) { x <- 2 * x } x ################################################### ### chunk number 42: cmeans ################################################### cmeans <- function(X) { rval <- rep(0, ncol(X)) for(j in 1:ncol(X)) { mysum <- 0 for(i in 1:nrow(X)) mysum <- mysum + X[i,j] rval[j] <- mysum/nrow(X) } return(rval) } ################################################### ### chunk number 43: colmeans1 ################################################### X <- matrix(1:20, ncol = 2) cmeans(X) ################################################### ### chunk number 44: colmeans2 ################################################### colMeans(X) ################################################### ### chunk number 45: colmeans3 ################################################### X <- matrix(rnorm(2*10^6), ncol = 2) system.time(colMeans(X)) system.time(cmeans(X)) ################################################### ### chunk number 46: colmeans4 ################################################### cmeans2 <- function(X) { rval <- rep(0, ncol(X)) for(j in 1:ncol(X)) rval[j] <- mean(X[,j]) return(rval) } ################################################### ### chunk number 47: colmeans5 ################################################### system.time(cmeans2(X)) ################################################### ### chunk number 48: colmeans6 eval=FALSE ################################################### ## apply(X, 2, mean) ################################################### ### chunk number 49: colmeans7 ################################################### system.time(apply(X, 2, mean)) ################################################### ### chunk number 50: formula1 ################################################### f <- y ~ x class(f) ################################################### ### chunk number 51: formula2 ################################################### x <- seq(from = 0, to = 10, by = 0.5) y <- 2 + 3 * x + rnorm(21) ################################################### ### chunk number 52: formula3 eval=FALSE ################################################### ## plot(y ~ x) ## lm(y ~ x) ################################################### ### chunk number 53: formula3a ################################################### print(lm(y ~ x)) ################################################### ### chunk number 54: formula3b ################################################### plot(y ~ x) ################################################### ### chunk number 55: formula3c ################################################### fm <- lm(y ~ x) ################################################### ### chunk number 56: mydata1 ################################################### mydata <- data.frame(one = 1:10, two = 11:20, three = 21:30) ################################################### ### chunk number 57: mydata1a ################################################### mydata <- as.data.frame(matrix(1:30, ncol = 3)) names(mydata) <- c("one", "two", "three") ################################################### ### chunk number 58: mydata2 ################################################### mydata$two mydata[, "two"] mydata[, 2] ################################################### ### chunk number 59: attach ################################################### attach(mydata) mean(two) detach(mydata) ################################################### ### chunk number 60: with ################################################### with(mydata, mean(two)) ################################################### ### chunk number 61: mydata-subset ################################################### mydata.sub <- subset(mydata, two <= 16, select = -two) ################################################### ### chunk number 62: write-table ################################################### write.table(mydata, file = "mydata.txt", col.names = TRUE) ################################################### ### chunk number 63: read-table ################################################### newdata <- read.table("mydata.txt", header = TRUE) ################################################### ### chunk number 64: save ################################################### save(mydata, file = "mydata.rda") ################################################### ### chunk number 65: load ################################################### load("mydata.rda") ################################################### ### chunk number 66: file-remove ################################################### file.remove("mydata.rda") ################################################### ### chunk number 67: data ################################################### data("Journals", package = "AER") ################################################### ### chunk number 68: foreign ################################################### library("foreign") write.dta(mydata, file = "mydata.dta") ################################################### ### chunk number 69: read-dta ################################################### mydata <- read.dta("mydata.dta") ################################################### ### chunk number 70: cleanup ################################################### file.remove("mydata.dta") ################################################### ### chunk number 71: factor ################################################### g <- rep(0:1, c(2, 4)) g <- factor(g, levels = 0:1, labels = c("male", "female")) g ################################################### ### chunk number 72: na1 ################################################### newdata <- read.table("mydata.txt", na.strings = "-999") ################################################### ### chunk number 73: na2 ################################################### file.remove("mydata.txt") ################################################### ### chunk number 74: oop1 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) g <- factor(rep(c(0, 1), c(2, 4)), levels = c(0, 1), labels = c("male", "female")) ################################################### ### chunk number 75: oop2 ################################################### summary(x) summary(g) ################################################### ### chunk number 76: oop3 ################################################### class(x) class(g) ################################################### ### chunk number 77: oop4 ################################################### summary ################################################### ### chunk number 78: oop5 ################################################### normsample <- function(n, ...) { rval <- rnorm(n, ...) class(rval) <- "normsample" return(rval) } ################################################### ### chunk number 79: oop6 ################################################### set.seed(123) x <- normsample(10, mean = 5) class(x) ################################################### ### chunk number 80: oop7 ################################################### summary.normsample <- function(object, ...) { rval <- c(length(object), mean(object), sd(object)) names(rval) <- c("sample size","mean","standard deviation") return(rval) } ################################################### ### chunk number 81: oop8 ################################################### summary(x) ################################################### ### chunk number 82: journals-data eval=FALSE ################################################### ## data("Journals") ## Journals$citeprice <- Journals$price/Journals$citations ## attach(Journals) ## plot(log(subs), log(citeprice)) ## rug(log(subs)) ## rug(log(citeprice), side = 2) ## detach(Journals) ################################################### ### chunk number 83: journals-data1 ################################################### data("Journals") Journals$citeprice <- Journals$price/Journals$citations attach(Journals) plot(log(subs), log(citeprice)) rug(log(subs)) rug(log(citeprice), side = 2) detach(Journals) ################################################### ### chunk number 84: plot-formula ################################################### plot(log(subs) ~ log(citeprice), data = Journals) ################################################### ### chunk number 85: graphics1 ################################################### plot(log(subs) ~ log(citeprice), data = Journals, pch = 20, col = "blue", ylim = c(0, 8), xlim = c(-7, 4), main = "Library subscriptions") ################################################### ### chunk number 86: graphics2 ################################################### pdf("myfile.pdf", height = 5, width = 6) plot(1:20, pch = 1:20, col = 1:20, cex = 2) dev.off() ################################################### ### chunk number 87: dnorm-annotate eval=FALSE ################################################### ## curve(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, ## main = "Density of the standard normal distribution") ## text(-5, 0.3, expression(f(x) == frac(1, sigma ~~ ## sqrt(2*pi)) ~~ e^{-frac((x - mu)^2, 2*sigma^2)}), adj = 0) ################################################### ### chunk number 88: dnorm-annotate1 ################################################### curve(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, main = "Density of the standard normal distribution") text(-5, 0.3, expression(f(x) == frac(1, sigma ~~ sqrt(2*pi)) ~~ e^{-frac((x - mu)^2, 2*sigma^2)}), adj = 0) ################################################### ### chunk number 89: eda1 ################################################### data("CPS1985") str(CPS1985) ################################################### ### chunk number 90: eda2 ################################################### head(CPS1985) ################################################### ### chunk number 91: eda3 ################################################### levels(CPS1985$occupation)[c(2, 6)] <- c("techn", "mgmt") attach(CPS1985) ################################################### ### chunk number 92: eda4 ################################################### summary(wage) ################################################### ### chunk number 93: eda5 ################################################### mean(wage) median(wage) ################################################### ### chunk number 94: eda6 ################################################### var(wage) sd(wage) ################################################### ### chunk number 95: wage-hist ################################################### hist(wage, freq = FALSE) hist(log(wage), freq = FALSE) lines(density(log(wage)), col = 4) ################################################### ### chunk number 96: wage-hist1 ################################################### hist(wage, freq = FALSE) hist(log(wage), freq = FALSE) lines(density(log(wage)), col = 4) ################################################### ### chunk number 97: occ-table ################################################### summary(occupation) ################################################### ### chunk number 98: occ-table ################################################### tab <- table(occupation) prop.table(tab) ################################################### ### chunk number 99: occ-barpie ################################################### barplot(tab) pie(tab) ################################################### ### chunk number 100: occ-barpie ################################################### par(mar = c(4, 3, 1, 1)) barplot(tab, las = 3) par(mar = c(2, 3, 1, 3)) pie(tab, radius = 1) ################################################### ### chunk number 101: xtabs ################################################### xtabs(~ gender + occupation, data = CPS1985) ################################################### ### chunk number 102: spine eval=FALSE ################################################### ## plot(gender ~ occupation, data = CPS1985) ################################################### ### chunk number 103: spine1 ################################################### plot(gender ~ occupation, data = CPS1985) ################################################### ### chunk number 104: wageeduc-cor ################################################### cor(log(wage), education) cor(log(wage), education, method = "spearman") ################################################### ### chunk number 105: wageeduc-scatter eval=FALSE ################################################### ## plot(log(wage) ~ education) ################################################### ### chunk number 106: wageeduc-scatter1 ################################################### plot(log(wage) ~ education) ################################################### ### chunk number 107: tapply ################################################### tapply(log(wage), gender, mean) ################################################### ### chunk number 108: boxqq1 eval=FALSE ################################################### ## plot(log(wage) ~ gender) ################################################### ### chunk number 109: boxqq2 eval=FALSE ################################################### ## mwage <- subset(CPS1985, gender == "male")$wage ## fwage <- subset(CPS1985, gender == "female")$wage ## qqplot(mwage, fwage, xlim = range(wage), ylim = range(wage), ## xaxs = "i", yaxs = "i", xlab = "male", ylab = "female") ## abline(0, 1) ################################################### ### chunk number 110: qq ################################################### plot(log(wage) ~ gender) mwage <- subset(CPS1985, gender == "male")$wage fwage <- subset(CPS1985, gender == "female")$wage qqplot(mwage, fwage, xlim = range(wage), ylim = range(wage), xaxs = "i", yaxs = "i", xlab = "male", ylab = "female") abline(0, 1) ################################################### ### chunk number 111: detach ################################################### detach(CPS1985) AER/demo/Ch-Intro.R0000644000176200001440000001443213461527012013360 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: journals-data ################################################### data("Journals", package = "AER") ################################################### ### chunk number 3: journals-dim ################################################### dim(Journals) names(Journals) ################################################### ### chunk number 4: journals-plot eval=FALSE ################################################### ## plot(log(subs) ~ log(price/citations), data = Journals) ################################################### ### chunk number 5: journals-lm eval=FALSE ################################################### ## j_lm <- lm(log(subs) ~ log(price/citations), data = Journals) ## abline(j_lm) ################################################### ### chunk number 6: journals-lmplot ################################################### plot(log(subs) ~ log(price/citations), data = Journals) j_lm <- lm(log(subs) ~ log(price/citations), data = Journals) abline(j_lm) ################################################### ### chunk number 7: journals-lm-summary ################################################### summary(j_lm) ################################################### ### chunk number 8: cps-data ################################################### data("CPS1985", package = "AER") cps <- CPS1985 ################################################### ### chunk number 9: cps-data1 eval=FALSE ################################################### ## data("CPS1985", package = "AER") ## cps <- CPS1985 ################################################### ### chunk number 10: cps-reg ################################################### library("quantreg") cps_lm <- lm(log(wage) ~ experience + I(experience^2) + education, data = cps) cps_rq <- rq(log(wage) ~ experience + I(experience^2) + education, data = cps, tau = seq(0.2, 0.8, by = 0.15)) ################################################### ### chunk number 11: cps-predict ################################################### cps2 <- data.frame(education = mean(cps$education), experience = min(cps$experience):max(cps$experience)) cps2 <- cbind(cps2, predict(cps_lm, newdata = cps2, interval = "prediction")) cps2 <- cbind(cps2, predict(cps_rq, newdata = cps2, type = "")) ################################################### ### chunk number 12: rq-plot eval=FALSE ################################################### ## plot(log(wage) ~ experience, data = cps) ## for(i in 6:10) lines(cps2[,i] ~ experience, ## data = cps2, col = "red") ################################################### ### chunk number 13: rq-plot1 ################################################### plot(log(wage) ~ experience, data = cps) for(i in 6:10) lines(cps2[,i] ~ experience, data = cps2, col = "red") ################################################### ### chunk number 14: srq-plot eval=FALSE ################################################### ## plot(summary(cps_rq)) ################################################### ### chunk number 15: srq-plot1 ################################################### plot(summary(cps_rq)) ################################################### ### chunk number 16: bkde-fit ################################################### library("KernSmooth") cps_bkde <- bkde2D(cbind(cps$experience, log(cps$wage)), bandwidth = c(3.5, 0.5), gridsize = c(200, 200)) ################################################### ### chunk number 17: bkde-plot eval=FALSE ################################################### ## image(cps_bkde$x1, cps_bkde$x2, cps_bkde$fhat, ## col = rev(gray.colors(10, gamma = 1)), ## xlab = "experience", ylab = "log(wage)") ## box() ## lines(fit ~ experience, data = cps2) ## lines(lwr ~ experience, data = cps2, lty = 2) ## lines(upr ~ experience, data = cps2, lty = 2) ################################################### ### chunk number 18: bkde-plot1 ################################################### image(cps_bkde$x1, cps_bkde$x2, cps_bkde$fhat, col = rev(gray.colors(10, gamma = 1)), xlab = "experience", ylab = "log(wage)") box() lines(fit ~ experience, data = cps2) lines(lwr ~ experience, data = cps2, lty = 2) lines(upr ~ experience, data = cps2, lty = 2) ################################################### ### chunk number 19: install eval=FALSE ################################################### ## install.packages("AER") ################################################### ### chunk number 20: library ################################################### library("AER") ################################################### ### chunk number 21: objects ################################################### objects() ################################################### ### chunk number 22: search ################################################### search() ################################################### ### chunk number 23: assignment ################################################### x <- 2 objects() ################################################### ### chunk number 24: remove ################################################### remove(x) objects() ################################################### ### chunk number 25: log eval=FALSE ################################################### ## log(16, 2) ## log(x = 16, 2) ## log(16, base = 2) ## log(base = 2, x = 16) ################################################### ### chunk number 26: q eval=FALSE ################################################### ## q() ################################################### ### chunk number 27: apropos ################################################### apropos("help") AER/demo/Ch-TimeSeries.R0000644000176200001440000003171313461527040014340 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: options ################################################### options(digits = 6) ################################################### ### chunk number 3: ts-plot eval=FALSE ################################################### ## data("UKNonDurables") ## plot(UKNonDurables) ################################################### ### chunk number 4: UKNonDurables-data ################################################### data("UKNonDurables") ################################################### ### chunk number 5: tsp ################################################### tsp(UKNonDurables) ################################################### ### chunk number 6: window ################################################### window(UKNonDurables, end = c(1956, 4)) ################################################### ### chunk number 7: filter eval=FALSE ################################################### ## data("UKDriverDeaths") ## plot(UKDriverDeaths) ## lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), ## col = 2) ################################################### ### chunk number 8: ts-plot1 ################################################### data("UKNonDurables") plot(UKNonDurables) data("UKDriverDeaths") plot(UKDriverDeaths) lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), col = 2) ################################################### ### chunk number 9: filter1 eval=FALSE ################################################### ## data("UKDriverDeaths") ## plot(UKDriverDeaths) ## lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), ## col = 2) ################################################### ### chunk number 10: rollapply ################################################### plot(rollapply(UKDriverDeaths, 12, sd)) ################################################### ### chunk number 11: ar-sim ################################################### set.seed(1234) x <- filter(rnorm(100), 0.9, method = "recursive") ################################################### ### chunk number 12: decompose ################################################### dd_dec <- decompose(log(UKDriverDeaths)) dd_stl <- stl(log(UKDriverDeaths), s.window = 13) ################################################### ### chunk number 13: decompose-components ################################################### plot(dd_dec$trend, ylab = "trend") lines(dd_stl$time.series[,"trend"], lty = 2, lwd = 2) ################################################### ### chunk number 14: seat-mean-sd ################################################### plot(dd_dec$trend, ylab = "trend") lines(dd_stl$time.series[,"trend"], lty = 2, lwd = 2) plot(rollapply(UKDriverDeaths, 12, sd)) ################################################### ### chunk number 15: stl ################################################### plot(dd_stl) ################################################### ### chunk number 16: Holt-Winters ################################################### dd_past <- window(UKDriverDeaths, end = c(1982, 12)) dd_hw <- HoltWinters(dd_past) dd_pred <- predict(dd_hw, n.ahead = 24) ################################################### ### chunk number 17: Holt-Winters-plot ################################################### plot(dd_hw, dd_pred, ylim = range(UKDriverDeaths)) lines(UKDriverDeaths) ################################################### ### chunk number 18: Holt-Winters-plot1 ################################################### plot(dd_hw, dd_pred, ylim = range(UKDriverDeaths)) lines(UKDriverDeaths) ################################################### ### chunk number 19: acf eval=FALSE ################################################### ## acf(x) ## pacf(x) ################################################### ### chunk number 20: acf1 ################################################### acf(x, ylim = c(-0.2, 1)) pacf(x, ylim = c(-0.2, 1)) ################################################### ### chunk number 21: ar ################################################### ar(x) ################################################### ### chunk number 22: window-non-durab ################################################### nd <- window(log(UKNonDurables), end = c(1970, 4)) ################################################### ### chunk number 23: non-durab-acf ################################################### acf(diff(nd), ylim = c(-1, 1)) pacf(diff(nd), ylim = c(-1, 1)) acf(diff(diff(nd, 4)), ylim = c(-1, 1)) pacf(diff(diff(nd, 4)), ylim = c(-1, 1)) ################################################### ### chunk number 24: non-durab-acf1 ################################################### acf(diff(nd), ylim = c(-1, 1)) pacf(diff(nd), ylim = c(-1, 1)) acf(diff(diff(nd, 4)), ylim = c(-1, 1)) pacf(diff(diff(nd, 4)), ylim = c(-1, 1)) ################################################### ### chunk number 25: arima-setup ################################################### nd_pars <- expand.grid(ar = 0:2, diff = 1, ma = 0:2, sar = 0:1, sdiff = 1, sma = 0:1) nd_aic <- rep(0, nrow(nd_pars)) for(i in seq(along = nd_aic)) nd_aic[i] <- AIC(arima(nd, unlist(nd_pars[i, 1:3]), unlist(nd_pars[i, 4:6])), k = log(length(nd))) nd_pars[which.min(nd_aic),] ################################################### ### chunk number 26: arima ################################################### nd_arima <- arima(nd, order = c(0,1,1), seasonal = c(0,1,1)) nd_arima ################################################### ### chunk number 27: tsdiag ################################################### tsdiag(nd_arima) ################################################### ### chunk number 28: tsdiag1 ################################################### tsdiag(nd_arima) ################################################### ### chunk number 29: arima-predict ################################################### nd_pred <- predict(nd_arima, n.ahead = 18 * 4) ################################################### ### chunk number 30: arima-compare ################################################### plot(log(UKNonDurables)) lines(nd_pred$pred, col = 2) ################################################### ### chunk number 31: arima-compare1 ################################################### plot(log(UKNonDurables)) lines(nd_pred$pred, col = 2) ################################################### ### chunk number 32: pepper ################################################### data("PepperPrice") plot(PepperPrice, plot.type = "single", col = 1:2) legend("topleft", c("black", "white"), bty = "n", col = 1:2, lty = rep(1,2)) ################################################### ### chunk number 33: pepper1 ################################################### data("PepperPrice") plot(PepperPrice, plot.type = "single", col = 1:2) legend("topleft", c("black", "white"), bty = "n", col = 1:2, lty = rep(1,2)) ################################################### ### chunk number 34: adf1 ################################################### library("tseries") adf.test(log(PepperPrice[, "white"])) ################################################### ### chunk number 35: adf1 ################################################### adf.test(diff(log(PepperPrice[, "white"]))) ################################################### ### chunk number 36: pp ################################################### pp.test(log(PepperPrice[, "white"]), type = "Z(t_alpha)") ################################################### ### chunk number 37: urca eval=FALSE ################################################### ## library("urca") ## pepper_ers <- ur.ers(log(PepperPrice[, "white"]), ## type = "DF-GLS", model = "const", lag.max = 4) ## summary(pepper_ers) ################################################### ### chunk number 38: kpss ################################################### kpss.test(log(PepperPrice[, "white"])) ################################################### ### chunk number 39: po ################################################### po.test(log(PepperPrice)) ################################################### ### chunk number 40: joh-trace ################################################### library("urca") pepper_jo <- ca.jo(log(PepperPrice), ecdet = "const", type = "trace") summary(pepper_jo) ################################################### ### chunk number 41: joh-lmax eval=FALSE ################################################### ## pepper_jo2 <- ca.jo(log(PepperPrice), ecdet = "const", type = "eigen") ## summary(pepper_jo2) ################################################### ### chunk number 42: dynlm-by-hand ################################################### dd <- log(UKDriverDeaths) dd_dat <- ts.intersect(dd, dd1 = lag(dd, k = -1), dd12 = lag(dd, k = -12)) lm(dd ~ dd1 + dd12, data = dd_dat) ################################################### ### chunk number 43: dynlm ################################################### library("dynlm") dynlm(dd ~ L(dd) + L(dd, 12)) ################################################### ### chunk number 44: efp ################################################### library("strucchange") dd_ocus <- efp(dd ~ dd1 + dd12, data = dd_dat, type = "OLS-CUSUM") ################################################### ### chunk number 45: efp-test ################################################### sctest(dd_ocus) ################################################### ### chunk number 46: efp-plot eval=FALSE ################################################### ## plot(dd_ocus) ################################################### ### chunk number 47: Fstats ################################################### dd_fs <- Fstats(dd ~ dd1 + dd12, data = dd_dat, from = 0.1) plot(dd_fs) sctest(dd_fs) ################################################### ### chunk number 48: ocus-supF ################################################### plot(dd_ocus) plot(dd_fs, main = "supF test") ################################################### ### chunk number 49: GermanM1 ################################################### data("GermanM1") LTW <- dm ~ dy2 + dR + dR1 + dp + m1 + y1 + R1 + season ################################################### ### chunk number 50: re eval=FALSE ################################################### ## m1_re <- efp(LTW, data = GermanM1, type = "RE") ## plot(m1_re) ################################################### ### chunk number 51: re1 ################################################### m1_re <- efp(LTW, data = GermanM1, type = "RE") plot(m1_re) ################################################### ### chunk number 52: dating ################################################### dd_bp <- breakpoints(dd ~ dd1 + dd12, data = dd_dat, h = 0.1) ################################################### ### chunk number 53: dating-coef ################################################### coef(dd_bp, breaks = 2) ################################################### ### chunk number 54: dating-plot eval=FALSE ################################################### ## plot(dd) ## lines(fitted(dd_bp, breaks = 2), col = 4) ## lines(confint(dd_bp, breaks = 2)) ################################################### ### chunk number 55: dating-plot1 ################################################### plot(dd_bp, legend = FALSE, main = "") plot(dd) lines(fitted(dd_bp, breaks = 2), col = 4) lines(confint(dd_bp, breaks = 2)) ################################################### ### chunk number 56: StructTS ################################################### dd_struct <- StructTS(log(UKDriverDeaths)) ################################################### ### chunk number 57: StructTS-plot eval=FALSE ################################################### ## plot(cbind(fitted(dd_struct), residuals(dd_struct))) ################################################### ### chunk number 58: StructTS-plot1 ################################################### dd_struct_plot <- cbind(fitted(dd_struct), residuals = residuals(dd_struct)) colnames(dd_struct_plot) <- c("level", "slope", "season", "residuals") plot(dd_struct_plot, main = "") ################################################### ### chunk number 59: garch-plot ################################################### data("MarkPound") plot(MarkPound, main = "") ################################################### ### chunk number 60: garch ################################################### mp <- garch(MarkPound, grad = "numerical", trace = FALSE) summary(mp) AER/demo/Ch-Programming.R0000644000176200001440000001645513461527032014560 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: DGP ################################################### dgp <- function(nobs = 15, model = c("trend", "dynamic"), corr = 0, coef = c(0.25, -0.75), sd = 1) { model <- match.arg(model) coef <- rep(coef, length.out = 2) err <- as.vector(filter(rnorm(nobs, sd = sd), corr, method = "recursive")) if(model == "trend") { x <- 1:nobs y <- coef[1] + coef[2] * x + err } else { y <- rep(NA, nobs) y[1] <- coef[1] + err[1] for(i in 2:nobs) y[i] <- coef[1] + coef[2] * y[i-1] + err[i] x <- c(0, y[1:(nobs-1)]) } return(data.frame(y = y, x = x)) } ################################################### ### chunk number 3: simpower ################################################### simpower <- function(nrep = 100, size = 0.05, ...) { pval <- matrix(rep(NA, 2 * nrep), ncol = 2) colnames(pval) <- c("dwtest", "bgtest") for(i in 1:nrep) { dat <- dgp(...) pval[i,1] <- dwtest(y ~ x, data = dat, alternative = "two.sided")$p.value pval[i,2] <- bgtest(y ~ x, data = dat)$p.value } return(colMeans(pval < size)) } ################################################### ### chunk number 4: simulation-function ################################################### simulation <- function(corr = c(0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99), nobs = c(15, 30, 50), model = c("trend", "dynamic"), ...) { prs <- expand.grid(corr = corr, nobs = nobs, model = model) nprs <- nrow(prs) pow <- matrix(rep(NA, 2 * nprs), ncol = 2) for(i in 1:nprs) pow[i,] <- simpower(corr = prs[i,1], nobs = prs[i,2], model = as.character(prs[i,3]), ...) rval <- rbind(prs, prs) rval$test <- factor(rep(1:2, c(nprs, nprs)), labels = c("dwtest", "bgtest")) rval$power <- c(pow[,1], pow[,2]) rval$nobs <- factor(rval$nobs) return(rval) } ################################################### ### chunk number 5: simulation ################################################### set.seed(123) psim <- simulation() ################################################### ### chunk number 6: simulation-table ################################################### tab <- xtabs(power ~ corr + test + model + nobs, data = psim) ftable(tab, row.vars = c("model", "nobs", "test"), col.vars = "corr") ################################################### ### chunk number 7: simulation-visualization ################################################### library("lattice") xyplot(power ~ corr | model + nobs, groups = ~ test, data = psim, type = "b") ################################################### ### chunk number 8: simulation-visualization1 ################################################### library("lattice") trellis.par.set(theme = canonical.theme(color = FALSE)) print(xyplot(power ~ corr | model + nobs, groups = ~ test, data = psim, type = "b")) ################################################### ### chunk number 9: journals-lm ################################################### data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) ################################################### ### chunk number 10: journals-residuals-based-resampling-unused eval=FALSE ################################################### ## refit <- function(data, i) { ## d <- data ## d$subs <- exp(d$fitted + d$res[i]) ## coef(lm(log(subs) ~ log(citeprice), data = d)) ## } ################################################### ### chunk number 11: journals-case-based-resampling ################################################### refit <- function(data, i) coef(lm(log(subs) ~ log(citeprice), data = data[i,])) ################################################### ### chunk number 12: journals-boot ################################################### library("boot") set.seed(123) jour_boot <- boot(journals, refit, R = 999) ################################################### ### chunk number 13: journals-boot-print ################################################### jour_boot ################################################### ### chunk number 14: journals-lm-coeftest ################################################### coeftest(jour_lm) ################################################### ### chunk number 15: journals-boot-ci ################################################### boot.ci(jour_boot, index = 2, type = "basic") ################################################### ### chunk number 16: journals-lm-ci ################################################### confint(jour_lm, parm = 2) ################################################### ### chunk number 17: ml-loglik ################################################### data("Equipment", package = "AER") nlogL <- function(par) { beta <- par[1:3] theta <- par[4] sigma2 <- par[5] Y <- with(Equipment, valueadded/firms) K <- with(Equipment, capital/firms) L <- with(Equipment, labor/firms) rhs <- beta[1] + beta[2] * log(K) + beta[3] * log(L) lhs <- log(Y) + theta * Y rval <- sum(log(1 + theta * Y) - log(Y) + dnorm(lhs, mean = rhs, sd = sqrt(sigma2), log = TRUE)) return(-rval) } ################################################### ### chunk number 18: ml-0 ################################################### fm0 <- lm(log(valueadded/firms) ~ log(capital/firms) + log(labor/firms), data = Equipment) ################################################### ### chunk number 19: ml-0-coef ################################################### par0 <- as.vector(c(coef(fm0), 0, mean(residuals(fm0)^2))) ################################################### ### chunk number 20: ml-optim ################################################### opt <- optim(par0, nlogL, hessian = TRUE) ################################################### ### chunk number 21: ml-optim-output ################################################### opt$par sqrt(diag(solve(opt$hessian)))[1:4] -opt$value ################################################### ### chunk number 22: Sweave eval=FALSE ################################################### ## Sweave("Sweave-journals.Rnw") ################################################### ### chunk number 23: Stangle eval=FALSE ################################################### ## Stangle("Sweave-journals.Rnw") ################################################### ### chunk number 24: texi2dvi eval=FALSE ################################################### ## texi2dvi("Sweave-journals.tex", pdf = TRUE) ################################################### ### chunk number 25: vignette eval=FALSE ################################################### ## vignette("Sweave-journals", package = "AER") AER/demo/00Index0000644000176200001440000000052711354653130012744 0ustar liggesusersCh-Intro Chapter 1: Introduction Ch-Basics Chapter 2: Basics Ch-LinearRegression Chapter 3: Linear Regression Ch-Validation Chapter 4: Diagnostics and Alternative Methods of Regression Ch-Microeconometrics Chapter 5: Models of Microeconometrics Ch-TimeSeries Chapter 6: Time Series Ch-Programming Chapter 7: Programming Your Own Analysis AER/demo/Ch-Validation.R0000644000176200001440000002577513461527045014401 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: ps-summary ################################################### data("PublicSchools") summary(PublicSchools) ################################################### ### chunk number 3: ps-plot eval=FALSE ################################################### ## ps <- na.omit(PublicSchools) ## ps$Income <- ps$Income / 10000 ## plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) ## ps_lm <- lm(Expenditure ~ Income, data = ps) ## abline(ps_lm) ## id <- c(2, 24, 48) ## text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 4: ps-plot1 ################################################### ps <- na.omit(PublicSchools) ps$Income <- ps$Income / 10000 plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) ps_lm <- lm(Expenditure ~ Income, data = ps) abline(ps_lm) id <- c(2, 24, 48) text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 5: ps-lmplot eval=FALSE ################################################### ## plot(ps_lm, which = 1:6) ################################################### ### chunk number 6: ps-lmplot1 ################################################### plot(ps_lm, which = 1:6) ################################################### ### chunk number 7: ps-hatvalues eval=FALSE ################################################### ## ps_hat <- hatvalues(ps_lm) ## plot(ps_hat) ## abline(h = c(1, 3) * mean(ps_hat), col = 2) ## id <- which(ps_hat > 3 * mean(ps_hat)) ## text(id, ps_hat[id], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 8: ps-hatvalues1 ################################################### ps_hat <- hatvalues(ps_lm) plot(ps_hat) abline(h = c(1, 3) * mean(ps_hat), col = 2) id <- which(ps_hat > 3 * mean(ps_hat)) text(id, ps_hat[id], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 9: influence-measures1 eval=FALSE ################################################### ## influence.measures(ps_lm) ################################################### ### chunk number 10: which-hatvalues ################################################### which(ps_hat > 3 * mean(ps_hat)) ################################################### ### chunk number 11: influence-measures2 ################################################### summary(influence.measures(ps_lm)) ################################################### ### chunk number 12: ps-noinf eval=FALSE ################################################### ## plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) ## abline(ps_lm) ## id <- which(apply(influence.measures(ps_lm)$is.inf, 1, any)) ## text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ## ps_noinf <- lm(Expenditure ~ Income, data = ps[-id,]) ## abline(ps_noinf, lty = 2) ################################################### ### chunk number 13: ps-noinf1 ################################################### plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) abline(ps_lm) id <- which(apply(influence.measures(ps_lm)$is.inf, 1, any)) text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ps_noinf <- lm(Expenditure ~ Income, data = ps[-id,]) abline(ps_noinf, lty = 2) ################################################### ### chunk number 14: journals-age ################################################### data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations journals$age <- 2000 - Journals$foundingyear ################################################### ### chunk number 15: journals-lm ################################################### jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) ################################################### ### chunk number 16: bptest1 ################################################### bptest(jour_lm) ################################################### ### chunk number 17: bptest2 ################################################### bptest(jour_lm, ~ log(citeprice) + I(log(citeprice)^2), data = journals) ################################################### ### chunk number 18: gqtest ################################################### gqtest(jour_lm, order.by = ~ citeprice, data = journals) ################################################### ### chunk number 19: resettest ################################################### resettest(jour_lm) ################################################### ### chunk number 20: raintest ################################################### raintest(jour_lm, order.by = ~ age, data = journals) ################################################### ### chunk number 21: harvtest ################################################### harvtest(jour_lm, order.by = ~ age, data = journals) ################################################### ### chunk number 22: ################################################### library("dynlm") ################################################### ### chunk number 23: usmacro-dynlm ################################################### data("USMacroG") consump1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG) ################################################### ### chunk number 24: dwtest ################################################### dwtest(consump1) ################################################### ### chunk number 25: Box-test ################################################### Box.test(residuals(consump1), type = "Ljung-Box") ################################################### ### chunk number 26: bgtest ################################################### bgtest(consump1) ################################################### ### chunk number 27: vcov ################################################### vcov(jour_lm) vcovHC(jour_lm) ################################################### ### chunk number 28: coeftest ################################################### coeftest(jour_lm, vcov = vcovHC) ################################################### ### chunk number 29: sandwiches ################################################### t(sapply(c("const", "HC0", "HC1", "HC2", "HC3", "HC4"), function(x) sqrt(diag(vcovHC(jour_lm, type = x))))) ################################################### ### chunk number 30: ps-anova ################################################### ps_lm <- lm(Expenditure ~ Income, data = ps) ps_lm2 <- lm(Expenditure ~ Income + I(Income^2), data = ps) anova(ps_lm, ps_lm2) ################################################### ### chunk number 31: ps-waldtest ################################################### waldtest(ps_lm, ps_lm2, vcov = vcovHC(ps_lm2, type = "HC4")) ################################################### ### chunk number 32: vcovHAC ################################################### rbind(SE = sqrt(diag(vcov(consump1))), QS = sqrt(diag(kernHAC(consump1))), NW = sqrt(diag(NeweyWest(consump1)))) ################################################### ### chunk number 33: solow-lm ################################################### data("OECDGrowth") solow_lm <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth + .05), data = OECDGrowth) summary(solow_lm) ################################################### ### chunk number 34: solow-plot eval=FALSE ################################################### ## plot(solow_lm) ################################################### ### chunk number 35: solow-lts ################################################### library("MASS") solow_lts <- lqs(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth + .05), data = OECDGrowth, psamp = 13, nsamp = "exact") ################################################### ### chunk number 36: solow-smallresid ################################################### smallresid <- which( abs(residuals(solow_lts)/solow_lts$scale[2]) <= 2.5) ################################################### ### chunk number 37: solow-nohighlev ################################################### X <- model.matrix(solow_lm)[,-1] Xcv <- cov.rob(X, nsamp = "exact") nohighlev <- which( sqrt(mahalanobis(X, Xcv$center, Xcv$cov)) <= 2.5) ################################################### ### chunk number 38: solow-goodobs ################################################### goodobs <- unique(c(smallresid, nohighlev)) ################################################### ### chunk number 39: solow-badobs ################################################### rownames(OECDGrowth)[-goodobs] ################################################### ### chunk number 40: solow-rob ################################################### solow_rob <- update(solow_lm, subset = goodobs) summary(solow_rob) ################################################### ### chunk number 41: quantreg ################################################### library("quantreg") ################################################### ### chunk number 42: cps-lad ################################################### library("quantreg") data("CPS1988") cps_f <- log(wage) ~ experience + I(experience^2) + education cps_lad <- rq(cps_f, data = CPS1988) summary(cps_lad) ################################################### ### chunk number 43: cps-rq ################################################### cps_rq <- rq(cps_f, tau = c(0.25, 0.75), data = CPS1988) summary(cps_rq) ################################################### ### chunk number 44: cps-rqs ################################################### cps_rq25 <- rq(cps_f, tau = 0.25, data = CPS1988) cps_rq75 <- rq(cps_f, tau = 0.75, data = CPS1988) anova(cps_rq25, cps_rq75) ################################################### ### chunk number 45: cps-rq-anova ################################################### anova(cps_rq25, cps_rq75, joint = FALSE) ################################################### ### chunk number 46: rqbig ################################################### cps_rqbig <- rq(cps_f, tau = seq(0.05, 0.95, by = 0.05), data = CPS1988) cps_rqbigs <- summary(cps_rqbig) ################################################### ### chunk number 47: rqbig-plot eval=FALSE ################################################### ## plot(cps_rqbigs) ################################################### ### chunk number 48: rqbig-plot1 ################################################### plot(cps_rqbigs) AER/data/0000755000176200001440000000000013616365110011574 5ustar liggesusersAER/data/Mortgage.rda0000644000176200001440000000445313616365122014042 0ustar liggesusersY}T.ú.fY(.2 5iŨђhY biRcjM65֯((HXEaYdK{wotI9{x7\2_7 cx+=+>bjUצ3ՉIΐrċ$m&?y1/;YYIi޵Ī$4};Z办DǾDO.qk)H|L@Jelu:ɒ2,SLsd)S f†8-K eZ V9VR6<um Qmky[mvDy3&-934}Luؑ^ցZj ;lo@a[ ]ֲU:nO-: k`[Ok9gIg?^UW֭-u3VlY7F ~-f{^K`_DiquASjv _y i1,QOX#^<{5tEY;sω][4sϭ{f׼}W^5;e" Vqc_ RpYyʬEg}O*?ޟ'YFv_1/q6k=Ӟ_wPr}س6aC{->4~2?{Q;\ :' iD.~UuB;4I.Ov:gϤmz$x1İ^wlUFW`{C<[1V${Z^Qߎ~'4D{F];=!?}hީyHKҧ:(}D>{oxkf{֤~ ۘ֐%3lק&K_%r2\*IMDL*{sM&SY7G$W3dTbU?߽SoH D./#cũZz] L R&D0e"R&F8eQf1-` r.Ĺ0"r.ƹ8q9Bbs#!1G9Bb0s#a3G9f0s#0G9"asD#2G9e(sD#Q2G9bcsĘ#11G9bc8sę#q3G9g"C#AER/data/CPSSWEducation.rda0000644000176200001440000002445013616365113015027 0ustar liggesusers7zXZi"6!Xm(])TW"nRʟKMd[_;zkAl-*  ?|븋yf KdQ. cO  5Da1?oIy5W=SW tMʩLD7F Rx]1A@b >]1z$y_,6mŒ\-Hw6F% O~-O 3qq8P +(eWc1+| lq5I!i\R&*75᭾w-NG1ԚրL>Gg57mX NFo$ ^ |@׽QݠgT#`JdH#cj梻|*xzV4Nb ޡ[d,% E/({ҋY9}# ?(A sli05/7QHMIbb ůh4[ cVM8~RY F,84I=zKK|p)7 AhTnyPrވws.\t[]6ȢSDUίႮUˆ\5 O`6aPtYꌤ}$UC m]|-h4 gHjώozh:ڎ?&_H7VȌhXt;0ZGXr83[تzOt0y6`n MJÖh(hŰѸ84—#P- X# !F0OwǕϗNo}*kuTxXzCOQZTF#.&<`0.DijÜ##?\-C)VgfR+&5߆:~E0qi֓Nz'rC𲟳\jppamd7K-j\PA$o:Q! B wRXy= wҡPKC`UeYqko0"g&ev+?لVYxC`ZgZ{1%4˲ww֩2{,sF{^j ԛ,1ڰY\vG>ayguB& !,̌3zʲTqyTΪgfNS!ʱӐM1s,BR/͑=q^Z(O(Dplaq+8jp# DY:Gv)111N~v*Kp(Y_UvXYqM (BTeh rɋ'DzYS搔UR']ˌyrs>:tiI[~_N`H;TK:=#բ>lz+h^ y7} 7}5R@P. ZB=e\4kf%skBQMnbKIS|f5.EIX1񷗨ց3B`conoF\k q94oD`MF^N72\ˁp$DQXt@mv/XiSZ!k%Ȧ{ꃵxN^ľHAJЃYX['+ &b"}w Γ<{QA6y,H;4F[?n녍M AGKI9?LQWPNQoWGb 7SŹ+'\%m/>`RDk<VK))j. ^i'\DG 5~a[ՁO m_O>Hv)&ӳ=U8TFfG,/^#z~JQ ')xhTCU/)û Zm䨷5uOvyBX%Q쐻Ѯ~y^Ci1f>HcEL0%ⅢJNT!KLJѧDƤ *%crə~N$=LAp K<CDg(|KZrU+K=-;02h$1utM-4Y'&FA+iSe @b7stk5*D]3|ނR-ʪn!$DϤ9]@i-ytAuUQbS$F &; a17U , QJ\e#gv_8o"X8D\XqJ#P[j 6$Ftk[%)9Nm`;@'f e7ZUCG֔Bq,Z=圏%\*l`b$((‹Z 5bj!YҤb@^a؍q//̄MoR,yS#]okJ$.\ T%er 6$?;\ Gl9`v!*Z%hzQ>^o8fsU"~hQq|-kL|b-^rZ0 =V jSbM+wNyXysy+*YT76nmHW֘ɇ&舀NzerYw|@v{ۍM oRj2%),%,_:tk74 C7c+1wT&JFFQ}=l"'7+{ʌv ww{8tz1G:dB@d$]F|󎋪lL #ꀉt'ED9?4]ck̯Vp-:.-8,0zQ5ʟ5Z#eikOw )!P`R(YhY iaW"10 4F Ig(GXN2Q/ry(hqQdF?q.d4V6G|HGr 9/rSW@O"SžwK)0c:&m7m܇A?v-jRK( ?~9J!ڷ Q>ofFB;/$͓.?^-1l~ -i1({A_RrMo6A#Fc~a^%/ds ~qj',@iT6G+BC)"[0H,_qʿi11CA/ +e/o-CP 9ñA 侚"-~ԶsYžPN~~(G+^&G@K>6JSg|)]ْw`a-J_X>#,l"9ƍsj;Ǧ}cEd/0:lIm+N>Wc˫Z"C+s!&d%0yNnGzklܟRt wIzdkZի$͡ñ [rbԈX6, e.(Sg k:$w`>&9;`Q 0]sM d/gdXxر$(Zܺ nv/T\C¶6顂!/e~gNb-c3~UОΨ%_uF}қBT >UN‚q>t={^|6v^80}';eY7FpVF@`qUD*7B'WODiD."$ƀMTk?t{=Gٌyc}r>2(O /EY4#7,T9ghhUhۧuGK\;r "\y&Pt)~Ѣ2qẐdL$*mBd@?T}ƹ(f̳{xS`_37N\񣬏ڽzQܿ+">$Eu "G34VߘaݮyPz/OzB߅>.8<0 ge]ʍGHXgNFV/QQ _ȅy^// + *5%[T脪9IpI: =Sl B_U;XKT8ܶ  Xi%#aUEeouX |W5"%c& _P=*҉7gqc[Mɖ/:S-J\ڽZW%ZնWBi"_*=ICBh *5$M:4_E^>k&۬t"I䎾%@^-&DN( UF 7ub $a(X/Co lȊrDnOB=q!*v"F7󑖡s C. '3W9Vqo4^U|3?O]D#ӍGim5әbtcVu[{(Ud CYP(n S}2>W镩MK1hq(J7WeunFMˆN됅#O/ʇ͒G1*bx<4ܾacl _F6H*6GVd3>pF5kX tg6Q^8UA:MEg.YWr3OOk!(^9ssʅOe5$Qz9@Ow*/Vϳ_$A,4P}5~a,<WO8B4Sgyг#/H[xd %ق 93EG>aF+!hFo(2^5e:.03!H&#tiwb SEWYdt~FYs鞮' c&}.m`VNB-lb-HgwBEnJ ;9?<\;'̓ L F-y1+ThMdq(N$a7/rނ!ia(P0ݎ==g#Џ!U9Y@61inɎUۅKm _^fd ;K[^5PuM/t< ~;:)fݼ :M H`)(ӏ bn_/.e/-Vԗ4LjXRsMXV {²_te}c%EE:Zd5G#Qjy L‡wO%K$Wz܆sSUu7} 2$GeCl};oVnC\|0{Ä́!!OE!2ζXrve<k2zĮAi=Dr05 :vu( j"´Bس} & UJ vFM@̑ 57^~y<lex<3Sm|*$0]HgT8#$Ja :%){ RWUejǓ)Gs5gR;fu~ 8#j˒(#.S.m@)0ƇAF<; dhl n9? Ci&g)QZS͕VZΣ/\xg(R <x3Dzf*4H!.f(m:7qU %,͟" 'yT dVx3i6gǢz(4kJmĠK3xJmZi!T~jR{˜~"uE?İ^P,lu :I}%Իm[7K 4)%B?<_† $`ƲRi l$SzCY"3@#F{4˖f Ϻ*z܈I$Ww@ĵ6Q*&=cJ "1)GX@9sb(hJ]j ǵcly n;|%T3610Rq\VbcJ aA~*9fRmT| D_PVk>wv4 qQAX{5#} DVtxdT 7ʲ)k<MXn [7[=jHbiXC_k2?@j`Ee4MC/Srd3 }Bĩ'jx_knaх]N1leiM3 sjdQ1U"5hY/Wے}H*YՂuu'.#J-uҵџ;ʽún ͉j F 2q BdC6ŸUŞ542?K߇3*L*GH3?\VfEgTVy%=lqQչi^~nJ ʗΤrl$=k:,)Y/{i&1R7:`|Qz$a (BL3+JABU4`wx*R^;1/tPt[p"{G9*F{~.Fڒ?!_J{m9 4|y'r#V2͌F?*7I5%v~ ieκ*U8k-m~p 4 +aGCᨁK&TҺ2{>xV_ÈAR~l}xWE"ntXPb MHy[YdK_DFɶt6']3ſ(^fڷ26D-%9N̞u'Riz*B4Kg)փT.(Xܙr 'b.~j6d>:cdwde4\VXd(v d 7>/Isn>QkGPloV_F 6, &H5&26&C*v2{]!]| m&un}vhp;PBMAiNPט1_Ӝ}S3n8r7]wMfWJ393y!(h%"kIouE 7Pۣ=R,Id| bhlCc5#4uhv9}9p-h4_7MVk|PS|z V"sB.#0ۚAIυـ>]H.#r9Qa&)lU6*:nc"B:b@0BX*N֋U|Uܵ#CBtv)-MS 6ͺiěU%f4k>B9;tC}83TBRAlӨJHS92{$*lW7p^{=T\kEDZ@8vzS7wE-!8[F@EB@;Is0M:,*y[qO!nXvBı!",$0 YZAER/data/datalist0000644000176200001440000000225713616365110013332 0ustar liggesusersAffairs ArgentinaCPI BankWages BenderlyZwick BondYield CASchools CPS1985 CPS1988 CPSSW04 CPSSW3 CPSSW8 CPSSW9204 CPSSW9298 CPSSWEducation CartelStability ChinaIncome CigarettesB CigarettesSW CollegeDistance ConsumerGood CreditCard DJFranses DJIA8012 DoctorVisits DutchAdvert DutchSales Electricity1955 Electricity1970 EquationCitations Equipment EuroEnergy Fatalities Fertility Fertility2 FrozenJuice GSOEP9402 GSS7402 GermanUnemployment GoldSilver GrowthDJ GrowthSW Grunfeld Guns HMDA HealthInsurance HousePrices Journals KleinI Longley MASchools MSCISwitzerland ManufactCosts MarkDollar MarkPound Medicaid1986 Mortgage MotorCycles MotorCycles2 Municipalities MurderRates NMES1988 NYSESW NaturalGas OECDGas OECDGrowth OlympicTV OrangeCounty PSID1976 PSID1982 PSID7682 Parade2005 PepperPrice PhDPublications ProgramEffectiveness RecreationDemand ResumeNames SIC33 STAR ShipAccidents SmokeBan SportsCards StrikeDuration SwissLabor TeachingRatings TechChange TradeCredit TravelMode UKInflation UKNonDurables USAirlines USConsump1950 USConsump1979 USConsump1993 USCrudes USGasB USGasG USInvest USMacroB USMacroG USMacroSW USMacroSWM USMacroSWQ USMoney USProdIndex USSeatBelts USStocksSW WeakInstrument AER/data/SmokeBan.rda0000644000176200001440000005354013616365126014001 0ustar liggesusers7zXZi"6!XbW$])TW"nRʟKMd[_;zkTN1 X)= Eּ…/8q~zl~(7&s"Lq ]yK"(Q !dv^?(J˾b Ⱥ1EM (jR̩]`.Μex)\wۿiUM*]ޗ~^WwƳf:t]-8psW>wo,&ԩe)\3sQ=HG%Oe/)w6Egϓ*Cm!ChA(BmDRl"N @sfpʋv7}^G߬l}89Iƌd!a_x†󝛏|.x)*aK-#^nMyJFGȱay ,Qs($j'v8 =un($ Rh^Jj|B"%+bOPb@mq*!:, &CYYk~grh~,Y*=)xO k¦xNt}rOVC8-@q|!-rb=>pW\ 5H_!,פ^G(UGZDiP-e킈I3b/hsCY3~aNls3 _ IF7DiK-szi,`=*oGwFkxuY}!a 'w 7lqQysgz|-˟kCfSj9X9s}@˯)pg)@r鑣!SuvC-%d0E$e q(oP7'G 5k7^;dxDzs@{fE"$_!o:*^!b&L+QгKzC_KAQ@ƍ`w{Jyf*G`38@TO%&0{Ay7C◯7TBaCr}3o=>oҥOogx/+@cF{G%N9vƚW0ahJ'$-$+Ux!ۉxltkRy{.=fٹ<_澮ҧ\-YEwpdjj.>vqBk'Oa=sنP&EgRp_WlY1.BeA-Ρ#VZKF:Es.`ͦǺRޓ&%Ck9~I:j'NnAxz3aӐ2Wa<Qp+fΐ+valY97* V(b^׏TPjVKltΦe*~y 9-B^:[yOvS'c!K&JIszOUJ LuzhGP;!r+QɶsCQUƤhB=:2XL?3)&3u锆TZo64]Ouʀ9^0,]&,)'+x2h1E)Xϗ77 ֗lgyQk3Äz%)h$Ə;¸W/H.NWi{dM&:'SI?Y[=4Cm)ݕxS,m@$_TrU]s}d(8G/M>PIT(b' ڡ藊j뇑ӁhCV /,Vb;Mj >14>"n#W<EzkjHpX F%njEocSwc#Y't01eH2)`%1Q=䭣*+=}8y=1ڄ]>RuYHB΅^/;~nIc-4웺)Pߘm|6cuR3KڙIij/ 4xɧ5_l &ikuJq|t"s )qDix{9[ S}u(qA8Ţ"#/"o2]E<5=g@U%+[ ǔJD?7|"썸rIEʚm~na >\SUAe3P*f+TJ. ?Dی+FW>D4N{p`< s s +|z]MX' C y Y vpY2n߸5{e2DL=hsb1$Wg hX XqʾwkdF)BDtu0I`X-ץO``x*GdyÝQ8Vi1lJ^CºdD3\1E ;+2iBYdMMYu5T:⨸AEDպj uO1|{i%I}/[xFz`cZ1b7\Pߝ~Vud@i쐵d4O5{:?YF9t>DJhF ώbk{i.Mwe "!\ "IgUn>f9fqoq -abE $ޢs ͫb[skeN M3BjA@@Z ڶP=ϐ Vu5HPůM8 "@! A(F ܰ/gp bN~Ew*qR`p% gH |"zmD:a5I13GĐ>t\AؘG8ij;A0[0x:޶ _Cĕ4ӓ|!h&X~\rb>Epsh$&9AKOoQɠ wb]M#ds7}:JG/?;w.8<+6~`icN|zu(iKpiJ`>MZ0M.O 9$!k: ,N"vVYdkc8 k`~\Lvm ʻ:᭄S_( Xֺi.nĶ`99Q'd+Bbl\C¾wܨʬ+B3Oddc Rn(aϏCEVtp]*me"S'ެTBPRhš|.<ʡcUgYE!&u&-ϴrWE0W_VgYY}ʟ)ԈH{;=b y-1.*0{8'CSlKl Rѹ2l8$&0XMh|[UrULStVSd8$֮O!o ؀ 1'p~n*msJ qFLG g޾ߟ<Y" {ľe J+퉸3h&_T4D.P jufݳbF}uc;}$It9)tAȻL]d0V3OIеk>6< ՀZ?OE?WKT.L}ioRBĸFZ гD+HqnNGMz΅^Cx9qQ%i=*0\U!}F>QA}m"DJDjM .==>$H}wG~ʰ3]V? JMcB=Gf`E\-T^D2=k,?B./y*-KTnų w6  QHOsF֟d-W6T+`*.9Z7fM3"06W@`)D0!'`ήvcBѥ1JuZC]}N ~(v_UYCs[)ꤲy >}T@"( ʊ+CU1+iLQEO7QRsMdV l Ah>/[JwVtTe8{LvEs3b.̍[ݦGygts` Sq P/,_:^Ca-zJq[W],.4 wmu+J2ShB)roT[~#BhmJǮ-l K:!Tʜ\fo\&XG\|#cre/!6tsz⒵uz!8\p"ռJ/X5S:rT>Ml*dvaZN?7bY\HgyBg7[ j.Rr׈'vlhC!+A\0?Vpe߲sok9P뎝mpRl{9g|@ }TLj;ʶň'lFꀃ)j쪛0{3cGI᳋z܅UHw/b?Z!Pn=·H $Ձ`Bb&n1,Gýib'3i’w~69Xw;WA{)[ʉMn_휀S:iЍhB¯r.3ӗ.||-Ho1yMhw:\2wAw9%#W*].T?,SkE0!hD򳎌Ǻ#Fmd LqiYA=VnD.Z9U T8Y\/x"dC>&n\$cCdygÏ/:B0U-FlFm~d+UzϒIO 6iaBGm lz.[l~vQpH;0:E'ɻVo>6^+;EEo*1`16h NF_R{] hӆU֖uyϞgk=)bQJrS8q1HC v|5Оws/C-لaCf,e10KḣY Y:yMjsoi젲v-f\ 9uk LwMKl hIC"W]y#GŕLVLtְM",Õ/ q'?2sf?՗,Rd/T5XM& %'v5n4|sU>p( U ;0!o%~BmGN=\j =ukotl|M60*R0A-]ȈKk0^ə >E̟6>Ϧױ0 oc'T!Hg0jJVRzZPe.Z;' HweCIwt[z{-p!.+Z J(۫X!`a+2²E<-D9Ť9%z!Ǣm>mKt];M=Q^S2.:l]qoa. #X^~CR)UUzn`yZi]9Ž{e.%x+ʐUt;+pډ Jm!QVƔܘYQ\(:TS976oIyJh>wZ4U=;S6-0I2GJQT5FXGd˞;կ9bG'ڗrvx,#(IGeӏlT:뤆CanɡkYd z:-ܣyܙw7IedD 81YG$zU6[|+z  1/nie! hcyoztd# aCj>_Pv;ZttG l+1:P@1H/#,6kF;?e+#e+z^|5]kfQ]|VQ9d/'5hI1M'ʒ:i`w!"RNL "{yk_hF쮀j]{mCd̄ ]x4*K23/8HPYܟ D`x?u"y˦UK~LVӈMjF!=R(_b1~.Qi8bJ읙'sNdצ6&,_^d0X ܨ?%:>1>Z2uֈ[YL7bN 6>oHVҫ4LB!tDto;egp%AJy1xP)_eMfp[̨S qg*k=Vv6ghyp8!8tcgc-#um׊l+̓]C㻣O"‡]x5%y~Nioǫ֝皺q!w&QKޣaҒ%*dc^G[ͽ| [OqL[? :+ W6Y=O4D˰>N(2`^r?qQnJVƇBrϹ䜈%l3>r]`zs6h/ X+6L cI(蜜eEV/)g0s]tv8<'QG$a߃k16%Na%ɗ]5yaC۔ݮALu\ĆpIR+N17Qvhsz!^>![2ia_&<1[b[#UV /G =OȂdRK>"]Kgxē&wBt#*Cq&(m$lt/qw8̨;F96Z(7TT;@2/󹨂"P%ܓnSJzcRcঢ়b &o0Wf tfӎ_!/>'Vl஻YOBx+kskle sV^!?kߗ(uS>tEh&b:ƶe:Fdh9?1t'2$`We0w,+/ƆG׊LSyXZAOfq業7EZ C1C\7d!ێ`'Sң?灍O9PC/TcFV5PЕ߹^7QhvQ@swE6H ļ擵i7ϐX7z?(՗CK|@NM&*վ1^Bn^s`!#+L8ї?!KϴF5I } IHN%>֕!/%0o(prHdQװr\sUk1 K918ĸVݍ$90yZYX?КٕHoG°"|ʽ'*Ye6iMhӵ j sWmw1+nu"Ӳ=0p̕Ć\ֹȈb{/QmoNf@Oksא}`ٳ`Si7Gqt΂P7|8$ "-K2g)-J("I-M$&m⢨)8NO>8)Kgnv8ұPt-Mfloef'vgP'VN|Zn:z=Q׻_l$6{| 26B?U$tZmٍ´K]+䈅(!yQF[?5Yn6i0X;\""J\/م[@n_BZg$fpYXpk)rbIc5nN-i xqK#ye9< aZ[v ,].vnœ1v^SKt])Fb̾%$2S)k`d)FtZ͜D:LДIbiy\lK=`jg^9w]VPj@j$=" M りkthH_A{=l10Le?pڏ>EV/ B"`h>i!Aع 0ϹŅ=nG`~}wͰsgo.ĩ %jzbyǀԼh$őHH#fhk+b<'w41gF[CaɬF8Z+h~W](}q$OBq>M#gi_}P{7]*X/"3`1|zozp".ā'ngR\Ku.IhT4Cx]\6.C{1ztʧRz$z.e W݆ɭoFaJkFE 7[Q1Gpi|MI| ؂tS+-VbӶFބػxN{Pdo?C"}M8\!C`Jlm Lz[sq#} k{s'qeYT7+",boZc1rĬ K֎EO0ʢ{[C9.Ku53%Ij%*?qig֭z(=oA ν}4FJ*zuyOL tu[:b3HU/W6^}:R{KWFbHf}tBzX!⡅#_.Wbă/\k2ȩZTA)|H3"ݦ'B7KSY#0o7/;gZ6(g&ſ?S2 E6"J`hx`4HX^!Dj~O?҃'F1F"{s?DLkpaGp Pwm`͵i@m49ǵ҇BvoBl9Tʩa*C/&q9'&FE@&DAgJ7-9D)Ue|pH؆<ٟ†f0'p(T4y]Ab~Lж"xӃG/lebQA9gLJc>g(f~iak'xS} ;nbeJa O`s_p͂/|;GU!*1+_w EWs*?$h=Š%O?a1*Obq]|R]"h|=IUXޖsc77iĩ0Vt 䪏m5F,0(b[9>E=7u}˥V7{[``2XVyul6p|Gdq;aORl& P'LMwS2qs;0+u/ cd:kZCu29ھd>d=}K..Ό`,Jata%ҎXaWh]MHKt_>G}!Z? {פASTWB;A%0lϋr&B:*(o,mVYTj^')BFJNz$^s/K(W)_]"EQ_0KxLnz[l@8F 9E?mk]}:*Hˠ^;Z,凱NHsPW6XV5fuk6ϯMQgsJr3+/IߎqGC3] Y+ A1.en9Yjt’U,5IiAYUM eڃe]J̢-՝Lze 9ły$K{qarwz119XR']N Y5SQu Ct}HùAeHw,WUۺƠWŔI%&Z"ls IDhZq& i%X Ƃw4{d͇3/bVCB 蒱@I_[웤E4`2JwwuTd s[Pc4/UIcKAJRY,:Wd UZ2lBG!ҟU`Ehԫ=Y9x6LT8a4+6oP2(J2KB٬As[#d* ]#`S7j)BLȮcb}^Ajb  a~ TpGckC Kw9MvSt|(B\MHة>D !Rَkܧt<-1'8**TAD 4mڌw6pȪS:pxݨl/E$h>BÔƁǃ+hE,e( ѽy6T{]+sY@s/>oTSWX gCTov±X} Rbk[@+PPy;84x`ηQb_\Dbs~j5>H뀏WZ6bp1ugcXY-dN;g,5J6jdK>(Gߧ(Mmc}i{,k<*_4zaqej6<5jnm`HlǷgN'VHFK6YГ",Wme -=q ūWKݯsÛz,aȪψ8{qf[¦{A8x*RJuc%';{y igX3E?Ƴ~3`V)iByeW23Pw:ZnPrHˊtNkxjC~$eI@!t?8$ i"N*T0=wP<K4$Eb5єb vws'yW?]xuAwm@?ǫ `I4Ӵut}$&x`{By5 7P@]eQ}}kILЪ3!NM#-;ssfīAҝtdB !Ώ|/E"b Wa6{*O mT;Hp5IP T_w7!ȋ!} vY;ic]Tp^`,AzBlfEF$ucj }6}sD'Is5^RSyi8bYq&\Jf!5aYFܙ}̹c8^Gݍ-bw; MMmP;G@p:灭 %[U@ݣ7g Ԉj tn;cF;m.`٫j?q}t%td (I1sVLoRV.u Mʖ (F5o͞XBY,okAy A ɄĹtwՍy.ٱ}GDnXM 0.O\ʹ/<?/P?{#`#תDU7]S* &mWg`#2a1ŧ|aHFd)(p?,a$VżH ;ml8ϠOtTX1OR<2DRo4KlNf3'{xY;OkǪ8L.bX|N]-IFӺCڼ <ȕ"%fyf,4$& m^:ZAc(r^=hor~VmA@/;45{+\y?zz1~S#\{,tQl_2[@ ˗*DlMP0@k J&sXDnp@_jN=el^ңքDP@z0A:$nr8xa]aE2^Gx\ źb3\;D+ZqF6hIm[]<٥\/e `^\lIY@Tfvc97C_xc!f_aً?oI3 UnNwāܘ$ә%:[.}:"EUзU(w.leWgμkvF*HZ $*o-#]lD4@͔K-gzf~ֲ@!tZlN]?'0aVv5Σ2򇂚0Q T뢑Pax^~!ng;&r >x<0FD$r?r嶆!v$-`V/}̖'m6|J6^mIUZoUVR;5t-[>\º r0wx+]ط1]TB.°/X!ZL~/ryX7PEDOuݬ{̗4tXЪKr9){u_xVv"pMK~$K@U0!cml$ >jW;l*ДC)>  c:嘏L:^mFrW niq:jRhHjz͐: )FI[uUeX0Sy'¶V, (L:Q4Pvb,@0 3Vk]Q~b=9RD_6칏1ѯGgfjA9iO{!$h*bOk>y8|AII7nB?:k\"Reⶄa{!WLiC5⚅ĸG鞸1AOLS9k&%%&\=4BA/SL{m?=i1}fY,V%oo2r,S82\fpk_s=̒]-u/HnCgv4E^)a PA2q]9 <#/3;T { 2C_ƊCM/4| %o<^}l7*?t'?/ e@뤡EAaTTmG4(8y.-~Mh(\k;^`8oW?47&â*%|a`]սM>i;=Ji#䙰JQC}BіJT؝Y¬W@}ďn=G쓴E1LY/eOj=[1+m?%"Q7UK[ol哶8G`(,d6Xq<,̦4ňW9- b`4 V'B(y;}#S0v uR݋).> eSMmk- ä́ Zy>b A )=zOu8D G+?3qxns"ϥ Jnܬq;TWAWg= K@?;@1e•2]ʔQ!xYnSD]Z3GKDB:<10 ^_MA'9diqIӻU.Ad~zwXB!>z^Mjr`cber7's)RѿLC^يOue*'_e$cv&A,VWr0 O cm;Dq3pL1M>kr{kH/| Ȃ =P>#6qDo` 3o2 /Y ЕmWZeT*+(Wr~!{ڛ9&}"ׁ݄8:2nQ_eƐ!Ӄ\ŝ"i:s~"*^[ȲlMNuI ]_9=ym8 Kmfvìn7)S(†!PE1a8?iC+/zL$wЛ` c3Y?Jv} sɢ ^{P3U˛x]+(eq>9EI;5*SlDmuL6zV=e(6(Ӂ VcruHBC?}!k5XTtXi-^a~hBPS^&=IOuc7fs}~wMYuo.gҗ ]eVVI"6Ŕp#SDOXl'g@pYݱNSLv7eKX!DŮ<\ǑMfZ]+,7fMQbSk4xb,Ub^W$01u8/qs|35*&EK~盦u){W g hlMsX5AɅ/V3'I!hG\ km^Tǭ|hu`lט@vo-SaWad ;H#|}UH#,v@=zyN 7Y9 䱑_7g2\jRxW:ݷi\UrQb L75"e4_ёYf3exgݒ"_?א ?n!bEp?Yt ͑k("{\nzVbxJ8:YnKXAR[iAS(ɖe)M+{0^cNؿٔS %ye56 J`KõO̜<;go䙹 cЁ^x"!_}$,b}{h/\-qQ#S#yvZ״4&/`\A|mP X&))HSH)F );7G&Ww|@Q7ƒn<96$N",.`+VD6CnX?w".ϡvɼ ,לgv fny%QG6 e;.ט' Xgt3XB eo0D8ER𐤰7PZ׉@3p-( #ʹKiX5XUTc\QYYB⿲.~ntw9z<A{|֪e9Y:N#ux帿v£dٳ pуW޺Kvwnp诖",] YCT4r}Kָw_d}*HRܥe).ճX.{vG8fU߉gӢwA׹Z7BFVǿ c!̿&U6ڻ{ĨT/ !s&Paٗԛn3$Vy39{ͷ>0 YZAER/data/USGasG.rda0000644000176200001440000000321313616365126013363 0ustar liggesuserseV lTU t" $&HTx"6*)]igLYe$BAQf""mZJ (fiPhwϴҞw]νN}?e9ŬiA34Qi2:i&1OK5h"LTZ 8w -=?m|Df-ż9{Ι<^w܍e7 bNuOy0 {\a_9Q^^b9gYv{*֓S>>u~+Fu׎}~p̣/)(02%~" JtU ; _a;pʘ_GӚ_/g]jz|,5x7?q6 + uyv؉RZVul+v bGRBU{:^?xd<xX2~/%fQ^ J=d)tA?jVC-K+@hXФ; zfaJ +dKTF8"[+~H-oA5\D5- ZT[>4T~$ρ/ +]ģ% > {O% _Y~x_[4 -eܷ:x/DCxT>T6"UnB_l!QtEwǨMP_MVףg#/>AѼUfwWB\G!?(U9 o%*A\JŠwtyJTeyJ׆Q)/S4XF׹&o#?U*]|ag-oz{|gAзMI*]ZO/"SuNŪݬbEOW%j @WAO帷ipEۨ;wOҲu[{|dϐ,i({ u9=x@_ xH4o~*38o2큌9ʎyϾ';0G n~w|,̉lxv@i}]ǵx߻as{'֫Y`r<{ M6P3?%Vⶹ2<xjseDw;j l>H]3;Į>rlsfJoA]f;iux: %؎x L NJYe]fje8pDAIEeRAAqWKMz,yR9|}?G˵k؏'fRS3 SS3M2imb-C񞣼}gE71151+_1QW*ĪkW;&NWM8 kp 7NƓг2*SŽW %yU;ߪ MŠo#^"~l>y~I>W}9[\!gKL^S/>^*_3owݕ2.x#pUrK :sս zRYwԝǼk߶^[z[;A^X ȩ!x%֯_H| ~'_r~׽y C7l#oM|= g3Iȼi7>ż}v$vMSGS*!w.}'wXg3]A]vYq6v  9??'>v=&ya|Q/ν|{GcK }ҟ/1 t Ѫ\C@OW U?*֠ī9~,UW1y?T+'D8|Cx㹳0QOùؗW%̅<BoJ kb<x;_zTq[O~/׳?_E3WwV4~g1~%oa7$}oeeԓx<> T ܪ׀WU2z#̯U<7UA8rCȟMf!`5Tg@nu3ms:k5c^bn }x5g9\Fky>ՙ׍"dc^-gFl4[O~Sm#(+b_7e/κTOyh] ! y W S cgm"M4r}S]z1 0'aa a] jx0E(&ezp ``/!U=?2!n("8.yz98 J]0?$^ :u8 (wY@^-SCyomz40#~_ 1cMz{?q{"sK'' W2\7~.u<'#,_"-q}HΧ X'/\/qI7~Jde%޼ xJL)]M@zO) |X>x"Dٟ$}iV_i^Z*Әc ţ8~1Oy+}q6 7c|MBs)=:-}9e6\ g/&d]Iy2zfP C^:%Ogq2* /M3X' /a y<+0%zE]0hA_I+q*`1\P6eDznK=Ɍ<_)Ņ9@|P<ǧ~S2_< _,9m$uZ)<פ'|Ky>)u ci|Hcޕ7sB+-Jv-E|o[|]t㋹O?$wŬwru`I/ҹŴwIt)5}*?|=aDyJ?ga>Qn?m|Byo'2G9'yu3K~s밮e1XX׳*״+>N:!.3Ow_|X^l Bq?eb^l_6-1K Nsx> z$4'r'.{Ɵ?\^LȺY/= '}'/y|ɛL:mwyk~Og}"33s^R>p>O+g*'_8-q__V *\]A_!:x^y}zUN#4!;eWVqxȫ,^e]U_U w7η߱6nnͬ+#!q֑jTżj+VV~Ofm;n%;-<0j g8k-!vq}TZuZD|uӒuKʩ+"̯{߇`m۶.9g}O̯г2Jr|1WA픻 oaPHu!r[0ݜ5}6ٞAo9O@@o;m( 9X߯cގZ&kmü&օ&~i;ήۙuwnCΘ þ>e?yfb' 9CMZez;5BwiPw 3ƧAݸdWSH8|izWp+\>@?a &xA7a\9F9\wםH?L&O]iD0c"k"k2OZDȸ8'ɔ?S(g :c*F}cC3.3H?Sr|&uH>/ /g}@/~/?t? $=(0_(>?t 1:;w"{>w͢޳&=u֯-k}NK}cPk c!'z~0ٌټ><wXΡ0>8/qg̵|Kͥ^sn<%3>'0oxoL`&pxOO_/5#j}1zK$_"If'=u*qJI_"P2L6wTɴ!/5&ՅbMOuۼ%?R>b*Č #Č@OF׈1&t,[>oz[[~Zx(GV'1N«9uu@N^G+ /M76/5s rGcܛyzzL>͸j+ df^z0}X}CnX)- o x6(.+kӮh[fSnaN,8O[גz[.Dnm6Գ-ٰ?#{MOaKvǁ|\ωΔ@Q{Ǒt'](߉]uS{?) uN\͙[γ}~v[[ovw=.\#%Cw[NWw>w ݅xsB޴@h#u5t>t=_q\^{'+xwj(q^JW?z}\ue꾆#tWB-n4f=GõqBsVw!Eˉ54it'u$:v&]4~rw1o̧ᄚG?51hgc/Z|>mL\U–b?.F1$zGw8VF֧4+D~v⼎DSZW[|-/Нt#ޝzq]m-5}p-Z]myaT?Zj9s3Mmm#zK?5HV4.hv[0z{.8OõsjWFtCCDố6#wxK<=)]͊=!oIv>~NhAJgc;W[ %|/}dqY7_\*|Kqm8zuovR6!U"~,<νIyd _`! wRU~PE|0x[,+=%/ᷓa iONG |O<")?vښ9K^J/z>i=D. "cbx9?1+ ̗=" + [Njp_l{ߖ}}dq48 >_,y_D`6<C du-/-A^8wA39P@p *;ߠa`t|gj~z%,cUO`k; <_sQU&s8?>};QRs,V!!#1/dOEH F! !Bҕp5!!jݿ^A얽qNBA_ EsSE#nB@Сs^h!~Bӱktp>~I~ =u\(B8bl4zjiC6KW!\CWqvvqhv_$-p wEFӯ#Ɋ"<p.<v-J!퟈;؟X'YsMY3lEH1}##~#b!"dDag/⦜~7׊C$3!csdk"#ɟ?FF܆yy,:{ķ3.]د{uP7 ֍FM:. b)'"Ou+ 5'*Qu`nYwHe~{ZQmWQ}Q_"Q <~N,޹i*˫jčVk.xT*\&iI4~}Xa5;&&\=b<5v__y;/k|qCȨNLH/pc;.Kݷ}fo<5X>/<^Fzv G%}KV=ۏKr'=x]GZ[V~Lzj2n=e犤_c+U_i׍]V? WGnJxORV?v*ݷSrNHOTm=Ut^WW6vyiٌvߗ]/df뿥=]/-5vqhh3_v~P<3Kwf/V)"v$7QlZLG[8v'fzz`!۳!yQ3' N/\Rn۶ﵻ []8}Av;zHdę}ta#璘e§= ѱ3R-Zi=ϗiOψ/:YtݝCtOoni8C_x[~"~V3D&${qul=όLŢsEqGNr6  Pu_#oX)1g5ɃOS˭G:#zIJ{,;+L{(q@x0n>P6>gqZ%3EdzC8ylk_u÷kpVto [,>2ո6qJ? ׵[9´b_m5<п:cl~P2R8͗\.>,WhSD9귵V EYM lA}9ZD[%:-ZLޭeG85Ng)Ur] 8ѺB){rrtFS?N /iԣB 5Se  5Jʡ!5;_F~>MNw0H/P7ͩR?HaT.BC^j^S8Wz[ٯZeM aj}n,4 :):~ Ve-:I7}6ϞZ1٣x*Cj2tkGb詚>{^_I3$ n®FQ}(Ut:A7j?(N*}el0uB=!GJpSē/JIy\ؤ*rlU ֟Q }LVw>8TݦaF{]_AztqOSIJɼZKz>C=#\nY,nWzu]zn')ݕ>b1z,]Tk,Z%NP2UbyTWCEozE}mcR6IOW9 JR)}JٿD6jHg60D: ~Il-]=N)}Q:ؤ:N~_UvPQғBys\^ڰ]i8#e?PraP_&;Z`"}(]ϠH3"[BՐ+rsoviZ63J]aw7Q蟠o3^V?U\&2m `׬d~2X.:Zxjybp E)a4TiM<)NAER/data/OrangeCounty.rda0000644000176200001440000000204213616365123014703 0ustar liggesusers]TPTU~풣 %QXFK_Ŏh9*Ø:.Q.ۅeeC`W]FR9eDilǚFL`K93{ss-)ԓTJ͖Q*{D0\o*6]j1AB(A-8qч\ $`>2 ,I9eh`mc+BE~?3?3l+kZw99kU 5g#l)Ƕ^fYܷEtr) >f±~T@M,SSȿ|6w!ϑщć;zx9pܢsksFսGu)?8tn8,©e49z-GvCxfOZgh8P*7u^}a6먮{Tww;•"i4톫qU;U׏ſ ]RqX%k2W wΆ#\j)w2ỷ.r:w!cӓ/=8?n-mһ:-i x^_}FJ9x y?9_9͙|U=/ki.e%k:9%7rގl _-am{q^wI?2Fq)keJbSXfm U0qBD:8!h#KYd3W/AER/data/MarkDollar.rda0000644000176200001440000001004313616365122014315 0ustar liggesusers]Xy4ZedxѠ7)6RꙊM2$C*2TcQl5+nu׽\c yooo}gs~k?^}889VZc Z6U\"҉59z<)ؽzE-,2rMKja{:pD8' #GNyΰ2ި42+h"usxut ́W#0PiR`^ݲu MUX&,{\>Ɲtv`Ŵlö14$V YAF o&όؔ[>|mgk d7Rky4EͺKxeɨ6V #POLߏA ~"Jh.;?HZ[4f=>Lv곊:m,y*9 fZ[.}=Pb{,!Yeos6<: \K1uO}D5rBJS> 6S[+§_OL<70 }rLiC3PePp#'.bEl~`M}HT}P|)xV.H3MTh10*`!RnjcxTs8TʇA g& ?3 \ϥ=9J&r1lzvm_GDV7I{B#f7!'ch/DbN}3k TF6xEaF/cr~  &ibߌo,a mN9"_1_ZǠ1Ɛ k!XtMu6K^/cWM Hm:)RAP 467ӣhݽ?L?'fPf]AHfR<@tL݊T3oվb* Uǟ!NaWkF+y<|oCs?հ6oy5֭kMрrgG?G,+v 6W|6Ew-\d}m5Hָ)u[UMRs|8O2|;oPFcᴽD 0YK1Xo`DG^U71 8ސ@8ϷB`gȬ_ o%E3kъ?N'ka"NkOvHR{##rq=wj ,cOA}ܞyo4K%"6nI?D#XIjx X=cӌaH=2?<:" =Y 0Al Kd+H5^~ZW}wwm{"氇pۻ!$1>睍=p"t>V^6/Tze 56ٰzW8;W; !Gi̦f;ǁl`h"]Vwgt<_yc M=_7B@#K^7ЫѪ5Yy ']?ƉA瓸mk;Q !6ܺ}&8h-BtW LJVhy ZRDVmA%L, ym_-QMJ,S>u聑 6|}͸^1(jy?>V'OJfʙk?zf?a N7{ a'LwuCF mCGE`c\Vx7䙦u& Efׁ-8+x qazn"_<}wj̓X+)Q =6:%iVj sDNkG;,^do3-|{Ɠ$w"mx|YHH\Tcf Ɵ;c لb7ܢ%78޹\/\^9<7v͓P4(͏gl׊0.6:6<8wuE䐎 ,WN(W̷y8PqќFNC8Ne H- #sǸvoSR-zH1zBʄwkh l7!fB A0L(([@R __վL~_Tߢ1!(wZ/oz gM`],,<|,%\ٟ}|h3ǵ#1-)M@ :p\V5s18T;}'16kIw连_˅;x/R[Cv}AͺF; ݄&A`s"nQ,d-8-ƣ:gkMҞ{us-mU,YrhkmnpSѠ'n:)+^crBasj֓7V ['DD%t)23&f }ΰJ |yl&;g`d<`՛㘹};"ucƒ㥦38)^`~8\V_ l- /e5y'˿?AER/data/CPSSW9298.rda0000644000176200001440000011636413616365113013535 0ustar liggesusers7zXZi"6!XH])TW"nRʟKMd[_;zĮ<(q/5L)i6KxА7u7#5ZRmo2Ӽv?u~ֻI<&wCh{OT'y]v BDvbN;@2--Ⱦ,]{-5,*K;nXEVLo.*^=axo7* 2R**rdwrvs>cw{VOuoaQFfHN!aE5yHPwz88oOJCLK7;'YX C?|\ C(V<,#].Wv" =RT,8b*kTV|'%Cu:(6{h_~s->p޻ %Uۓ*s 2g9oX3617^l{G2@ũf+.jB'cMAd-hrNq[W ڋ6ң! hNUqAz"lRs5j[P_uv1$^Mm#>@%MO5YX#nDm'A{=Qa\N+Ul.,rhIRp\c(({I٨&} 7PEBӹ}t`2}O1YѪ_X0SgCOOz" >ΰS-ˡ% JZ52> һ=Ї28q~`wXn^@WzΞĭzU=rIcx*F'&i3H.B>̲P?~ETs š"\6I(any?F%IPa ?_Rȁ`%jw,Q¸Dw? uqy(]+4^j_&-R" Y&d8;]7m5 %M}}S_Y宨xހQds"l/  ˞Culj4|&e\?cIHxR0ēOAҀG#>egDZɉdϛh/iCG9+ 5Һ* ^v2ӵTuӰgͷΡfYOoGM7]_f-;MqJa76Y:[c~>քN7oZJHilBǦﬤih@e[3&GJޖЈ>c7nB}ĬbjaYLۖ?Ր͜΂J YfEJ; @oe@ {`\qN .\*Uf֫k3"naٽލw9;-8mJn5rXVs#* nv+yf*a~c2M~Jyg$H8 ; w볣Tu\+ _q2` *%$FTh3r`X 0feS=E:aRZw>iB&շ{ iOeQ4e{uf(h5ZHYZ:([ߍG!|TLax%P{֎LVqU[/RV:etL\ԩ#OMu7(ߖDgnR_Y/"Fh"~ƕ;UUQRꛒx%Z%m{Dz/"i*)L>GBul@,ѻF%rÔ:PHx0h`Sށ54E*7R aȯkjPxg m֖ɣ1{TMEdl.Oq3nK'Á j  SX=H ھDWnvɅG j9{ ->oma'lߋZrMp-2Yb/#᝙1wϨց%ʄ/1qҹ oTD9*}+EHČ/UEJZgw 7*NlJfIMvPZ; 4VkڗC̊΋tC ̖w;}DLd ,Eo<<.lpnҽhniڳNAP;O~b"r"XqNH`QjfNWHL oɣD1ÀN l,;o}U08z3EBBu<&. ,ۇ\u1fMq$%K@Hqt h?֘%A䒹^#U|fW]@`9EnaK Zohi6xXzmī8Ӛ,w<@2hd^v" F1Rlڱ* b*ќ`fAu?hgZv8'OWIlFu ت/ sWșXC< ?~=C' P^y5<=["M иO>vm9Oww捚P{.c)TD§!K8M[:6^ S=$@LV㔂_%.sf'05'&M-ct}= ȼ76M9LcXG=f^AH%e>yA*|`ܔ8] ۺOo9GaF~S_5! ZmPi !,ypZ=7 XEKirN[-]J p~c.jvݰ'U+lm+-gy]kE<㊒%‡V{ESϓz幺=F:[i,텃8ZH$L Xa(\6~=,mbߙ5SdSNN AgI!7a\C+OB'&+o* l#E`YV{"?C瑁hFP16@rŔ羈c`v'T j)6-JT+Uf0Й6_GZ! RэO[[2.cSf1l8^(7;!;thY<9+CgDP>{0Z6x.:4KfaB9-c8`#g(Q)Ic#(?zyz}kW^ q9<7u'^nHk{JЮDƿao!s߂'U6z[^hMїWiޮr֢xHIdMTv3}GC )8虪c I 8.$OڔNϧ Rc5OT\! Wꌆ;A"*4L^]!7ԍ\]@<:\zw2O7 N'Z2s1$F^NM8'%L'Bi@OoZCp*}I7xT)CU+=b[XJNH촊?[UxՍ$Y'QXP чǞ>{C&! p :cHΊD]x$La:B$j SվX! eQD hvX/!M &QH_d+flJnnBJk`T{U5##kc̕Z{|cy^~EMWc c6@2+b:>GG{@ߜ1j(>#K703|GFl\]-]'c ]Ă78ɑ{ǩ=GBS`)|>Z$+Ó\64P(N ' X*./*Fj QPY9/^EhvMH=ޏ!a "'Sfo>z PZ7:x+` yrT҅]Y\H]]\0Ԕi<Z\(*m;R·cEE;gﵞoa}0 z56lGzNsdN>Vd=I͖@fP 9u`s|0VL0C'7Lyv aߌA]B  ªX8Tk-<,wTO Y\nm\\GB@gώդVcbވUpiR&syy]#ZDtm)Uٞj'7kH\k1Oдc=[XWs"Sr%F=ҹHv)Z}*[ĥf8lj`ڳ@IF$XhafB!1F'5нTG@z-#nJݓA>g(`wF[LlȭMz!bY(7HRm'wF~}%C'v*pd]jY@Vn8d Q~&,$F*2ok}zͳyvJkj}&i0  w.OFl%Pu:ܖ2@{H4vЧ*wQO2k*fۦ.bQT;X&za ׭Ng[v)cհŠVͻe5'" 1+ B]e"sb,39bB*1>aaa_omI B‰(N^\@zb=*5ڕ}\@z-= M9G/fq{{*$PSoQ|Y(mR6N}ᛞ@it~Q-AWeN\gRbHa|-nkpYLE6ahD.E v!xFh /tN-7X'U kr9W&CAЄy&K8a%U{ΥqzTi ֆ']ЂnJ2:5eA D81Ǟ51dL߶ X=F6ei|1v|/ M`2Vϯ[㿩L!޿ێx uMj mْy`%#@R< 1mx ?fMr e7*U { yC#FNY2ͦ8R2zT~B>9!p#17Ȳɮ\GgAY$y׷G>;oY9 RB`8ye< 2d\I3=S[N!4IxL1{a@ozXҎ鑲m=dTG 쌼< 9ʔ!fj 銴 @*Έb֘q#:NJyGOzsMI҅|2 WW𹵌Mz%Rv.FVMXA(tM7-266˩Mܓ|6wnZ*"ȫ`C/g()Ŏ.6c?OT))m%Dlm@ NI-X/N?P F[B򐪒!M:nbs%1d=RQLGk#NeXXDrB5[u%g ]ު\x{=PXߘ  ~YZݚ-Q)In}GZٗc q*^tT1uYkH-8xcd:C>CD)榠xMg?l+ԏ6Cpn3B@׸-(G*Kv4I*&^J_l#\V#7OQ66ڮ,zQMlFュO \Y](#RVpn%3t[K39}.ͽusKB.eh%}hm C[\@>HƦZߗ*W`JruL0}U?I]5O잭 K-xQN,"TeeV{OwFs}@whr)ukkr4WTxMjhS)ht=9WL]]jr^:&)Y[}o`rQn>>aB߫ c-!^zqCժ/(I5̇^nL#=$bzt%&t7f10#ˆܱdGh9HhPL4‘dEMѸ)`yloTRM/\֙|6/#гV):MTT$'G$#1V f

\uF@̜W!s8,?9&Uj(͓~EslUU7^8l/TF Ȕ'!ŅrAc/Lٹ)2t6x]Xok9Ҫx۱=ӭMw*@f/z7Ύ wGK#k*Ob̍gYX6%q˦RQ*F=֑مֻt+n39,g+M*E#5($Oȶ"oUm=7op̝iWH'0ڀ6pVǶͽ|zH*ЄOנ闪_{JvzZ|^Qlugm1#\1;\,ż%Khfe0Dt3Y?;#OxU 62>@%oBof = h̼SJoZ oIP,?il63d\`p?  c3b畠k#xĄ -uI%ZYY?l򡚧#zJTuJ7i(jYyuf/RzMEb1Tu2b S5۴% <AoᘢLUbn:S "3W:nv]=Y5$݉{\m` ` 77 <,ǥ|absdGk](ɉ<~rf3rɀ]R <$Wr1n%ciX?tSTAiҝv)vaHA'f%Nuh$ c)4)SɚNU<.:: !g3g;d - ? !SF0wճfD" 6AoE{rk5lfQy付:&[vy1`RqP6DVE~Jȟ/p(F'kpD?QQq^)T7Ueu To7]59@.WOwٶ!G;E!f`W^g6bMQ譣{Ӑx+9N&ЧI. :yN|}-2&4$2iGE;te#45O2U)o"%g̨o2?c$E?jFLc(k{Wgr߲X=GBP''Xiz.1H"֍58Ë< kJ7.9>4ISzC y99Ww,-Gzz|Ỵ=IWg9PlR$>~V'>^uDL)LGㅈЇzf8RFH/msS݅H&r7y;=%4"||FRfJH_sm!p5]g}06̿aGrt[XYElME|c1gA-0{ 7h%`u ,B3##}B#/E1ErJGyהbį尲ZʇsRp/f])ڇ\4=7d.@[Uq+qSPU,2Q=h_K((;D@PIҺ5}'A^AxYIYa艆6[V53Ɍ!crZu8\8y|i(|V~Ɍv bsA?+I:$I{P^ɘ>gLj* ݑĂf&<Մ<_}Hb-KX{hmUmHư/q`Swi\{9Կ3RDjظ^*QӗGxӍ14멌 )s#ig%_(揑?L83%2>$`^^{?kf iź &.C~*>ϊ OP aN7ps5v[ܫ?/h}E^dո~I)mf1jxU|.AyJd g4NLuSm Rth7%ֿ5ɾ+ bt,&{Vqh}i[t8{"8w2pyLb6AdUb0H e_|[(6Zz" ,ˌDљ0F뾖m!qVUM.0ǘ*sW)GA$< £3P{pNjO,(WѼ_IޮDI cpWlv+Chm:p†p7+Lj_+ ѦFԋoBǯSiR2eaiG{D\|O!'g@h;:NQ%6sB<"m Hbg*HN\ItU~֗2NDR265=*~3_T T\;M]CJu%Ye'KіhpOQW-TiY-_i7/w4q=WةB[aH^IU%;-Q=UlTD%N9 xiŪ9|uV)r=eT hUP\f,31fNj[ rH ݦɾ[$İW[pm0Nv-:镭~Xo݂{IYYHhm,?^&襅JN0*Xڢه~' zҵLZvޛP+D:s {y_g-VN` pHnÀ]x4Lg-Oȱ\Ǔ/mN\Ve71Gn7oeD`5W;&?)n¾l\NԾ,Guqhc7V}HQ)~Q@5';tߛʒh;nXFWla19;VhhRԵ5u_ GjM4^ B 1HKxo4H 胱c@FUDC/^C!ꗃaIDlz~ըwiknIj6|fOg$oD?Wg~ρHڑ?$k);AJy#VM-Y{O;Ęm&8} m w;2ӗxAxF5)E/eK2Qnp nYqOy;_bUõ=VRcJ:qYHi`p5ptwܕHSe pgh4f!{Rk,u6%_Jx+J;xZ:c_Ļdqzv$`pd6;ChsHm&}v(XO IMĒIm錞?~Ƀns"(0c^;OS ePq?c~e>i54?:ѱ4Z*BCR 3a -Ye= q$`Ŷ^>RL'ɛ DQ$,'QggZDb {RVɮd) $ҙ5;NA *Л{ҢKBDUY=E S!& VI3n~%13(_TpF :*T4ļKnGt|@Ж*l |I+UR3r$q L5h$HzYb0qаXb9>);L/׹N/ecGR25pW/5p~x7j̽Au3\t Qrcx샤@}j `'xMD t+=)dO; "чZyoCm.%z+3ܐ8g$ {Uq[Z¬ŅQaoJ%&S:؀4oAw+<ԫlPz4*f]?,GpfX5`:8B aФ4>zYWӳ:z|G[O] kiV {g>TK6QmZ|xCfaLhvCU>u};+m9n֝@l6ηU`8)Uux,| O=Pz_l=_i:6O؟TA݃hvhp;ID_goUE[8$ڢZd pd]KRa,U}΂=A~8|Pbd%LrlK3v"?gb;W*Mv(E9eyc [ wi8ӭC <df?"Cim/ӿ p>%!LtE h(2xG %l9:| xGNɌO?ųʋG;9y[L6;eWI.DfÁWEIqzW2&KFUV AʨԩւVހ*5z̟$͟TDP%$Ho=mEVlS! X!<|vHD'i'NM9 }RFz'2~'cG|ƥ@_@T+基l6k#iܬj喕W_*ic E.ڷf=ױUz2BO̵B69Mywzh& W࣑3=NG?\ yNjW'՟@I<#'PaauFƌzDb_3FxZ+Al K;<:U>Ko.;[^1 ю#Vl(qIt6vnU}a]_2a}y$REʤʋ=A(r}4oZm) PS0F b$ jқD(F7o.eǎt ̰X\(Gi(clv*tC+$8P[Sa;{Hot|%**]­nx0Ǽ@h倳omOɮUWRfvB^#WH&6 g V7f0l`_o}lu?I=#ٖMDzfTt#!5y%izũkUߝ{gl5,gs]O>⎖]Z}&n:f*H{e!duVOʡ]Z>+w#/p~2}Вg q)}aJ灷iKOrUfiTz˪Sɫ!4",l,?VjKIXP)s&gnmuvz%:\f'bai m8:t~;]b^53^@'Q1%P`]I{ Ŵ ۝XJ6)Dú={ 3aOG42J0жB#RQhCx $w,rsA C܊9@"'6 ~o., (D! #h^agӚm|4 dQ#\4 j32y Uv ; X0KD5Y/ڹ@u3z^1V xDII ~0&v 4n>i _K[! &S7m_kpf 1PHn'`ܮoc?x\Yi{3ٻZu:ߞb|4f4YܿL"܌jmU]1(lNR9b]F%{aN> 뼨k S##~l^0 ^9" R y~Pf( !7_r>Bi9x>?w%^#e/ Sɓ>MyP9'+euܩn|"M43Jݚ H|ZaMMT0!lJA{C͂pa .X4D{T!N "l"k/ IlǚkDup t d0@m/t͂u0z / dW5hՆw9o8߱|㸮nJv,;AbM{w/#=+SD.D䯦7TB֩Y %2.CL73;hin@~q lb8-HvW3F7ԹwQH}(wf1e. f};3{ȓo0-q3'Xa*'!mYuxlIAĿR?ٰ8`(wT$T7uZ<@y|vfPrŦy0]V^/>WZs*>B؛m@Z&\=" ᰿ZD7͓VFK7Žd$\m~:t63o wH8V7;Xa`k=QZ:$4Y}؃o\f)4jiFu<> 6Gz*N$JJApJt+GQ`x77ʸ&c}/bkFETo+ޟ`Rco[yCz~|FyNúv0cʕ(QiNؼk^j8 %W: !IC`PQ hԎH{ )A 6*W<ü2LpR A "7ɻ~1W;t˷Nчb8DRTZwSEsbC]mJMotQxG͹as y&: s3<s (PD }R"/փ;W nyϐ;h*uK)k6I T|<@…OQ;!qJ2R$ $S*FѶ|u^?`9Ըكyk@<<Ç4Eq%<$3%$ΊK^qVEwTe#yJ<_Y-@Ú;s>60 ?[dGpJp], hL*e:(w᫢k}g-$[2~vwGLk>;gl{0@_`gYeܓ6R"[2))@{ bM1_#LrVJ- ;ERS)Kb?O+^m:|ܷIlP 1[1 Bg(1x8lkg/A]2YqgNw4zvaƕ%i yL=M{f_t;EKEYN}S8-f׫TN8  @oC{jA_Nkg q4@SsQHk+ /97*\^s_8T콹iv0ә썆F ];}H'l+gq'9+Ym2lksQ_-6ᢿL +tT;yI9pO@XenW=Nnq6_89y|;ڬxH\0"\?qxPwuw_tƀZiB&07b3QW6_te+XR+%6{Lݑty9ln٧ t73X&Η_$REÍWkr*uTxZ,.Zim7tKLGi΋Fڝ ґ̇%S=8@.+h@'!qEYfLESZ!aw09^3όOEO^6BriHTVN]96ٟGy,DiR;Z(iw X~Q^G06{Jpk@MkskԍVS2aϳ%8eQԠl埋ЭqrIi23Z}n Ԛ,rδ)>#y75'GSXmlE'B}7+6'C3a``nރ'lwCƳuжhp@Cn4'Hgq~&S; WCbKG{,UknS6l  C֒͡\%6(b="}5؝E߉/^>Sp#3>6v踤rsbsG5Osj1%;Hg+]KЌ?y_X:q3. &Gǹ6kN z;;?RVyXZ[\$:%wy 47j7KԠCG)#Xo\[ҁv DiŦw:RjQ܅bjj3 W79^=5*iG'lFۯeiNCd8<{{MVZ7~cFW>*kן ".֭xbN}]gDWTDbtCU m3@oDʊvЕkG 1Y^}tTE%nZ{#d0bvr1@a,> _Cp9O.,m/6"i"!+.oO`ݽq,!0D[oy 뉁n&uVr|\t|vr NEy;7wgQhB3}#nx@3$:(eͷbhL[3u\T,*q#xL+ѐǖ#LnX礽(ޢT#ӊC.]r[8fw7jJkH@aƔ+Xd}Cռ)!;?>g9*6[:f#Kq:ʂXX+):װ\u9C;U(D"=O:R@FۏbWI1%W}LLƒcSIxu#2q@2yfAN?sK#r k`JVbEIJ?r'Os?y 9h8nrD3,hɀ&v*Nu ĸlg@]sChy cR(uP@= &|EdZ[scc>Jи(1e+wQd,j)Wm9?o>qLnX[~[jojUtE;7BB 9I'KG*483ķ7F'+G-`\ɿؓTw>dE<7'O> w&R\ ]3N$%%+NGA䘓iSikA٬oy0hLhs[Ѓ,*?d-v+927Ι>ATvJE$ [Ss~F!68EQ(9f5{Nu8'Sr )^>/vFjk1J]3xoeJ2MdsBASe5ڑݛmkF`t:}_|ХDE,XD y`Ïvϯk¾h;,e{kDյpYl㹽αe3yAaSV_U޴ cg]"1uq |/(/(YЉ?Y&D݃PBRk;ճ"gדWk?X" %FBGa:N"S: 0AKz2譕_"ZtM098'ƢWo qV(#Έq^dng˥;ZͤSŰѯH+VayS3 > 9-q(LNtXW q}9FHTkw/_n_4l'pQR nYOeG@t(W89IQ:xb4X!`#ʏMxQ%)༩''& `u!}XrxM!Ek9ZR+}i @nK^[3-GQU1]$d><5.8~.#PW&DEvu~|3{FKIACj$M^9HK?7D)?,  jfLl?6hg<4ev۵&S5 )ݼ.e[f cI3揿sU1H?-1Dcr^ /3K6cp<2Oji4"c|UF} lo~%)ԑ;9[K2 h)EtSO\`#yFrYJzEl*+ut)_>þngGjcL=L*1[ho4)yX$&SqQﮭΠ:N +a:h%:QV-G+_5֤ rMG? F?x f뺗M2jz8˼YOeXgrnʠ(%m([k$ +kSIFsa]/H3ߍַߒSUw|B8Q8)ɇ,.4ɱKP\]dYv˖Nʶ[DŽ(CV沀s2 3 M~f4П'+fwNlVXORrlT/vLLj+N ےe4m5Y@LdQeͶJ{Di\#i<%Xfak䁸g Xk;"NW^ [!w6i(2Py7jح;4ZPh)l'pCL99V/dM 0)"j Tm]"e]T҇2ᝄ7]5QP_X -]c[1( ^ 2̦Z"Wy6tcʦ?`@3ClE5rIm/;]̾pOd4[o|u䥶2I(dɋݪDz[!O7wkZh9w#,[78ӻ2yVinAbWťE$ qQBfԕ,~:7Qo2ӍyZ̡cynGs4T'PX5d6cPʂ~"R]oxlVD-{2IFǡ$:^tY갪JTBEz&%Ҩޒ Yռg [H3~DzZqudjRAObaV,/ K܌)hO d}1qP"~:\0*{k8V3a<{{1GU=q0ǿ35eNъa z䕓wT*.ʰdZ88Z#veq? 8-5%&qU>:iZܪȬQ3Ďr>,9Җrdh1-j>F=3:-9oKR^% ^KD9vTW˼،{uj ml4}.O*~ 9cYTld{u9gX^*V;K5kFG~w j? |"-|򩳆Qw:I6֔Z;b"UNڝg/ܽz)λYʢBɓ0,ʷ&CKv#|aXz.%>]S'vPXīn8>(o:jax4rFN'BEogּoZ&W/EpK1~ź<`!hswuOPPg4CG_odffHS:N{VecuU%Y^>ћ :G{8<;bb3TdžX}۵'$)ҷbs:uS⏏?GmJ@!!4sBnuw2ھn2-nB}'ml27^:U9$85[+ܪ 4)Ap({62 vH]@:!kYMh6B®[S,IH›oC ef:",jG-:ngJ~Qt7G&?;!u\5xOJDw WA=Zj:K |9CNtpՐzЩ cj}yϖVp.R:1MҐS~=ʉ[T%<^|1kڷލ!lÿ OED:0YJp F.Ik3q8Wu$dXif7-c#RWK:zIuN# N ZtXڀdDށ:9m7QBƈ} ÛrXmswk DH{z;l`n"n{qG07\d†N)拣9o m*a2@_lWdۧRPzx}ճuHP"Twy:U&!Q%ǸPlZ_+ƷrA\qcbDԶ޺09%Ow16ihzzÁ*a]_ٯF'{,Z&#x]1[Uph1Yo.]k SƘ!Ζ i ieL<)!ƝA 4yH< ,@ TzƬ$ބ?{v="۝s0ti>tۥ``մ7ąJvnӝ Γ wWQ]? ,*Ra;2l=vޓlhu Ԣ{Rs "Gh}$IY~abu2IW͸+~G=@܍ylRzD-!@0DAB|EDp F;,]C%]P"?=!ֹfRGaobH>FeTѫP!+gqqn˕/\}6?d V5񺺾uXl8g~y9 RK'ƛrMrQqj5nax݋Pqt7 pZbr4Pj:!)v>jD&hu`F(%'^6FԃA:9j$2 A z9YBjd{/D(:mp,^Ja?ܢAڄ,*fա÷6C#cQ:RU!21p/{+p"_):/T@:dK~ |~.)i,! Wko}! кG,+g€GgqGL~Ǟ|p㽽3fuxE>Ec즺pآi8>f<-[ \XxndxkRpVF5deU1 %yo M z&M,;H?\鈭%ՊJ] g(_ߵ& ڬS'`v:#>k˚=w앾 NkW9o a$W Ǖ姒803JQ-C%ր,c~COmd8խR񄕂!![PX q~Ia er#iOD//| ێ4GՅ3`WPVR9 miOcAɢχYw!2#?y(3p۠_T,3yF-p>dojZ?{)#|\ [QL`_B &pēTnGb8auAQR+{,16=O>bWCsgdCPطm0¾+1+{np, }_4r`r-e8@/HSn3{ڜ3O:x[0I6Lai{AA K{e67m= 3@#~$!3/Jc\*ᆒzZ5, Y׿i߬dzk׸hkmSZA(!MN,T;(C"p2RL񤭳grDk|PA!;g̯0u43YȊ-Txl$iPo WZIY$/ E+tvX QkYsW" pQM Q,WVl0'JRq{r|)&M~\cGhmA~ P?uFeg vtY`xOpuc )c]34?d^WIg54~|{1tb4,+ Kׇ{G [R<4@ /!2hM8fܩjOYfTL8N򴈒Pc);ҳ|(getŞPGZUQ)P  Me:G!P : g{JhM4ek"d^`ΨDg%^;d{KA8u_A^PȽBS-h7l)=e~]gZDΒ )9A4f-RhR;\R,0&ykX6s<& ; x3rM. բ "eɲw'0UiM.D!):f73[ӫUM@I6cֱfQnd%L7;B֎y;YԾh V`зYpPj.d͠> bm޶H1/f[ ?&s~2(87b^`CbBlF %{jQI<\lҽ;Fw k Hr몃.6's/oɾBZ}{TB:X^$4*32c7ޚe8V9 g8; ޵4$-dtsBЭW=[ SoHN*yaw2*ѧ=+KfPi 8'ӷKgl);WlSoG[1!}Bm$H+;L@%l_7ޝ [è*ƭG@lh@R+L<*PL5_>hR3t=% ״h䮿!75Lh^MHE6Ήu4'DOL*q S<=q P 퇃ia1k2'ܡq}Fգ$tyaA h /pmg4Ef-༹:|gSVGi }#] N⢲U3m71fNIܢĶFcy,٬gT/Th {<Eh;-ow5Գ-#KH ǨI:C]O5})!n|%7|#qh]?KGHoI (VU$x3LrhՄʯy& %'W]fX X3in̮Ey|K$)^j-7`jѶc~/e1= ˨@ķl%0Vi1k:=J&^R}ϛdP(`$\g@y5ƕ= ["qGJ8j g4<X[78=|;9tsw8SA2cW O1Lx0F8p5fxp1:81za>KUiGswd5?lt<6I;[Tdrq݇ɲj=OKiAL \5G}|ufL% tE+ 04ŀe@ /, 奈W7q[Y~9ͲMA ȕ R&IeꚤC|O.#c-G`#:,:I'ˆ. 3@l G,rz.mǏ[?3-lbe 8Y(8mHS8hQ[!/_RY[l<\Vqu$Jx'(t @EtIdhN:&g:-[q*>vs@}Fb?s_)2_$޸knvtM)Ck_]Sظgn/jH[H1sDӯ} ĕbeZ)3f; emBZ8 ͍~OAqv~b=){D[rp-KAw?>Q\Ggͽ -Ym5oц*1Okc/fC@8Fkj:yvh\('#m ,23_DR9U{tQy 䲢P^K&d>k(vV U:x.| (b`ah[);0:}0͇J6IjUh~]mDQ^-mn {'&樫Yb< g:o#KzP>DւFwIb]JO|21E,]qr ؐ7uP<` x= 8A8F@t]atPz@ o1n8Rͭ!Dp98ɰa0y&-IQ_iT{y\aKCC/XLM@n? Dq|ͽ݂ *Eu ?cI2 $`sr@W[jtJ!OU+53e*o3&ccڑu]gŖX&=^ܹQϳ)nšN笃 ]DَAh I'Z[P}g]u.%H4}. 2* m'Z _)!^W,ixk|ثr#|*&|Ud@=瓾֗ݠsakl_VG`=Znh߂# TWxf-|[" cmEnQ1Q؛?We2j7 c8jmm4CܛqS3%@ZpQ6Xf-QPe6+ܙ=ߔHn7 )|KRԲ z ; n9ڹ1(%z\Ba:Ģ˳\C6^LԢpQQ[faG;_rUXB`5S!C˨՗EcR$A=29Zz_<ydFێ}grEZ# )CeF@j8I P*ųfrB&)tLz؎Pʡzl:Bϐ2ѺPo?*o $CUҹ -Y \m< VsaGmB`̨?;MЦ+'o!UJC,J#lL q]%FDw@苰im|d7`‡Qkx53/j~ (jqC0*9S#P<-,U"{ TvU`\ua[w68+ Dƌ!i9u-\_TϨZȘ*c[eJ ϸv.ēkK`n!PFoQ^Ek!0BR1@wp8mm4,],D4k'Tn#"XI6,|*T)_7\$0IϻY ǞԵrZBtu) lH _RL 9h$Dvi"3EA)!9+i~'[a'CkJroH]n e|<^w^[?q5`p:Bx.1Ok_ȿ12J{8W0uCixdh܀۷@ci).c2Y*9:ˎv Eq> _sq $%UZU0ʖ~I2-B՛NZD6Bt&mOX *MC#:fg7O4$d[pvi%`n 3@ŏ*tW7K_S8эv9]9(˕a2mRtU.'S~;kaMg7>|̒9M]?L*k&j<)P; Ӏizm1+DJP0XI뛢LBg&$s\tRAop&14oåkf}7'ji]}Oކ7RȓeU<1m"r3L=cO ZV^+(exX;7)7X5̙GzSF?Alqvl)?4AXC=9\es׺hUlB8LC@2_R&IoqZeՆV.ݧv)@ DJ_T1 ]p61:e{}@饋1n#sf\u֞֐^MVՂP!C*OU٥>KHB)B5]mt' 69wK$,V٣E2Zw5%`pw Hǣ3FĦծ:`wOf- +Y!7 ?5oSx{,d[kqWwhܐyTdD[:6&kXr6 <~# vE  R'k6 iWB[\0z8$m >`OO,tKPXg\z޵QvQ2)ΐ<"Y2{z"E)Ք̗g!ϟ=ߗDB܆hmQd.J4HANߢ|lH?iFKVEn)| HQj*wЧ$NVӝD_$&u kD Gwxμƍs&67)yS4S|/ !F_jJQG͹XWvu> J+.TG[GO͂:^/ms!&qxȖ/΋z ź ΤKK7Ghf/Qu>d9KYQ;˷5ղS\(5Aj$kGQ ]UUarDü<͢$ZZc<9-ɴ*nC1HZ rQ<>L ģ.\tP(]vis5gFˏV?̗ X K >ٲ0]|vszJ0^2(1/ݗK"<'KrC&7DwJ6%XU4>ZMG.-pc~zKٱ(vbQOwX߬s c8f>M?yؒVT,Id-;~_13l -{ vGx}7PuW?/)Ԉ-eYl,bYbos9JE 6"o6u$:.I b9 AALV` Pf[>MA[˥'̨SfgzT9x{Jg`3@#CFVvXQc w<$J5o> DUoS~<=$Qx6=IЖYp'$PS:_$|Z6xh.,VS(]$  1\*S5tT%rt=xͶ#b7+s{z@wf b i߳F|f!QӴ )pl-: ;r$5TktW|P0+G)nx,1Zm\|;yg:"Zvf: u^bzGÕM}) 㫬 ՟ϪfpĢAF8oQqk\oS9n3.XOj>7US,<ÇGaX߳)3ɷO~@d'ߝy/B |)9Vo>Vv A#%B S[IzSs>zH # Ё^v'o}`X;&?:+a]yQT#a9h"\՚駻/j cُ2䋠DaZ_m&53ԂD6V,I| vwqMk(d论7ƅB8ٝJ=6"A«hdvb%p|g=v?B #RЍjc=Dw+R[xc&BZ^Hsާ Fv"fE}j~C妯Xa0<۟Je…~͗SܟjaLMDj;EP+)n0*lm|xZfhwN$/]aGE܅不{*RG w /5&w*R.9R92 goE5xZ_mhxTNg*lyY]1NH A&#^QcjKAyvS|2h]%y[wm d/xj DijOńKU:F3b PRha1hhr\e[I4;r 4|3? L:^K@K3eP4e1 Keb٩P"oJ}Dƒڶ5D_7 ޤM?ޯ ?6\G,`H\i ֶ{䯟9z,PqI1%淬{?FzuI eC+j|m׷Y=VP]lf75cVv'0{q]'M-THO3/{Е5u_8a-V­q04 O-CzpjڠQ:`h c?<~m#<)szW$x |ch&\mH|EAX ,`z-ulQ"*P$c=<#D9b783+t֔> dk.e͑'nbN wƭjOHejI>+[>6L! dID$mzG/RvDbf~iBG5 QZ(́GztWaM$dMB "OHWb6F~#9HIJ΁ ^E Z$ @J?A\׾TBDszyFk¶ڞhͪf9VطI9RV'B8c<2Vkcon֫PVԖƈxȅ6:,Ik:hꧼ nKifLבT|z_,jqB}ikus %.r&"gXH3qqxݛNWO/I+aZ/l5c+A'U:+}+'"23RV}:#Gj$y\ȧ6b(Yx I23,,ޝ6Fxt WZΰ/ξ]y"N`h8ˊfV#<чK%0ڴ^X&Qd j+Lǧ*C_ʓ B)P,u%ZF@GK F~E;cBʮ)*9?=f:;ۚ501iS|9|}o36mYsmw(t,/wj H ~8;Tq 0zi4 2~?EDK^7\$5idcsN90ӥP.u{R^eϏ8ƁI,v07UlmWKaƨDaA{,BGogӻx:u \䫨$ *u#!vȦ_w Tb L 20Yx(^QR[iWE4 -gi81YJ -v^紸 4f3[а6{|g0TF3_ 㒃~\r*BR'Խ5'BW`'ؗEh%}o Շ؄E9[( h+[=&Ct&|R jCJ.iſe(ssxp)$/_T#Q>,:>ez%gz7eCOٶ'ЄY;W އ Ll+eXW@F)K0pD]ϙ~rLr^v }L];~qQsR+Sj2_NҰ= gb5uz,iR&\̧.g +(`j@8zN1F^ I{ݓ,0N;UR$XtU Kg+UYTh_B2ى)w`D{o#y4YRW|Svtǫo|VJBkLL,0a}#Qgip2,TB\8Sr>Vtt}5$ eaj{[׫IҤM%n_b#m4rG2`uj@$N Gw!CVnT. }zz4%RdK*.t"DR =vrTYc1B2ͯ]]uֺFm2tE{-3]J$)L";?+pf)@Jr|=e8tc[CGYѾ㕎VCNwg6h5{t[47xà<@WtrM@g_ƝLwpky878o iXd\{-Fʩ=eO_Ƀ@YLϹ!>0 YZAER/data/MurderRates.rda0000644000176200001440000000275513616365123014536 0ustar liggesusersuV leޕRJiD$/ ȣwJT%qr+"D >@G-^(X^ -Q %(V*g?p&|3s=&Ǖdp&8 xF>w*#h0*$LaXHK1D5FO-6vyEB}PI,b@f5Gi{)[/XQ&=LaEօ-f=m }_C݉VkNӥځ:ZaV5ԕ:DO,=X-jD~H^_+ٴTsCbzKTweA~A_{1Tu?Bkan~4tq~,uE{(!jƵ_CTRF{vO;udUɾQۼ}i7ΡOU9U`1+o9=@ʵ&sf"|8GPwMnVyֿ`2o)glF.'F)cc,c"Ϙ2g3a`<8k 0bglf,dfXϻ5Fop(cAF[Ԃwo"Cο0^fo++Mi_o1M"ߴ'Q':)p`_|侤f{~*6웿qn\^E~:kGK;g i)/|r /wm_!??K_vzGsvC>xo2f:Ɉ'LzG ?Sukc<˔~z>dVCTm.SF| -^=I3W{r ~?_;:[~SD {;j`﫽Q[+}_cM؃WJt_TՖuZB3k[ɳԾsq1>^wq7;ŀ|'`BapZTBj0$t6vBqh8R[y?%7 C тp`I0P|&>Z0E9$a},̛ }( L, GPLF"7ߚ ܈KWK JaJJҵҒ%jiki4Ks4Ks4Ks4Ks4Ks49LajSs059Lnnv3Ip^C?P AER/data/UKNonDurables.rda0000644000176200001440000000101413616365126014743 0ustar liggesusers][HTQΘyIRBz)'!aD  GaufD--KtAiMeA$F{H-')*' 00C-)Yⶕ{.,1" w {!/>? gP4 PP/,J+7ŧbE( WPqE|q(_VZ#pA=\,(lA9CPܦ=Ҩo6)h.34H߼ s+o'o<\NUg;f1vGЩu}yS )Z]_nE٧(=WߣWԌ $=CIP*nJRJ25L-S3ʔeJ2ELQ)WTr%AR+Lүnѯ^ׯխ)5"^MԫGz4 y5 ̫y4i41i>Mק41_k~-_k~-_k"Y4f,E@k[@s+J5RͭTs+,J5R͢T(42-o&VibeZrM\kn:=:=.~-կeUOQQqWMǭYx4tLzt zt ztytytytDytxtxtxtxtxthxthxthxthxJ51y O W)גkʵQ5];;^]:v:v"z]Z_MĔ^%^^Q^&˫cǫKWGk5իk׫իkWTOL4c^_)54309@pPRC5-Mu fCsj>4%1P}]$Ԭ%iD)%T 7ԪXdf<WZ8cw ]p9<3aYDB uJn@5b^njR1RoV!XnٚXp>HjaӪ"X)UXw2orU"\Ӣji5sL9BqnX;쯎7̮ksyѭF`).1A\c"HifƆ%cñX%zfIm3 }=t&4J9:UDL2 'jtb񖚈)S%³LFa=?56<>\39e|8n`ɟ&. 0pnMnDx㱙Fbx\ 0jr1>/>'7FjJoc-MލoŘ҄&\HωԽ:UDq:WtLD-.Zi ӫ#qpju|ՑVkBƴ|Q̛*Qɑ\Q&WXZuKC]5hn XNᆚ:nid( XH@M"Uv(f jpS.p'ÍhRDX,S,KV261Kě"lkQZuS &/L 1SJ6h 2x3uatQIpF\]o].mcKs{ t3G; GN*+: smGo7z~}qy9#13 ,o]w9y@a܌w2os˵ǀ)@;i;CtK]6l6ແ{vA93!z8#63ƶ@[ 6[ `ܹE^1y'A0^w 7FN7 +pGSl﷡@'ڌZU@|wh?Y=@]a |1eF=A,7l ihnOYxz̹\ȁ޿xqn>q- %, O wbKѶ6|^y<0ڭ-p{UCny$%tl~!}3%jn<&7(uos\ϒtw^,$~kT?koIy>zItMkw"{0]z+ʝDߙqZٮ"C{~orNp\뛥^| oovݰW{H߲;Kk;qn͒D6"ۍ^+wΙ.lMʳхn&G8~dۺc'ڡ{__iXS-%Y|)^m^7m9!w$Z1hmn){RS77Jxʑf1#˦y[]rƏvR {v7+7Fn]~f߁w.]?'mWa\nwDžm n wx?<߭$/K6/n̿-Wv" wXcߊ3% 7?q9/]ۖ[`myᩨ=6D0^ҿAD=,vnl&I< ouHnO$Ӷr|/뗢[y{NǺֻ1ymam4ٿ `G,-E{^umfۮB?ް 8}ڥ˿^7P曆Ixہ@w翰oÒ(GΈzo?K=7"߭bٺ<|חs%V\m%}z5B~aӒ?߇zS^x;>,7WbY譒׷(SR/_vsd_6~d^'le.3m=V.4焥yۜ˰~|Z8Χp7];ao8߳Z9hz=W;>蜎eৰ8CڏO'9Ͱڟo=½.眄9Ǟ>\ V/^yt?N?鸿릎G\||z'ȃO5{ke/88s?Og5nǴ=sIȇ+p<>xޱ>9 ϨV8'Qm[zXwI{@8;gXn^Hu=/uDZ1{9Ƨbz˄aVn|r95Έṕd;x g^0cNλ'lƠgAmˆ 1tL4_aC{ <}^{l8g{փ>ANsNf~.@χw\73)z >!<mc|r%~2j;R t˄yojfpxǚb|e͆r"^CLC;,읱@} s!WsAItA~wk?+wYa¸AcZ5(Tkd[" 9w&}'fW.g\Lިύ0g%12b" $ p2.-]c2!g@Գd/Ʌ1t8ȄZㄵ>@fA.3 ϸ7Tvx7Gw6(8s?%4N@ND0HCO$|G$Opܗl)&:pPʃbWϤ?#zWхxJ,'s "<;S|e0De{NOl<חa6:,G1g=8RP/~ow}N[\s5 k\($w]=Zpj{S;Zg=OI+ D3j3n'LAksrq8ُԲ}<=g>̇a|m'֓Izr<$c|K%ቶb;muB'*}o_Y6~\G~[o-9θnl~lyʴַ7S;x+q7-SW<~ˏm;~@2}8>y}`y'H:q"ԄEyߙJ|:&YnG!>ywjyf8>yXE8yHnއx_?%3wMW Łғ#ϰs^r@dںp\lyߜlހ\4q0UѼ/yofx_ldj)~Glzd>_g>9jbxx=ank~z} tֵ\_lui,ՖQP[;>|qv֭s[")_|} ?n\Gy=t}WeN!{BW>9 9y2c{ 3Wp>7g~oX΋^DV[jo?>%x|Q?ϳx?p~zvb~ }9oy#\t<^?ü}^gӵX~v^Ȧ׶S~y>^ ]oE_c;p`>̟[s?_a;72_n9JY߶فSW">,ӳ~T]{p\yv:w귝>v^}:0e>IOYVx9JlzG\g~yu?휬-o]8w?N>~k2ٗdp"ڼ+Ű ./sŸ# ǭph(:О|-yN>T Вw;G7>Gl*1@Zin Xsֿ*o*YO}p߿|{`'~|aQ,"]W/Ü&׾9fHKn#Q|@?_)<P~_!F Zt\o7r{Ѿ7%BxChS1FH{y!bh8, ^((7DmvQgEweQ¿{!ڵxcq4\*OكyxCyPy5r3XtwEy/< z` xD`xn?c~ 1 #.3%PߒeF2mB~ I({'k$le1^<|,yG@Зk,8|i⤑XHF{&c|1D>O}Ʒ+r~a߇1K~Ԣ=<ЮEXwcgQ0Պ*Oo`yȯxjj[4 h/k#AxjP6aQ. ڬ-.jR[FmM|YLo>Y~gN0ӝag|a Ob9ٞ+eS7Jg>+?I6<0!.d3],_d;R봵$ǂs<ʎ}llIޅ?bg;|Ⳑ9IŤ#1:l/_F-]LvQvyLa?:hN8`{+>y|A-aQq^j*븊O?a=z,g̗Ylx =ymNI43dzLc8N%=ZaUn*o\ٞ濜`[oEo͇/%c~.=I^^7Tq}[mMU~"^XS:_,G3e{|\[}]fE/-Q_٣8sX[{-z/`;=΂I\o&QگuNjGtվ븭k:,?l":/ x]b_ѵ^|t\NS?w}߫?Ǒ[ £q3j}df,|o$|Nj\<΅ N2iH,ĩo%*}GzʓqMFΫV;Ì_Y*dL_%?2َ |,z>釮^o%~[wd.?J=+9O>%|hsgU}@ԣj SQKr~Lޣ_;;DIz@d["+K~IZne#W 'TTxt*;=JX1-m Wv[-#_$D{1.BipɃ;_M yhDp臂/e; ɽ/XS8ԷR-z^ԫUmPSq~h٫h=*^/ٴzH6lWݥ}~=e|{ An5;оWo[g_y= }{xg•קEyPk/+'{<ލW;f?:^V$ϫH7bW݋ ɒϘ-7/ %ՔDb-4~ Q܊%|ӎE`d+<#wB`] 8_)nbQY_(4bb5=Qew" Mѝ"#_-T-CpxKv鮲}n/`C;vPxVpS}F31*xAƱηo0.Ɣ\? U~a ʽ9^vP o߻vy=:~Z :ؽ͡?!Lu3p/7 ?=QT[88лn?}OgX~ }i 97ly ? ꏣdS"\G}f'̿A=~Duc/S:%F@vt{|˳#"/h'<~q }s~ѭAn)tƕf׎h`FGvvQ'WGtEҹ~sCo+Ѿ_?E ~DʝO,憎k|Rl>?h z@qyw|O?;}OPwW[G{j~%o_>o%յ!\irR㘵ѦDٳ{}KC&EՏ%GZK55Fji|f kjLPs\lSM"+BkA2o eBM6~T^pSM<Y2cacٱhS^D|n?>-/'~ ['uK|cWĥx<̷s)?.]3)ɫxx웽_qǗ⡮R{7IB$uV0'S<:)Lݕ)꒜QLPt|˘-cS<Խ:ww)62}ab2} 2} [_3n.̗inc\mxr;rgGs+&VTB˵j)-vݷe& =44L LѢ 4hT6ڍ4Mhh @44)Rh4)Sڡ z hi F2ba =# $AD)O=F !1A4L A4FQ'ꆇjQ4TyTd@ 4!A CL AhAC!4H FT<<&Oi"z6S5  M@4PhyC4܁~t8d&0< m$Le i$_v%&UȯݕZobA+/trd6) l1dIfIAH*"m|pg|}sz|my-70ʠg OkԨhn蓁| )én4)M"f `^Ss!X$tOr@$!Pi M( (HD$4b=BIMB E% AcJ@XP*cR,Q")d@B$P@hFQaD`($Bc ia$JBimCn"cmC# %:P)u Lb%F$J i/ԋMr]MW_:'as9 GPҍA`1%]݄U@" M5tĂo#ofGlܳvgm-&c_C>D8FYu#H4q1k$v324h`#LHUT $Dc^$30B2 1+k%RX<I 3B2,'Ǖcu-W:'}4UӅHuMKZְ@1x^fξ]%S/.`Ko $uծQ$ThY j$Um8V P7}C8%ӀVY:kv\Y Cg3˨ n.U7-BF$L-fW?Uz.F(yYF'T͞Gq([[]n[lɚEROZSiBވV?fAgYbpK^.z5 im(%,  _33<(:r18 r| bCxͻXFkkkT8D2\qPd6/QdIlA dV3YcW)B ,e\] hp*P.9%nLF8 E'zВۉ %(iR3”4igxM=<~1;CVT-*ՠ-v2MNfȲ"pv/5pL4ldk6 +8U,'ZHfZfמGCf@+Yq~Qj,VDؑSrIt%Ȟ0N+Y L(0$xT-KXPS̶`x\IB5sB2A{!![i @m05Z6TVT}Q+d&zSss<譍85*r%b%"*HxEEhЃ geLFvƄHa``ttײT֋hD#r I!a(ʥSq!+ƣF$&O&auT"Pn;/ ̞ڈ!ӊe4r%V؅#u6fHYw41FJQ.QbΟ` 6я̗HLε 5wb&=X@ac P&s9K']ɱ1p(..[tY5UB1ۍD+2.MWTY/SXI+ $kaj \NcL7Rb.$ `シ$. B~W7ձ.(`C"8N.14eJ %\&t2\NYi[:O+OGITg9*1lIWithL²i6 W*mQ)uTS-X 2e7 Ę *h VOch\2G]amࠞƹ:khiT{B1RJRʊ&f1/T5\\ K {+c$$*- ֋ތ  F5FYn(Pa0$1 @DJ @K!b1@hch*X"62%$"F J@BV\mT}M f::>2A5)h:5v8"@Re+l)+d{IXD ;hI#|֒E˺s$i E-@@ ;y$lX#RIɽn#^tpdfaBh ~7SKkDܙE~iąFWD! 6N2_FtZi[hБf9ax@  ]tБGGyI}`%H -R8>CHgVS{ "KM[¼ Jլ,zZJVHIF<rEDKn.lʶƳvlلw^itC@a7 $A3-1F6^> >8:`+Jʳ~MY}o} +w6Jz)|jV'R\ kR杹=ɒ*oP$ش 攊)7JhIem(DVS32{&!)I"$_O8N*'qiiM8ڢ;Q EST>k݅hq)>{Q $FؒJ`ڰܑ:I4-wm4i/VdpZ1"#܌ͤ!k89u։}~ۺC&L%ʐ!뛨UMUwjZI/B ǠNM)v(WnA? Ss$3 lp"JDKeoSt\h di< 뙮%3a n0ŀC.Rj6Ě kQq:q$W> @" <Τ";M>gi&]mJFYݬ%BP=Eaj/7scl QQ%Qd:cSG|}8o΅h[ vt V^^`)Q93tB$Ce7 3{|2{ y c(:bHHVŲ4,UPNmff84tSE%riTA)z0@|a k1.VzpUV-wD&z)_( DBކc])CbZ&<, Ĩ,'ڇiy^db6(|2-ӓ`@~5YcN60YH Hv/.EYf %oo <@p|{WS( '=VicOgfmfYfn; 8[!)B nj>A&戇4I C4 HkG`ql5i:yasAQ(řԄqc_/s pv֍ }7O`1 i6Cc`160.qof&8nĆ9R.; LW*Y3ץH*m=> ,[ѕ.z>cARDS-e =m6'4_=^rԁVmTR^E+Wzq[y%rK ;YZ-}.I6v.)D$iٲUւӮgXe%L3BEj!Pj֦KZi^m[6v>mPySFSA%V1AzUZihgjUhPGk^cKJUjNl҂D'&s[c00U&%HZI`$;1x.ÅY5HD]yKE";LE"AA&b)JЊ0'AQJq)uE`6YM k&U8KYo?1Y;p/s?]B@8[AER/data/Electricity1970.rda0000644000176200001440000001470313616365114015076 0ustar liggesusers]Z|EU@"HB 3iN :!A)!. ]iR^DJ/wi@ɵP)R/0ygwvfv?T/Cu;T!Y]W͡C]Έ3=r}88P4PAߵv߰e<1oʋO,tg{QW0ͽ#"7L'-]SIJV7Caߟs;ֳA+tqE!6rȃ,rVAa_Wa':p'vf [. {aP>#"IJ Eўn4mR+;/Ң=l! =|Ŭzֺ&`[jο7~nwh*~!a~MUX l+ŝ[O\cF\}Z=tFe?xA_}!z65\>昰8NwO6~~u 7VNI@ϏT鹰,q>~t=qvMY{1|!ӝ0X-;;5̚9 ߛc\~֍ctmVuq)g!w:q.^c kN|C,`x]*pc 1]q`éj2X{q/;ݿ50GfVRжtuFqN6 lynj~'/q 7k3bd4rɢO?8}»,Eo4/YQ8&(_0(d6+=~t[w}>ܙ~;Como/t_-=Y/iI'L8νEW.[k?9`~=>xG-g5|zN8',mA%w卖*jjYѵ#RiYe\ _!ռ9>/z-9"[8\^CsWVQ']rh!=*G8VKQ?b;O~C辿ڱY.`b5 ,Š}rb'Ikg7qo}fp„_7϶a[]n<D3Z|zSG}Z|E̷doݜNm7ՙ|{G3ߣkǢYjd^ +ݕC$+yKWmQ"Gq.|B]:7'0?Y7O՟'ץ+3b~W<} oXލ+w,.hnE/e1/LrPxK*_D];vIe٦J)c4!&qlǴ-wVy\]J 1$^];_ 5Cu564~'ht4̜ҕ;D~+b=GE`}o o-75/=M8:_ҚdHuM6 5hq:Jd9R&% K޼?w_嚼xŔ>vkJd%rK|6brh#S EfX>==sH'[@jAc߶!Md[%`'ۀ^wAj@ !p!" 2xċk"q!"ӓ˗yf֯APV YhUdp_v~81e_Cʷ!AW@a_ O؇m!O!Ux[JXswix[A$e5NgN$zM!d,zv$A<Оl-3u|R(ڔWAԺFȡ׼!+);h "J#CFv'S53D@e_1{'@ ] _q VMt1 ae%BR7J-&!`)|P >iUY@Az#˾ 夜 % ص뿻WKMT?r\R1)qZ kn<2t8!uՌ!K2d~%dmi TٔC4Ǣrȓnr #pnҍnCO>WHNeO~N*;'[wd:4^B,< \3iA풬i9,Yxbp C,'e#U~Gl }s; xҐ' U!o8.\&['1;qv\_ށ q42 ٷ% kN|%r0l\1,3Cr| LEգ0 U)j-\ >I GפumCbXe/KiCן6|-bZv0:lq`M̦:&ؚoqږnGJ%EjH#$s7i[(iֆ܍E@rjǠDFͦMD<*\lTGrs ϼE /v #^b^;3%# JhyՀ̣tnRW]o6n"?q3IxHQ*(v>r +~LImJ PpM_+ VD+yvs7@6PHi(#ke7)le@,:j" ':u x񞓪| `w纅Fxx*xpWx P=i?BwՏkb϶\y'7dGn˹_8KF|{~\(F/'/Ȣ,R8(>}8ȝC9.Np=9W43h.y~/$zàxm贇MtEd0_Gtq}#=hE~]8nzgu|tQq>sS9k4#vݫ݄w`2!!Ƶ4m%3!it㶙-eLwXi/c֙軔QtVugrc~єb@1{J ,7)""S xvě !'贜J˟ Z#wU#Erٮ>(>ӱ1w:F>s2B?dr t;hDwGnǥ fJ[rUjH00:E+mcC1Twg PZT}(eS T=Ӣ(+W'`RL,U,o.T*6Jf3vQ\ RTv7`TK+%#kQxԢE^XT^b[ߦΔw )ށQzt*iٌo<`"X;\pQѬ\U>U@b;XpcgZ|BrzWdMWkV(y2A^ƚSמ=^|SSfǨz0wZDLjy/2"z걾zԜ:?{]7RՍMr[Ru7RR fZj%cAm;2.tQPgA\i)ނX )[ _A; ${$EAF -h yEq&& ,(Bg \4A}!h yf Y] Z(HwT Z!HCY%h${%}#[AVA$Syh yc yA޻et@ FGtL<.;!H^:8)'A? :%AnAg+輠 . w?](HA6R J.H+Hg#-d + (OP y7PPbA%}2AUAݑk䝖nr1׶\l>kjWNr`+AER/data/ProgramEffectiveness.rda0000644000176200001440000000102413616365123016406 0ustar liggesusersSo@c'1?@KձSEPS.'RYJȶsaf\V ԓ>w߽{߳ C!^ 6$iCl |pMτ)ΣS%&'o2_ʮ%YR! cӽ*~@3nBqwep˷SӬȕP\Q *=Tn9X*^$<*n&*ւ@`L|1JpYa4ԇڇR&YXz`‘(;e #F\?U!՗e!ʓȟz%9%'Ɵ 6@vwHJ3:uj6>;zz.PTP}zt. =k? sΧ_XWUA 󌲸*k(|6/qDbYCP݃E uiD /\+]ڒJySԉ>b3[z%>;#{O9A OQ%x@onȶ i7n!WqCQj`w5_, AlX!C2uYyL!~)C'"$ t5͂K7}c`3;]ÞKUFRb _9zL`:5!w?JPb: $jx|)kn:G^ gD Vt7ˇp SDt( |#ޛ,AZdzvDLVBV1v~!Ԉ9W9]vSŦz.')U/H]ckCUpӝ'm@ڈ39x&]"X8%V{B~5el4tX3 I-q#dÉ'H!'8PuPz+h.ZDlZ Rf]ئ 3>yW |xF8Eo­i.QJTw- `A~ZDLsO6:Y产 6h|GA;6-?`_;+و|fxDM,Ӡ"JB;zYczkUMӠ57%0j2v[UbcE#CRTq-ͷPBҷ Аj"S7NApx[6)͵Mf'p3Db#}if6_t띟0ThdO-`.P,֪B^!8S$ȟ8Gv*yŘQEz+552yf.:"$1۪H1g{f)#8 -hj\o  qcx?I(˃X}6+û=DZW\yQ+F\)4asj^ơ{`ỲyKd8os[/A3Jlh@^u&]zL鿊y Q'V#ERݽ:&ͧ`'_.|h9eZLsXғApmXjnYX購%n sC "vyULoRGs3mgl 0Af4$ 2?]v_/{>َŽiK[JN%BWe&pp J^MHR<B`Rl6A+j%j~`-jTUIl\K*K**i>s$a"tҴx͍_S]l+S)eQ"?mz2 zi$z?}~4 piL۸(|h{E^S:;2Ch & se%4 |ҬE4Ҫ6=]@rok}ƛ0ꤰEiF n;oDEbw-􀏷!ZW,Jn*,]dw{½qMJ{2aqE|Km_Y Z֒?G:%i52'mj+~ߐAQ`;Sj@te~̯b* ; 3sFiv/`3=?R'f1O/偲 tnjy"6ecb +cᗍPE5#ؚ-9, D*n=7ũi" 6vF B/qP3\p[L&iaVR$#3-OEor΀5X^cPRSgMN\We<-ls&LL)J d夑M't(8XgjzZu-Zc:ovb=k^Jbo)N9nmJp";.pٔ14_m+Jpu.]$ejm954su&3=!fcNM l9slzE,<G)2{^I#inqt"'k{C?c@p⌒ugUq\Caѻ9~&I6Rh2L]堯|WWYQ]7`kq*|OTL{<|1㮓\1iz9ےd2NbTE}܁Ӱ rPš /΂Mԍ/ˇ"Vax*Z] A )PXkqTCxoI(.Q=E{s抢Za" ojX1jyN=@ʔڙE̎=謍h,Ad_*u5gwWqcJt[J.I%M[&4嫙OQ4mBfFM޲x=toa:vapB [i}!a[@ {עZ$~wPH} Pz}?tK1{e{y1 ZГ[#O򼖻>dFYRhA)vi2P;|ѧ7if=IODɓp?f jc KQyj{aيؚTȨ]/MgT՞p@Qs EN0Oz>k̂)ƫju+_,spg{ٴ$\Cbeq~?{2*6[@qϧ3 b$RHt>>--Ż.҉&~!&KL;m}M~ɍEdrm:%>H&߄ATA t }kޛ9wm˛ɒsޢ`n+ ,BG۩Zf&e(J-rDt{YlaC"U؈A 29\Ғ3¿&oSǜ'ELeng^l4ӝ874W ׶@ꒆ|~^rPus0VVE?@{?kBuLj9Ae @@#o۸I^RȿےPMTKeV`!12-Q1kg%|(;hSКC[EK0/iF7Ȟ(UfL]mlt? x$NyJKVfg0|:鸧QMBvƒ4_F`X8DRk+5*m*c5)> .zʊJaS#s_Y {n f&좽OYURN:Je9ޚo_@Y,Vnoz) =%-37FJц-7 ^LnG;2*OWV ̏YB?*uh=aA < TT/7_[M9zc͚,m'&\sׅ\8nȁ!.K6hcH/y iPU!(wI ӺB1>">ev<Ɯ]!}eg! L_s~:]{$jĸ&fǓ;d"p}Imt%3BvsUum}.jmR|MuҢV?|4 6E[ÑVQfIEL':Sǽl`oeCyЗP\Kzj iV+2 SX?Z_L尢zqb@uX ʭo:"E\?l!ct;ٸ%;^9!Kqزn|D7АdjG*ȈD)}w!V|x賏sBiT{]q珛U!ij4`g28IBN?$Z@ "INUr0 ?DU D61jϏܲfo. }΍F `K➝niY m{ K]U/^44}".c(>H8`i66RͣqDm%YW$g)Yc[=x]eJF2>?\9d/WVߝ ~$8ıjHߑ"W/w$Wpej ?zDwN{𳌎!g߷ث>VT8 $%۲, >ϙ% t!P :d\spk_Qu2Bdz#]sAVNX*LP/aZ+] ;:F`B`  q}蠎) Wһ>X 1t{(XAq.eEni˺W7koy|>\霊zxfTYqC;~u1 PZ%GJUp*=nZ-kݡze#řv1" +zWAXe0@@ aZӛЫ* H=Q&H͹tC6% &5jۦ:y" ,P:Ѹ]dOBKY|>)׸KP xE:BSwOyi- c> ;R+~7R [#Ogx(ׄ r"D/ Qwl,߮Aٺdb#TYAfG!D’J=\H59tUT( cNu^A|Zd6Q=l84b=ŀ:jhAc> 6D^}$p24צC̥LѶ*JvJYZ|I tK<89oȇTX\C#Z H`&37[ }L#:GGnツG_\MptC_VreTmAɠQ\fVNdfݠrf1W&%o0=F{j{pN-3Y>[x7<icϡ ";yl%̹G-.U:-3V{6k謊wylG1%$ >ZjxPa^($8.@*ղ "%@-`Y `/ODƚ`1WSejBd)ڣ0m5-`W /zi?fH2$+qLk$E}4uUۢڇ`ۡ˹ڍ /g?8[`L%>@⦰n= K}JBWhmMT;>cQ5 r|((x3, l| zk%cGp=Ҫ#G., bfЯ.x C1@&Pՙz szAxߟ IEXeٴ* !;2$L hc #g3XFOJCkE4Uʃxq)fpC<,A $@XZTW/t?ջ>kzIPPy_gKS}̂ MIۆu\%=s^ T#οO0-@y!UhDLC"p9@X$Y[uq&9<!"W *f& z*l?uܩԼ Ge2ټ䛭ym-xd(')% @)jbHaTG,3 )Bzq 1_ p1A`.RnD_i,;᏿7|c*=ukliSU7TmQb^mE4m~FnĆKbFʹyw2dWaS[ֱs],Bn:Zz4Gj]"U*)+TSA~NWS@+b0oS[T(3JսӮ1sT rg4QLscMKEcC lInz`L^2{I]Wm6Qy`6ozk÷-95~95GdZ SQu8X-z̢?H+}˞ ƏJ̟y+ַK>S?fWѿOi8V%P3TH(\FJ*^E bO]+'Hf{M;"Bgi)&{1YOU}T *r\ySY7T?ja''aMJ'rצIĚBE==U$U]sB|FƗW-\$ξY "#Hsa."p˲-+m ZefC+//FN ElfI.4J*9M:"œX1CR66ME|>C*O"k .\"/]"KKIIA$rHjhɼ6Hʏ*Oopi^kv!xQ%j5?Ӄx_} TlE)U$ngI`Fy !\vo3N*(`6,GIH3[C$N3[sI#]E{' &mK(-~H?yS~Vέ/&fA=%H[ akmKSUXiqx 1;/D~zz׳sm 73X#ԍy{|fYT?$Z'-9l]>noVU{@B~>}6 ՜53{u G/mBBLuA -57X @/MKSuҭH?d>@Ԏ9֬0 6y 3W/5KtFN9ƈQy:?7'{/UuW"153ƲR#/ÙCzj<;1ۯI8 ԎXm:ꔠ1_(u\]2~\n/3IDGJ!>XL֐uc4e E)I!$Fw] H6rcLIU:E4PV[Xn;m/#ىaM>q;Ep 2J7q!cTTwT5cZU-$8 |Y+E1kc4mq†5]2ɑ.\H%k Q70c@HԱ"9q!O0Ѹ|o0O+,-eS6em|`F$A v4 qZף3 T׀$ͦx/1Pwtxe@^8VJ!>Ӷ mك*q+bˢa0O|+Hv!^wkB^[8>嚉i<cGNw'tp|~J08Ӻy""?jpXgP*A_{ 8ԸHc:1V֩_/gH% ?8;JY%գ+Zw5UI(NTg8d9gghDbܚ,5!R`!' \l<|RXxQ_Ad uhpﺇ)HQrDcŔP? !+2um^GYuRZ[\M`w"aˈ5d )}IewHΙx3+^,X~l[!ǚ.,%1|U%|(”w^Ej&~a(uNw\ K , wV@ ȚsI*픦Rpx1Dy¶]'1W5^!A@^gVm#]Me0J,GlUJD_Xc Lk{Qrp3_O}ikοN+I0\md~tB?LQV˟@<-` s!=O"*4nDX%# 6̜4fZ*$~fָVտiEܤʦ)O"=Vx7wU_fG~x7)IjevԤ:rj6ZJVj VEy^i+L넊Y*[_&[JːV# DB'W{NC%AlRYYhyU>jݬ<+0 /6|͍i{pK_[]t@^Im80@׷Ŷf5D͛TT eX#X>6=Q` }rp@ȣ] BO`zwEiyWa-RCyˇF_wu>P,dpm.VMָtkuˆ'᧻'u㱉#^qN#ȑ ,^-̇wd4zTՃ7.5RqJ eLjo^O)7 ![l}&\'OۛlOd$:@1b?6j9`aCqڃlʎn/+ިkJkrh^sFw2(q;6wAL ^ $m¦;@)2ʠXF++&I?V] 6KH?xz LJ.vNK ,V)-jknX@K hL\#_oքUMmBWhu)񌂦0ޚ/أ+PD~;c;lsrT[2L[$tCMFj7I:Coekp!Yv |v2!4~nͽ6WtUQ&ѯV/&67 +،ndȢdY1&I/ZWa;yAfH妩3S3)1o\L#\ 1XY /ݞEt9ϾY՘x yf7e8'Q9^b[d<ָ$bfi^]v4R`踰!)C{4kO y&N޽lC׽4 Pk 4iŠq" Mʓ:z%p3e I˴aْ{ICIgi|;~')S#bSy2l`Dq4]4k.ݞ6@*EyAq7/qV"\Ak3:DAcB$Sc59ס , b0B8e 'r궳i2=~7uh\L'3]qe7 ǎVU;؍;nس >?TDdsO#B[xxU ʧڭ];q٬;" ע)Jŧ4D/ِ>zѴ8Gw+= ?Eo.$3V!GuP =i|2'Ufڍ =<||ӍG+$k'(w%JF?pPY?1Ѫ_$8jy r3eFs/wȬ Er\hdFa1vZkz) aOFtÿ6*ҟUPw:/~IHR F;un%ެZh-dsp)<| DwyʋCn?2HjI+T8eD{y4 +93 KܤZ1cԷ*D5o2Ƒ[ȡN|DVQ֒v\GCr%}Tx3 4+*yrG0Jkl\-(&ۨ7ʪ{K2-1@O"?X7g}MoB]{t b*~sp5s܈tvt'ܯΔbVf]L5*zIUDz<4uW45E nKjZ*[?pmzt+!5pa]urv$C=KVFJQ[292qJy5д7rPǑW)㟡v$l̇>ӏKχ 9XrTuɕ `(/^ Xix5׼d MeeѣUM 9FW8f* tՔpyc;Ve?hx 1zCxI+JZ=ҁѾlÄKt01elh6m~@~)tiϚj&I[| Q(שPT%:s$'.jn,_ {itw4l{ 7n˕iOȔ˸u$ -'B̟^Q/ W/çi ;^tnoRt-,`]AoNف $J8kߟZ+UP\&fD-pp#}#qáƍ|,da܁L$﹧"Τ8j@KLa> UKřA!b&틸'i?fo"/ѡ,96pMs]fzOJY>M+bk'%:=0|ːgHhY$%71 c|h<=!_}nѼ&u4 /g1^({}H@=ǵ\j@k@ιU@Ҡ@?Ҩ"(%.Um m6ub |co!cRnҤ_+ Oms(^w!std>YW$,]m]BaqRJX(ЙÐCyQ-.rm+1B~yC\ ۩=Fw<[h$dUv< *HCRٸ¸q!} !׫W9KN >E-9n%[ 0"mV` ~o͡c%#skL>W~?oOmmHm|UĂ6fI znȠ&8vh2M-ՇޓL:8LY+GL5˪$s=iWEI RˋW~OQi&E(p.~˩˗Ow9rri3tJ-/W7c=4TiX`G5IqyWr m/ǥD^[ۡc],`و,qo_D32ߚ-0`gvfj]˛ YSh$^0@08R!ƀ8ܦyHɄ]aZN#XĂPW P$bQEn "4j;PZS;Tm1%.(h}LJW݁z q8P\]{7=|E)fC~C?Pm҉2Pzi[W"!EY5@s=(E~P@Dt|pK"}ޝ<)il1g- \C/#s 6q5L3[L@yk'P!;DH6;--\P+I ge0O'O$i.PA͛ .r@[uݰYS\;*[c3x# ܻ7vr; ~;c^qЃ&EV*.gpIZCq(q kvکB@kw&FGI]]d[]Ȱ+዗a:f7\S[X tAX5<iO4WKMS×g ~;Cn', #G4UN^1Q֢⋙1+PL8 B_x62`MALCf k`Dxcj'[P,p{4 , |>չ $@etF?g@gKVuO*,9Ս$4.:cQsx.gPJL%lJb!^Sclbb5 x pUR/tAVbWQ1-7mz437SRj/i\z|QYxmK/;ԁ$[  l9zPpF u }7b Ė(|}npbP j*lQ m/Fv͊ F wϽC|,Hζ퐦~Ix_r}1|nJ!SDGOL{g qs]! ?+SH R,M չv<{Z!.BXj@I=XeЅo4C&ÃY9ArOvTez7et?.De'ae5_$?Z^7YWYJK'ʦne9mt-4㑠42 oZ tӷUuQ>JN.a;PGY]3eA;'1};Ƶ|4][>/2=L)BTÙp#I))%0ȎCl"O z>D= E! טx'9h[<{\&?=g9ټ=E2ў8KFh6kyR tL} qcN&Ua9~ fbQ 2D=TYGy Ǚ>& }Fl|4Z?<+fM8-v1ykqegmDt.M 6J+ ~?Ӝ[2i䛝ߝ/o CÕMFadO|_>uAfvșa=6wo^m:`鸨K:Kj/,zD 侩]V)W)0Pq|p8_Q:ZEAn%Gkydq)Jsoິ"]%a32> &$e%ƽaE酔. SՅbM%E}+sEtW*V[܀K Jg, w`i{fgP?y.+t9K`8Vʒc5At#7^)|l#-8@cb񝻔G#:˴׼>t945ciKf#*;Ų_mcǾ 7" ݻoYQ4.~vC̣uX(u'j-%f88kJ+q|^ Qf&=O:alh^$C J[tμ6< ZJ 0z0sMpwdB.'N#NƤY0G1wQ*Z0+Z3*f*m[n.LYਢ,(&,8 ހKN@XXf tׁ6M`-{lӞEBEx>wRga湻vTa7ఙQdJRpNȖԌa'% Ǵ}R ! 9 Ɓbv3rNF:.\KFӏAbF13ϙ9,g.oW1Q=<'!5Gqt/(y' >$5IHZFX_0|e[?= ,R-CadnP%U*5oyfQ%TXV/ JRzj܍Q旲r%޳*K^+]}pG>=ǏtrZmDț3m ]\%iJC .⼬w,pI&hT.߷YtRq-it 7HOl8i'7S<!7i3Y@ls$*^>'&?YB[B?q5-Xc3‚-y@Ǐ;I3C`] uu<,fFF%Bxz ettA*F؅kXR!H3DWH' \% M1rJ g9tm×^$) Y~,,')5*U>^# 0[e_q->(C Xu~Kb'H9tʶKn͢x8, k X$4"V&fc=uuBpX5-H޽(ٯeIw{n Ḥ2Y¶PF-MP> QDA~c?P/X躰hGO] jYlT`ò)ڃG/I[dlF.m;R%WQk+{2EB7Q}Uria9+a> 3ȿ;e$SRW[H/WqHuУ ,Tav 4$Ho"%S)ґU%^xx>76:獱IsH3ȦH/VciyO|I3w2_ X-'QMX'l0BɈv' Zu'4BfHcz^=r!RWD~vIw?%WeAp`ʠ>X0:J9Э⋆xG+q l-eZc F FQ9]:z P =a푭k <5J#{5BzZauذy^O%G[X0[k$NL& %\\C&IEt&`uz!^gh&Zb+X!{Ɂsۘ\V>"2#ۂݘpَ7r96 ~\\pZy]xֿL ̶MoQ =jǛU&:r9lLzs9%"X(1㚜>n{Y`LuT],5-"YCex`GxIQhZσ+$pJ}QK2(T܎"('E 6hծ:%Փ']s jxA] e523 >nMO,6qv=N(=1x k?%#+ ('y0L#*=vv8YW Ox9H2l}f};cI}9Lp?/jcF9%-u yڿ^0XqrP1O;Jb b_چ3FyY eV Fd1-no%w.M_.ͦۙWqN#KvoK]O5GPKcak+^͝ $!nw^. ڔB!W@$%٨ŊQ<[>)TP,y\_6Z ks > {L uIuq|6X -vf7N4PEA7 mpT69Acue I);EׯX]$Tؿh$$W]\>4VO"_n`c z|9rUx͛YCJH:k>)S{v[ u>(iC?0Uaݐz7rϡ^P/qxB Xo,i{&Y^.-{s$٫ksp{օF0k9pk()^<:b.?ytіvT?3IiU595Жw#aʹ=eC <.*xF9_@Iz ^Fή 0 6OMߚ@PmPJps lX=8z8/?i dI TUV Ix%5ۙht9G1-^ | x-Ob{:b>Rn`T.*o&MJtc 3{I+0~ F,.G1M,P  \אӃ+F;"\3NrOcwꁗSϥrvWiG {B7yGHq՞BRWlGAm,x;__}˶3ækǝ Y_k3.o.ZT䰃,5ё>!w$5`K:(7i޴28*2/:J+N8mӂ٭s-?)ŵ|$"Q`d 1C)r,`ߌn/h bcAp̦xQh',RXmr~pÐ?b"5-kӿ\ ]--om |+͢4z@Ԧ獲D&i 7:s/C)~bF@Z)3dj,!{A}ZMVF=;`m>W3c!G=ʄFMÄ蝰T}^L"(1w b`eOddD-r1u*CKiYn$|6v:j_t6D9:,|,^6@4(jl>q)IbQ+B(vOCn\{xhd)E4X*>̙7`.%UEGNkk?0>5ȯH gva 0P0M9m@<Ph v.v_bFuȞC l_ 5mP!&izR;;wLn|K\BDdn1#{%SJkQ4d0~f G?mFކjSAQXg(0 4O6C)|HQQ?jA}[x:1AYMƖ`I2$͋5g}9En GC+_%l{.F'`:$ mqqoD.,\@"ـ޸pg)c=S3o=r|᧶mzi覾+p/FA &B"d/3IFn7.⨌(']A/TGqW@].j,V_,}-Kݴ#7}\ϢtipR3:I n͐]K"S5h֒Xأw?@*2JJfe8jB 7)yvzd@2f:roN«Dd6L, iL^~ޥXvr=YB{  ߹Na7 2I VoӉ4~V+΍`& |CR(pyiMZB1 ]0?N(D)-,1bw/wkf!x~\{C_7b ;)(Xzv!}\J2ӧqy>mTץꅢYYnmE9ոzM5PP%Op"fr:cu?-@c- F\֢&[?5)TA-f0 5:  m)CVb;HJ)?p%Tg)&ډ4kr?I.=f1Y3/bS?K- LOi/ȷO mLWeP;=F1p . @WB+DUO`$=RXpɗP~ںrSU{$Sr0 6^!JۛS\E4KgN{+@8tA%?* `y,=^ژr#n e=9ktŬ'a{I0mXECK/u=nɱ?V/<¡F$tMKZE. 7X+~Bngiw4}pM=w.!Pe il**I/s2#xW@n<ϔ= ~o7ȞyB~.vd'.[ LarskfӕOy'ޑK9{`Y ^8xN ſ$8.FeKcHw5WOnyNmPg hh_VV&tF*3wވy(6Jh,6$X|d1aH8  4i{𖪩7]dVŲt0|̢ro'}:n,8*%Awe%vChD-*[U19$ZYoϲu`7˫v35Ie#S\&L%#SAG2WA08kR#4[.V C+9*@^r=R} hT :_U1)pZFWRwJRN<̷SBxy,% fukjov1s dqԠ<"H{Iu!=a< g dU9{GԦ3n4{'Mm7I׊M0%R ff9DRx }wLdDhkbU򸲖u S#F yT`xHA~-µj`83;37J큖哪FŠtdZF Oߪ긠ei/44%+ vƘ{ I`# psLw<9Ti ;UUc& "x&>bwgd{؉&J|i":E EE>Cp_!8v{+o=~)gfWwuau1)\rV-tz7w2RbT o>(Al|=Ң U ;,Cb>/9W\¶k > 4nq?iA2ӱ Cih8% &`i1!=:AC};鍅U11]'x85[|c j[Z?nQ|VA I_䕚Rg19ؗ%|3w9 <|c]qFrs&f>t̕-:%\~[IkB+2>9H8S[n^ny| е$l=&,ơhV (SW:ܮ/S61-?Û]6[TehVO ㊷]VL?0tYaߟ k|qMIZ 97Q0 Cñ3)ݙ_`|T.8,P7*:krWOȭSXB⠗b*5@z=piĻV z4,Yga;tsVAQ*бJY}Qtn]x;D|#3z'c=gff l O4m Tn9$Kq:&h {,w|-Ŏ)/]7iˇQQ%B /ȀA ڶMS77-Bʲ3Z5^`^4q3#נM2+mѹpF |:hi15Yےƥ Q(YD)! p>3GEto**./:wϤ>m4&ĝ`٩u*zJԭ%mжՆS c,H.N4鴿zQHznY9+Wy9Ϸ_6<:e@EK~M1fr>ُwH~[- ío%/KlgMmT x79^h%Hʲi#9՟t>FO͉`O\OW8$AHH-͉m2I͚aBH@&j* *>ڡb2VU2&|OuF1OI4uVҴO _463H#&ǜƬfmd3A"TNs*Ke{>W%z uAL3z?c= +ۧ}JTA>~8|Cqv}\;,x Z[ ܋)t 'iN&w?SPe|g>RSm Iyzf9-}tr˳.YV,iB.\ kV!:i\JL=O7qcvؚVVfbmAO>lTp#O&:-\T1$-iLl+S]M V8Ch1I3Dg#.޵+#`of kI6a\k:=^RuMhv.5 ev =0!$"-i';9i,}ScF;!kԖ-h8P ?˪Q_ӔC";$G/.ץnI|f=.Bn(„:Zr r,N3ޝX0˵xq=6$jP y*3chwߥE0pgg!5!ws6OwwOj{LV|H2K"е^8Z}D4w6.ENwd %!+X7k:,ѿ T| (3XBcv8!;ʼ߇^;zGc/ Bh[Y)kk8V6T v;n))<Ȅx>+M›EY\Kfbqӝxbq9}߬ e.Mnu&NJ:AY֎?hgbt& p[ؓW-F +,$3 %++y36Gr[儉(!3p ժ6+5fX+ʴ#nBp$oN`!p~eE[8<}#ozoR>0o &Q1jBD462>C8LZ3G_;ݨ5̻ŵZW|٦gWiOy b{ǢM ǂ oËܫ :E--eMm-mxw% Q!=er4ž< T>锒TsCFi!mZGAZ|TΥwyPS=pل 8.Z*6u[|7%$f-ǃvsُ 6]UkZZr£A.=m@,k"p,|h4Le]: :T٦bg^ onnp ǍYw Ta?qD49$^KdX <N &W>nߢU_n)ڪK"ImၕpOV#* d##BwM&vgO8uia@6?(V]H ՠ0.8{Y7tG YGftPk?FLr @Bn[\[Hcr*#zqQ'fYc3\g_];O/k0@łz 0cЕ$cb+c[/%o(8*l %үV5J/h ƙob& f X:­ g{+$ߤˆVbr6_1G7W0F̴h|R.P?6b^SӋ%6|^=̛vOgQ=N~\9ʾh X.Ez?X[SF-xoo$9xGl2'V>0 n-Și%+(3 l <D;yy C+)̷{5R[k2z](xV{5v9" nii7 d*"׏i<Fdϵ}NP|=wr)YiNzpxE/BP7Fm=}6&>9`ԫ#q `Iu(t_柗$[؍O|N(<`< ;9d3d^Ahޛ Lmyb#8qWc, h44*(tM 5n6fLkD^1;U(]s/P*n{%uSLܡE>88}:Pyug%z AP^f e(w/4@氎\akӼ.3q . dP1Ƃ+w`!3B1VjQzdl/MѴk7h).P;xGD}6;F$,@ WdMN`9YhY&+ߨڝmٝ_ 6GON)'%&XMrYJWG8p㳙0J:ԓ𑙟"YUew|w/9LDX#Rüj_5 Prm<~ϰPNuvM<Puy5ͺ竸wN 7jt"#g!SouNrA$Żu],$`FYDb(u8gٯ&t| z[G<$,q-E#!6o"&`RCwz6 ]b ."٩H7l5`-k TpKւ@LV yeNEAR NYa^9ibkOY@Ipw|zyI6o}/ Ϗ+HSkwsmfEk>jVe2v҂kiqiWEH7{r]) D oB|+ʙ~#kNJ4!7M,.5e )BOtzbOdbӣiXAbvyU^YԻ(h^+ r/tzq.(-T!ӷ=fZцl ̥JECJT""B,.gJ%Q`t;A)0}iv¶-6῞9v V-!C+&T}9ØUTH'ȆʆZŢƴ]V>vKU+8XTu٭fI=M 9$3tR@ݱك]ʩ_ԟX- 9]Ykط9ck`opTaQ2m@׉<;cOYlvs:> *Y,0{P7 5HRcw7!_4ʢR-~D,M޻jr\ efy/Uq?~䱫k+ݠg:-L0 cSXAH,#PRaלĂ,EE:}lDeGfBI{a}Jؽ3l6zbBV^n< .us5q~V.I#zd T\("hZLV7{@{mJh$*H&e\o_>l*05Dz>q -N;Qc3nFdCAE-!hIo9luyj0 ByXH2(MMvnlư\cႼ"F/gxҐFRaݧsf~Gkh#qh,G]Qg--r}1%2,hw ޟ_8D[3 :Gsi!(K=g2-u7%8(v':ldz[{t~4 ?k\ }aϣa/h˽Fb|w&e!;Fܺ}9e5]ߐ{$[6OӱxPzzf;_hGft2UGALߣ'U(="yRfhǟr^i!>Hܫb .D#T%nti#7ZiAm lkgtJ]ĭ۵/_%G ő $9gW/_CKAN)wXI!V! )^N+Kw2a+W >OrKw~+ģu";30c")al\j U)8)HBPKm9)ԧ7?*=:DS k |aC>s`#ܠGm, HCtI@9֣p>_5&rʈ/Eg$>nI `&GYM x}f2iY\7ZryX~WDp| }gb)J5QJu^L݂&&&+8%kc)z&0r{7-C &w )tm2g<'0rqztiQ6 醾Ru`nKĉO fd 8A^BͷPT5T׋f1锷t 6$M0~c]? .7S'V7ID1]QFD@*WĘ; 6 mЃ t ~|>4 ei.bؖ:nmwn iJ6y@iP.3:.| #}츀oTJ{jbu5 o3~H~ :Ыv@47a7 nקQ9)8=>?"^{գ.-(&W ti߽f?aCf 2akP8c5pUAgծH4-;EÞ>d(@8 ?L'nr4#"k£}@N:ifcL8@tQ SŠ 3F YCvm&7Wk:䣬dQHmI'R xhwF 8k\"HXHr|ڳH}"WD_ ʱg#b@(!Bd\w .bcڲKpϤ"I5Z9z>-d"Y#/f 䡂%TX̠3Zb]*ĸ.=dAoJ^sx-} DY)j۴8w L> nO¼}  ŨKpBzfF@Ӿrck|Ȭvs#pYU?Q2*R [/>u+I^C6J t~tL2<6jOGU^)AW PeeN (SN4#1bfz}Ƌ''UlN*S=WD~[CDž~Zv^5b;Z7ضzE!$uf7CE.%z\=̿w{@6\]u}]iU%;֭ڀ)#c>2&,ӥDlW$WdE4ڎz vm\SO~ld6vWb܍p`{/\u{ L.m"rTW` rOn|!E[L< .zwOX(g3}ebHk 7.ޗ󦑃7/ 3r<wZ aSҩ秐mG[>8_iQçB[?rK] (Ø@h.>ofq/mwdq*7X|MOXp:-6pۢK[МMmKJ <6A&1ja{b#Rٯ~I7t!QpA$؍&݈d/NM\G>jĪ k]m#xV|x11=bwCW4LHBW%m}.ʒǎbLϫ7 ";εW2ghnke{ =H䫿I5 h3/}K1J&^9\ia)>7јg}OE*bw|jrNHn4oQ& ]?8,5mX9Y !r"ſ:4:2Nh3rhÒ$0sԪ N,k 7O1&EG+uٚ>~G,c~40F#uOҁn=s>1K޵{gt ۢK^]]cFӯ.LU{PR $͵ZraI`W[%Vsp/I-;ϬvA# ` >u@tY1.!k[&yyvy!* .5\CL N{'=MdeuCn$G78ʼn@{&/;u8< vsn @%>vHvk#ӜҘ^݆C/BxxaZ[.m{Ab* Hw[k!It}гz9ώtH^cx>)Sh1*[um/ }@A eFr68d`hvV]$(Af"?m `籄RS`㡾7'*5)~ԏMB}N jξXLWyF "MxgѯW4vTX4-Xe橻n/-ۆ)N,~;FgbښQUO#w'aaUlY'"1TO4>;p_gTWZ}>#= 6>Փ, Z纏!8ϐXcEWhuݟ$n|G0݂C >GV/dOlK}Y0= Qi`\yYP^?ؿۤz&2~-{5Y0t7B4['u'BҎ>k#1ν(J'$zrCL0T^&jד-/*heE4jQT3&k:T 4}/@dz6e-7t >i,T6N@f-jrޫ5{I8p3Bξɞ ~ާz+mqe52*ֽz Y_ƧS~}as~V=8qU$s$!̎KЙ uTW=nƮjXkv8$P\QMZ H/p?|L3_2$rG1M }z`R&'?5HhWë{߾lUYIv:˝4-A.5I%eUBMc<yď=)l%O%8")p~_Dj||(jJ."t[:EIjQP=5/ˌP9{qHP%B7֊̸0fӲ<8unN3bF;_zcSn#*<%3Yw!eǢ;~X8өDxn7Fz6hƐuGySֲ5t+c l \oAp'HH0?й`пw51Ng8 QW NY[6Sșua`_ҫZc GЏ9Rkg}spޮt 9iϦPRe]=$yjFD`p$>2xݭ,te?B1L`Ϸ7xd֊Q3ߘ|V*=(m<SH~oͨ8T?=ٲ?0%úUڇeGxs6ւWNW'=)ǵ=NgB9!BicxKB6H1D)vETI̷MFjɗFH_(lݕ3Dݘ) \tIJ?mլ[Cqb6<;ٗ .aK_,n GM1ߧOv2d/Npǝ,?^hVO5|^lO_d!؝M!TEfܑ o1U-7iG&U cdM =f^{%VMӛv:N9۶J㤤"Lt/$yȱ~WA.}gK y@`茯'4š9Q=/\ )/XpX_f_(c\<%ht/+!7$*^j=19n5-S :,(ܖA`., 2/}Jl)o)@_[}E-IQ*ljV"v>3ÓW>t^6sq?7jj# K~ʯ颽Ag ǍOhr\i `@ 8,?_ܠT"{q4ҕёJ= }bcw,٫M#()[ qc.e/3^+U|TrzCi-T/Wv1c@X[߭pp4DKPz\v()yNۀ'k-UKTO0gRKQ8JR&?~PC[C2GTǔkK 1 @S\}o4D7BO[CPi9wEOLn"꧖k8z**S+Wu);t=f&bRൟ+ 9N溽1.`#B ExR~|->6/RZj4AO1d,F0esKqb%g?"#\Mg>+Ur7z005\Hm~Je(CUCL3V z({ߑ|{1yI<^P謋NNMCZ` m`tںNzKٹ hݮ7=QX7ezg=r%=k^"J$Qu7 Œ7+ZaEQ )} 3 HY4JGU]&u|Xzh~IxT2iJ8"#oU_X"V)KA1w$\;ܢ>wYlHkj;n%P1@N8rmo{JaQx*h ˴t5pz>(7&f%ZB+* g1LjL-k1 i ek=ۺY-l}<\,0L'@޷XBcV4 Z ap)GFnCd 5+A./4~$e5,Eqt0M9eJ'sRw,uӼIr咆:OhuK V% z!6Po6C8p[""`,GFv#SrH4#2~H0"ܐ|H'3HT~ܳ ٪*C ?Eagٞ6 #ӆt`+t4g&Ŀ<0B+_G@ǩGy1pYh9ȼYASZ5Ϥ-Iɻ ̧9Щ8sa2##5sե0zp{ zr7s՝p5{R8SpmZEy*s‹sYaN:C&l˴ءW w?,edD QS\en'0/%(XZip̦B.5k[\`_߾j`Qr,b'/O` ~v;%z<^M¼,E:n>Ak5|HھqA*Y:xGK(sG~Q3p6T34mTE"R 5B%) ^x//6#D#s|A~DTyk%[. K{^3 "Pn ,xmRv+8FOԬu[ưg2|]Ƌ WC0B;7~UxE:Udh.^N*6Xsy)L8ݚ:. W:#XFgT\uM_+Ͳ75 {1:u6֥1OEpBB~BMwXDN6'`<1qp=qNh|7)("s}Q7ӿP6Љ::?TPgϾ5UNP藺]WNe@+C{{X9=ÓzȄ#ǫZ\79SFQ![֢a1\/BxBqGNpQR$bIe[uKuB5AzЁ% vGԸ=0ݥٻr `qfi.O* } *=6"Mw;Q d8L[I^(锅 lj(ɡ.nlB"4;JYar6 ҠN,'JZWkH2ӂ/4~K9*?%';:d |O0-o `ߑ'5Տoޣf/DR0_Q1@В&FWQ 1Q?ѐ:VhexaWXogFlXr_̵eqmJkm%4?␩-+4ʘRHؼG0??~\k 5j .X6ok2 tţq^*6|o怄d5*GUyQ( Nylr $?ĕm)rg%[8Ww,og|3ESVpI#fzVĠc7K񦃊>R~mr״9QN(KU6@QN )\ ly^qMU='i&r&!8T[jk2;@{6ËrrG͙s=eqDFtIM"Y~:LM+5 x ǬG|]{½|3f]Ct2eaMf ,9aLeh;Vv-J=X`4w|bF}--+IF#?Zp :Nh;._:h [@t #I2!FIq%8{a+JNT$G{, gPe[7 CyyU'4P:*tcsb aҚ gI_n_×*@? ^WUn'{U\.Sz6R.E.Fuk_.hwʚ)!َCeB4Zn~RR {EB`-?6_'@Amzqm 0@Ү<}@%J|Ⳳ$8-#Wd,8]IV=؈O gtDgd])kmpW53=uhz$:u{F̼`?뒐kd_M03.'R=[zx٫v"m'ѢT5wPCB@mspesCZ{_xmto6 DQ-S|zgZ幱BΆtLj+-[2VlJ\諴gp+YQ#gՕjE,@GEӁd=JI٘4\2g×;ticKߤGLjrV%t n==j9Te:MDފh8(ME!6bܳ) `;d=)^rBNi w[--m?I)zUx@ }`T Iqt7ƒ@eZ^T(Q'=oL8dwQڧ}KѺ>^]jyVcc4pf{l^ӞG=W+rp;wj"M 8+L&-y4Vd8ǽHb/h4_M$q81'='zԲ> w{x  ^I;68;NcvVN&xյKو$O#HA;y!$G$ p%CzL{IP]E}1K)/p5 ׯO{,%OQgLإHFpvO[\Vk,iQD4mg{ur?tA jFCiҿT2U]@jV #-8}ݙA֠N(Knhzw$lJF@o ~R_Qiʻ 7']Vo:~8o>񉛀Vio\q=d42yPdONکO^g>i=:0oÑg$^ωYbMVy&lR&8ث]#K%5zvt*W!>E^j 7C!V'R$iqۏ/w8*@a%_qWפ-f/: ק\iЪrASguPHv{f̔)g %w8.LZCDȉ>qX0aMHpf-X*Y2b^}%_~aIA!Yi/{452С/y3o 0Z45j|H$(U:,0^UD;*yG2\ AD||C* |wFԙ(C #)fٲeOO SW֜OQq 9Q;.u5̮r";Dޑ1()o)%|!UaQ˴vUU[7`KYD sXϷ &MŪzL+Kaǫ5. Fm`# HS (^ h6bQ iE-ƸM:fjgS:nfDvP%y5EtRՎ_Zߡl !<,t>n[eJ͹BE0EwF4=%@YF4ЯD\SpIqqA!k!MRxeZa8*N7!aOwY[V0SZk{dkiA NMpZFtw&HS72>0 YZAER/data/GSS7402.rda0000644000176200001440000010037413616365122013245 0ustar liggesusers7zXZi"6!X]])TW"nRʟKMd[_;zk5uyYIV.qЃt"W+u/% o% aq|~$}3鞎x(ji%hzZٝ au)[E^נVQė^Naȶp{Ɉ\uHz|aW5*VԹGFg9[S_(E)0p{}OC6&|"zjN%mD|p۽{3ږ|&.'^Gq_Bk ΀aGx|XnF6m=E[[8e}h6wCY%+QO$"Q@M+&}HNۥm9>I֐ c } S넣.7)h~#Dʟ%(g8 ЮtZ341z4ß4܅bCEW`$-S3q] 6FXdrcIɤJٖoj9 5et˾*jTyLojEмz]e nwFR<ׄ1WG`8M֝2Jl$3M^mk.o*jD, n-ǻHɵv>UԳ@sX-ɺ6raD<ޕ9A4|| /:]ƆU0 iO"E]ܿ;rmX&TàTl{Er8=uj=U*d  VzX%d~վ3 NX3g2ʡ.q|?6@ŧVMt\.3z!U QZY~4%VWMOc=) m-vCL. Ke5-%]CêS种 UM7Mgж' o)Fo&4@(nF?Qa %BM E{X D; iߣ:X'-+WjƮSG\Y&Aaj!&3X<La*pE=cƕk !6gJ=sEBX ew o-hB'xd{<0qr7-)60| C>m;؉H^NG j9[p$`Z. FoR.He-JŘLn#W5)rPnFr7nI]t@~J㬍@חyҨyr D%~F3<邭?»?5iwrF=8;uG(n+6! x^&a-rfr rf_ Ic!k- `zK$tYZwOMz/ޥ$޳8`#R.p@[@&2ƵI;PO0;| '[S'q^VGBZб}P$„!w`D䨯<~[I<)j=A{!֍7[Rr]*h I #| tZ23'AݰDՄ ؽ2`S}i:8䰽o;Mv;+|*@6u xz@3UuāEJyaə{d|ƌ8ng9ci_kBySs 12ĭqL$O7z0X}دc5 ThZkDL=8$,se _tZ,\į{D|[ȀlqG@,( 锡ݱ溕Oq70__[JlOCJ ݦra’%7 i)9!WX'!q2*bSHMTRkM-J}y )sX;IgxZM!'Uu3:@%}vpe&cuKQlqsJ#$2_4kO]Eo*jw0{mB-oBO󧁪E}|wMico?Fi?!;|B^=I}ř UTnR*CpA؝ &I_m ~40ѳS[vkdzB0}6A{ъ-N)C3ԣnλVHeNh 7S_UIpP\\F#~ҫE#@z6g(B =wrWxjn=M[3SN/#2û/oNWBǂK/W' %}>ĵ9p jsނq= I5X8 CHKDU]W`Y`!LdN=#("FZ9}dpl W%J,qmNwRE{!C$avq13G}|q@uՅOKiw̪-^f]Å=LO;帢)dvt> + K1ʛDe rsNP'C9,.PQ{篵^>I==Ѯ<90@w>C}x 89n:dfxYut/9WfR-\_7wE+3_Fi^BJO0pdXIA'fs UenF [\c] nJ P<Rغ@cg"{^?4%^' "9z@K5oXVCJ /ꭎ JNd W!Х)O-<M! ԕ t`q@˭: jC&/<(Z#?+;hYֈXN# cʁ3C6Wpb,gximt0X:}goFcnA/}P&jB+w~$*Ykj#@:tr0{ԈSzH,ScpJxMX;|E &Ajh͝|EV<sY%ȫ Ұέb3(t20)cLʃԁ6?0}n(K4`9Rps}d(  쏯%|׋úUٟBnhWp6yfb }mq˛C??њ(}tK׳ n9`g``g`]X$κ7&C9QZSI|zkxE{Y u}[ʑ(*i,S@Ktuuc)-ܙE1iHsCb4-5hfɭ_ܒv/WfpЎU/I&vSF7ѧ 0nPT,ڠk8*MfGrgV&̌ˬ;gr8@Yzw>GS_zëq?Nm5|'\'.z!]1LYST_-)ؽb2,4r5[7;ADĸ+ ߫K,O)EX3No9-3.D= G1W.ƓNIWUVZXwqi &Տja. YWh/UqvE/xX +SFU䜷x M27=48f?I]w{Y.B771lv̋6uF_{A q)'>*ft 6{@(A8Bt>ݜqXH>T~y]NGYp5L4&25),kO<hBa̓Zu:v3Fվ]ztQ] ^5 Qƪa: Od%uŌ$Bޢ)~l9ܯh_Sx* 4hK[d 2V&ڱ֍ōyp>.>t.J=@SJiצвL^_2`tɪGQU) IwFW|*o S氇Do$hO^dH*ZF 3ֲUWzK#~*@ 48[Cu`E Ɓjw!`*ܺ.I~P뇙&.j0yvCdlhp`臃b[D؛Cs-f 1.UJ3(Q"6UUOq6pzv%C5!~e8QY5b_cՃ-iA9):>԰]"ȡXg 7>Þgm+g'no;H.wS]6$v(MJ&< 'γ"ic : A᝙؋ңOֵVéMPT1FrI"pj5Z YLn,ŏn_45|؜-}?tρj^u.{’ɖX,rTw^ٱMZ)1[\QHG+XUmi#BqZ{t\F=[Ԇ~%gOq 襀d+ӤBN8UrAg4mZ.,ͬ|oz.QcZSѤ?:G&賓J,-і؟vBB=:cL ct#n0|{Qȗ׃p𪛯okoKH]'?_uᷓl[cX@= J1hU P3br0E69^-J5;][tKW"/WeD8W[`'zdxT#v4ᨒ@lbE }CZq&׾fذu4=żt hTge2z.u6k F.^1ө.*SԆbU#c*ψkh1H/*>7bIs;]Rús"#;eW 5<\ndMEHOD QMSϹ#Xs ӇWPrvVV qo2lQՕ8$i|&PձHҶ̽.-8;Rv ,NSN9MCHxIw1+ es[B[RUӊnW< +PsM5`W0̡TX0=.5G!'ʳ T@eCDIw1P)(J>e܎͉rMSʼn iнv;6C ӇeNtQ|{b/4z:BO Cb_61a.qj[4& G#hU~$؁[SEhK#W}x¦]G85D!* 'k/)D Zk,-^{۰:o Vy\5lXs nP}7'SCXRn? 316+Xd .lI7%m m}Lˆݯ7ɞ6I&=2,\%'$ zJKؽĘ9hA Ia+p,*$4bydqqs6gO9v> U)rnJoƽ'/ar`/+mmOyM쨻|v[UN?:X$9 8SNZnv̶-` >DUI# }pH,wIbp$V&噦.~\X_`'yZ8- |N|)9Z&2NO4s[/ZjW"Ճ$bΡ5lz]KL7+>ߛ}OQU%d;(]6w}~x>%挘#waH\$0C5*XtQѠА7Z׵"Y&Bl&*)bk+'&ks!(2rcM?N!zտƽFL.I̘*Uk.?,LޚT;0bl e~X}ps  O[xFe uJ)Cp'fK_V#ci(#-21cY(MZMn. ؽ4R텸Ч/zBgF2)3xK24Ѹ1otˈ "\bO{|HdB&<}@o!\[Ѯ]#Xoה=FTfxr6'@$N# 2kWIFR4|mRt#7Qܘ,[ weQ9nx@QR` d7.b$;:T__Jͳ|ӧ޲"EO5 a/.Y̑(ץ_XQg3X㜑jI,G‚U{3s۾oI{`KXElLg 6lSHcʦ8D|qT rn kY@A#x.3$.$P¢8L.k cnL-UmO#ة^FU4U.ɴ/TsC䄜εJхb&&wӡ?~@ ?A @ Y'RV$(rw|Oa1zr/䵟eoqN '?ڮ^ޤm|0 IثŃ}.dc͓P/-8J{FU,'|B{}{xͪȧDå='A+_џ 7Ā=ErT 5iܐɁ(EdlQRh3g3,3u^@54`X0 IHmH' _#o96j]1$gA!g$ŞêY1Mo):KvViAه)yKMch>v(JE{N仾+@6u8"FJ4;N_[R[O#C i2$T|;kquA2N uB!JiHVDLDܨ[&.CDёNT6Aa8n/Wlפa_Rű2&ǟXD?7{x۾Z&EҢ(S6ns;Y C.*3'c0H+B?ƚV~ 9 XʸoijъŔ=mqN^vi/@7}_lz~J\T/K$M; Ŏ7&,lZW~tal1D!bmQE0R p*P7AIjS=IQEQzϙ j4X/Q}9]ww+{[; 61e ªM9vżbipK}`kqrX1"y³ѱNpsi:0=ρ~yIU59ZQf;;\(6eL:j /A9Vadp.D0qhtŐ3r4ϑkvsb?9}'[X\^y9K %?׸B=R?`6g4e z /Q t!XYք`J|O ݓ#֦nGyFV敞咮?WmKn0ߜAItAڦf ܧ%:RRP4ɐPd9 NlN`Do0K2eINݝD'g@DCɟsn(#w< W !&۠e:Hc|&!Z4C"urc&MU 5Ĭ'>h'BGR QIt60GdLoy% y׿ ZT 1r^&qO;ʛkXK /j9UgMQX}ثw&m 5Zy< (K-oh})5a^VP@Ѕ,!mİ͝3h9{^gabͲee@1j©'%Aoo0J^=J[ibϋ^ &J4qqcJ0SKn%Ƕ=\0[\ѦHΗym9fsͽV}3m#aȑv qsjdx\D85Ӗ=ΌUgoKJ&N-ݏ}$F$-CRIƃ@5F$l0|4 L^_|.@U$Z.XNmё?og 0xx*Ԣ*,f#XT,EUĴ_鬋ZWpfddaoLs_׉UO]"ΐm[폇|e&%T;#EAn9@]Z 9E,Od؛8z:sXHV+*!P*T-paw~(#mP`{)p R37 {F'j5D]g+YO_oapiyoK.ռ'~&Sri.jq;^x<[5^3220mwo(c9-3 *Mt 2*:U}[^:d~W/Aҭ 04mNa!Kȸ5uJ^=w|E;s ިJ̉Ԧ/aЖW+AHg>v;ONm`A4E!&8V>|`<)O>čg4pa(>At3oY.y^= y6)P}![T^ sR51a'N&:qYT8&:V[zi5Gs"KcEfG'Պ<I!`S9Ptz#ANwߜQB4c=- p+ly;/)Hd/;#`0}voEPs_Ovnj=02H1^]$n Ug" 5qU!">)ID!㰌b|":xpޣH_|~N{Ԉfma$O*s 1Kyt-KHmOLrzYi sx~ʈ#ʽB ;UA= xWOFUq@7&PѐovpH]D)>RO'grmu?PGF[Ym} P) v2XeYGQqYIĬ&%ZOݜKGH ;+ Sh|gɜݶ1s$afC`$$BU 1L71z9iS2x1fA[ $ÓYv|}bZG68"H|8Frlp$3:ub"ՄZ$<+}NU[o,gīKFINS&4T] GBwEmX9Z Lx&T[7%n۶t0YYw3ao ] $uP3\0Y}dk?τkjp`Ibbdi_56CHgז&} ۸}'z!C)Vi97qJ 5 e)IzKs" 0W?7uX@ܢD+^Զ@ nîF!UGg7=vy3$,][y5z6]n ZO$Ə)U^I@e$M菾0@GwlGs#_tOwvN).bq̴/Kw8 x N?U)Nb˽hsR5Ks<S/ضۦȕ1e\FUV["-ߟ{zyo7bsͣO\h" ,?uV3*.[)aOTF TbC&VRdoŬS.=~is,Ч߁8ìV;AGbco/o( X!QAOϛrC@j=lZok|RgEunXYY׸Yq`?z:I|d/Q9@*_uXTKpbULA܄ZE\rt\['%k>ڀ fkC΢y%_X+ /ЦM;wu)[cozaLIlwi$BpZSvtVjc-P%05pXDsOI VyPChGOΤO<`2q G1"C> ;k7ODq|哫, `${0PvŰ#K"m-E;йcƏ3pg\C' >̂:5?+i#KQ|N#ݽL ,Y0$go;Űa2oBI0"E3{4FQރUյǤVa+;7(r+͊veNQu( N6P{߲yg2ϐnnٹ=ocq_z[V(wrLPR L+ڬ LF7$!4aM@W*J:(ϊ:ew4fhKP|?ud 5L,#,%vA$KvsB".APGD&W- 2 51xo)yo{ FF&Z12Y9eJ7Z;D>27'a ]g%H_&Y"̄p ٌ*F<%+=*q_]#Yn`"hY2T22sfϦzpCL0!1o;EYRtWǣW"'r}DC䷥Im4,Q`zPxL;ۼ.=݇0"]^e*i(AW=a%0V+)>]"XWejyo0Lٺ9iA:1\Q8n(^׶6 $#MןS wNp~('BϡMdTRT،= 84`7 Ez r'Z0],PFn7Lm,-TR18M=v5Sۇb5]W¢ؼl ~{{!8)Vk)MVxuL#{MъUc>c;8a2/P8A  $5gFc^g *|_,=ߑ+IׇS-zef>n,J:fyePYttX}t ^6"Ġd2Z)'>S_pѺ2;L ~!s~s'2-T|[SeyK{i{sV"iEe<3dsfA(>'<ș_רa82v,øvʣhtQ'7vy杂*d^VusǸi/Ֆ3+v9+vh:cN2y\@uBh-/;Aicp:bE9ߍ߶[tF u^=Υ R6Ys>CmNp f<*Hҁ-F&L]sp1iĕ8膬^d)̱CJpvQ~Ē}NYfTnt[P(PϏw,wFx %'wolǡD wC0Y/!iOp\q<ݳ7Šq|T>*4{yo'c C4.-.:-V_¶Svaf4Q5@Vq[^ p#j? A͢9vv %9" AX dmÖݦ-y53veGDdWYI!*333ЇWe5V6; z9xE /0<;&>H5%ј/Ajq[Ba$tU{ M EfM1YYܡ(E6踼:>-=t3  Lq>#5IƑoh?DR)7ޣ.z6#<:o!Q.COcicq4XsE눣Vo'D|&O+Yt)Oo"l(lQ}, ʈn 4h<^tCrb Օ¬8+1̵%D bO5op+$t#WT%>.hW큿2:WW)C<k v$fDcY@]zLd-n 5N ~ ,ヶ-wTx@Ѩn)f舏%6µqg@ovOz\ %Ŏ%ABGhSs FNb!Gj7kHգftV ɶ|!]fŋR?&g= kKhep,6i&kFb)Cl) ˺5!'-bW%OJs(8|s=^)x}1 YAJlCVftB0ѦgA٨sGFTK!P ǑVWfI5{k,xOAySԯ-TSϯaDٻ,>=f84Vo<\ю Ti'`ԝ i#rVMf^UdVĪ gكGS uU^븉sϤG@kb:'z!D7\ϤԂ%crH46ZK5ǿwNqofIT0d1Z{F/[me,Dn7SLk4* ͕yHޮe{iEsS t|/ſS?Ҕ˼a7';#}7d;d~Xm(#n@ {zqڛ @zħ:C3v萑"!&82VNh Xk(qp ;|ʶ 3J0m=R_;:O3 fTWnp>[)fdO4A?CxG s  >$ ||Z+l۰1ĩ4ѫJb)ʓp(fF^x[&y;ϼ԰驲x~@r<ѷlCLke4a6%a7_a}] lݘZGa<ס{+$CvQ MA*!qUV 8 m7HFe,-X<Pr;%GeYAD>Yо3#ˋB2dϔ,gmvQ: 2X[ YF{Wxb)G5)ozq 3!PLѢ9 go X∳h"1eԿ)HQQ04S|Nˢp lӜL%{ iS{T`V߷23˪tGʰꑹ066Miro@Oi(0d pF/]oǘ{C,hgCcG8U@l'ŭ}[\'0.%×`S>JH޲TFlz 1ևE,ؒuJ(3ME$o0`փaӽnv35(<ؿ:܌C510s;p*m p RnFrxzJPhV~|Q}D]gDdW'NЙ["^v;"%;7Am*Y"Do8=Y1e" 8#|\OzS*l[ֈXnW&JЍ2F{t9{~%l@ jezTКU+aN$ w$`Qi$,u>/Z3_)t=LeX{I"#Jgiq'%_S|1:>ڜaF&%|QfdZTEZ~ E$$M}ĠcTk=#a](3h9}X0x}G  'X9|z.+NG|-Ay~0t`vj%J>m[Ǩ:jݧ7'Bj~gZֹ,#ՠ>TYAd/Vpe.Dlj1mع]TmεAȏlYSo8<Ӕ‘bQBoh9P"Kz0.uǽXϝ`}2S`DŽU>JvpU>)5 lݷ}uq-y]G88r29#`Bf߸SMG.hA%c=SV%ɑ~դCau[&ATt0a50OX~0,^jqk=Vv&+uuaw-Ȧ&D-鴨m̶MFs*<`a(R싉+!ϕڂ8GtP-Eǚb^z1ߍe[V?B3 .ǀ@̬N`2K<~і0IfxWu}J4>cl иf1;(rF!3I EqmF$X$A#_ [fRc!/RDmZ U`d[w-blQ.=`BW+ xulWW*p !E/}FlKLJ#[1$rU;c7$J43LZ$'< e0f7pCFKʩrh7ago c^N2Mr)S68)dZ = ob,׫ s||ݢܤY+j).k{ٍ$<t" ~sA=J''Ϟӱmի^oKWګ@ 躁D- ػPUI'TcgnEj_AžeT6[{[r(@0A:@ny<Ό;[hoT&ӍψZ,6~\3N,,ںS N6pGe@[ P38]l߾G{0- G uc7JZGhv| F!Uzڴ=!!(vݦ,Gu[u8=GI|Quvf@dUvTwifSҡ$D1%`48%v߁XK}Ul A|R^E*= ֞hy%G QOqP{AڰÄ0u,3 989}fjo3F򋑦C޳/ȖU[礝󛃸?7bNK7ѵiּ're8k>s/:-rvIגPvp}#]H3h)M|ژ3c8bA263dZR)NUJ9A$XE pp᧲#9^GgޭT1SE;n3ldž evXgm bĮ倁@8\v5p?-]8[nf,YhmfD./=gCX3zԀጷ}6C=ap;+IFh-pvcY Fϭϙ)oѴ`}dzʼF:!YY聆O d@gKę_xAWۥ@6I^g !UAm~J ڷ1qk Rah87Mop0ujtei١KȄ{f&{K2!{ [e;D0FG($م9ǭP\:@NC:ۛ v`(bZޒK>PꏶpuC~5L; ig;,7J$2"d fRf0T˚}xKC^%&3{3D ;8_N+F;|zk'Ծvj2, U4G7<}klN2L+|l!҄7yL֕ ,4S곙8FH$'|V23X.y㱨)m85&ĕb!cϻױr2oOt5Veѓ' N%:2)2`SaH GIV ґf=6po 2FBqYa @`4`cX&["G颤? z=譠Pp'u -7k>t{/K(XWfZIIMi?/k5u.ђ3cA/QЯTö殐tQ]&E!WORRxK[>+E||v>7L;۞q:FLOg5j\ 8tSb"g+ rpbz /]P+ƒ5#P/L}iv9'o}hh*7o:<LoxMl9;Ihg*4GaWZ_ _)\jejg~ىb6ϝ- EKFuC[Kֳ < F":Y+0`41. %Uw[j|m!khIq8-lǤB5G^o@$X13"̏dwF`G\{B ^ZUq(!]*.}a#AS2~)$5RJXY9Z=ëĘ+oM?,bO&'j 2^5i3P'ڐDvxj-JP|JƋQ]c, b:ڵP*kw.Ys+W/ {6LIecv;0p1(I]h92(u/83κY%^M)g`>t_taF5^Ho#blnh*48=00SDxS؂⸁˗5}B{$\nw-ϽSrtv3;#;;Gz9Yί eD>驑:-DzVgCsbzS0aKv0ߣ+,u2g:^޹7~^7ƹh]AOf+[Y(E.ŋ[ua?oSJ4%ve( 34f`ʔsg y5ʴGfp*qXz`m:ݛ.Z}^ژf^GNJV<\rEB숫(#3+Y!(2֤Dʚ- aj(dZA芿jD\b46WU qz5>VTod]´%>7t}?HގceJ- 7}@ՕcVr kb$ax0_~yR@ƖA\_6KS;ıt“vHa~[32]lECѻߐ>dTTh1~ڵ!RqgLgaxWF ޗV\_ T#w|ݿ@tbS3jpf8%NBܠeV$VH!n8$M aoRD^Cc*7$2}?q|[JL}u^MN:z,b2x;ek[<4]^Xۥ(!5l@g֧ʓxG6R,\[%7pca1δjQ|O:`LnfKq0XXC :KϏ S>6K6sZ1Z$m ֹQod(eUR'YoJ s<`v{78[,oӣΏ|Ŝ~h[ >C B P#r8])) a HiPZ8J`b`vd* zU񛷐[މ&*nAgcl$նG Lmv IY~`8iҖ*?h|(: W'ҵ(byBzpW|]:GqQn?M>u򨋼~TgQ?G=>{9>ohL`-$[׷6L %іYkͻ6-cn,RH+E"c+-9ߠU䳃V&)JQ5oFنWwnNqڵ{y@k3J. ,XZSM,SYZOS D/3-|uٷ&X^k֬fI2sR* UXC>*2GC\֧[wh%bk'ZQ:צ~INV9 Ze;{D\n3V4+&*Z![|oWxfj = :9Q[e9[*eKC9ۀ/$OD~mzpmf`L b:\WGy6jzn}5<#ؽ,@@Bdl>vΎms}4p{;|vhhNF;xREŲUГ5WȢwW_\e@A8.3y0В#m4R-?M_mZ96IZ6: Ǭ1ZПafԙF|!=Jc= ,X\|Y+U2 4*P!D!ͷk'ؓm&$)HH:H'k)v^ȍ0#`L5`V8 VL`e݄wm~:j9:">7a@DJ8AwK|Pp`2yYc g!\[aO-+p=Eb\4ݘp6aҶD2!|$} Oħ)3rPy`kEqmsDVnIPY܍ōpd=K-Ǔ&#/(y$b0 +1yu[([5!!ap5V^Q0zQ[gn@h?M'+9q*2 1 mLq:eقk@]jI.+(E主%~r5lNa36D7f `,hx{? w6DY8x1gJHlY,\ɼZ)gPZ4}J}| [Y VVqDSڨJ{B(Lϲ*rcwK=/4 O$|$ЍЦcM 'XSTE}95y?{I{ r$#Q&wȌ<8J{D&xpKaH!5՜ u2ў'K]o(/ZlHkyZR# G8,V/XbՉ|."/ĭItK7ޏ1t\.Z)Hkn ~e`S o)VU`F/t nI\E'g/رNhjs ~L}KG Kf@k?Own`Ɲ| 2-_~Z `j#XfQU0sf]زuh<ax6zHeϢA1kC8sR r$N20E'Dz8UAsF⋇&Ftr.r[c8u|҆Th9 S~%ZqTѻS3Šl.-s̹tKf<W95 dayoϖd$$VɹXtVS Oy͗a<"0A [kXLS0~+DB&f =tQ,CMQCZ$}PrvMvwO`Fߔ2u1ɓ:bl{ZEeWL\܈p@;}t"b MTxsNm}D,L{kI̦=)aFҳ}1x,QR)of~0 uEI1_wKUA!IZZ;H4v㫛a#F]ھw4vPAvfwCuK(E=L%E . m1=|QT+%м,TV']FmFg}rf ""٢dwjߩxpXf_;Np?V =e=l1\˘M_S!zae NSVo\ȖBPA5SY=Nv&Gn7Nw0 v[٠*_+"T+5*A$y\3ҁj;F@|-C[Q$ %`2yo('U".įaO* mU*NJYL; E~^XcP\n^?JC CǀG< _6% a&ϴ:-+2:;ebj.yq)xT{D`pvzS AŊͮs^\ .׶\SKBiO(pz2sg z{Qà) 7lٜ̿VqH՗Fj=suz+lc|{E2ɹdN) {\1X* pr.hu/#:V{'Al4Yr<~KxHhwVf/{}8F~CbVp 镣k͡y oe\5M(:>̔w@a 2]r@ޭOܔ@ι p r{5*nQu!T+)]` *fԟk0PXgȏg 疌?_oK5!±hX5y֩/)RiwF %Oġ{;T `/  d~3_,> 9Vt!IA3(sBP8&K]͍@҄XTvRއK<>GSdVE=`F+bOD.Kc6髦ƪjV{2:8d= 0o[I7OvDpz/x~ %AaD1aBAfm-X۴1c+,ǘqq٬]i>.\J ٓbmMCh'27S#`sҏ+(VNmYϻkfG4&UN KXUEԄ =A9=G\rD̜bFOp U mS߆:;L +0kmijHRpgM`̣ 鐊pJ3 @֊Ӎ-? "_SɚmF20]G΍Mrz^&bH*x,l/nyKJ[RXES '^aWj31+烂H6o LUL^.59KƋM]VqnW} Q ?1 Gc*o!4NQ8hԋmT^Þ{RU5>"aھ+p@U*I<4~Z̤Y˻{,ffś; c!*kV9n$72ÛXJ0;? c&[]C] P+_i2ǎ ; JZ#GpxxeRADt;|q6|6-_ApD!Z0-#KP(PKC, #K&ʼn p*3ڶ]pW9j_{RNnc5칱&g]3ݾi[t%pbi ʈ(ZA [RRX51l% ΡͲWGln@8q\e#X \ nՌ\;Os`P%ƞ eNm`泣jUt&hTLw3+ifK8>q#ggqW_NNv\9p=?DYOmEJwOQR7@_&xM !6uDc7M8->*0hN-8T0t/ #[rAnReB/,u֏GB95O追#p+%"`LJ467gW 5;um'3GDKU؁!o{>0 YZAER/data/USAirlines.rda0000644000176200001440000000531113616365126014311 0ustar liggesusersiTot"1 l j@C4%&: k 0nh .q!.(OD1bDE AgƗ`^L;?uwn$d44^24Gv~ẈAGD_%U֋&x c^ؗ0FD:'4 hR*V UB3# (,0X{]pT^R' ~AHL:%Z[}8u AǶz%7fu6Y۵ϛo&m}薶A n=lF+lFlF{hp56\ Wc(A`m@0-m߃g++++++_Ķ^MZ.}V.l-ݔEW觱K_pC&Qָ(kP;QDɉRJFHw>E}wX;tX(rw?x5-j ' J6]AC OT !U@7WsW%.I_Egsя zyq>7ȹwr|'fևUM,ow8c;OPS1&(.6jFEӎ{M."}`Yxźޟ[; kDW+XQho(ƒka{k2,*Q{OsU{k d?r#Ro Š{ } 4\=v` ހ#ڛ-;rbڐUX~`KQn9٧=x{ȣ-|3 D_,7bSsKWlq7, I"I{`ALk w[M,Zza`"xM&7H]~>3.p5l9eZ%gt= }\3E3; `d0B^`?a;H^Bh j? T@n|*W`ܦS0: AԭD+@It@&5j6@Zͥ OkE}]dK⑮ ,7@aS5#Y L#` |)\G@@ ?g`Lf!`3AFHmUT!y#PF_$΃ x"0ٷ[|փ,m&ɊRsԵL>^%P xJ`W[ ";[&.n/e[^ TSfH:0B`%d2@ H@l<sg; ̡b`jM, }} n}96tkjD%JKO!\V^9-KTU*r[[ZfYiGnhgmsj  ͻ/-Aϊ3~xзnVp+oy“g M3xs}tMSfiS-=6S|\!w[4O]]~'x;gU\_W45vS hД-wM_XR?ȼcu4{]M<\<Z ;s-O䩾Tv M72ν>r ϱGSI|͍xoGԤ'J+3닺o"Βhl|s1ȉ!ўF; hN O,+ENHI YיNdy:rS:8 _Tjw-jƾQ~++ߵ[rf|{jgigi{>Agy17'[Kuy|^@sVv;}v{UME-LRk/IAER/data/DoctorVisits.rda0000644000176200001440000003562013616365114014732 0ustar liggesusers7zXZi"6!X;Q])TW"nRʟKMd[_;zk{C(:wU5](t=3kUrQpEo{՟vAՖgLF k a0 I< U 3\(+8-~8tLW 4PIZ/㖚w) x2\m6ƙ[WR JR5J ]PXuZkڠd,^CBRK/i OxdɑXWCnkDS cחE*LN,3Zt N(cY9uMX`)UFYb':bk|c/^0 _L.UtT#XnF@R? 8nF[2Y疭Ac 57T USTQfs+t3ֆ\qVG;7W$1d ~AވD}D`B:ǞK'd<%#5"AD ,N:t8, ?\;lLPx`F |5qVNL6N*?kqjRfͲ\U&eçPcÞxwSD~+fb.|KŨRK '"B~eY{?HN2DiM#/lo1A1LzzcSxh+Gnk8G8x(NOG3Y1:fꃝBiuTC_F]ˉo?nf kh&965@ Ǽ/6Xt ",VKHy'٭2a׭u#Y**F&v^ V2=xwg#1 *2}OG|!lU">-W,+_u em$/v]nc -[U fT%{Vg* MۢX&KdfPb]Pu!kK_(PFʰ[K&@ۺ$n:Ê pped{/?Fk=lJ3ͫoGE7КF(U7PTZ@σp= g\ǝU"\feL|Z/5ؕTlh/`cZ^s)^ohȫD4Lae8DnVipjըfޛ?0m;{b^^N=^u~N6\Ih~OKM6azfԄ773]|GJ+l|{o<ýNUn*K YRF3.1%>\R = 5rωш).Sta5!Żm`XqLjT_P;!J "S.G%- pF-ljg՞͓ZsذK/̎  '$ Fܙ{LVyks Mp/#`͗I r2nB yz\sE۾ɡhrIASzy濁"K4I}4Q[XӮa{Gq]O>sQDp#LĄ#^W l]x L_0׮}/]Wihρ .g^OS!`܉f sˊ;8!8dΜ>U.K: ݷoWD,fK oz=fXa*=$ pf(N2w^iWyYЙG ƹ)نDżXHLˆZ2 4 PL\ޕ~ɾ cQ~LmRXcU1;o/ 6G2 (Im8b>"=.^)>:<wqq~w%˼ C 4dڄC V:r|ⅷ5+0!v%cB \/5ɍ^2?wؤA]*F  A0sh\{>"Q6s"ڥϗO;{ۋxG\{5fЃRie*=h +=PVr9jzEnk@٬`oz*Rrn5!B82v$T&8(ˬ?z(&P}q+QKrc 55Eb{+r2mQo`L5@w!C7wbxRcy#J?;OVԁa!BSt XJФK8Ԡ" ;PEe./c<ؤeht{!Ak(Y~}%y~+( S ת#5GکB/ZA-H ť2*//?|[!QƷ&m{oUoO`%zXZLn%Ef_bmѿ۠vov䦐 T+&8FaJ\E [|1gO,  6ۂ&Q 6l펺alݔD]#V҃y%_خJ 򓝋>2sŰl5sOUddA:^[6"`nՕE`2x킌1Vrt{jJݔ<.- 2j"{~Ђa.,L ]٣ wjVl/\,N4lKzۓR'52 zVcAIgM}bY\˃m:ud}1u!ic{Lr bO?t;,P53k**|m^ T3Q@\'Rz/n' N5NvOPv@9=K:]:(-9,&TcA)}4v[^λMsowa:djXFr'*;MasR5ҙlwQ;1q0`1Gw8,ؗr}r:ߑO)(dqF)zԟt{V'/k&SPXC}cHfH;b!*ZBD|@$v#*ђ VojI4 (V`4xS_{22ՑƖ:Tkʨݤg͇hI7uŸ#n/ {/E:O0,Ɇ!ۉ )[ |iv00p?u& ? g5Q/v/ȴEtD\#[=yײ֊MY@iB&:c Ŭs2+{ y" ݠ6> 4VLE= m?pM/j tm8_l{̇b'+Mk('2=Rؙ\wZ|5gY{` U*r@V0#e4ט.&oKw]@aZ ):z׫ȶ:A۝b IwY-, PH2=sQCD8h"i6Rbq?Q,!t#68n-\$ي#u#B+>|nP }Oܨ_ILf5׼RM.99Ƥw(Ufǟ-c~tSCmm"'([M ̢!W^i2sg*>)"0{`Ij'`'IIߔl 2,?$>d6c0Iz|S&#nxdBWlnk/u\ۙg%ܣ~ϩz‚._6.ݺO,9ʱPeh ܚ O '"&ӣ? 9eM9Ž%(·Ŏ@鱏))_J 0"7wr%ڎob(;/VGv龯 w lE2I}JX m쑅rŦr&PqA/BOF|$gu%O.tζm 6Vk..&vt|i+.w,`ܨX8\=7gyXY#kKXUƌ6pt8nKNiOGCb{qvU 53f6?0c le*EBtBnbUqfCt ]3eo= '쁆NgľG Hh M?t( =`T{m2~ JdmW@+|,q//oRj mZoj3 7nz hf2kYK!l?Fwra^T}+|:cbaHWMrÚa\CRlN.zvhnvK#|R{d6V9|2tR͔z4X8xNpx tײ$§xZQ2&ԳsYK<*kbrHnW.XVDnx|KIpݺnҋ- `Ƀ$ka[ߦ \:)1 P$>(#Bܧ|\GԊItŸVb~po.w=~}r8-iFַ&J<ɞW`ň'_ݵх >#26/o˶{QnoiNZ~56G~85l؅Nƽ蕑1$R-Dc$y,ei~Kcڳv6C }i'w:!@Ό`V1+qteN}CْK, uqf'D\%Eؠ0v6:L .l-G-5͡ᠼUP; ]|? \N6DxY.Ɓ:VGR)m";;zTפuU!Nx-,!Mj6qn};i^ſ?NԻF탑R:m+b̥ |W5串L؞/Olr@ ($e%z~/Q[u Wy1ȼerRm[C . ) &=:׃~.Sul|HhE֞0a5 -tI!%y<#F9b"V(67ЖҥMu,9%rhQ hY?_g@ڟA?iQ'W ~H(j{~?Ff2||GΕu8w="Q>wgO/΢ڹfߦ=剪P l4lgj4X>k/Z}ƻ]$C_!M.ߪR|P4a/ *"9;.,)m eHnedp3ޕqޣO'uԖ7no^ XWg%B(ޚ(1([uY&D4fnx5g*Շ3ir-HuH˒wi_+*R;Qy4%." Ƃx0氶N; f84#g{kHT vT~v3MOo~O㓹F)pFf'*0 Uw#xܙ88b1oo:ǚx5G/ #4XFrm%HN']i1n%SC[s՜ 3c?*; E5$BCL4hVBEMyǃkUJZ:_%- R:{ݱ> '<=ivSWԽ| ,{kMH!ؘ q+f}%>2LPĕ'qlUjkqXcs~;[P kdOYdF.虥RJsPM_EV BI7KnF攴N9Ca]eqLX“攍KTzvstd |X4b+@ { }301+ PBea׍36* PIv5S cE$E&I ةs5\Qsuձk`RzGo3Wc[ŚXYk<#aY${#Ց:)fa :ͬ~ժi@qc%FMam8*Wpdm4o(:*[3&֔+;BjʼLb2C*gJF_WCPL?K\srr|sm_T"#A oZ*¢9h3,xD>#2LIA5|$,yK\&FN3 ''SPS>m-*H+l;3oBNX޹J\׹Al4ɥޞaxЦjԟt_"lpe :%+#&*h QtgvTŽY{R8#=_Ė5Jk3#exQ^7g`7/M5'sQ6fC,0PѐG sJ7Ԩ]-gc̘- o^?csޘ\S>{yPq$htOmAQ_NX$5Xj.<%gvrMFa#%ueKLLNoZvv3w 8G1}zg{."7t۩d|wôJS^d:pAW6Pc~t|#KEyJ~xQtT8qKgJ_- )HĿm\T+ J4Dcf~x-.uМ(e# eA(@_>mŚqw8mll#⼲ Ix@vW-t=Gۇ jzں8H_*.+A»TCB^p}wݰWq1Q!XYΰn/ʲtMZ dF,R\A sj'cwͮZ^Ket4f8&UGPp;h%b48$?^>{'I75oT)= KA?l#g.5j掕i c:dt]y_7>98JK7J}KǖG}K={>ӏQqO#9lbPVMEgD#}|Qs+}? d-7LlXVtBm2|B #JTv<'"_fyq1YN9]/ ;4{NeFU0?%vrjZ4NӮD}8!5f K&jH %KK뫬ٓ8D`7ͅB((o'fR.ݤV*ϡNQ8Ms2:diAKf\~j v>a>$׬1_?LJ p<k[F):.WGWg)}3psv&6RA[r$kyxw@`3\tqmhf po1_|8e ^Yх-u^b$_.>YDa{\ a4D_̋Hh㢪DV dl /8Pq~ JV6ApFնTnJEC.R`|>W*-gTϣ' =ħahoN_$߁EPal2OMW4B gnr^?)(5{)xQjd^ӄ,fߺSodҞ0`ibn~zb9_gU}(CZah6"K׍Ѝ|d @xC+{ŠU=06[Ԗ2jٵ Dž%YryLw(',.mh[SkOO/.)Ac.Z6^|G(B&!wrOT\Bih:lzV',&_e6No:g{ΐn-UAX;1eEFoX'p;R(qo{OTMy ԨVL8/*lP JW^5AbNx2ީJ(#t MPz-bdƬ7qm\o2s~]U]tYf"hMnQ1s5-(>.@fy{$%CY+ϰjTF*TeC~V--W+6տz_CCs_#=,c'C.Y^BЗcTPKv_˩ό"z8 d8)XpIX8y#ʛ)B^#fndM@TPTRYeֽ(#2\w1ʥtr@!f|Y: tqBٚo8kj#˘٠+;/~5)sQĿ5x}ۊ4i jvblօf蘕[Džƛk/\%ш&YUY8؆닅Dr9]-cOѭo{ 0G-mG(@ɘ:J_ZdNZ%VnU,}0Q3I&Jѧsz{@f`΁J+4d@CA'IjWu2D1#JI@!uJ N@nklykI. 8;z8p ȱ[;F1b޼kt Kkmm3F}U( E4Dn'coK:^kf|l%Pf&yh%?\_LG+W\j;T[{?jӀfd>:3]pa0"1=Q7| E+7s; p]=#;k܈NV]j)33{yXl(+5?YGXE_"4E߳aq<^,XvKcI?*sJLKZJlLtZ ]]xyXs2%Hȃ0?:U㓑푤$h ")z;iD䎫)+]G[Qhff e"$߰ !褙u4၈|K"_3Nɯ _yx%Bi$Dƒ*z\d+{T@/f-EʠZ'2u,*ydDڈJiS1N8D5%sܒu!ծxn,sxNѺ(#61saJ*" $tu7F4"PRx($JϴC,- r_ơ ϱn kyKxcRAk}׻*dlDStj6VzcPa[$T)Rc{k| 'H5w!Yu :32dvXCk2dPf`)IWlI.sDv/y2ۚ4L&ʒ3( jx__\`kB5('$WVc NIajt=E`ƲzegLRBf2t b4i34Gԁ>.`J1kwMAPv0)p;!)ݩ80$frwoFjT|fP#sP^:P}'vTmT p졩`NlV'~@ :7-׻{&mڅM~! !pKp_kLi n#(dy4Y}u"FF\#6IҮbxkq y2&F?XKăa/LWw n+O7+|Y٭4tMipEVJEa=9G}n}4bEeuR0 YZAER/data/MotorCycles2.rda0000644000176200001440000000044713616365122014621 0ustar liggesusersmMJPFI,".%HG ԁJD6FTZ}A[PСKp ]{` 9aVEϷ>"klp['DuՃY\AqlOqsnwuceB>u"C |c[-źgCc)iكĿ&fKi3es.cL=~)'M>{9)}f#H˶X<;~f+ }.3R~_G7AER/data/GrowthSW.rda0000644000176200001440000000415413616365122014017 0ustar liggesusersW PT~@1C78Iy ]Hu&\v.6Ll';DcA[5Zۚ&ԨX `0 7n;Dz4͎}9wYmm%pS`P?&&pU|E*Os!CM;E}w~]W#͚}gk hv{ n)}bѝء:i Poj-;n6θ ZL=|@gTph>?5^AɵT 0 gԿ5?ܷf:o~2x;Ɵ";ZW~n|%Bސ0O7?]ò9,ӟky|k0 gŸ xW?3IB?&1z:>YaOyp=,=?z[)4|KQ? qVFtB{Zг1u{+8,u?Wp_ s1)`W#XW ==yľ9y5 ΁;( B2o|EݛWbߡ1ގG=V b| q˰G7g ٔg+2oݏ}֣.a¹w5cOp_;CB8WP6cc4? nr a-wlyvp!gE{Pw~Q_8cmwOM< <]s ?-'nC3Ļ} =K6|mz̘u<|d#R|wЎ>0V6Bq+Jo*{3 uO-7~a V uݐ<_BJςҽshK1 S}g+^kIfFZ9|}9ȸަ;Ü}Xwܿ2~ߤ:R** nnug޵i|؁{8Ө5)߰Α~x6#{go:gMǺsdx" 0Ϗ;Q\/i[^:O2`vk8(;M|1RTqVOk{Hk翤LiO}| TO&rCZǕn=~nq<F:bh/m7ַp_:3_!sypml?/?7'ld"$Nsq>Ͷ{v*G,~C~t~# ww3t~sOߠu6ll̙vo98^Lqֹ@:9_PY2nP\Qjⵌ,b dЮk۾X/I,n#@_2A(joˉ{$8w{&*WVVd.7}6?mf*4nUJC2STj #opjKz!L=g(cT ў!>_uF*%DWkAMBnk% Pȹ. [1b'%#?WP;]ej sLM9㝟c$SUpDzF9vp 5")CU]h*7VaY`rwuCS'n>vվm=2l ~ȫ}I\BZlks[Ƿ !7@U[V7jߎ$yL`^;$"{mIVgqXkMD[T3("tܿkJ]Ϟ3KC`de j j%!;&e7_P+Fs׆EZ\\qgΐ#L8:7tu(HB)'VCC*qjKeYMzF_ VUʮz9Җ OĀ$$c kPc=B٨y58=AjM י|YJ k>6N] ~/Ake9Az'Q;@NZN05Aٚ^fZq1*? ya2'L$!B>l'cGkO4hm? i f{bCakBP`CxhWyŕ'`ӃC-D9("e,>Z7,E~v ӟ+±{5?̮ ZYH_<A|`ӎ*kc+] ُvܶkЃXk&YG%gҢ2dKד@U: 4T$-D&"s :BQ08@w$'Z鷮/J F,,s* 1[ͤi2ԪшCuIplm ǖJC죜~:q&ܥ}4# ZuvDރe9JA@aȖ/XZhsvب@fz bNW%An}ޖ/dk6ó, pd='S(0#¼x8.7bƥc-5wM&)PX8b! LAJ <_K~?Wҫ*Z(6t})>g%[>?5Q!We#z1'5Õ[+) in/J_=uM?}G*@z@{NLz +[ g-T pb)J%4+g3٨W/g# Lxb* ~E̯Tt !O=1f-SlByZ$8+}T>wHzD^ S<:/\U[>gĥOpx q΅MzayV+t}緉 HPI ^x?u_`VO?Q|"EkQI"NI5Q eG/M>(zKԜ"l0+Rķ{Mo񕑖\04;t! 3xi)|htQh>Co kykd;LL;w=ֻ| C; |lz~:lNaJܽ~[)㏻zsaOHѦgsåJ:%~b:d{syn+_ǖʳ57eh s۩:x&U~_DBjQY<[ s߱4C3G"U!WC%M6g ċMt+$2cXEF,vx3r;mtݓFy݊#ĐjvF&3uGͦxoK+k|oa(.2]eY|%*B7lЃQYR϶j$^G A oɞhִ1}˒v ?^b9=l7i72d&$?ZaNB pU;gE+++HXvJuJDUS | d(Zٌͷ S?R qP1/I1g!SnΎtd?m@`GǓpKx]frQx#s^w I297!zlS91JҘT6îy˙ȯk߂+jyWn@MKB] 88 GQ-n|:Tx?H pi}qYw3JwdOy%am>Wվ$Ok]!8HThn1ϲ<] JP K4?" di9&#mW1 ϱ5ul{"9ꋿ5sDaj HTsb Ex u7&ǢT?2SMW a #/6*H#1 VgٗSE&'"Qoq ,2/h"nP1_&.̑M2m,'0~R쐧b mCpw3I/4H2iYc[`4z1i#墕>(|Y;pp$Sd@w[W5JJ:>ѩ6*e|&꺓WҼZ 7%~@Ji,e8ݳ-GZ-@G F$W0 *U!WKǻ|jʦ( >f؞cS!H3iʈslK)vaKCz1쿽&|Oc h\͑#Z<3uHq:Iƕ58Б:`A~BgyUn$iE߾a/a"LW\.48Ŋ@#BTZ[׊WlhIbF:O7; .-)o8};oEs3??q|`Cb쿯i!*C$L!=0UT 5XqT m,-5;xNYH,r ¹3,|rte8O 2'-7ަRm ;:BR6x$H0pNh<*ɁIĝo}?@ D$1zNi!jپ~) }\JMڗokLv.tкD@ +/t4\i8uJ"/Cm^9b5ĥL!,'OQE+`d*e},Ǣn:J$O nu㺢rilxs}5Ǩb5)6 S֢jvOLSyz`7OQo'JA-E\ AAt?D7,(.czJ{y҆e(#:ȡͩx*B^@OcB;zn+W"k(Flu"L++Gɱ4TV"F.!HɽbU4mT;O=6,z_ Xzvql鬍 xw?v7H1,i<{$Y1 w6&l }oXӬN%rj6a0􌓍Ρ:snsa4П]͉£Gkؼ-ƽHW?ϣ-="+cXM-sA7 { ( CYJ!Zu9NT0?g6s-tݯЧgYUv+ yW]H_ QG駼uChvzo'U;j?q+#l}ӥNcr{!{"vMgQ/ݚ665m HiYANb/T,]+KY*nv"mL C̝/ #7ĽЅ|_F26RFV *S\xU@<&[]PST7cڠ?aT?{c`t$Bňn k;mtD}~|"@x L6IL~❕.R<6d.p^JRgZo{}z} eat7y)B$-\`lV}b6xxfiѯ9BqZ+{1`IA`vr.z#).!yzaN:b:Q"f[ -;h`0, c z@Sq:FL[#ʺNf%5 ejB 'iE<*+`|d rWv}L NSl .шաe8Dw:\x\ߘw-'SJדs4aE4 0}B#d#K%*UfZQqW8]+,e6C;aǩULt$6Z% ]$ }TېaG€҈~q=K V>b|}L ד!*!@\䉎6 nLMLJˠEe7oV$jtv0X,2UsIiAtߺRKʋ5ϻ'+;zp6%I6bdy95~9CcyD"rNtRDM/X忲ٸpRRb2O|زlFOmc9q[ ̙C;h2Bmf8qzqZ\B(Yo}S=?6&JFZ-wZ#I/:u sgʦUH힞CϕW˺cH:a=$D3p?a(xo KdU4d] RW&&V9JyfΝ:#$Cab+yl DX)lu Nkqd`s(%Ɗ".!Ml}ZdlcC;~R"E bR(fRhw].ZQ8Mez@X.Skg)w{åVc[mZ`O  2^5ąOqXGo_6姛;{ϥs·B <`NS,Q0@ɻOW9C *;nvN S]UGORB9 _8s= xxo?fS_͉S0,VE`;M5"]{%#/ٮDҡGh*vMRz3Nl@Z'! g?uȈwo:4^f?7ٳ7>&?"؊6İ_X ՆkcmI)Kuë`mK?"˫Otrsߔh$Rr)hvp0÷?y&~G=)Cgxfgcc š3:ιFY&\jjI r8m+jZQt<82 ˮ;Y¥MΖL?Ꟃ5z]Yx];1ܾ6k A>ʜT<<Ҹ-DVGvFY)ڮbHС[~?M}-6LǶw4%楁5rva<wB* C6pB^%A/Lz[SgC Cj9N.9D6~fwKZ|z硼н!aBA"9\x0& Q:LA9ֶ%F QuI%*Γ1*9:zЧ3t+diUwmJNOrǐ^qTUeb` +n[=a^S[;ڠN7YCUu) lvp щ4:8˘Z,LukSkbqhLNO:SPTGM2ں.]UvňωĬY /kv͍p\2ֳR-[h`q3P4%4a2),+z>|UYct\9ՇK[tC!&֤s-,]sohOZ,C@Zgt93v/W5vNxzt`ܤ4 FX%(27u1mml,C6CS`\ F"Bs iȆXtbxY*$ 6APfH <7C}gWeau쉼saa 2%rCS>0 .)SIwIU^($<bv%%OL Oshh(^qW_${OՁ'Ar܁?q?$$mTm[([lLowR. C<}M\mFq 5TT@=є(8V+56Q(=,[nW];6o&~ǓqNdΩrJ]uӋ,@yaE]F>-u"m:Xހ]JP-v9ҲNٟhqMVy;% ӸZumw;uI}s1H\ҳ燰Q >(u 6Z$P*#~ VJwl팕4o9pb/d~eB9s^jW|+R%/D8K|5ILKCʄ֋5c62([4)*J yHQ>_Ab2Zz>f/Wϒc3ĂJC[9Q@? 4$J}$r6Wirʺ ?rƬo/v`\{jiC[(zAnNMR*nr^jcΨfY3@J-qM e TfrdꟌz)s}Ć4y]uK;RmM`5MM; 7H)OCH͌oPʕE(%oheIED Cyg^G[UkOaMǤ]u38VN%LY\7^U{>ZRywugUY˺)Ezijp>p#vN {UMځۈsK£ {ck6.cl3kqi Kj`\4# RGnBVh`Sϳ2Ƣ%gs,IXj;RNqf"‰oR3F&[~lhܷ9! +Cߍl #hG 6{䢌0Г}CpytNbuueEA&,q|EKox+/&0J_Hm:UܿzSWг7Y0  -ck _S̞Kڕ>p=(*P+~_MŭJ75qrȁi!Hӑ?Qu*|;dX P J,ċ;_}](YVK ]]-wp}>-D|?K 9ٹBT  auyTnLѨ]^Zt`n.izdQ;d s% n?nCQb2'xel=X@A`P0!'`W+ ʤ]aB0"-3Y"P]+[Hq_lQR>#OHĊ YL+.ޝfG/9@|%N_zGطaV>X,fiN{t*&R#IcZGb->3~4y}ڼ} *FJ=&#BI=Nٯ.R ^v5 ~#KG7peQ4[oPV<v$ *e\hTaTQo7HSE|sab7-Acv"&K^ǭԙ2Hr;[i44)raefH ;}~ʅɆA6z:blz?,nލXkUy2Qj}Gp/ޘ9P(o]%qyf)($N]j&$ fg'CHrUcňZ=<)@+ @u:^H P|6ttKb]vvR!U-# \?}ͻk=S< i(ߚwX5شrkvt-&|KG3yCbdy/{yAorNP7BB]@6mղN:vR*krݾ=bG1@\MrD"{~xBd'BSsux&6n6#%ٸ=Hmkp&!; ~©#N$S\B:s0Ǝh5/Z%r[ qg* /{&MZ ˺cV|U]/CgI|>”IŵLwK;yWERVJ N?^ʎb%U両#h0"6# BӺؔBi <"ff nlEF@;nJn[p}8}@{xZ}2 w6wc0+B!A_B4E1d:M@NPtJK§> d ]$l'`i u$pv6F尒SZTJ&~仡 8E0&"1Wvq>)0-J`"&'sEߩ J fоh1FC0L11"&ɲ4 ~w<,MlB1J2gq{5G8(ohoMj Vs3y]f`)K87?;cc`p=ϰ p^Jf{/O' !]d8?N3p\ZJ-.(3^G|؏%IF^u}gn&j 9kҽ (/U9 rap`2i, Ɖ rCиh01@_њPnFERaجPSoOF9G×*?3/-=p`kl["zjnQc=k<"P˵'Ekѿ:XBהMۣ$|O&{O1NQX\MTLo(EԾ>dRu .UJqes&ś|>Uzk{zc+AfRf).54f{D)%(g7'R6ѿa{lm{48taju{< 2BPұ+WG_)Q.`/̓z1DzOh֍iF9نh=N|e*N2/ddNq%Y hR+&]M6٤̨!r_EBdD{T.]Kٔϼ@= LK]q b@ lTӼD?`- Ey6i:W~=FrFBUi7WaOmD[rxDd+og5:o'<½'\j^Jv]*=ȱV^a2x, Aզ詥܃E7 A4|yv a&]dN g..EƇ92Sg?Iͽyu7zIo%8G->Se@ޗ0|.3cHqɰS3yMu {r^[aٕ9r`[JK*%Peg;z*4zN蜍Rq49KZ0vGh8{ts;&.GmѦrN#1<3͘[* pr mux ' ˗XeP ]aoEՕ; Ԥww CA5m4,d9aعCww2yz1oF :.cyKP+ 4ׁ oQ-]M x8W%|븒,Wz޿/IsOH=&S8} w ӊN?%T36N[0# Iᤋs,<0asy <<\#P蘲a }z$:4 {q+LjF&6*I d6/LAa5 &RXm ub3mdZ/iP)nFj3Oc(1qg4~LFv x1Ƭ,oZV='Pu?N";UKQcrG+Ok24dE穥bmqF$6\&XbN~ŞV!W˲tֆy۫;!\Vkɶ.3" Ú-P#*7ɃuūװEC鼂<~<m8 7趚Z?h4'^6cIʮ2xRBEur:d.!j $q.K}0F#eq+ֿR!2eoN^.oF@w hL&_숥| _r Ʊ}SNΛSUC_woL{i~ ݚ N1? UjYfڂzx#u8칛}"_>>Zcjˇߑu~eZd f9Va{x(O}t8qvRblc Þ"ʌ/O;+e> *#"rӖ,:U;w֤٬CYr3}Egc43a`B'c*,5ב*'/65(,^EJJ {;q U{/að`uܒsGO6YpDqh ,* {{JHNѡnu\":2"&ҟ|{jD1p Eэ]nd5;TCQefqaR^;7/5^%mIeFa.hzsuy= >fqe]L:I('X{GѩW !%Uh+tnԅN wRi7ہG-*?qI/Z5obXD.Vs;@Uk\,3tyl0dǏK ObXIucq^薮5.ұX5lj+咴ǜ=3I+X颚xD>>ň4w1gY" }Bl'n61:u-90Zt 6Km(R8]^^ ֐D~ilr}hʄŁ/7u+]]EY(**w.P!lv{nrU7\&/ԄHèAhӏnasyAiOLlL#mW50WQUát.(7sgeo`B?GOe =m[+?ͩ$gHG -j#dXǨӷp'{lo'ƋbzaBCs2+Ҟ$,Kw"oe"5%>4=ÄQNjQiO/K8 C*џ)T-ʫ-D!>,H?օ)v*M{+FXn}.7(m,Ъ럧Pš$-*Ip)N ܓ.뉘H4ݸ\M.4Uځ)_;*qur aʟb"ZܽAkTAttB; JSX9!`U'?8hyPX_X]|'E  i{>6Ӥd:lbwO1VaظZX9(Mب iuv>+ \Z ܞU~-HnV:o+Qcn&gC1_Cd%l,AF*l4諪T'*Ó (uNܱv&pLO"U]t$z26/&7C8.@|jMYN@VyШʮ9n)G(Y`bG=1|q9Z*="E˻?"BmV̼#(#!d^O4v,0?L=(_aoY ZKtMvr$8I !TMJdֿGz _.C<WAƫW|FAkMbEuV?"$`eA 8]P[% m OP4R #﫜s (UcFnj\YPSyj$K) 퓂xuuqWγjܿ>'eA͆~fnKѤ~V (F1rtŬa s 9+% Z1G&v\XίcutɊS^}փ}Cȟ5iU}J㤦zd?ˆ9D/6<9i *_Xe9(\ӛMZv* 0 NU2DmX$@v=? s^:Dw(N5-׽Gsb .BNRvd4i5 C6S!_l'S}~q~LEۡ&fs62en{wv!:+Ep'? &11@?@z2?oݼGDVdKxnRv4]1f j2,f/--)H1RoIg>1EC`*=IJW@f2Iaugzz4RڴpJN`/ mw@(Lz,,@bG7K;}fDIl8҈M}_ɭm5_:Ho8W.R ̾{%f2u=mɱ;V.'~4ei)?f>}91.+>S0j)g(NO f![FvoYΩ|V% b@+ 8Ez5 2ɇfgf-ÖUn| -5ʦ< xX]dfUA+RwH`QuH_Ϩ~ "AV籈A'@r~*SLAL5j[o5~1@ꇽ2 IBt[ ̏^+9 2aMg[kO(E5M4C"^h*hU7Rp'󧬆I鸒C&JϰTwtk8l$iRq¸#`߃?:GǗS㣉dYݔ=Ԫ`I#`")1Uc;LPr+% 027@љ|>h"ntv4`k[ַٟ<yg!-H)1/+I%2UVf,W _q+AF $mbI35Rls!\Ih 7c'\ G̷"~mv iX#9j<5pnjkp @ G1oxsr,%'ѾMTj p1X64Pw"]'}YOs`ѧ`uC:c^a8.H7|7 6]{$>c7j$SH g8oC}أqvh:Uib ' B\30-=<%tε#-q#f!7r&gK[(ϩk L7s+iws?^ w;(@|C_g AY%vHQ%zSz_X8i=6{NioFFT&W= Z:]purGbBEU@@ȟ wvTpWDAr"nkPaJ2Fo[{=k, W?UvSƈuMeQBk$Ya$* %"SDߎ^L| xZ}2$%e9-8&\rU7{ ISqQ NIM QVeP?c/IQ:/Nܜ|@~¦ Ɣ.zZ$@):][ '0\'s\xEb9|(Лm`vql74^%6^29G6R-l΄ػc qYN@2Ї O1 `eRuppW S&Q w;0lSRQ iot 9UW{iH h٫RyxG(JL6l\NEraaٮri@?|CN. \a)O\swNWDgkϗs]lm /1?G{iIU ae?rR_F{k:[1* uP]vv\ 4j- YcC՗nlcrv'YsT0 vD؊ ΅8f)m PJ[ëYtH =z>˰NuчوË; 勆mo2ZFѹ&a 6yMF1l2z&^i b :AY;s&jOǐLF>PLm$ͧqz wlzllJakV2o{IwPCኡ0i|5Xz!hyuh* AjsW+R2oEb^OyV&p{9Xbf V-> k/O'8E2*l2^4E9Č??4%q[YYdxZP\Z +h'W^:̜#H?Ѐ|ܬ7(rEn1ñ̺ݺ,1fsSD-pyޱ1{yyU2Er(Ⱦ"3H~ :*+;p % BMNϒZ(:Sņp6hNj i\cm]`29([r=PbLA9i5νa143xFi(nrO{჋=&B7/.ۂ^B8V;0鿗3̳C^t |2zƟa{VKނП34Tl[5vo=/'+:&\G4$W0w@;9 w" SmGw*1m Fn9!q"D"X.EzTg8ˏ_ז+ˀ.ՠ_G,1)pqjh]-mqk] eӖ~/e$ ȟ"aM+\m>޸36X}M:<8aOve?ś0*b4R LK4m`^O td4|יwTN=r=q׳whZZtåbB&Y%>;^)@ikoO >}~̽^oU4)P2_6\'ǟĄ(-4E>u/^q6[(o 4lϢPXiB)EJB Ol(#7 U>>=:ۿ;e>VI+`r}6jrl``& Q@WpECВ6?)vgā?iFPDHwfX c5,x f4u _8k`Ht 7>%(]ɧ,^\fgmb'.kB1eK:ʃ(L1A>y&*$C󓑘#;v!ngEwN{wctsvzWnq2g% +5*8jBȵƨ;>\td*Zz2ޢ. pRgSx{>LkPUy ~wnW5- FM>u}r./*Eƶs>J[cyvZ`Yogok^JjScN|򍈷e,2l=`,'9hJoIZC,mPbHD͗ #Ϧv3az턮ZjFKhfŤTTά!@.9m[p=kѢ9=[.I'YLq-vEk=:_՚,!3(t>6 NyH `,D-4?`!n51]͡nbPöS` d} XPVEmwQ\İYӓ_x9'[E蓞;5BWUBIH;>|r!,>+֙o[7 0 31i*=]Mh-Mw2Tu`z]i'zD%*nfS Ox N!B! \c㤿0Ir8 PȄ-_HytXg92h\I J~ANfw.+c`_D\fZ\G{vou,%.MrciZ2䞰M+XI>liܜ[YCVY&XUs#ڟmi3ȃ^:>k U\|חi'Q,?PkjXe#ilx%\{eV-0)8x9k[O~ EIǚ֊G|%yBhejhq[\ z3ꨔB`Т{OLى0+/zA܌2IamB,b;+.Ԝ~7׫JzT>UHC!?.+cFQ(MDX 6C(c[ 4X ':HcYȻQӑZ 0}z})~t=]D& 9t$7 IٻshÎ 8CĠe)\8p 4vhẀ#"`G>;>Mz0E#Q.f{kDYj$캖ԝR:>Xp>Gsʋ+d&xo# fvL&ml2t}Ln,oztEAJ_洫5d CynA"Nb`AT`CU{4Yrz h<`#qTMj"ŝAt17LFN3hv¼Ua.6ToQ$:+Vb'&8ڕɢrn#5G=wT 50 619ֿe!QhE=ԾoƩTCUgtIcq,BQ -; Y9v=סj?ãyl^NọhakwC2* +=LqyJqDJ>~:{eTj[*rnRto;~??3v3FsDkq!&h7/O$ "&6񃦚1:|TWj*hCiO^Q>|}eȷJBY\{X'C8xr1lyLL<$;%"p=3~kcf= %nXsvR&! )aQ/(l#4 bPHȥH&9QA> %)s/;aDj)ml)m3 jD޻G\";,jUj'P|XHD P\To%y#2)c3(>w8:^ 5/`]A}z{`u@Ņ1?u#ւ?bH] s94~֊lYC)UA*EiVyE`?O\]{1g)#]er*yܙ_;0j̟fO"(4hd*8ʖf :Xͥ},!w󷳧WV3 MwrH<8F2< | ęp -f26[QeU>ccČpF0K1KYk&+KԷS;tb#Hᗉb56[9_/)ZP &?~ƻ<@A'ϐ1OTA'=ؚV'N!!* [}?xf0c"꒍1/=҂lp%QC)4 嗆JL%(rs8p.gAf(pa:^uh,U1fjn[+0Ji)pKF5+\坠mZx1*9ƯA7gfӇ\KD"5/$62 Z!ٴ0-mdoȳ81<==VEԣ`M^z"m$>wge0NuPG&tj6]mH $s Ѓ!?hUeXYh%ʂGMSjkj̰-yH=%#]Jr3~;M! Rsx*>˃u͓5(O`ї[}OBI社/ ';Ql('&!QGV壅Fx Ȍhqj }1Y5`ܴ^)$yݢ=Q)SÛ9]MըFe#8Y~o<9cM 3 ̦+ʧoc w@. (\EĬT]Rtb8GKzJ.na=K'Ab#GI(1|1d=|j_cGqm|-jHƏJq*OBľ <7aO[LX&X+jpM 'ț $0QDŽC!\c諤6| ¶ 1jn:VŴ)4e/̹ 6Z3o:],|H]ZBf"DҨxȷ;dz}?T87km 5GH.W~;̊mwNb'lcE+\؛Paĩd% -kU8E}=fw,j4ghp*-ݓ-*[QS֪^*e S|FDN4wy ymTmSnHJKdĂNܻUw5Oβ &1 ʪ‚%b/! ›4FjrA^q59hP)7"j`0[/baHp(28<==Įk޵>xKxӠI+`P.Og!HK5de(5x%zt .ugj&&_], } >ܥl809 Dz?Vؤ倜Xr,Me`YC pJMma >D zi\-l.,l"v#)+?C^fs>X}Rj2*w\`C M@\~CF`,Zz]0jv-iD =QeXٛ40\+[u~q<J5+ ψ#H}L3/=/2% >`uWMaǘK <aϑpAjB<$.)*ؓIvjexݻ^²a/xhU.JhVKfz$(X.wڙm`)g 95; x⑮F})lW%:R)}!h\IZ0mt<,&KpSϨ^b"M Bm }`Is롢uh3LD~eJav%d2Y}LRKNt'S^! Zt5Z`PExL7v'@#+Db3z9KXORve]`VJHeǬ.oɲR\k| ŷx7nRNEt5嚭B8nm v`Ԏc:x}>vn)ߕ劼xnQ2=I*XR(zHCLr/y|BZW@ֽR%j}7!=FgTL),.uc-w9xDd$rh&iOVOW0fT b6d绞q=1)пৈR& @lx7O}'@ j0mE5!3h#͵kP86Q*gƮ)ʛI y6ձ8ǃ)=2+o"m1/dBt F?~}k@Q1hRgn% A_N2Д5D;ϨF wS"mo*$O.֮Z,}F՘yMw:0d`:Po S %@Y gC ۥ jc`5iJM'=_L&ZLm(f- @ BuV ch:,HP7@k$@jO`t97#9ϐQ!uoZ'Y5aĂ+f;N5S,!WT$1)W.T3o2. fD,9LX\egd'o[IJ6gǛZ?Sva,㝦}:>'^*ѻo Y? FS :a%Q!,<ǂ݊4Tv2=_7pF1X9n*8דIR3e^@s."% cqG'둕egӆ@m mx_dp9 :=><X kZxSd$ /R]>l'߻AHP4FhQ^tHVn#űVGGHqW?zJϜIrNjJ,9~obzRNg[K}mg=c+Kkz ^32r_ ׺7/VG 'ĚQZ`qU7@E Χlză.,6&k Y"/3X1usT 㩺M@փ\NQ~UxDf]u8 :-(!5텨nl+cM#pwkFC}gWӣ%!,VyRت f +TYdpu# aHΌ'Zg_({^< *7ByKRdA.Lb8!eکtۚZ< n??%眩"VpL qwZ, Z@؇tiH|&H1m=k~K(85^ߡB3BâYVh{VP7 {dkK2fz V6#KUW}r˗2Y)Æ"7Dab}UǬ#MÈV\kQ,cRҬ: λsO҃Qx;'KvoB TB@B'o0S J:4cc<2!"cৃU̵Ȫd cT)ǚ7^͓ 9qY&״kfH!fx-g>m!P]? 3?J/0JPG H4#YҐCΆ8xm' 4RI-@A7$FH=.R)_mx#ٙ9WŽv~ fvS5}:n/><8(Ԣ}Q, |Ώ$ HIyŃpk2d0|ַY*xϧgkh]^T=Cj 0DvL V0(T2tIQɫTZ=!V~CsY1#BlM!1)N!;?A5N`0iӒԏRZ81Ze厰gڈ#(=FB9VISYD3݀ "PŮ=5ptְP9)>뙔O">P7bۍy}PW`"b܉G̾ڤIY$} IIoñ:f?QO!\i뵡zFo f-I-ܛ;)[ tɪ;Tq^}A2CHO 316$s-,.](v،[ Lk@6qȋ1VpryW3N5TΝV/f"img Ij.P:yF-j َڠ..y.bmy t\etL >rM\(Y9sh)"3 _ڋ]#sI8ܣf#rٸ>5JZ5 *hՑ `aɎ\ݸ|RI@cZ7nv M`Dۈ9֤5:0-Wڡo=5>Zˢ-xW_w9?Avƍ)no$t=L4M_猺RZ[ ˝PIEy3=*hz1Xĥ\tCs> {;DBPRB^6Q$xuYϽUͬ:#O/  H^4,mkLE%cb{1NmgxcX_PhE'{?6/kb]zY^HPDlTMY7v~#"̔ qH$`RB] f#a>07ٗ8"" ~WՏ)cOlg@j=4SZϡl/@&` f#XshӰ$@[!XثBz=4\LdL=s}{xks0$b,CGVܨ]JuC B6Lޚ#cz&6yXJDjnFAӍUTy [3nӐ7Fv嶨HrFOqiILj-U3qi$A3Ӟα@Zf_g<5NŽD_py/q x*?P]pJ~bfg]s޹(`˲^opM>*`܁GbTYy O2 E2tnR#z%)/I,8O"8\'b~.P.&~a|v=њDt!$HPAkfVzͭL n?@"=t(LE=Tq_66*$bڎ I`gIMݮ%xEop0)E~1%ly1-U5vB|pѹgE_ rJ'-Qq8V? zGN,*syKyt/}Lgjqj5n m"^-÷yN><&߬qC qܬQ lO-epduB37/o=I)P9D' mBN* }#Ic$ՖYȺW@7U&964-gkZ0*G{Fr] DSifGR!Oc*AO:̹Aq)2.9IUBN4b鍷vMfY?mK ꊅFaAnLD2?(D|_׉xm289ɟAz&l@&+*H3Bh`z>?FgR3B{͢tP:O<)>a qoobQfu]+ >Pq0>gσMGxjL^X65xn#f^3w2S=G*k%CStiLC*M-<AumטdMGlрXz P@t&EfErI6\KjqyzYc4k6gN[Ӿ} Ezc)Go"e@?/g&s`~2 1&{!͓c &glѥӻ]mnX=RIjBk)MKm 2rB;Os|%*ɆZׁж#B~{W 2YdE)G-4j/#Ѵ~ߊkai0EMF Y\1}1|ЧA 7y-ѦAm ʐ[X8vXTvb>Ss}3a2zŸq:^# `l:!ၝ9]~7b5SqmHO٘@Mϭ7rV|=`Tok+ҢgȤ{72m>Qlì/RWknζK$ =tx}._BT!ߙCoDl] sӻvd/ˣ}M\V, PC.`Vrv|FNkdCL mupU{Z>.*FKCk` )Q"ѤNqWƊB:} )υAJ:zuWF|bnu3eb2zvHY߲Adngƨ j;b n$(1ů= ~(ExR%bkH=`#F`?31Ϸ!s (Q#"k?'띭%I.,b #:Xv/z(mg*(DI.L"ѽWj?`zH(-@czhU;4e tDN(xb#r!(Π?MLVy:vb-"#/lP;$a.[Pse y( ~4K) 285F6[#kee&4pPܔ{U*n!,tGiCB(`ȬUu ΦLF$-X'}z>^AG69K2X6U#ۅj ߶5lRD('r~mJV_W- p<;3FvG]Y-l'p\𶶥2tr%͢p`( ţhsCS{( w}2U#u~Z]aJn+T +L,EeXn\ )ǛP_d2! =*Ta_߇UҎq(2{ DT*ݤ͚F@`: Vhg29\#^3dԛ )nIfMḼ;W?'^8:G^uˡ(R-*'D.Pp)cUV=pIG:h7#/c傥P* }<$!-\8<%VS @m)u<Ӊ}AZAr(/Gqqk툞s-(UnNo:ַxuo'0c1Q,@}yT_e?wt <vƙ+ƽ{Ƭ/gtXph5rөȃPY!~Z{J1 0w9S )v+e6YФhMU' ]_-:>Ψ8˘ *c?.uu[قСّە-<m1e ӿ]ګpz:1Jᤣa ~ dunFpgԉ6N~.)=lj9~X\N] YQg!g:SX-q;b/ԁ)k;sGФ_+ jktrJRzz?.WULR =FTnR'g곆kFZW:UcG.EP2 A.F،wkAԗ&Z*lMqXoaUzT׉9Vv{rI-U}Ҭ1;V 0.4MS+ILN>?khZE$mxTF$`Hc>M87whP-Qg܉^qٷӮZe$&ߟ1TWXxxe~TrL9RZJCN KW+@J,4E7\RN{o*v7:5H(gFkKqyx/%OBV+7ݻs&Vt "%ڮC߸͆5{t8+k 8{rUHg.D+g 0~ 6xL&AΘOׯ^ю*"'݃:N:*߲ūF׀ܩQ/ۤo35K*9 @opzMT(k! }\ox% iJR\20k9-bX[VuY'^P~T@ Tׅ4>0 YZAER/data/OECDGas.rda0000644000176200001440000002307413616365123013443 0ustar liggesusers7zXZi"6!XF%])TW"nRʟKMd[_;zk:#)ReC}R̃;Wj-b"Ph0Y_u(X0dbЌG^B+rr ض|O[9bA8%'9HKWȺ5 vd$Waϙ G0i4RBpu"LI>Aʱsc'|ȥq)ZIƔj4*K0Nd=%p=07lM=ڳڑ3YԬ` C+k[JDa\${W<=dtE*;>+TXZSNt0P]$ƶGY'jaB/Y(s}a$D?זɢ 4|BQg_S ݿ;2tρed|e+>s`5ZZ^H وpE3M5Cy^=^5C X&%' H킻Z .AC c\` p{VyDml2pr KkWJjn~F,bm 9)]-WVehg">p"g 37AύsY!'Q!Z'`Bs򭅹j9ԯբ`VSXocFiL5KGsU/i/Vɡr;苞D</a"G'V9щ)p OowDB6UgG1=%wxf 4V}/R+9E\_N~(qhE±ń*ꋩwU'*T w(כ^Rmi $r +GVA2O;ghnjc' @@3ك=ZZ}mcn?龜T~!QW]tZ|ޜo˹{MF(QI$՚`+~8ÌrȇLxfE yDxN)8=*x^f#'鐰`7>j5bEuFX_C2LIykSr9i;ήt{zn_RKEfTʼTgpe,k~jeT(8$ZXeɞC0@4k@& žko]fZۍa݉G7^΋6M"(h.^M ?c 4wT8oԻ+!d ^~T?WaORbH " NeBPk4uPtw+&#`Õz r-tFn]_U팅7̱`j8X71JӰ:^G#H */0ᯏ貴cF3嘟Jj:O(#SĠѯ\}NT'u3hR^R98X_Tњf]p|CKPzP1%A=9 1X)u D YRyVm~R?ZSy(n߇]_[ԆQpj`\ HUΛmgq+cq%:ym*#7c`>WCݫ-? 3ftοzUlw&K+9[tDtVs>C(IwXΒ2,.!dLni 2G2 zG TFpq֚u.kꏀPsNdiU- ZV;-=U$9D9&*2n=;:bW-)qܨKwG3c vN$wa2GjQ,j㦈bU-6q=_F nnY\2K6#0Kpo{״S-BQNmsZnQ6;+,il7et5J$*L2X$upZ,nۢyPQ@` jdo%[ϐl6Sw-koU7˄Ф+.yȌRS=tn8zi}1ї|d:g a0I잩A6|%XE}bnr7)t>阮hVc x#\ ^T7xz:r+$K=;P5@qbe6Y[7td`;0F<<[B +Δd28߅pN 9@#`&Ifypy{jW\b '\HF!! (vǛT[K`QO1={B?0g:j[1.t~\Bl3¡fDS<˜qKjɼ4v kG[7lqeMYXd~Ru1;KH@!jMo?@u1lߠ'=Ǵ5gR(-a,3K(OsA(&G!]ذ݁q3{OeBp_uQ­#L۔9R^r$a#Bh%vk~N x=Kl3zs9 Τƒbgu.0^KE15<=QVI\LXēŘÉW?t 8zwHꃀeދ|WֺqbUѯE+pF%gkR]N8Ŋ>8m (%_]P-|ٰ-Z-G$u_8^)?^$ )x<稕`>3eCV,'EkJ`??zA- 4.S6Z;;,չK-49z}d6mWw7 s{.iPȇs<-ؽD`{<.\$.lnOCHEkMP.KFk7 UY8o) ͮ6l]wցs+L/_&Q$p/ϐ$E7SC2g@h%A{{hSN( ݒ]kpmJ|k*іz"h`;m@_ dfPJ:=`–S ]<6%+J ,@d8< Rd*Gd@+Qp>RM !! V?ۖ©S!oo|G I{Qp\Rw:io%| s|Ƚ?VBB{oEZ!Գr` Zك΁eyt&qi/gO%p]"fAb(i-PPb)Y44Ѕxy$)~tO([mA,I;`4wΆBzj:@x·C@U7GyG(dMC QK}#Zn *ZNMnpI)?!;0S}i%)KhI,fF zd"E8eS32 d|u~ 44 NMSPsa;2#Bv2(̨4|󽘥c=WydbR>ezh 1$ZYI:uAqN\9!IMvYkٰn2TI3hZjRȡv+!X,~TT/f`祐BӐ/5U Uu-რF͒A=rD|LA8BՇ}ީwru^>j@EmauJҟZbF>pNiY9A j5),혴:jk`emq{E34Ti~+`PLmG_kuq?Y' p˜A™C/R+KKEDQDL OȅS"RPI͓҅A6)|G|R+i{qH2ݏCƋo$YUwd"ŐHi#"(KM,㒦ϩf6Sx[MꏑՃɔ,S|- G)x2a?B;Ă{ZRY+ ' */T *{fND;VK$ 4DwIq\G+^ADkvx]xίgG#Ԙg)mv@tu݆u̓Lď T!nX=Ac]KM'Nn1[μ9|^Jbn&z濷 vn)*돜ɇ-Q& ۉa3I=m8;.մw _o-a@2^(!zq[bG/=W:* W*#k&opiVG󚣰gF?'+FQ `#bhDWr GGmtmb&).1D`ԀV9ҏ%T$k۔{WP T֥BxV{y6Ew`-G@֗6y?6($)qŜ}i(rh-3EKo6pn1hGY@@Nk1褄FiM)/;]xUHWՁ$CK- ʜ=ijaKHoD}|ۄ+Os_8 MiYJͯ:A.L} Fg2IhKH~jfX&aHojaE M"`CROLTse~~O5?򡦜< Tԙu{XsJ65YbseTW (zJ)">Sѽ Ń\lw+qj&!̲V1>Rxm cʠz>Oq$Wl[= S%hcY] ?cq]4 ?J"~Bރ+PӪe:(ӡ~X btڸ?F XbYy՜ umc b뺧,ŜyF?.Վw،TZYP UB`"%*eJUO7!iz"I=8]6;KSyF1oVe@}4Їל7swJL%sxr)=QɌw|V!̣yG\ +8Z2Ϸz^+G,fPkwHk=y$D=X2GFDd3EpU[7NӶ|V{C8Mзbi\-si6߂z8mJk25K)!XjyD>/F *[aK@%=@_r(<:꣡ BG*і% =>!F!>mAkRicyR- M$we{S{w+*T)g9iBXHx:&,cQ˟St.& Pb)TYlv3~WٮyL>0/Gp \Eվb3ɿo^u8UuDCXǮ~twOkgnN#aaoc1hoAeu<&ކvV機O(עȟb܁,;F^hM/W blygDQ۸jLU ͮ}($Z '++x:T{_+y_U~JJr^٘ y[]Ϭ Z.bQ4.g2K9Q~7H@2- "W6&[(apʎkIltL4p4~Ephh"5%0XJ^;?8Ndi`UU?`HBq'q_:z p>N/Wqfb%\in7义 ǟLWeD4|YfD!=/SCro7jl'ݻ}*:VdB}Fr(8} >S`n}hJJU]I䏮xhr^J Ags gËhY |u?JZ/V'y۹7,Ơ~  8sV!!W7o,_ W*LW"ddYXK7|҈a,bt~EBe8Q C@KJ*T72bWуE`_TKUr\ɟ}:'U .'Il-,W5˾K u[]7 2Z4kN0~ɉ US.e0c'z;V!/NpWnթBw)/Izi*ao2I})5?ޤ wn02=" F f"pERON<

⭽@&z38Aj!dI5+3sBkguC7pE+۬k5нS;?(gih2hqIt3;̌15+E:zp-0]bTj٩׽x.Ͽשug=|Teh=Vo-~BI.+pC𽱟J S/5xz yмVǺRc G=_63^!w"kYm"yp&~*Í6Kh4a.wp9J"ٍ~>"ج EÄʿwԌՋz\ Ƀ){ >E]Ш8Jwsj`Q/{ 0bN;>}8If(u&{0 Ayoq)s'A!,kDMDbf T-jydG+niR0 YZAER/data/ArgentinaCPI.rda0000644000176200001440000000103213616365110014524 0ustar liggesusers r0b```b`fcb`b2Y# 'q,JO+Ktd``z?FfI:׹l _ T=n @\_r`QoX@Kc:pY`c kvʋ@b{$Az! ` T!C@s!4BGBI(ktHtC-Y2[9C.D] n0y>Iw̷س~؟K_Cԭb<^ l߂r1s58s lΉx+`-S_O0vX7FP|92u# Ƚ> {UYAh(wX tx4ȇ_`x`~`:+X].|S`~U0nW|w;}(ĝ z9'>,Ṵ0c8Q(sI1ș0$*Ch(@šX\ d 2D0޲AER/data/CigarettesB.rda0000644000176200001440000000252213616365113014464 0ustar liggesusers]{PWw Z>HP=g&E% XA@%-|"QZ(QD0HP:`acj߽gw c亅|͎f޽x?-X\_SE~Zr[R]oI͑* i߮,iCm{:{f7{rxѰw|yn7>!_D%$9oV$.sϢ9lE+`O@u7{h2~+zvV"gd*ʌl6Sp)Ǔj}]q s]l>AsoIUR9e)ܑdq=PW*\ϫ[tSF `=OC!=#F *ST@4PhDxPi 4hOTU<4h42M &C 4LLѓ&#EIODфPhzSdڞz4jh5$ &S=Lz zMCAhzh@) i& 6H;} 7x&УBx<>[#?{M$g_0ޏ~oo[pdRv vBRxw1 ?8(kּԻV%Ҽe|l=&6Hc=SѺ{?X\\i&iHk_5L#:z'2x?Q㴱˜vY IJ UoIՈ:`o$ݰܓ+zwAW:EgQǷ>c4Ua«x.܎fd%48Kdl(4=.aQDv,`Wi,@R/LR1~s"%R`p6OE*HsPgLy% gpm 7aFQK3){PoTr S %Cc 2\Mi;ܨ2%ū)RI$4Ml(E%4r0Gv3|ij;JWs}5]b`R+  bkq_i`RbHj.F@m͎ TK8I%+|>$ _)2"H%!R"G&1'>͡$4[I(Ohwkj5rKKq'&zʅ +dk*w󔠜(턚Rhf &c8kC[-z׈ᨆ;( eyҿ,gzҵ 0>΀!D)p)YFv+bȯf&9;=[.H7o.~q, vMֻ(ħډ.Ttٳ.xHif4~|ӖSf8cߕƈÅ>ާ/-ݮ5K!&$lɷ{-†aQefd:Лl.h]`JU*rd)`E$mJa|D,g%ˉ|8>vUlR7f5SLSh)s_.2cAz@5wU QʴT7o[5D-tTjp.vtemVo½{V"';}/U:`S$kFxDG~ o,WBB>[, ]S;S*Bo+ZcKsR/4jD 6IvHy<=FkW+]DQpQrۖQj-;5&sbZdEbwUH$[xAF݋s5D@$ʹ\(dwL54us%3$Id(G+. 2cE%EI2#b `%4rFWJwuuˆL "1%$WFJXFHj%ᰈJ٤ #fɢɩ IMJL]CT!.11w%I$ Z*UgWDhBeBH$4fI,ji RMcE*M)M52 X̢1lK+FH㷏R0IL&D4MU0$6*K c"i4h0F)1$c)$,Q)4Иb &KIX"%L  SL0i )ի;wHJ3 E2_ `J!%wW#r]+wX9 cACTm%Ah9r(E%h55h6lV6Z&,l=/p!PV> XvCTAO`,";(Vlr5iǷ5|E iJq9Ώt1K{Ϗ)xb¸~[z֎Ego;gC CZUY)6X@RFqHcj{s\uEnj15\*\ŮjkE\-jFm\\حmmDeR@  "yRmSZUW{%w2F@cPuwD2'=6Q!C~O{·R"(Ggmz`$+JUٳf̀}mm^I$I$I$I$I$I-JҴk^%$-JVZjի{Vׯ^vbmm m 6l@m؀m;1{maNֶ1+Fm m 6l@m؀mbmm kW^4MVm %܈=O:Y9dQ=9pxI!$I!$7"幷gWI$ $BI$ $BI$ $BI$ $BI$ h"Zֶi|3+1$I!$$$I$LI$δsl<1WI$I1[ZִI$I$I$kJbֵc1WI$I$I$I$I$LV-k[1e{$HI$KI$I$I$I=/+:kkZii1ĒI$I$I$I$I&I9}W>ADYh?aɘA_n]:*0֜/[v\O0>8CyT(xi`0K"=p ˫ѧ~6 N{1vmˆedNVNVR$JZ$sGFlJ~xV1!L}8Rx < vpJ5_mb_}gL&-\ɀyo# Gd<~V/5O80w {>8sN.`:^<|Ҍ?;`yVӞ^ӫQU /'!@A}Tگg%խ:NR/CO}.s|8cYM^4gy L^>Et8u9lW >oݧ==&vW^ֽ) M+*Nt+5|m&tgA 1/]r\ Td sM5Ӟz҂V&07C]m'Wm'Á?'-"@?;r|D +F%b|h{Z`j>_N'y 6bz?_&@VW$N,jQL=ehb"RRm] (kE61#iyni+!$ 8;A [?9G]D>ܑN$ AER/data/USGasB.rda0000644000176200001440000000235013616365126013357 0ustar liggesusers] LW?[(̨ns12[)ɊY,ԎV$|P^P,Z,`̦ R6 s .cl=mܤ}9פ+Te*q"N,q" &p\@ɪejCljC_w ƒsdŔ~-,}n4)+&*)+N`JQ exzpm eEз&rf-t6<=IiS̟Li2<||8.+ =OY; eî>#ei 4J'_ o1@}8S I)[ʀX@|[8-}gߝ_v3=/{K#Ga= spX)b:p@OYn~⋋_zusg0rW:cQv\e\>Q |m|ۏЗ;c϶0o|*OzDZ"i:9?/ tnC2t_rQxz~r䖶4t%^E+}n:]p"5AYB%^5R~{wvh5d;jD:_:eLCՒW%z@.UQ#2T%Գm4!`\kxr3u>Jn=,X'7ϋg #dȬЌrIȈMl#]V;u6s%T >yw.Re-Wj Ὃn Hs;rT!c;acsQ4`PDRN.Ց*>' !35&Eo:7ؑ73! |*ga={)XCPr᫂ª㫵uHJu>[9ȫi}Qs 4qc;'ީ}W4.]Vds[P|tb^0cs{Ԙ>zO#C"; |3|;_ϗpl-Fz>zy,_3ʫK`y įw⥬޲vN- Py1^#|V3Z'ro?X>q ѩ sL7&|@|f1P12V6ژUL?$;G8ۚ?ޟѠ@ߵo,V@2ıZ u~S"u0O l5^AER/data/CartelStability.rda0000644000176200001440000000414013616365113015365 0ustar liggesusersBZh91AY&SY8|O} (!SFFHBx3Q)#OSjiIѩhMhAFODh=M*OQTj4@JUQC@@d Ab ESh!i 2 @ѠɈЙ4hdF&@Bdd4M2`SM4LOJmC#M~<d`C &4Mz#OOSCC2mM&$()މ=M& h4hh4h pEAHIY$ A \֠2B C>a&5H# D!$dUdJR"޴E1 %EuYA5h  7D$ py/I21ʂB%Q -@El)lv`ô9~41d"g\jԕu9YXu7uŚgKPg^̜T6o|4hDD6\lܦXԳ> '$ѫD*<k]"ݲܓ^OK I[f/!0&e*}S)aQJ SR=IաP͓C)y"V쿂`lNnQ0b17=2somzm0z6&q,޲YK,zRR}|?ǧ|Ç8pÄbHѳkʡt緅%+$bETQ@ HxQT,V@$Ad idRPF`hQw?`/+m4b&FOvD2|8l)<zϋʸJmVḵW]5X7mp+9SMv} Ƙ߿t<@*;l_e,'K8]h:Rصic1)kTmK)mۂS - L9p&@dmU4Y C,Xbqz_9u0L2 L&@Ps>7ya)JRK)n 0R_ZZ F JR)JRI2T~Ck #-by&VZrl(I[P@Pm!w<L,HIYb@%)R@%)JDDDDDw*9s5U$DDDDDDDFTO{c5@05,͌0@, J""""c1B k3333c ٳfL"hH/FNM2M"őEV*EE JYؠ)"(#V QFV`1^+P $ύ0K$%p#J,Q 9$ A('"T4JΒ5.B)`FO:9Q}R >*2>څ AS@;m9ҽ%gK; Эpg<'RdmicHP !].:R+M_d;D_Z mkE+ g.?Z?\/c𴬗/5zXG|a]9&,I[0çA,gMdvE'Gv؃qp(yͺ)0)w3 A-{/!0 otS"d ؞*^ؼɈ|Cϭ>L>u%j ?h9Qu>yſUiT.ىfB|:}dL|:1ضĤyPZOaD Mvέ!9A?-"%#sNL࿱%N"6X:`Ҕm5]+39}2_uh`ů+-~ff8U~k`{͋,D#>bG4:>BDr >Y~@GGxw|8/~c3m! A Ntd'\ ^/,2ڷaי%jwB+Yh^^j\݂T?Zg:rNd+R6ѩ A=yW[>,&fp ӺwCdb|_݀nS8Yt!UR )`qy=p#B,(0qW"rRL+C/r3cL3$T>{SAzt`@V>uY'-0kB~"34 7JsQfÎfE4 B.bܖc!`1:v-;f[ǽ`OԊqSQ*0gТ E<8qR$:esgCy[E 8ZVx{e.n0ȣjU0`O5I45]R[\ xK\= +9jb,ʪбzx⬔̄m .ːGbfl63 #pbr#I._ceI)$+!P`ծ74FTbOp:p86k{y9A&l7Sdh\R'VuAnyyLSv$ǯ=&rĈ[Q >;v8:![~Ofa6)1X)3 f^/m;ˆ-ZLd]6TaV%D'8[HNƿ ^q ׳U9 rlL=ۻTM{:29vkMw+{_`YQRcs1Ǟ&N8n@N9d`qv³?$_lSpr"y*h+@?[P

C̯ϏWU"%&AŢ4L:]; BhAAި` i[ ~/o_]T# "w/+N&_2VX0[6/>9Y𢁟=ЪLee~n\_R`_P`z~ >*F03):j)WS֕CQWɛ`Y׶E5_o\jxǍ"=NJ6`|SOª!3>%DMb)I LKHΏxkN[4>E˖\ rD3-^}DgaY/G${4Q20Fw¾; 82l/Cq(F Rf7b"ڝÞ ( | p ΏIS5OV9A~(kc$Xqo0hi{v5v,8@,Oej&}оTk9oݟ#E*ZĎIG+ =|%d{7gs!k5v\,/liT q#z7خLm<3$-4@b*N37AQOŏ orW ƗKY~cm=JVA1ĥ$W_|VhS@xT ,Lcᙁ\ F_O H\Ȫ?k|NE'НJ.F"լݢ))oUps'_ewiBCh ezη9z)wk=PG{ $i#F'6X7]%->|4g'wK;`3uYp~!+%;H5JnqL*ѭSsU*LZ  fE"ol0Oz} t-#%?P'Q@5NvgCy,DcmE5%O8gF I\5R%3{κQ #;=Y 4œҦ?O}ZhzsO.I˔qTw;0و@>_3զ J%~ (G@8a!n4| ޸^[Xn9Nd2Y_,{LC z{KuWM[eYȜGgݻ!wh+UG[FZP)}>9=rK 53l]YUy^7DmK^ u Lef%3ATGI c}5߇*ӃŦ?"_-(J~Izsceg ᅟQ` Uj^ ۩\1k3QcrvrcKZ ڤ0AASdx^c{pa*70-&~>GVk; 2PlZKWފ"<\+vZfivgvv~Ԯ]wrW+=_ep:VfP2 &vgwb,iaa167[OZd+v=ib]QK͋HݜI Ue:7g l,RFNA|%iY+$u%.T'E`_;G]2 CvIh\Kb8P;/oLCԶLԔȵlk@Fok:f Yg0?i'+`S}rw@2}^Yy5U 5Hsh'\: Xg%qٴW\ebonq=S# -\HO96ʹ#/TNMfOӺUR? W =(zj=֬|Pɶ2YLUӊ?O֣ɏ7 vZ[{g2/VYOԺT}Sxἇ4(x@ 2 9OBaE$NH ACN < ΰ&yt9[:ǻ=vbҹSZZ(̬oS v\ZGQNr츹@ ۫e |7' 8,!PVɢh|OaOo1 ^BC_BwX NmN4-'N t?9r:#Z/@~M/"?ZQ Ʈ*t@`L4ݭy9 OS@`X~Q,3x|7!t̤N gm5LV:B҄a͆:Oj€+oO,] !D&ä: B43$@ˑlK5e]ՓM_īd01B\pI8tDʹD'e`iILi|eks[UWE,.ʬP |-S>a@|TAAgăXeS{23yŇĝs4SX$ݱ@9/br YS="\4ަ9l3&G)n> GioJA8 ݊{Lxp0n.W Q;Ŧ v՝fK;~*/, ƘOpei"UB%9U#Z$ZP{LM&`aWq7fB%mxw_Xkb;QGHœ^V,TJ͍W\g7+R]=r@E2zO9C AI1\`BE4%L i{Dy{:3HT*ldT v<rP.3caP FٸqP-BR¶PnPy=P*jo0S{]vAt&KF3 2:t-VC(* +agO{?0>NvF17h 0@gTg.zkBfh˽L}%p=D6RޱRI _,"3mX# ~mŢj=3w#AH1m㓵6Ė4`" Ԥs^,B/+e"'0p^Aʈzͼ-C䴊Xp`:N^:!|HwFWTa{/`'"ix12j] `xSL1;TuaS8|J:1=\]=w!_lGHbg2e|tn=xrO+~ 5M*Mt *LJSK07>ZLZ8sb eA)z rx\Mxn%bwV$`ml5#hX-J#4gz!! ۤYd&Lg .4H~s\6:wa"$Bs<5Rv:%u~Ԟ;oDD "\ztU]V<9E^6(7i&H81躒.뉛üVM/)8I'8kW6\;QIӃo z mT)mg98y٨bx ,bO5 F+܂͛G; @mpC4b< ]Vm3'k$AQl*ivz u,{< l_d$26o|ʦDW6,&~ѩ'.m"M"Lb-쭌S Ɛfj' nȱ ڀwֿ" 4'7.L-Uƣhaս,|0l$, iè3z½S!Z+%_|6,*2+5ţ8D3Ζup"W&p_dj:KtZy !՜{9M$i ᠁`l%Wϓt^f tByY Z4WΣN"XٌRDP7XhUaď"w$xXz2Pm;x `@([A 03!eC@SN NjHwd^ eb&Yc3fd:-rLI)K˂dy yhX9ت1zF=SFRGjmR5+mZrF:ά>R9dk (۽KėhJB>*`ЅُT8kpTnAw=Fn3tM,翩u+h*[#n\޹y:cBM>ui6o3Id* JXINH\,V4a|*f;/,NC]jO=1*Q9\zr)Iqb c=6#LpRH(d\ af`TR^w ЖUki0Ԝ M@ ܭrZPfA-?~ L˞RB"3 WSY i2ofݧsF^-9.K\hr!>v풠3g2t*=gy}ӹc{CZ\MՋΖU2<ǷWb5gryᡸADIkTՄk"$/q?٘3_M-qSf9ب}'-`[{rwc_Y J.v|T*S4KV.+Kv[L`;5g3A:מZ^j5m@;$cO1IR|!&89n8&g`_ ΛLPv[rcn返ᜑ}`,>LA mF:8P:Ԇ9h o5).U.Wj`X޷E'2ٿ0(4\5 \V$4Ltr[qPDv|/OBIO)Lk vrav&=H)Kɀ":KMN.]<Y.tJu>4(ޥr_+(& 0w_ m9j`*z%<`@Gj!΀!4I",e5聬dFNo?}\F8B@1U=pq9H]!ȨwU ̕[!GPw2NݥG6N`l?' 7f$o2y3özFɥrJҙ6! 9͗IiSv2w'Rn!`9ծ("W-"W(|gHKq.bs$1xegGE?2j#CGvO+FVtRO/FR[284tѾj@ ZsF *vzcpbx\W P"d79M G-b[=!],MSLx [Ϛ5{,v žySgjM!$fD'0V'N,|KAaKGrکIwzjQyQl}UQ,>Hjq9?7WSO۝G5F ~D W8/aW`_y$'f喯"vs@Ln~lv#we\ܡ?;t߾{ IjtǪOB2bck'9qJEhⳑ )RzE-Cw0 ni G lGԡ'Gh<$˻IR1ѳk;x6\}٣gBVxdy Hri|z/\%atCB&Fk6-{)6E'x/3=ݘzATa}"ۛi1ಗwBƙ6dәKLޒf3YS^`;t,]R>8[JDO!3|9UD!\E/LK;~":W|8L ҬTF(Mv%nnj'-vE^OYP"Apj\\.2fĕP&1hZ=Hi#b3/ly0ӓ:S[)wjñ.6>R L}Ҋ[*Cߐ}{tm {p(&AD= o ZU9OlFT&I5{$\n8J2 ?榑˚VF >.r:e18ԊDRKxt#Q"OUX}lbic!Wc +oDkd 8*2\=7CJA|lN hG'|/p6,F<Ĵ5t } Pp74ٮF2p=r9lї;cq8w|Imu sX+m᧡Wjhd矘`۩RV`8"o*rLz]'wۣјCAxW*fyJn'87ַQ[X0 I9S!1bX)r9$3o78YU"|TU6Soy6=Ƚ|I2\o(WN]Ԏ?;!ddKvy$ZMVy%Afݸ8ЬշUkî3BzjǼz6tIy5\'wPFW$#%-֕i] kqW)?VɒZ.ʆP @G>~ Ⱥdww;؋1xTi-ފә)sox=,*ێUk \7tV=?3"SHuJlJH;VOO/TT[)4F0ZC)5~Ǎ:ƃl!jSY%ȗt &5wO<; oX;>wޯZBXơ?x' 'S:NGpƒuy4ƗAFf2EiA}z5Dj)Rg.Lsm \Y}"&eX|uv:'Mh5[퀢k3*OҰ6J 𦸊J%]X6@nK급_Rx6Ͼu.eqۢ N_*`.}G[?|l(c >\) !դ5kHK8_B L&ndjwd1noo;)ۣSlWܕ>Tl?ڡ:s1`W/%8iԶ Sԑ&.&= c;WܼezQӰJ^V2?2>0 YZAER/data/GSOEP9402.rda0000644000176200001440000002246013616365121013466 0ustar liggesusers7zXZi"6!X$])TW"nRʟKMd[_;zį~W758J$SU=B`tmu|k22>4o{w٣K&3[מEUmxҩHE^pp `{zDQ{B5uݪÄbli [<U,&7r`I'qP%=`,Uq}ȿbx*FzV.,]m#y2(,ncd? s~+^ˏQb"í2md|H{Tۉ'4֣HNaz`0ɤ'5~UIs~oi_Bϩ@ @©NN&½ٶ j,k{c|R1A+HA%j$UfꚺI;J mIP!0d::[AZ{_#1C+̴̩_Z p,+cn>+:zO[WZʐҟ挑q> f`&#?,9~h~e@g*cQwztR;6N!xTj DP\ͷj'FNы\2DC}PBIs耬wax.x Ʋ">l7Fٖp=PqmfN"VvOk򢇂V#VV'V} Ԝn۞B%8< 0璇+-<:VϓxYIa3Վ+*/ {`x q#! =h^')-h"9-30!g%Kʧʺo3@W8 s&aMAUZKpB8|F' `RI`Tȫ{>]aw'JM^ݿYOG&kiiZеH>י꜐+?Up;8wHsHd\rtK̥`Τ滹Ps$e:nZ(>x5bh!{{UGьނg[PFK !79|9aY|pbCyNX"TveFYyJ^l6Hi5/..l*DYPhm2KGz̋ȵ~>xF2@"zl76'q~fE+OR㐣KFMi1mjV4LC a,DrEX-C ya5)5@>;XR'&CcuZ\1+U(FPct\>Z&f3lhb\C; XoeԜ@.*%mx] );J8XIn5S,ˀe'锜r8)f4cBY | [ 炓m<#$P' Hi#>(U5_}?<30*k]|V<7%*NK䄍tVbo9$ntww[[*H2tܽy˦KlJ٘=wfhc2XO@ˆY%/+E!6ZP<4h;oǿ5=XB\$ήj'>zG@}n D>V?udŭ?P{Ά-ˍ Xr_SgZ8u)4Hմxh ڷX&w]`ųlT$_iC. 4!DOm,Jaٗ DlDyo` d7TUQ'ԑYhoBpIej s|-gL!B.p.S=?bBEWǓv*(Gp =-H%?3gRA{~3gcO3,C\{+*ٟjc;RŘSxFsRIi6]ufFVtH!KrX^ ͒DOQ!dA}jȌ& W%pQM/FACTIBĮAEjjcqhҪNU!ⷤ9&Ic9Eޒ`֘q_s&hz+? 2V2o$($C m,2`0/ւj?PV4s.lrWfBo.> Nj^˼O8%brCK)}Ueݲ3&_?TRVo;ZcM|qF IӲ"~RMi_BvA',~r8? yj0S0~JSZY(¼\eeI[qt`% QR5lody{a ޥpGuxH߷wP`~C! :#Ur t]WfhC2jeJwe-SG:hKI|;dڧA(rWiS6aC"fwwS8 7+Quwk|?X]6QAt8+:Gn|K\Ed[0 Cnr{@NH|! & `]&NIsh:R qG]Zۧ*Df[byAc9V9+#x\ 0^+wԃTV:~KCkac|:4%47 <);n<\d[Fc,a}.lhMai}/tT=^:N@G&IaT>exKӯo+;HX$#]oȆK- slz_ØӤ?+/$< 3癮ŏyfJu!ОiƚA:jkMmwH< v=Fğ"a -=F Uyxke,V~f'6sgGV ɜ`y/D͸P=PX:ZJ Yc|{d(+C)Cx9d:qCODd6G}qcR~})Ď~Yg&  -<5oAf5e4ard q<_cMU`{$ 2Ka{GԸqoՁ1m"Zb1'}Z^(o(#%c q}*캣1 mV Kt$S_VE/%$Htr;h5s=a=?/'#:G>Qm_I'5**.p6ⲭ fXKz>m>7!rL~K kQ&_TxM/'%ϵ21Xf&M=O,ztɛJi/t`:-q苺@d-YZ:[V< ccq/aZ3G(;)њ 30[W-|r)0.fb!?lxUG{.\ zx6ųk1 2Fnܾ!1#4b7;|rwOsHm0.oMߪ&3Zm#CH(6S0DйDMAChm>x~Ǹ\ pDR Q4c(ȐS- Ay*~&ـm8zӥƃa _îEi+Ov<wkb<\LQiG95 b`5>9gjy%x; w j;fC:Mor]ڏ"ctƼh˒SMnLd9t?HMC=WXHmL<5o(* J:)_RӲYq|SB%RuSHd~K\XoyNOdx6jhJz"JioUrXƾC{J1=Q0b_y7xQvg&yO)L fº%q+JMqmKx@>bc/S;YߧbXKFRP;=>a>*I#zOY1An s;Cy,Td(g_}wIGĜ$ YU"g)4j~U@zmڗ}q?.䴹Eʁ,'VHPwt7%*"6A0,nqh 0BLAӬBa]cZ, O& mtWJdrZ7N}Ob^F8;SuxT^mp'oAQ1r0Y[25{) Q. E蜫OEZt<Yƽ~VWiZ+ƕMS!Ia%A9Z$^ByPrj=64BàH^+K6kއhs<>=C xW+9 8d)1]@X-1/e: 8%ag((M0ByuO7PSsBȢE)|<+VϹA|*h(ȕVf հeqSy_]|L']#Aqwc{܍ B@̝7YiPM Lk`G L_ ^uBc|̴&@&;S~^#hF޳6ST,9¶/eCHY,;s#:=&ȑ$T㢭ڙyZqNc3AO$ cFGcU_Kak*)@8-:oVTWͪ5kZFī*թ̔z>96 "!*nNhdccp㨕»siFJݮztܰ ڽƿmb|R+{AC-qm6&(̠lZ?}ƫT"e9K[y)Tm$͊=fGYCha9.]z \2. ;=~45pRZ~ReDQٳk`֠-#*L}j,ֲ̻{{FըT0c-tvx\϶J$S7&:U\ he_^8 )*E 6m OFj@Q}uB/hƫP`A vkV/_sp=^wAE΁t=2֞Ìj4 Z9ϝQO/+ qe]-2_8(!rPD`(ΡА$c0-"mc>W2ۙN.XNg/]R8H d(o{qa o|VԨ_5ʣVb焞s &e-hedTzN_䆼5U;$xV -Sj1e c[iɺ!;`FŹ >蔊pH,]≆-H7OZ|^ϩE9Y!2i7A_ն!# `=3GC7#hhQҎJA{(_X(Zg%uUo!̱h3hgE/hv.}# OVBqS Ufl;HW.mS=OI)+'q'-㚠9c2Qq̼3#1uqo`fzppSbLCkh;WWrB;4FƐ' lCou[-HK;O0Bid"%3jz)מ@^ŗG՟FAfBo|R7xES*?0켦H9 uNG1DmӮ 5ѵ+a{;7/ҹ=+e7ֵ (@[>ܞIP* $}yp/V9hDlL(@"ʵd9x5MdF:;1贆B}fs$ʧO4l6\&'<|,_FMFңH.n 0֡B O(gXi:Ƣdz|bq|9&yAn =%WB=v^G)y=/^JT،v\ +otjAMaG`Q,?k}XJܟ3T_ު\\zQ'6k5 1E6ui9 xfVxkoÐ2"0Eln ,O邌 ڵ$Khx%iWLHWFq(Sl\Xc(֍iFv%muUl28"|^D[>2420JsK>Wst)I}YONoJӸjapes{ZODƐWu3skn'K:ŎUibJYޘpSJ&/2SfX[,}-ddQ5g0] _Ti7j:*-7O ֽ={"4v~=LnH +47zHGEȲjxA@%snKql,Ζ Б( (P' @fe0{B5LHno[*- hڎb]V´Pj439iܧxQ座80$)ta"$k ,:>׍ Q4r`"&}LM*UF0EGa}Cݠ 8FD>;Eh\n] 5'XHDHpt#SVYo0]{VdAEgIQXc K`s{z꺯g⤝/s|>?^GDkȹ_GZ{z Ss,( ?#!=L&dڑ6r(|6x,䩃#8ըZp3xV/hQ,ڈ[u;$OEqI=6 CXYل:h4vF;<Qp𚾩'"#b*{O22Ol/+SHz0 YZAER/data/WeakInstrument.rda0000644000176200001440000000551213616365126015256 0ustar liggesusers] \eggm`! 1@΄sPB 6HH-! b! !*Rj*j!K_w-vwvwޙyn[cdw}y-/t6G"h)׆ ڿHCdV}ϗ.V\KDϭ}\6kZb}^/48ws++oy+*N{?gIZ57hO{? ߟ[?TO:n=}pEpoqv|pBp[f?g&ڨpV` h25\:^^۽/87g.`?Ro]}+>+C7%wj5۾S~7jqE3ysƂ ~D=NkΙ\Kzk 5c3̥S=x7h2X.н?kĉ7SGFϦZmE?|K8$M"HG/ׇ  S~uD{Nϫ{kk;Q;)Չ}z n?u?`pA ?ky^dUp|.Kĩ \V" )KlL@v^Riu%/qνxnƼ"o+*Ji!O%ԹtD:Yة|y;"qLe{vߍNQy<||-R7G|oz뤬O~,򢳜Cܴb}Rm}!+s op\D%N)xzi;9&H^V oYKN U?=F>앸>ؿ(#|`t7>$k1tpPySDºrr}Zo0lzJ[It"D_8'KB37I>_Z'q] uA+MHӧ'D>FAxFa{MEyU@U BN@ɺ8vHQ3*^0:wShU>g1C߱v[$e2[~O ;D&!/>` +0.~8Y~M޴//D>u">{įF} t:< ;}wC;:A~`E%Wg: u[ч6NQ&] ;Ҥ䵭#EGs[~UQ*7N<5:w"&gN k8*;S?)sJu|FOB\!y%pgP>_9uH+P ݲ_/wnA>>L]y\3\D?T>s?]F'k) }$g]a}_F_@3u8g8_ $t#:t vȢq~*#'sK}ON=6zm~ 8"z^q+(3B'$~>"zHrN0/>mas5rb_:*2q]?\oqLb~ o8֋A9? }8>'s O\݈soð>#ϓ)\W߻ww(+| {qyIC؏>q:6u9MBQWiL5#{ zH'1}2'pq ~7ρ ;xC {8>â'{ոlKW˺Ga3x~yfy╋ky햟|Q;͍cfA f4f6 <;ّcGq;JQҎRvQڎ,ñ 2p,ñ 2p,ñ 2e-#nqˈ[F2e$,#a HXF2e$,#iIHZF2e$-#iIHYF2Re,#e)HYF2\p-õ 2\p-õ 2\p-#miH[F2Җe-#miXF22ed,#cXF0c0CGq&taJ:TZLi1ŔSZLi1ŔSZLi19Js(Q4Gi9JsWZ\iqŕWZ\iqŕWZ\i %PZBi %PZBi %TZRiI%TZRiI%TZRi)RZJi)RZJi)*U4Wi\Js*UZZiiVZZiiVZZiieQZFieQZFieZԴ$A"DAER/data/NaturalGas.rda0000644000176200001440000000702413616365123014334 0ustar liggesusersZ{pT޽$@;yf_.{!*  CAgZE)0U|AER0M_:ô (b7}k阙9^^_=ޗ:>4S4t5vpazM]ܦ5usDݰ\P=GϣQRΣΟQ)3noza6jY(Yf/kcǜQ?n\XMoՄ3?$I"oTeze5 T-HKr`ۓ@qs% %%1;J^"LiOkyQvo2JzeyRݛT%I@R=TH'V&V&ᆒpCI$Pn( 7J %ᆒpCf:{C7c20]F A0=F5`~*0~`Ղ Ɯ`ܷ[+4|e0x:8@M1F8~ YP tۗ:=j_FΫ`c>0nQ1Z;` xmCc0ca>whc/00_c$0~zVA}נ*Hd>+悽x){5x R˗>)]CAR1@ X6e 66 ںtl oڸ˕g5&۞ Og$p=6MWC_p\kkY] N59G`8GFplxOcﻼ;GN4"ِ9h>9=EE>ܓ:dc{L._oKNb4uMbٟY;;\&ENjq6.a:EI]hy^aB}~S#:e?2zi<[i01S?ynfL,d|ɷ/؞RaFI#(EN~c${U\d0-~pL;ˢ<~,\v^69JK (b^ R]7I?*|f;5\e^NK~/7ex.gB 9>.A|z^:WdJ%ҵ䯞7I!C1bynJ~|8{Wko5?Hk7&r|~0l^#%<^y ͥog0;˚ɮ^ŵ Ϋs<$.a޲Hy1IuwpyVL|3o7R)/*(@l#<u[!b@(bmK=8(CN]5 + ~>\eG^"_9z e(Jz\5qVy[}3_SR:?~@qq/f *g(_|UI8mMai/f[nE<Ͷ~l\w>-pc:W >n:u묯~3B뚿Jسߕbo7IE[b~CnbԿt,vw& ceol3m {^agO q?yK|ݱ'Xpt#܏E|{8ز:%8-edwxkbߐ})ѡ4/ڝO4/)k2"lj_֏vۍ]my~f߱NR·#N/~=Hυx{h,ogw1znݶts[7<&#yߝ=v<].}'?nfeA\ hϚ,;w55q=~V㜹3g7O('n^/uSj:5#PS>S>S~W~nuW~W~PPqdMaF@aFPaFPaFPaFPaFPaF¨P BaT( Q0-5uCiMNknZS7Ӛԭ5u#hMFk.ZS7њ-5ugM>kY )++++K [^nUVgUV5`UVªVZՐUмBZh^ ky-4мBYh> g,4|BYh> o-4B[h~ Suu4%qs3mPt)AER/data/USStocksSW.rda0000644000176200001440000002072613616365126014272 0ustar liggesusers][tUE !" iһ3HQґ=-A JTQDAF)޻@[Z眙^mZo% C|(ғ{[c4{?|$-e'EPo/?tAy&W5XdƐ޷Ͼ6uQ\85K,`+Ls-m'S{z{avz{Z(SzݎK΀mJjVZCާUZڼ0~޾w"ߞ^N|:Ax/J{#d¤1a%rgR/ʾzSnJY;D&5ur Y/AmgSlY7K־͜T+([W}FV#qﭔ!xf1RftAHqFt[.HY/ z}Ȼ+dȑƙ ΧWھiL:kzbwoiQGg꬞@f\O<`fCtvets|KLs9huw/SqU'eE_oE*͋>;Y&W خ%7/zu{ 87ť[FGPg.l",:>ڍXvmP%~{?r~t͢4}*4CF}2,jߜtq |ެnŽtGm@zz[dv !c)?&uF񒾁(=(~)_\v=q]X^1YPEqzܒ- P uޤLoSO-~wЭ?ܚۣ?7f2Zn5zC縻ԾS{SUn+kt?tWTקakˆxܿ^ߙ׶|>Z'#Sȅ_j5^D8zN7>] ^UB2*יsej^+jg_]}xH-@6;eE)fb͢#nڮ2u*bnG>qWT~z{W^GJjwnԛ'Nk7 [hRov{)۬Lۦr ѻX,p_ z2t>aD&=R4f/ɪ7<5Mbut\]ӥC*LM֍[ܞ>tWo(KT])7E>G-7)t .gqLhz1]WdJ^2Da1u;Qhx?RfF]/Uda^Wq]gkEc(=_X9v6'K:b}*2E &CU/XIieSvL|'ksɸU= [zQE)EhVS34wӟ<#OA|DI2E?;}צL} ƹ]'Q9\2{~D}_зKz%Gۯky]p %DUch(r{?rJJZ?j4J+*#b!UMs.%Akj mtV##kjZKj \A=(?.ykKA}Fiv-Jq2#LB_&ýLUG!2&J)olyL<7E<{gΈ~2z8Ʉ5q2ttUP/+ݍzS-d⠰~#nȄ'w;} POĬyl>E%2f̑~F׻Oܽùݲ"Pɭ0]HvPRJ <(NkR-`TuwjNnI, b dӋMsq 2PU ^{d:-Mx]dJCTScn dn)Hf )r1{GM(ϫcu jTEf1+ RoLR:Y#{I8EE 8t7T!-rC$ePTi= L=Cs&RɊd GCe;Cw$*D]Dk&N,Opʼn^x{IWXGۨQ.dV[݊e(F{|rJI bOS~ asy|t+G5V&h98q?t0%et=dOtRLп+0/knj޻ְ7.K)}DRHa kITm5hi-i).2V$x^u˵}Qx<r/U\p2i*D,sEŗs q^[վ)([trqܗ2+7~X:?y}z!|Lh$gV|Mh$wKtu>,:[&n{{?Oؑ_w9R)HQ=r,-o~z_mw~MNg!+yzewsFc27vO;,(t$7zA9)λ^pOy>{Xyc&2dj%T?Dh?LZ~RFbqLo'.D.O\tZӿTFQ/`3=(ͽywCiTe*t$ k;UhCT_~%_+e7)#2OSl5J. ޒ.=CcxsdMO~W&dȱGefƗeLWVs?tyxb88Ul2YXlQr&q-s@?Q*.W0Rz_.TQ a {u s/B鍜"ɲj7,uD=?^C=O7SeԻփx|LYewsb'3dRC<_r;|W1QU r@W%`%<:*E2 =)/81q߽n JOφ՝>)#~:zۦIf*z0S=9r 5qʹ\Fz#iw4(iЄA2 >B4*׌eSQSF^M-R'Yu-(+zé(eAt<>W(W[5{:ݽSXRj!ԪțS(+'ce)3{s,aܝ^ MT]iÄE h{z2L >Rp_"OFa^EJnOmF}U/>\1.wraaϝ}JM^zr>6fbs)seG"e)}׹) qOgʛ C'.ޮF7l}3Y|VS*d}@EdҶl<9#pN Y39)Xx,~zdNbx?<ƛ%o,PvwlykJzXӀ/\dngL ЭNO;sJ g/o#? bwSm)#YU:_&+bucBVAd.|~ d\N@aAg瓔Y=}xOy?1wkͺdN~ƙ L ,XDR.|fq!9D3Edܖ̭;sr|̾~4Cb5F}}ٓ[2ck&@P뚙xa>boaL':̧,ms><.pEykL|oφ*x5{ '/pe۬i@khLO1 8_p-[r(~wn4 /ؿ \c9(7A;Xcdld]Ca؀0:B/ w X7 ؍יiXoBCT2@kڀ Wdf̖Oy>Af'wgI/}=pZAE)V=7K_K:l+S;:ޞkd7#k$d.Ӂת'9xi6m g]\0a36d 䀟8'c?}d鏭E[ƚj<%_?Xwa@yGctn}iv-'aOdHy?bp~ɳQyCYW?G?m>?'? R}ŠH̹ 0p0~G zOd eIL%[/s7F!\0ڲ~&%ϡYJQbIF yh42ύ82#fz3N$qc cGym坫Vj:Mk&^2Vg)E+kuFœFt߫/Y˽eJV_Ew?W>/3+dI]0.ty>JeHm֘Zm072O_Mk6`R5pLyI'dy5dWsyjɹYBdWd#X$~^]^Ȇ'9<7'{ ^_a7Mcc)+v3>_ j9$Y7 iN ~>]\SKӊsBrbqpiz=i"uBN[GG]7%<%vȺܱV'k nρ0ٳgv?ܟ&[+dsbd_sdAhqAmL:N CnģDS Tq|ȩser:/<& tutF99%)hm} rO ?X*py@Lr:=̻0=Edw=|s)>|'d<a< R~aTr3yjBni\(f?T6yh%7[ [)H>J<1{x!uԿ^մ\Wz6|qdE~`z8o.\x^sdc=̳<$s/ʂZ𜛬 xd!׳ة+[oX8?U: uaGsd{zKvU [<8&N7Zv.*{_=p>ɞh$Wt o?<nnjk;xЪz8kFaXoIZo{%:\[>XC5.(:ybY'x^G|[=t5b9YDZ9rk$+u xlUOwI=>_x8c[<\=ܰ"C[>9s<#xkM^kԃ"/3<_p}Ȟg ߰1z|tO~wbٗo%_] ^q~9- *s:30wr=cg6tӂM+yJ9,L_I`a@}`qb} LD_܍ kfn c.AQ`?'ρ(P0V> ` ]PX 9P0 [ Gw S>З{؏jAȝ 7 +Cnc^}X\ ,L]؆yA`w`u &| _o<ܤ`.  sշGd^^2z@SUxM1IJ>x}5Sb|I.|lRy_-aF 0\H壟-ӻZ7AER/data/MarkPound.rda0000644000176200001440000003562613616365122014203 0ustar liggesusers]uPVQ%EE Q"DP uI ( ( J HRtHw/릻CSA_o̜3珽fu$T.ӨА #!۳;K{ %KBiYj9@BB %4"WVG!({e$֡L|YZ38af5L3{=0n!Ĵ]k&6k0t}yfs aCBЈKf%]\Vفzk#&OPK ɬ밈M21/ 4v_Dm6IE3lo|ըL?ad S]HPMr@>l9{FVR!*xees%}A;/YL<7 \rxqO9ZZ"3ٰ( ގJھe\Qff&X18\BmEDehg%%p"/ʝq9I UL;0H-$>ܓ?Zġ7f&B`RᄍSjm{{~]JЀ=_Ȋvyj 6= q\v<8x)$^aВ_b,}+4Kgh*J0)3z["O*U{ cizBY3AG &Ӷj s 1‑Md^(Qn^L<~א|- Kv-A~}lրtְk8/Zl+O0H[Kno+RG"z2uNjkI P:f3.vͿ.eh^,68o=Wx!Fؾ\ 2{"t[ʾ u9,QEՋqƧ -[pUo}]99:ϿqckPǭwIJծFs\IN0*us?zƆ `ecQ!}ȯx ĥi ԟApڳЧ&*t9]q+wͱ5k9C]? jحlj#hSaKѷ85Eěh-43yyGp p9 [d'TūrG"a>xW =UᇇѮcsLDa/$?I VζL_>QO yTӓY}`.90 3Nf9:)cs<Υh[Ta;]qB-҈fi?&ٻCBd_Pn6q6b E2 8;%J[B*_P FUp8+p12@$l\sr;ceTrh֝}Dk$*lđ3j$|w[cထ윐0-5k4+&0/,Num tΟh]{ &rUqBhU8'g{ZԆPdGo Uz3D9l-+*ho\vB 4I#Pn,ݷ6Gÿ0Ej $qo"V}/۶s)#>:AW\>DeQ@vܜВvMgά=un.{6HZxݧ5 C j)ׁorn4F ]Q37_*=ꠃwձ-,91?8' wb!՜uH7/11u>dVQun^c: Ҝu˺`hapiGn ͶO]4ɗQ M2% ﮶Bx湛KrA:7ż"}0١iĭ9yc1bɊ^ԧ|@D,QY(C`ֺtѤbWieb_焓YI ϾbbSUb6 O?5P1w9Q~oad'96Cn4 ZbNbN[Q70x3aR\]ԣ^HV[aeN‚Fͫ`>WjOܾ; A[HjZXǻ1\=*['bVunt{>!{'BӰ ,=h}ׁ 7 Y1UB9X {x;ʞJqj11zg6N@?W#|) k^шuNj~@0o$v' :0)l_҅>Ek&Ė7=-5 U&Qf*>]J n!eX;Sz!$}\;C V<___ߺ !a'C,S,?ˆŷG^~[ϸea)omnb FDIqMsQFѪEcr㽿Lji54=Ғ6䭳UV~U~wC 찃\S. v _u{ћʀQ['[u3\&u]ލ- x&9ijo,.~x r5 쇒/0Jpo@56S}m9N`[g?qܬeȃzmn)ǿμXfxBWJ #c;Ml7e0fXS*4T0G?ۄ.oRN ZXy_/??Դ+u X$%p/\7DѨCS:ݤ)yR^6?>ݯiX@Exy70`aRx.ӫQ(?3f=0h*vJ~ 0JaT,|Y4s޻;'QPam8v-Ֆ I@IC2k(qCCݖ_HHuxAfmi$q+^gR+| ]] H?qzs܏eijǠ;I;HЧ,Qǯ*' %KxiZNTԻ%D˾w{p VǢ:חX#r|m|'UQ jc ֿNݼ4b%n֗i ڂQ4?0c5K@#LV[g`jr p//^YXg'~zk.&}4|8v粬 :KDp%RB+Fd=:*#*FY߹V蔐8Y KUIHZH!%I8ʚb +IZ7`DXFyN%IŠmoÒֻfKb/aHfCb| ooL,.QfA[@!X-5tgzaޏrF 2[Sߓ7mWG ho&ɫdLX] U,Fzpks] 竩5\*C')O& F.<]EElvMAsX.Ûy/j W7Qh.zaFxg:?hqV>+w[ 6Jz#0}O#=x"Txa 6tڰ!#9-F4I!Rb:4<䛽'̞{7T =\?SfI;hKǎJ1%Um21{6MUauƂmP4n,ѽ Wz„2Y)gY q hzGOi0Cpֆ-5Lʛk2}^O& נjzSbkP*Dyf 'U`b h H>a G s?Jt0E ܸ}}pzuۿ1uuC*R&Ҿ, 8Ա?|?|7G{\a$ =l^!'O>Nh9pNV ؜5P9p/.:9 ^cgYyu62_#c~ڎ~ ^:8ïnj&,( Ю͂e/ I#_)b<-:_P"2>1bWMyT&Xtaʮ>\3 =*qg/귲pt9e4 2=m1pPd=&`+ko* +ɪcR8veU#<{תsZ2.~M,ЗзCj h +G͠ͻTPu'D :30^qpJ M"n$k\b8:%Wk-qݬ`D ǯQ%; އ$ yMb+p=v+GI2WJOv>s HIjk-Oe`AOWp#8<}{'jzrka0bX_4c0JP)luTkF.8s}_Ǩdvjnʵ&zu26&nQ7$ftͫ0ZV<%O~Ls&ҩN6Պ <0\'M)KAҎaNm(=ۺF;/Z`|p3ͱ kDm66q.Vpvnm+P!N!.1 U^ۡǶ0ھ>0f)'%CclJ9LCp}&1~=wjb$aHێQv!&*,~h Ҩ#J,TO.o:m8I<}lnx{F` g 7,h`y׺V'Dr[zq<}}K ӗD >ıkYҥ1&U s/&7.B{z/KY+a8| nl߯"Q)9Ykܑ6{'/:HJ±8pIl,L`}/"'1h6vaYV gTi(!Vk$ڌaЧYҧUC¦Es }$Ӏ(]W2k˟7^㐃ػ!F|tLObd܋v\8IcL-o<DŽ>J3'~*~+4 ^+-1`檐ٟ+PĿ" qH@TiOC:k05!Ŵ%f4؃ s;T[!IIs+K_EF <;棋&Fu܀M89\YBX=el+` m'̈́hGNm _:T4`]}Vhíփ)YV@p#ZeOJŀ뾼ATwX.au0do@k ?o"p@~ȢgKpeQa"3ڔ[ЈCod +Oh@%TJ:!23R#Cэ^rfpl~PrFz+ :o ǻDsa?qX2/)?ύAvᄊ~]J|ĿعUʛ!;{P#j?X_"bIÔPDDǶk9 g\n(Bf2o'ul[~ uHE_ hx|+$ H QUPZ% .{[oqǑ>51<Br{!K^s(1;O(:yIp*v5| y6S('0q.dB&1RUp#Y:tö?_Gb/slx3(4bbش^ /ZB1squ2up;v!eZn8GS#p2i6T:U]g@wW+4= {uAqSilM;mn X!~|;ӻUc~˚ wI=7jv廔R杇dc.+?0nC,|S li4lAZ.汑]H-py"Gc(9B^o? `_ͯ~Z[#!br|jgԘ%tq: |+='#8i.9GYEn@M*1A;Fbn:7iq"F=s J:_E>7z^+c#e&izx`1ZM"AgQG5~,5̺DC(5 {܀ig~(8bo}B& ˭kB{eF)4t>(FBӚr6^Yݒ T} l+7:k<$OB{wX,cmkn1ؽ%iE|Yp=w :*FGr8d0(DiBONӿv>p~ƒ8JnBN#uhC\[;W>˔^ߔl*^FpޖL,,r9%nOH[>&<8ZU%, L]S5M?nq]P)\„ĩJv{JX1Ǎ'uW3C!oډGuJ7+TR")7 6/!=CIXѐ/+5XrD$Jޞ8nm!_7ثrzc~ ѝhIZP97mv8ݒz/֗޿4)*R _fEn@a& f>;*Î%w~ɆWu2gQMJF*ܹ4x]eEBDR Кen#ٟfܰIU蒕8]]#O wgvI_GTrN|K h"VX-n}lKZ^>{C:8zr 's|{u# J| /Clއ/OAͳA1|g XwP${7z^ :g9lBh{H%K?]P.:owVbea5Uc`8샆a0>:zcƌNUaOmCGJ*. ћ sv/ӳ ހsi{-05Sx*(C|CvWV%Hx%mA52N8Jb'Arw-׿}Ƀ#KdpSf!ht8cڎyi"$ޭ]B6Xfb`+eإt9gXij {pW=Z5Q6d{;ۺ!ɡ;z{$RRPHГ}h8yL߲Daf2Agr2[q~>Ur(vay\qWASjRmnqxa>fnntFvL~t mVGA`]!X/Q쎰8+f#ǒo/h9 B=dI`ds2J8t%kcn6H&޸/sOciR͍ߕ"Cp0 ).pW.[6S~塧9&cT8(}k$=?Zw.oЙ3]y@[tJK2ƺ9:;SϤ,ù a0\U+U0t#[~Ej]QKw|eb_OAoٰ,xXdzteF`/2S5FSkO>"?Gho0K9qQP2(iMCkEHNn3RAyʏ{ݱHՒ6PkY?Q:r?72p"Љ[; 9:AS)K %n[aL1>[MeS"Gqyz_!4ue|וCAw T%̐&^J{+PK D75 I~ h+KC:0M*bk4 u$h:EMRTv)6{M>v*r,:g?5q~D?u(f_){q"[ӱ@:M& v]mKsa6u>wĎ;\H[t 8z/P"r?x~X)Fip- ދ!%5lP R x>rugXqe_ fS m<&yOރ;l$[6@{&_A -JM]F1`}̧jxX39q,&6TuAT_j7?H6`IxX ~\!?' {i/pKՃ}O1ÑX ^l 2应8 q}EbiZZ޻>ޚyUC 7C,w 2ts9=SW}'ؗ1> ۺiγcH֐!&_v,7ɪґiQd͌K^|bt3v] uLZ‡I-0Avl\ct)P [35|ŸoSXR ]iOO_uu ݖ.&;}X|i;OJX$+b1 #'] tHsưҋXX.q>M*e0{'Kg惮c9L74>0gʎT/? _T?7jZ0UV XXf5"Ȏ#cxYvEڮcF'236Q@G$>ǪNRPZ%F7OOKζ­5B~:hm :{L?2Ph2@ɡo) @ Iڼ1cL3l4 VO1L'&F;m ^ Jwraԧ F-,Zp$.]Op޸/ "c~)o3JI߸(m]'(Ta70qI OU~HAͯ{nVx(Sϱ,տ"Os>ad7F2CValt[<7Wz8B4xmjN^Ҵ0 c*@"pوxagO,b1uHuls{n8!,w%/5vt$fgL6F>AER/data/EquationCitations.rda0000644000176200001440000002210013616365114015726 0ustar liggesusers7zXZi"6!X$])TW"nRʟKMd[_;zk];s8@Vl鼬re6F2AwAX" l~HtKg=Jlp@`iV4bAag'N'b7 K@QYB^pSC?)Ҫ0)coD+)"ru<ExU|j,h"N]eIThW{fs\^U3erS)U r"JφFՑeWJS]кJ>ͫԂj=Aq5LsfCdhT ,HSBvY@%rPے^ Mv5PNGqH͊S_{.blv}\iI3i1<ei9NQL:@6(خɱQ]S}virEGu BØ72%hs햟]] 7A'cH!*"yf=-b^yP?țt2(}2.)j&g2TWQ0wX\/yAz~-`iio T,  kr8tba#",j>Xnd k+j c[!b!wa^Hٗf[.3,joQw7+G|j[M4x޻\48?N:^鄭 8F[ܵ.ka축y:}h6\\]_c4 uȺ2rv.~R8howЇPJgAdL}TsWҪh/|Zfbl/+}VKeTnX~w{bgJyzHvzM_1U}KvXc T߼o[n"&dC6A )EX'V jSAyVzެfj )D> p 2J+d,$7?0)~X3nBJPyLZejc@!J5[sḢ{ / /t]HؓT+0ҧq$Mf* ~i7#JfjBeeY4; WRrlUOC{s?҅ _d `_ȥ~-`nK֋r>'XIQ\IM9]mzFj9NA/ ku|moAeqLv VLxvsԐnay2Ck$ָ2$PBȘ1Lc.j7qjR4 w]w)ZW} P`c2:Yg"$,' J3$x&(htC֢nǑ(:5g45 _yĎ:B¬P G'+np1[Hg*pΨ~kJ=szkl4JECeNt ')@zT/}D`s軠Ͻp2\Oj74[MB}IM_6!yxsm8R8€]_׼JFf(86,:z )][0d\WK5.㤠Gň:"hv*p#PwѨȢ@fkYms<@B]_K=s^Y![ԷvZt` W}q`ldܓՖv=o% fLkUl:3JCέ٘~^ { Z#cK 2(lh\Q>&SFЬP6"]f6,=J;̸x-<|=[K cks~4r\5DL\E"L` f <=ƒ=H[-j 'us :0ζ.BqWkOd|FvpURFmwHTJSLo%s #7^2ΪxҌJ"@[6n^-aq#u;'L OkzDG 5+|uuRc(Bg Fch pkx*] * ++ Ī4,0b(? e&6 Ib@;{vc3/PUZ+ט3!p!K 5~ %dǸOErzI+su5`]4W[ yu<ʣ&#@T0 w9}KO-5,:n V2J[;WaϻŢ\j^5BLd;]a"̊moaBTozxc?] <>`nI}gh*{#Tqzv[L-'Go-oI%:kvozqr}-rmI@@l 5^Tkƞ H* `N#y{|Z~ vыQNL*wjśv~Gz/J-.Y"$F%r, ֍q,>'OCpTLܜ^z¨|h{ /NJSe8/f0"UΘ`\;9!ÛYΟd3 1,ۢF~j&7k_$G.Лx3*r?d1ZjmN֪#x2UȫМ[Hdȷ/yᱧr9=X{^^n(@SšuZCG&H=0{mPTxXTj /nsܦe3/w|ak= r{62{$p~2H\Jx]]9MCvX` އ,yAPT0D<#'Tcb>d,5yIѨqG` E;U9rԶD#< }j"A"G7d4盷ZPL:\kxl{}8`NˉؓHS3$ MMmlI+\6bAW8::L+uP[oAB]< .@/X1p@rߟ*aa-#a& Se Vgӣ" ,u dZ[o׃\y\7/6e18ЙAdyIG2eQ'w;0hO@~*DaU8苀^@^!wr1z5%OMUXYYͽ5y ivx5;B߳*I؃IP"ȆmsڍAyKkުjP[)7#!iTb&Z5c}q޺w (]S#FmIr [iQc"lvX j3L|s>wƳ6S@Z9yb ĦUWDUq͗'y~7"ny(Tڥ-C,]Gn-z~hwJsۤP4t#/p`X̳!25pC<:'˜,EcLz|WW} aujz%fsP濲R,}sNN{QPo2FLRDR*{@ejy}c0RZx6Ӏ4BA[UA7ɮ}f\D3{iM2F,p4GBߛsf[> }.˗/S=5\Ӓjt׏La/ 1/G&S)w J%O4$_ɣ>dUu5($՞keFSoX?Pw!EkvФ*Qjr4K{hLI?-;φt0d׻=*Q)4 ӓ݋ܤ(?ti?\uzϿ'4ulk gyڨyCs ClxZ%o73_,@R:cuɸn>@D5kC FEo <1;կ+(ӡ.$]-$콾@&wrmmFCSǖ X9N0'6!WQ#B1cX/̹ Sh߃q4yqpӮ*˞tS ݸM2ϛ?N;xa|{r Ar<}jUhk 赖*EDCTl{ILUN23C@LB:J=WKW,˼2df֖|=PQ$pkBۢCm0ˍgnAE_dڟ+. dpcA˖akUSZ ෥rʼKr t >GS|CU܋LJ$ N:J, Y24d $$k:I& רT)|^)oA1_;}H+TWO%J)*0E$2bUR _*XAVRn0[XrhA c/9n촫Td!r75_F w)/=X-ے=vE5mqqu1u\\ _pKXYHC(U#FgB"&0:wr (|aR?}z3 40 9 wh[Zb-/c.D][WYl;P97KOo:&u^,omjGvet|$2Mŝ\CB~+)yGe3'й^m_ 42H@4JDA6MP dgs9SaƗ*yeVYRs9 "NɅfBIKRL$8wV*ц]NկwS6wzY-i돡ZbRlX`=-)^ve HS~P:HW%:|Nku͎hX0ϣ) `e\#@yyh eу vGzFqW? $3{h\)a̾G{t$VXjo':ny[<{CWR=ԁ.:ŎJ5$X10X!H>#mL@[}BνP@aVY#yΥ2YicE+}y uȊрO b5}O_#PӉŀ⩰0g,lO:uf\~R(3E8@te=T̵c~GP 2go~ȥ7Un ˁ8Ĺ,]G$$}zQ;2$w1„ ?JRe:],}1i-6^̣qRllo53Q tVt6R;I.<> X w4֭B\-i]CL6<*mLaX+r_KS롞,9;3yٟvtI}ɊAdEW\}{^G)/4YdTVk ->(OuTaIWWmf/a nS_S>ڵd r$ogN_g O˺ɶc8UsBN 8HN'{ ZE|oԥIB1RXvl;sM>]Jf@K$X U)g^GD~RaۃG]׎NHăn2$n-t CAz*7[ۏJ5]W[V3(8~NAMVsP (ڡm1~Jбg8Fٲߩ4NvZR[\>b|u0C5ACm({Y[޾9!EW:0_Cm`(EbM[9 7n{cH$d*i 9^ĕkי(*wJMOʆg. ?j$G/nHg%O!qCC9y|IiTW $&ojwHK_H7e<>0 YZAER/data/STAR.rda0000644000176200001440000066033013616365125013053 0ustar liggesusers7zXZi"6!X"(])TW"nRʟKMd[_;zDq@hĎ6G)}YכI$%88^JUfdKu0tХ_9EҕG[ۮ4 `V-ڏ5E|QRYX%z;<^_<ۜ]]^G>` `*1ˇd@ ?Ի/$-)`%xVU/pC`ؽDi=~K.8uA!!I5RfAܖuJFEۡ߁]Ub3knY;6l4µ.{,uP/ òp3$%E7ehm⠩e<"lJuC܆CNKGGƄ'̒e8K⏤Ϗ1 >H6%ywd]wRXK勒b>TTm攠Nip!<:sџ3uHYB2υCl\xGpca2 B߱PHPl)׶qNHhOײ^ &U5UHw8p%:%=+&"nB:jHY?3Q4#8f6D:B]>tK J_zq&oa iW&5rV &g)N.%X\ UE  fV"l SlZ zҮiŽ#M ^MqWOsKXA'E0 3T:SzV[a9^{p9:*C=s[q VǾF X:άi] \ߨ }'=CvBZȪCetw  sHYSR 7W`V tQͺhJW#^1cd>2zZ%jO_EhwblD!!dz78{y֧^UzDI>N-ۮJ {Ʋ+y e ama`Ug߼>rCx7t_tF|DOHUվ[łAccI;8;&xUTØ.~t"J%7;O}Q;nJ]IB֊''94XF8orV$81zRoU%5J9UV 7yZKt|F쓔IbD0҂6W+"8y|Fd#5#V/~ꔙJ4x;hec 2, >>xΪ8mTEi!peJytH`,ON%02:Z| ȧo5z6b8{( )!?p񙴱Ɯ!´tap7lՉCH : /I/{s'Q;ӏŅƧBӸa E;Boz4^mhz@<P22an%VI;۬(G EmIh^p(Qg y*uj{띖d`"xhjQ La | AyBGUbyf-ǶGB`0 y%꽺I~0<:3 NZ~H Pv^)quBtMzIՍC5SXK/j{¦;i`]1[67Rjc":1ylVQ4 8lQ5MIW%lO7u=#߃ZW%bM kFF \?D2_" Ћ?E B~m,+ ƮY]eݫ?n|5RoLF!Ņ ŠXk'lfIjgS b], ~#E:[]1y酸wP]Zɧ@3)s, IQt=N*D-a%t;S8 mU@yQ|*]D>qx* k,r,)*rbӂJRj@yu |+-4r𛈴ZJ~\*%W䕡[8:_fN+SUEY1\=Ƶ2XWО =6 rX!(}]0}fP_-fTX#E<ZjLhUM)hֹ3$\z~/7a}0ձc]#΋|%;;.1 fUÝM¥G?0kxjCbHU#Z3Ռ$:pik'TjSN;hzhDWzYW4ӹ P+;%y |@)\Ӌ;V_?V3߫.Hh0:Qm3Rl^]gkiOLei[ym2C;ȿRwnjMh,PU ,rA;_]29sEh'P`}/Ne+*DjBgrV'kj3@0 ZG(5&H櫋%zNڶ 1X-&LqTJAP』!mWYB_ValtilF഼Um1ˑLoܐ$AˎT#1p}0<&2 FO}XB{ϩ-\WJK'Bx6Α1jNI _o6"+zx.su4dYr$IԘ ".mMVɼtGʀ#\vң\a{pQXe<3[gC}l@"Щ"_kmP3ǃ%PRb@qHd_j2{df&R3>ssv a'ŻsaD,qjW un4\19bO]Z%T}GUkq#GRE.tF㖞uj BCtZ51xOJ"qD8VcWx>eH*7Ϧ µޓ飓Y{UM  |ygcW8:L3]A약rgJ/}T\K#Ӳ0=hgg! #Y!J nAza٘ T d\HbL+SŖSHƤDa:j2aKkgQe T[@Ri0WJ_N㒀{GCi*/Mק1<}a̖۫wD S/g?;I]bӑk޺F8p1آOHD4q+jO׉ݮ1T\INYmL!7&euhL!MHBP 3eN_|&=L2G$\Yh#"+'a$$&w@2׺ZAQUz찞:7J"ޙtKz-< &j=P>,ӫnך<{% 6֋ui¹V]ӒSҢt޷ kВ0e,e3Ͳ!x];G 6 C^m9ט g:U{eiOdY{$x*߅eh d璢wc%9?!gyQ1t^ak3;LEź[SZ$uMc KQ6Z5ne5!a'{v7Ӏ-@MQPc)rmP%g sN01^PS}+|at=}ЁR7o9lO!zʖLWZQ9-cda)GbbuL ]v 0綠U&_r:mT|a}@|߻el`hoip>xu>IQLz >í쪡("Ƿ}6Zm|\6_O2+G+d5C3&KH2;ƌ 4:8ZD\NeM涄M HƂ︣K?+Y%#BB[ d0V S拗%X)W}+~f[u,̧rO,%4Q?N2$ͧ›D}wEepvz2]0-E'YP0Γ:p%SIͻ 7Yq3wf'b776BbY_ṿ&.2pJB~#'3H2}_Aw:E#Yft+!J~{G`9$&\#?!=Ͽ\bFHdTo}[`1E﴾`O+E2-~pcBFF>0|<eu&_q~ё7.,=f±76HԤ~a8IKm)`uZ|c Wh+ˑ]9B 0l /6΀ؼa&}%-w bE9wC`*toG2'Ͳe+HS@MC2"3]NK'.r̳B(gGG׀4lDh9(/#Tso87:|6\ʿߥR6Z'}/5/Nf0D{-7CVGӷ"dcQώML.cn赏#~ȜXF>&p_!^..F07c3+5s^|*!Oq:*[X~k^|̔ԘLɠ_ :hl!C2 <3x W:eŪd{_n*l郄Yi[o;.3u_em7= <(n-Fߺ?'Hbظ{@TA}޸2>=ORI>-BG+rYɣJ5G5Elbg5a29GRɮL5#Wӊ ſVkbp7Dy2L xmOkVR.bCr"J{@FՈf{y"Iz *L^53K~d: ER`+菳OںjtT؃b%q@-d<䆯sj'|wa,gO;XaLR2ⰘԘS8;>C%Ԙ4|m,xPm/he8~(y4\ͳݘu HP/w:Bk̕/ßN7!hUm٪ѢfЙM @^ Z;{_ɤOHm8cQ1Vf5M>ޑhy;p?i> }&3=AjR 3r2ԨgdYbh3m48ޚ`QX,nMWb}жǫ2-/dg5d[P^ ڶe*~wj23>!7-Zۑwuj](eeZlEazSøl?NPNT<Čػ`T ?{zf80{q\O!!IL(z2#+uw*#'wr/Dzc6*8?/|;ok wPq|bm3*{e< }E%t&{2al )`CJa8A [BoQBWآE/@ cLg;ɥ{}ݵ2fKQ䖊,Q4u"ds(ޔB9)lq1ȶ2S~):DT(azd Oq$-Vw&=^]X2@.Bᔙ0t!F,[j,DdmJqCgq@\yEOYJ<.!VdFaŠPæ9BZu\KYKSK :mjhwwv^d~Ovۇ|[3jvΣUxbU#{isÀƙ~Yƭd<%2CV-&<3)ycDŽ!f4`=vvR%䈻ȉg14I|3K0>wϮA";w)lrsԥ  yj; LLN-&q,p?u?k_TC8P>wso,7<'دBw$t2L@.^-wPōAo[}191]]^Oi8_nVMKi?owco,Ȇ댌r] Pt4 !F؃SϜpHLU6tKh5,!p9oǹ-';-S٩j KM̋CƦу՗'YJ/8t1)`ɼD ר] a]2>93,9Kd?܊% 4%sBIUqPг"'>]`"Rߒk06uRݍPϚ3 d3 ]qP៨EߞA@ѻ7OL)85ZvioH+GwtF`F J[ӑ,Q:pÉc9v\4+ф*Y琡Z}i|a71bK*f1$)s YL7WO>ZMoQsZW9Lqz 6Wњ,Bn f/4q.q՗$83 #zƹH[8RPP#~>n2 '㊌w(JiyB@=C/k<= kz`t@,$9fjU4-w\ku!_rrߝՏkl e *=2?dx&z߫3 _2abpKղI2ywX<@JbbҰbO!ES3-zNl򁵑 ̄H|䂜kbp^ЅJ^h]gd-ϝ+ wr>_;KUSm1s$HWNJ H(n/5;Q`Bq7JVxtbtf2ZN$VGZTZ4+w84U-5fNAj!1`^w}K[U %!V(RYƹ<&? my4LnSwpM-)|m ͢Nvrlg-ȑbo#_>‡qxh=]XOD hexrYA61_o`ʢa -#ٌ: =O~Z5O?X0PTݠ&JBA%fZ-MY 7m* D|Lkc"!a>bŖFj'sW fuz![5ejMuc m,qa轖^xxʦx͎15Z,FTx'J8f}-dž AOdKXKNAXcJ|*M0<,K݈}=0ݝI{ɵDIsj-^sĜyzuTlLWyofimK/Nrͤ*YVDF[xS۠zgciK!TEKj J~r'ZzWW'9^n/!tD*"NVPDED0=z j&X%X7()f0-i:Ώ[\F;\aSB7;xHb lW$k\`T\Ώ [~.ݳ@r`@8T4'\f <, B<_ɢGx V3 &wb8 է(K ݺټN`b+4It5A5$aA橬66J$[=2LeB$A 6 GQ1AQѪ=Tif};9o)*IJe}@!g. F.t W4y֦;֚~P%Km/wn$i6: v&(l$'3A; w0p]QҢu h=Q&"̴ wa| !] (}O^e!\,MڞE%7!wদMӔ׾m#j0p^NZt?<_Ix-l۾Q;1:kb޻%lT",x]vcA]`_8|K+acHNV:? Q820J` <)rkv~VW^)D4xRaGDZ$

\0iwn dn ǵI nm—ɇRQk>RX&$%(^sѿ]pv2|ȟ )sU&GAGO$^cVG7hc+#"y{/Wuzvz (en,@K~̀2EdV 5UH)*O/IXNO;i~T\^ye2lO^ KG:? 2%O('CN4 o:B|u2 x8fmuCw&6։QLem3Z"Iߥx΅m6w~+;% Zײ4ߩ1z(8';= R Sq` Dhn0^-Aн-W\cF\I WttS桗k$DVkO 4x{;OM zlnf9IP9ݖ-MRn* Ggf` ^i><&!)\Qtn.LZl.G*oqgZq Iس7F{ʁ|Cv®K!#[(lݯ1+^s,9)^@_?m\#]4|۫!Rt8و3q$J&I8Xv~O&F.吶qhIy{t1f|H|O2~O3Uh)a9.e h&gvlv#A_xۤB*(X)3PxW/|#6VA*+F ?T6t"'7JZgN=g_ܐ95CO 5Ӯc @VK`xQ+<\6e¸{UkR'rno \c9VR8n훤)E8_xoQQ!u~Efl%fX[u*IZ'f:YaXr`B4jZ8[xO^ٻ}\I7IΝÖ鸯hL m'Xy^,#s$K/QO ts7g%EU;US:.]9hΓ"+ @I“T0BxɺJjee(p6r {WZ!M jE䒁pe cyp 3$.CZrż#wc+LJ@G涺^)2ч1`V,,,y)}ϧЫs)<Ϯ԰͡rq2cnȯ̜V)X;Rn jSCpR )P*>n{TǗEq2RF(C;:d0&mX; 6"@y4 fX_.̂zSc5m,TĊ\5 >J;1wmiZd2@hLs=YK*YJ.( N`Q)1'shDUF ũ;sG[AKmÎ03 V>Ecա5Ir! B+IGt2w 0si㕙n&qÃVr݂*QE6q1;k> g-Z{QC8+Qt<@dh~kv `>2rB!"z\F^=7B#pJ( Rcjړ马$RUL :ꑘ9b27P,J*ʂcx.٠@R ]%6Ӊ`g`}8dE2xY/ʑ;Ym+ A#*+z/ӸE>}E6Ui||&%^GC]ERJLh̺h|$(RbZ i; 2>i6ۨF[H66 uMfg-}h~"0z8Ove\HmHgi r(^3$?k[ ARe^^:Z?"F3|6P*3鈮~eN?HͫFϗZƄ_.jfSi~y)NF)|T0Z3ةrrY&g^^-6&/wWk]!+pB+-{vmgw;SO,{^Ig פTOƸPfW3WLV$If] r \XtRJ݂M<81MHZĿ YNXf_C@!p(eWVzV[@m GχfW-ry- #_:?D`4Is} F'U0$٪ k^WG.qNP|W6ARvnuP_-c?nO4_"A.Ly9ZAeCcNR'޾e}uLR% (jMBj i*鎉kC]&hdx] X5oy[riZfĊSϜꤱ$A)K9Z.yT>M8 m(Cٿj42éPSd,tbAr$> c0LFn38oYE;_?RQ; ,I"P_>82a]P[۰l#)/"T7uNU1|*|]'LF Ɯc/au_Q.(zxm lX bs魊x :a'[gca Y]8 dsBrg ɨVC A/njF_ w͚CFA̓Q߸T (&}skpb'j]3ͳ?z%319 ,2Rѕ=~ A/tOCڌNV˺qAǤC6|lD$^!üw bde#tc4o4ůM{&6z)\1Y<3& i *lo֧`C( %"H(9۬]U#r MBNx[;ʚlg՟=ZIaR<΅r JAըJ$fj*i8)K\Iy77}Ţ3]joyzQ{xKiiէ',8P NEZL:&Y"=ʣ;w;oSbF:n 5E I ~LWCT *H!dw5dRh cc@g$s2%蹱KTJ|Q#Aӥu,7ѦmaḨg vI]sI\lmg(NbV_0oGo7ɽs[Oq 3eVUGݶ(׌wE^^2S0ݙ|GBtյ 9#['hIݔ8~n3+"]d'*CK|Uv\gw{+97V L"kDnIP'mn9qqE.ҧ;{Yvn `1+PwQkkWEK+4tԢ#W`nVO`,7PVP~YoM ~PoMR7}ےAK,0hrEo}dzr9%RDv̓<`UomXPUO󾚑#wYmlE[d 2MT[!TܽMð|;BcH8y]hj =qJTʆ'-o_]\Ȃ[=&Eڠ~%@Jv)cdiuՑ=,SOnf5V_"AQz(+/06 Lq*؇'Dh5ӱ+tQ-<#ik-3Q^oi )S ž4{8>f4#L&j花jEP29NKaC{[ЧtƤ邠 V6G1.8N뺄1; TtG) _871iwVNK84:Xnre\pRSϥE% zv3ab׍s obJ%. C6[0Sn:Sew,.Fx%%l}zy?nCT=\S")*H-cv byx*ɛR_ t m1˽7!Y)c(^T]9-;" /0f cBSD~mMJ"H88ɻ*{.L0P_I3o%36B*?lYl#`l򌘽3Ͱ8̎MGڞ$ 8DU@V汖nqJiTHm@ؽ]Q [-v=c~䊭&2-H uNjv\Di B䠖ȍ̚nM~vliER#XDa$'|+Y"  :>0e^qnAe%#^H;5o3x%`dԭׂ)3[t?dxU|2YZ U"_|g€W0콲U݀O2A-D!wFS̸]#Nrw**D,!%rBqD{yfp4X54fo7K5AੱFjT8 *lւЁX!F06z"hqd^/,?s$Q\N?P [t²#w3 S[pP0BB~ "6u=5^'>}֯}K7:ŢlىBml0 tڅ4uN%2_aP-dY;P `BQ:ayLzȞ73}dK Xt-8!}X؄ ٸ@yQ-afxOi[TU@:l]uE;\ %|uؿqOиrlR<~hC7|B |qH.AGFLq[Wd0:"8'kI2 /blz+D%Wj^r?SkcoW&yE⥭pѱ> H|Z{뫮0[^su ~,I9/$Ԯ?\#ŒaWs챢=j?0! .>n1˄7mӸ& Lǜ6uR'.1+Ծgn5H4 Ck\CPobW PڮZK, uu8CfgiU|W"CjU H e?+y?qa%iqCYr5rDb&2zY;."XO${0˗Cx~xaK;CshV~?  L2 O ~hLKfkE+„ [x8p@(NU0__R *\ +UF2׊;9pmүxr腽\Vt.%RFh9ASl g''4x7.8BKy: ~ߗ L=a3.Ō1G| (ܻ(>g:)tE=/9G9ԻK{Ge=JoGf6 lDEYL>̟f̥S#瓂Jb8| )t!J-,MmF <gH υ^HP.~wj֨y? R#}<൯M@iR?~t[M|"CLZ!g1\Aӝiuf$(:^&!ǔuЈX[]:=ܞV ia"s$/Ţg^k?ٮ mJZ9.ЯW.5)jl7j6DAV߱{PQAvs:tMD40p Xۮ ؞r`Yal*$qJt>FZ݂3=[Vr2LͽvH_T[unzѐ@PMM|gs8fѥ.hs邪vz`ﺉA !`lބ$Ij3۶VmjSt b$X@lZ59mTx[sk*O#֝>lШy/oIg >̅qyd$t.Xi,$f4k` @Xi/U,QeʓF)aМl+aH0Pu68*ͨyl>!ҹ-w.?Uϒ('XZsh\@*B:_M?|&uz-A5!lIY6pSSLJZ`B]Ҙ-#pmZIqW9wNącz]z~}#,OfO VMd¶h nuM)?D .b0\wq|\u{2$xڜJa-:n3:6؄E&ᮍ^C)np##!&!`,S4vD8oԼ^hCUqZ[XWPjyD>e0ku[񺷿CjQjŔ=_ ?o,B^$klJՉF'Px'4 !ѢR{p?^b.U&ˎkSi`^u٢Y*Wp.C(?ojKNPzT_vnG`p*Pc&p~ዜ^җ)#)9?=@{N ,x7J ?AS.o1CY8i#/åx95H0cٱ vj*/s.D6vj{ l+W[C*|nYf,lsfz 0 ݖOWC@a8RL\Syd:aY,odx%7ak1g{QJ%:iU8Rw&݃g6~^z8ey6/O0pc|,g)aXS_S1 .% b3({o'塢QuU5c,)i7G_":Oh5uHY_oNlzz t$hC>Ss&}m}?*Y.2,N 6Ù6 ^E]Q- /''OYZm#\ E꘹UdoD+=<P9~/#yk6) D R:!k\hBWIl5'5TLgnnnޥhtJFf>n]|m/InA\a zujRqڝcR8R']U[Ҥ?ŸԍaH'P/wM!!m:#I{z+y2+վR܁mz#́U$<Kof@S/@bV|9Mr9ŏé&O45A9F>Q!w%hXlᴣmҗC^W&0"1,z#iF`;fP]F!U~"'C@E>!!iN?.0X1nA7p%Ӹi菌.Reievv1uv /E7p]SHGRA^͋C_AZI~:5y˱ w1zМ:QKɾ Ғi. }U-G;{uhq\؉GoW~ R .GuG:6Y^ˌEN鿔òtB. <ёxOZDZD -2cc:(Ӳ?8(sT_=Fؕ]XvXBQP8a IMnA38@W(?,sMd 1,iD!0ϧ m/}SU%[)`oproPP)0Bt jk ZgN6}K~$1 FeSV 4!T ViK0a'ibAR2+!gR&+g9 w}\g݋?u  ĕL2N$qYVO*n L66NUA莉%KHM,f~/]{Pz6i74[-'8s3!ؗUF0˽YXwJ`>_L r,g8 C<s|qQua91Qk5wq8s)޿87xq_5a`wuT޾Ug\w}]u}K@AyO/&wOM0%Ax\Gl|kÎG#8 )3Ƀh=(硊Htz-oX ,j \p۶ C V+do<,wJTcMt+\OC** NqOf=g|beF(]$4^QeD6m^//QZP{ uvfywS}{zYxur!>I36^jm2u@;FIrI<ӇļwƒF"P|H+wDO;9I MI{YwR18sd~5nӣ7ND3up+F§H>>D}^nK YD5K^mR=`zk!̗=p.զf\Y8fhcwWj%M'[v|Lu=V4uO4Ձk-V X A3}Sff:I`EӯB51*%̻(Glj8YGa4OoUw@"Kpn3Q4Ks0.iEV&fDUV8mШj",Yr,)'kJudQU2w}YxKM$+D \>:=ƉvA!7\AO]^ˠEW^LKyV>(su4!Hܴ}d(:5j[]G7 vj߄{1WY͘1. k$RY4 9. Dc풸Ʀ\&9 ѦxM}7l&T\tmA&]3^G,]Uj9gr}~˂]CX75΅;gM<⵿RcC2q&~%WDdѕ3GxCۻ+˶*.!Tj=kYE$(z Xhb(?2#{ 4!^HY0Og(?@E_R&24S3e<,FHS4@ƻO.Y'Y8i:26V>3eX2F F`*X{ֈ`֍(6t~!}.5"ORZ(T 6Yc,9Vx#{떘9 Hջ%QE-5q.wgHd2T(U~~}uͧCzzMmE*%,n}GO^zB<ŕxuizb(e 1R8Kbb!,yj"ӊ+r9H&~vƭ\{qĤ5T)aika'ĆvzS& @xh4`,~ErO@-[)zp<v_q< dx>wvp. Z)_'10+5v3K4UJSZǞ/02hҢ_ P嫍g tA8Gj"%Rc&7}|u9l%~ۼ\?"Z$a%θ5OBi4 PE^Ar)[˲Ҟq:2WO=F/j&CT~6#|VJt5 ^ezAeqT^j4@Q`bNU7] F3›ENQxoh#?~Bsq7腓ݬ. _)c}n ip)V@]/AW8B<+v:@Kf}]/4_W!1VP,+u`'"Xmfz kǎrnR3o͌445+%wa *1=r.KD˗[bHƧ4 7 P⑼f.K t>Ъ{X ߛT{O?*r╚\gD h̻Kh?ό OƱEX܎4qբ! =#БGOu-^%Ѵ"&(M_ŷ AaYD['@+|,IڛGg#]?y),juZP 19p0sڨiDYymϫiW5ìt( ηD|?|gގe>_p5U6qW/Re1G޽v2im+ lu,ExuY|^*ɌNlSǔ O툧6f9U 1Fī\~Sge2۟Cy @-]ރЫ&c  =m.AȬ?7ۄ Ej_[%tRC'2J2<6 h>p`o^>veK ܎ XxˬQ "tiyn)?@*FZ=zx"=UC[Ql- +n\=WdGfnze/r-|b!=;oTGͬ"UV:|DnyhP1Qzd?~Nu<^N-| I!,Px ~/y*6*v36yxKbZ|}3QUugLt?ΡKB Կ@Wh+~pdQE3TUW=oM#ڏD~a=h+,Fm d [9?jUxGdJ˳R J9};v㔝۹\EYUx.Zl_!x؝5mSNICQ4(es]d;JRgk%~w#x44oʴ#<ܒ7(XiHM3gYáLkKI,O8;T-Oq[/DZ"$ DnӅo$O+<@Xv#'FjfjH9qlGM<.=^-?M&Bo%E`UT\$ac&gyBL/HbCHXH7[ rIS@m(nyq^̥!)sZ@G#ịTKJVa#"ޜO'*/@%n)q~'[A\H s{ ݫhbegy+z IwQd+6 lvdJgUVcAp1"LɅSTk( Pi. JAY$p- ['>8[ǣ;>k4;))F?&S`kd4, /Uv+ Vu'?G簎3$՚>[ Ҁ^Pb[G` 6RA(l3'eNL0hRu[P!C!bd&>[f@}C6S 8SMCYdbmB?No4NJa#DzUrmxXJ#%7|bTp*joPl!ւI\DZi} 1x {Ne\Ms^eMFAU(icwi S=)y8xXTef-MDKkqpv cdlBG<ϗ[D?y<31HqNW۹/R>DR_4ݭ!Hv7G-,_c4eD7'%%ܒէ-Z%dFrn x;WTYqbq}k̞- cXU_T ^};c_ժvkM-AR?zt6d7@:&0f)!֙ YŢ,^ћNZVXx x'Ḉ4c:ݶ!ף_YK:*˴pUc6"p;a3c0Z(ep(YZC/C|3]Fnzu0>=J,ukK=pp^:hSp#~vuke|SѻoЫ}F_yFS̑W#b>:aԠs9k"Vk:έijd9,t@*ORk:u%AKܩRɯ6P3c 6u`Y):FXذm齔˘u1ypOj1DhkvF9>/F:)޲rQ&'h@xP*G :әw5ڈY17\SV#^h¿w*(QCS}3']ܳQԁ֒b?BgJؿգ"hqK5S,D5np4 BDY.LɌ%XP*d8֔|f;x1"i mI"?'ts)YK K$Q^)$/6'|Yɖ|w8aVB 5HmK(Ts tԴ@e[9vbwL28.,|Uf;4}K[0|c-9TRYkd:V)٘bzDeS铘ReW`~t夤.>69@y6|ڑmԸ&MOxn⿖wGIFG7n{f !iU r;\>Vhͯɿ_G;F6b4p;T|͊W{u1%~ʮ.ze2>Dž؏ >DP . ǒ\?wȝ[ ,~(]T=`!iKJ4V) BɣgAse0üYbt==dXW#_zf1x~~ r  `1;3oeOm)ҳb~4 q|V]d%ZzǬ5 DtYހkO`ms. =p6 84ɢZL m\>ZgtscIVRAwWG8ΦpUIXw'J&%\]f)G&|?Wsf073.6w.~{ԤWnݹuw#0]|Ezܶ}1ճG7Rm}t+"G%%yv.q%tqXpc<ܐs<+i ^msd\i d",}κE5&+K,UK Rϼ{6r4q]p(9 H[ ͣUȷ"yv{/ty=Q`1{j 32/{+#rurbn^>\voo#g؅>zbV*^9OO R=vY.,8}(gW@f)VzMU"))JI6幣1蜘O QJH wg5P, kda+ąGS|6QNiK%O#na{#wwU?S$);&qc۽F1R#[˰.ܤC/Lù @I)B4#P 3~])r"ge{dc0%MQ MB ne!ֿQq]&`0+ʟz;8v}R;.9m65WHBrˑX20bژ*h^}K/# uuÛ_PT~fߛkJTU[8uYSH8u,m$D!bEsVmg㦝0 2a_E|6e-#jO]|!4LՑu2ݾlt-Ws9{鶒0k؅7*~V)Y<3$Tmcgo Sr$!*qJG֡uZA f#@7fwYf?}Q`k;W_nws #}X"O b$hl` yaa6+?M߇O]vM:ijYZםq9? + kNۓBoE8W&1\nWz.d=4n9"j/fTL(p2[^* maއw4O&>;RGNPNHOY6#{d9-mjT@T#q>zWrWɏ;:ʟx:WY&"I}̜++V-8 j%_z G1?IF\rܭM;Xgej7 gP>4K flE1w@sq %RYnԓUغqa.`^芐|߾R7,ִ$P\FՑ< ^,ߎjWnSWC]^>D`XyL^b Aۀ^=?\q*Q/F& Jq䜨ͳɕnS=y+y +U>3YF6U*z@tݵY3E}Ra (S]nueZ/F\}F-:Zʷ`=+:n.7xpzYt2j  rp\^C{Y!"&,d3C2 Og;-,0ŌTzzDmfn[|N~P|z25L%v>D!_ iRb>,RΛ]/b{ggۙ@B&sT<(:lUɟ?SgVY<1rdjT!(c1)܎0?0L\,/f3jAfcێۓ52![J-; 0g}a&-J*S`Xڳc1睆6 ֬>(ver $|~5\`NkѻƛRb{Ư^En Ypīڸ\ck}V"Q C.pw.E7Z6V"%/`Ӻ .U:W]fzJ2I܍HȽ}~RKD3gf7x'fKĈƄx=tl} _\Y 9DFp7:8Àq!ʧdę~x=3 *f4Pci :X lY :=QNfM@>)+ 6b:}cM5~IX0́8fۛ}{ֱ˔)+)k KhwhiX| 8cيq-!~^rCF7zeQ>Ś@Ɨ;?FNas+?SFLi\Q%иI眠P9 jG %y ~8WmW>著nYZ4Tzf44l9 lͳǔti*aS 1HyeW;\Uo%qG낀εb>P`߲OOHgL tVb!:/ D[*PٛE kQ90b첺i^"!٫[A *~{PzeሡhQYjٔ F2& t2zo" 1}8JThaE;\MNtKJTF{36mh H Dvh%-Y_e$@gl# (t1Gda_<'w C^wjb$c+yۯIp!3D']}k: 5JDFeXJpbMNV^`?i겑}U7?erbnD|@oM/?;F ^ qi՟}wp"? m.6yО42;eWޒ*A$g~@:K? Pד$]ޙ"m u'VؘM@zc7/C:nA>,b>hju2xV.ڪ HG.CIکBBRTCTLkekC^?Zf5&ˣr;]kHlh[I[׷2+; í-[%|d|KS+`]h\7f}aJ$}^grtJATÝس$\*58})L q쬶46O {(n7?4^st!tt jlx HХdf$]Kaɂ w(xi'Ku|LJ*ұ "B]6n&~-4 6ZЭB=mkB˭~KENU JY?t(Ov*Q3-ql}[t5-Vg9oq/K +寎?e=-ߦjY5Cݑ20WNB*qrrHj$-[K+V@ ǿS"83Xrs$@FXNw50 ѶGo{=aM Q}#qr^'lp &%7pY4)1HZk }qy: ):B.1 a4a$p=^]"sEP1.pBK Ў]DD^c*wH`vT"]H[Y}r%s35-ViԊ2&2CKN2푉r/; 9y]PUๆ9L3Lv1UޜRdžk͂= BH7̓}1[Sx%OӘ1CTTznMoHR(L1PuӨ̯`fصr|QsluEObJ\`/t8_f%q=Nz̒Ze ko^] ~xrD4 ՚4VG"{t.DFWJ, ,Z2@_VK; 큵>S$!lү` k\ΙثQ^RTIU.k61p5FW;Z>GOq:YZ!QTiϊLd.C;E&Z`Utbp<נ@j_ٱjl_T_^hݤzQ'׶ʤ &zl-I;\hcaZ;nz#TE={2j$I$"ѓDb27 y:VhFCдg@]|zrX_;ƛ]P&ٝxmIT҉uP:r|2irTz4Wp=_PK5Y'<=)V&/EfacDH+>Srր-9,]=؂Umu[YШ8-CU3ɎX+ħr6v%)F٨Xym@ .S4=cdTgV7[%_)ky&Ugƺ ɻhLf9y9oq] ҝ%6T"im@hyN3}"??ʐ8^AbɡUGj(2j8e#+3 AxvUa*zh{<ى| g϶d$,KT_\/Ouv}d|r;j}"4i!hPEP 6I?>usMYȴ|0 Tc4?;{w_ [լr\W=$єEf/tH6ވǸ UMa1ngۨbD Taod,6#6[ sllmP3% Wҟ C9/I!Ч.Rx}V6;py\9vv{|cAoƙ1vr0*OG8P F°ԧUz#ipbT.("O}3ڲM4Vg>#i nn')]{<~S7"}~yb/0tNBkB_1N Jl77Qg_ ~…ӏy-FoL]{N%EtOUk79>(> PX c9NUڹ{a[q+]L) I]yJYVM(˸Tu;V[- ?m#<-J S; pDdFL.U{1XcIЄ` ڐR%/utCsn;}ʀY{1Q+)68 uC;&_oZXwp冩8o +"ř p*W#8 u_'+UY:FMP2!5utO~VSdU'r=K$G!7ITq,,`agWm̿뵺i@|:={tUX찛"^'@{ˑv݃J&4ț_̚eIGH0+> zZɤ9Y>t3QM=[ M 4Uzq% SOK~ouuQ`T(ڶR)yLJSy]~D,A[1%םLGoFb@UJ: p &݌. ơ͐N>v4q/K1l qR\﹚UTR\''TI-e-yo 4čm|ͮ@43;ϢRź*,ym ݝy 60(GSL󵳥_*D><5>0ʂZ=};W~9YtQi!`<7,C=NzW)GJc Hג6qH N+o"v_Y 1]DO)}w"*ˊj6+(߶rC%MOSe(0 y͑q'L aKBl-y3ێҝ-;@Avl "L0S*ٌ$U ! ]Ro $$vKbBt}P|.RcVU܈Ħ˦j c{]'ҹSAr̵"pZ&}0e"|\h?C:ts"E p=$]Q> g5ݦ! @!R;|Eʆ*5`nϒz4k y):myHoE;2W[9Ύ|@9`l"?3|7J]ԕ^ 4آa}t}H%m I,)RZYJ own)#QכBGrIU]?,aNWr;(zfpy͓m'#|uyUE6=)Z~;_JlHȹ0U NSE02<μ@jɸ%7@_16e6@?/ 6XT* 2̜}h}?Xl:BTteG1:%Xև,< h.ѿ.헨,pzaCUmt0Y2#"˸h4F :jV]s!Ad0ԮLlb#q(''pSR'jI.}r⎰y]= Eڼ҄b4oًR%K#KldB)%U+l䘊Q?!o|{XPЕ!eX1gK_6r>ר4|[9+: *7m<= ?f fByvnQK D1sf dzf6p̳uͯ8ߞc!Kh-DvWI'4ijxI՚KgHM-0紉&c WҖ(={os Wj (˔+GNP9&=-Py usZTLhՋERGsA *C_BHtG3Ȩ5$@ $;!A{Hp$hs r~z S<?r{ [g6*'g;,vWdS;;2eD#{`T#v9덋q\53 l~.Wp~$wuoGj&$&›g練YnT^d`bz+pJ93 eKy!m䪄̇ѐܲ-TbPD l|۬IO_TEκ!YzQ{8=?y"%w_|1Ւ!>CCm43wS Uu0$)j@ WbX\!T@.qnTȏV-A*W|t="!E'іB̗YQsxYj J`*ŏ!vgu)%i"/9Dž.6 S굻ϣ_9ڌV cʱ,NXיGlyJ'9SC`LBd2lJ'Ye=t㥅:.  $k4Im)Tk̾_u9~2a!-FrzHwg8M]A++MX3zuж"uΆ y%n(v9Lqi'BXW">B0SA H%ڃxf+ 6"+jI-槸5.qZr`=`u3/K VF[=GF]?ݤ *'{{[|lҒ=*޻u{MVs-fQW}ž~Sʿ.yQs;}s52ݍeZۨҽ8gO.4:{xwK OED%4ԟ-egLe`UP'Hny B/GAJܾmU4cq\B7J ǽgce=Saj"XT~9>qHҍȥZOαb,L7 | is100D"#x1r=WmA `֔_V+۞hwS5A(, ZI?mϸˬEϔ3jK=݈Nm6JkjADFTmy DDs$ OvɌq]'7CsO-s$ 嬖Jk@'VԍomUC[Jqo%wH-ƿ#@w^β2IÕߦ9GgmY; 5G 6T#>uexa$ڗ#0IcQ:8WP9>DP0P3-L_5sբX要p;*C)H:$d: V7^NaWH# 4Nc}lBe!1%VP9VAֈ _|T4ĪM[D~rp+]KCC\lN *!dZ,ZX6RiTl0LU׻1BU!ѬijAN~yv~u`8URcͶݵXڿʭӻU[~yld>oJv""c^%fgEfF0@ @& g gv J.H9o5 g$@N *r|]Nիk￸$uf+`@gD F;eP GrgŒKT,2QQ W<6n4bF2#7-!u"oG tmM3 6Oe]jNi;4\` vI %_laVp4xR 6e3x Q+F{[Bũɦ+k-5x-9Wz7tkWd„du!3W![;%H8AY[9c)$HH2+_4Ȝ v柑z˴_NouYkxϝixbMW$8tmo֕؝R^],v=]ft/?ˋfA"TRKY5Ψ^sLҟ*uP+r>ܬTrnO υrDKyG&hd!'Z&ý|F6}[Z>Aa;+mo# Hb'3һ)k7Nd"V՜ĵgP24:Wjl+l oKFQ4+ʋ}5 wbHq^sKҍc= `(Zw:fpz 9pfd5FX+GT^5b4:kLH C俀&t8QȄJ;ջ PwxC-v<ddI5&c{Hc]řoxQT c˕owuQղYnThYڑ$5 F P8|dc͡Ѐ-hqG{Ll8X'F!2jI@7?9Z)ځUfG`RGKrOQ7E G7R{g+n.n33_'%ٝ.o°zBgsq354WG+ߐ?lðV@C W3a,BN.UfNR Cnh/oF;ȝt׵G݃>DxlM-̀IZ:ldzѲ1|XLIa+϶sVB0ʷ ʏ{B| X("EfHw*Z>ѯ(9g 5^ݑyl442ZTp} ڭz|砞wkDEwU0mTkɵL|VB bn͎e˻I v}$ ӅХZNiZaYT+%Rؿ x4i2KDLvVܾBU: QьK"[_-%T[= ܛ`QReb$3>zL僅~KwAdլ6Ph$㴢kDoޢ+QÕů#s|tF"@EEDKDp_;)iDIa"H9=dHæX81(/5`. :҆ H/*PD J|du DsC+j޿#TNGX!e떷ֽpнxcP3>bf/' ч5Ȫ5f*rD-7J6 (A=GD81!2bX *X G?.>զ}R1AzZi\WBHw'ks {$MFE`L&յV9 _fhnh L<l`KE&{]qc mFɣd)#!йǟ$PIR:_b, vz-f /bڂ'vŝŬDjCilE<,_KRڕ41I(H; AdR6dR>fq؉ ĎmyanMmOIm.<}\NJo !Q\ zw_B1T|V/\R\Dp||r&=T`\Nj>MHq.NΝ5Ņ-j2E8&y~2Jyc}k~!O qv6 q$ͪyfVSh 0{L3HԢ.O|(v)-,Ρu;y!=ePۂ esMb5ߏjupg[X>2LNxba1"y* :t !O"Ы; v IJ7ezܵvbz6X N3+tz86@c]!8fAǔ^ѽOf qNS6S9(R_=C?b+sFˌhnZu巐r=)ոTS*IvdũBpȎNanl>-2aEy@w >butSUn%K'z $T dSyrS |q ܝ&ɼuS ># I9),:Pf&{[V$7:|65G0߮Ce*JKr쨙L |TȂÐ2g"`0#kR}7P1)${bIe5br y\sUϷ=9Ч%oۑh\ Sz%3cdvNs eO;G%s*jESqc.gV5sδیHPEBHueKXi{RL;wg-]ΐSz! ֺsV|"۰i9#H3?Y\S[FdODD=nU(Du.0,9ۊ| h@ 婰<ژeYPE~Y[i1X:VpX=q }$%AIBJrbÍJ"CZ068a)5<#>(<"Nߑ܂w>ͮ xBaQ(!Q*D}o) $c; t m)1_NQ'o1T_6H%$-:p*C[3$z(  7v *PXw=޴Ϟ٬1R@uu=@wƓ /XHΌvYbS*m ޏuܗ|gn$:R< AM g\k-HSº1C)g9Ť}RѽNqqTukx^T !bK~Z'[!F%8d2zVPh}F&hĹxR/Li.5rC9?{Ƀyu tG˥jrکciMwjnG䒴!엡ծp0!.7 ] 8 r>*fGcsKpz+8QS^`ncX-aڂ(qttsGJ;EQbnqA֌ɛǏC+k捻FMBw`<`{FDZF+R:q;>䠴wl1@6zxփ $rnFK ! o kɨ8Y3'ro[;@ ~C*gP`3D3R/_:s 9e:+4`:]]/ 3nRWCsX>;N`_s|ev#yޙpd)Sy^J? CHk Nz좄m,K@mv@rEĩ 6mŰxX34EB3C)+]4=P;c ڶJ{Y't,I>e i$z;?=H7y{ F(: uf:"Q8xP2;N7!`"hle =ykgZ}@:#C,l09&[۸|BH n%(ZmmS r磑.JT@j! H.~³jFU S88Ȝ ٛ2sns#ٯؠa=pRi>o_^YeQ% PD>AmUCRg  XPa6WR-eá3nkޔ52G>61ݬ?o)vLŒ(Jt mv𾗔vof ?υ. w ~0=ȰK^A.l¿1 +DFUb{Rvew%rVY إ4fv;@$C{?L*/(G;ìoQz#*|#I >weW[U&%OIаf1!!:aPPo\t6TDhRn1PhP_~)#-'NO@7Lz:jE0薟TY%-O ࢆ(?^BdA%[d/QH*h'.Mk<#*[N7 K:tOi>-y Sj3+OZvswo,QȯG(4 &{ rEY񏎙rH,-d(KXs)f]=1p(O/rtC$YqY]@8f22 lf VEZwr0++3{$/x!fm[B#:fz%d^43I4@v}c/T9n~QxD,LOWnx-y1Ĉǿ: |mXݶw,\茪!>D(?|o/Oz:CS;G{':bOȲበRbҾ6#gy7aA'G2_Α8KOv?X_k}xc/ς)k4f[_?83ͣ s?HNxH~ [>݋7\C,}O^Z".ZyǫiE/ 'rx,kyVCw|j^5tv - '+Nufl[2ƙf8IsIퟵ}q#jZEj4ogap^N.xAbj"|~wDYZ&[hn@%-a?=cZgi.)|2H5:D9s#2_Eϱ}J BMa%H:;]d 8^Em/D-%Pxz~Q|K<SSQT]v?#E #y-*R0Csl9ȸuf?;"!ĝf4pÝ1qS?V {hCoSxqfm@oYa?c;t ΔTe~e -FÒ}-Xr\2 6$i)8@]4R( 0]B ~FFBc^\= rt\ Z])'wb#N)ѽmiK ̩OJwx=<%6kX/+mR,9a " 49NUJ]/500/׻.4W86$C# 9|,.^d0T G^r_Nя54kܙB 4%a éUe4?\,wY70_3!-.sq/owc0_Y> fScF{&-ĥkJ: >9/{.'㢵C6! ہZ\2F!ͭ ._qT}Q CG6G:mJ:]̗d9ɁJj0Bobj{(n8טearcO83Ygx JGҊv߮[:aDY3&9J*!{#JHZ^t'1zL1#t`tkmXa)LMLA+S2*bܒK:w`*n+J 'h8bkbHOdPijy zY.Q0eEX\9fGC 1Qܹ "mnhEk8>`(@lh Ҩ'|XXQx-na5^!lG@~Z.Cty:Ht8Jv!aVȯ+w,&28өW_[_ aBim=I1%͖m!|]Q᝵. *6ի7'.N6և AnB#׫7cz=$.LD@&(D9 Ә}-.[G hQz4&3³++f]1++[MW ` Ղc/F8/֫}DZ k*È[_Do`=iv5(X::CD k?Be`1&Jd#?[ç%J)V+Q׮tAf۽H}>B}_,ǮЗc9Vpz) ڕ-vV$w(*ݰ4G8`gwUifЊJR%"L*vdx%g6WJ|IDz6cWALp z3(".KCS1XLW&aR#ؒȹ>NFgmd.'0"7s2,i8赘Ew UƬgR$ϊ#3wG̩R<=9|kלm/\PT/4t31k: w"y<7=b ݇OhkHԟ 2gf՞?)&䚆ә5U'0m(\OgrF?r_\d#]^Ayd6T +r>W2^Ha{, RTĨG++KG;h.UWEeĽJ`+!z{3CUǨo }o;~pu7VG4XkcfH"y N+rS[}vgQ@cD@ɁẇCNRSm%+FT"$(?-:bg |ry:}|5ȵS[p.g%t;T~faQ\hR&jǎȭhkK}}3{T5^Gݦ5EJN(u̎&9-&C_qoxc3R]K.up1-Ag7ߤ>i_eMw$Acp$,F_7 Qo!uyٕAWsVER>HHvdGd3VnVaz)Q/E8\@o?rza!3m5u - RcۆdK5vEoS q6.aF@$ x u-UNKq8.zn#F<#73(8"Q1.,H "˂ǠmU}კZCAKZ\|;4=5QemKbXT/w_AZ"}Mh'&$5oL>WMD 0FtD}i8S'6 Yo\GfvrxP~%!Οw#Nr6ٞax{Fݼϫ d48ZN> )7U"E:92E2N_]-_9;҉Ls1_eח= l+%$H=aо^#vw?DZB'wIoʓQwc(Ezi :Ғ"ePBf߈;cO:ɤˮpsOΪ,w#3t{ܥ!lGPU4QdCi&gblk"j@QIyRGc>~^FYɸJ7L3<l2EF,|z-0 p᤮GOj;6S֠i _BsG][v9c, ^/nuoH ڇ S3bom6/s!H cPq@:Ēk01[P oe_BX}&߭LIȆmSUq0*aJia!PKHu 3LK"zgdsGD9Ω`_6{%pu?D^XFƥ̊2y^. pVe@R!H@~ z9w)W4J{<3\-I12e>)'BH"d;es:s˓B."jRĉkIٸf>LD[8 &@1 JU8Nwu3ז6^ߥf TS*TDDL7n2m^Z3#(VHuie;F OeW#2.O N*5yͥ<*NhYo@iҺUz8?,*JZ5vjGHR^pM]Fzщki-@pcb[MERS|]C]\8ۢ& `f aT fBukHډp?ǥa[TA+ahեy?Ƽe.+Jbpttd >r zcL Sg8*dc3H[(%qa_i9߀_c0qe_$)Ao ;nB7IRӄcH \\ƍc AG%5F-뻅T6Yɜ;o|GiC8rz9dρq8{O#pTeO392ƇZ$ L(2. KPL}&w8JcY1| d`ËF[#p ^ڝGH&lŭo$k7ې.y|<|> %GI^gEv"JF#Cf o\v>, gҼ{)Pg#*AU:p-:7J"cϑH,;.s7신x |W@GZQw1Kgڽ̢I*=f (`YmWy}AMgbP4DaJQ^wV-#1 P+d KX{9;YHmŔkC!َ)\\(O;hԼIT9o,t4+E/Ɏ_r1ƵizhLT׿'PHCU߮8'<g>KĜM``gk^e_1>|Y"݀ߨ,ǎ{QӆxEşMd_Itƴ8Y0/J?߁6rxȡ=m4 (ʬ^CQ47 6A<%9*Άq8,Uuԗ*2]{4Mс2Vq + ٷW"ⅼ[*T;~g9g֩S|^`.rd !'s⁦Ht |vwŸd;j|*IΓ£Q<$w\2. |40G]etyִOA zGsjvgQnS?ȾF1*O1&qf+^R݈ڱ #H@"<4!#Y )e#UhAFe%Shޯޝxmc&"G{&Zc<{I^3Jgw@f3gnuG'{FbG_';iRm',|s'jdGgdB vn'nVenÄ||څJ*O{ΝYuo#l/FޫwdY8/pӄޏ7/MÅ'ư#"/F5 D+ϳ ǛEM/6HhGNUodz$N~h ]Kn(EoQ.r4r+3|fp2ꏾ[`pXyuqXԀU\7Yh:[&{ғ4`LSJ*x1 =. 'طW_.|H 瘂ѽdYp-뷶S #ǃi j^܃uwDڝ ^,AK!#,\t"4dϵ>@ ij"DҢy۬kHq,duLױ];SVAtR,Pwv> P59|T?D&v +c|ϔivX`QyZN +Ջ Epi\?MwJ>GÂn8x쭴ysM+ pMd;lďyc}Չ>!z;$sf.K8ggN+}W(nxyf$cP?eGWk|#=(pOq z>{BV3m{r@QKZaVi|.N?z}6HTޑKYV)IwOm)OwF+ ps6j5@2 f՝,Os>1o@y%ӌK[BF* 5>A<Bo#9~ӿ;dY˯z#Dj32a)h\F*w>[^7?Nzfj2Z2XWMb6=bl\v8N)+5л,͙^wv-)Zh j2o$QfWxXzLxqlpn̎ȴ_jBGqfz?ܰ T_Bt$PDM,5S} ew ƶ| cmSS?T)uE׃/f`(Mi> ?g7^lN!!(BP,&-yMQ䐃2GCPT:E*]ȸ}5PȞ%{].:4\%H Y-bO%/'HȏwR`MyF뉑MV.hiV/>),fJb6U}yat13;޿$Vw5fRtU$\X+V*U2L2( |R\ϖtch #qউI۠P f`Gr;R]pNj}Pnf.^C261/Pq\_˹T{ _`mH ݔtPϙXLp1!inI)*ȳ-r =:霊R*_6 xψ-^+Dt/SoP/-^߈`yqՖ#;o9_O-oxJ!ag,$IZ;`q\%'ݱaiځ/V4_RU~zFrG jLf~jM!˫SU(F^%}4a,(91ԞEZ۹cs CZmbOR.Uf%#>]4;@~ҀѶݷ"j ^!qaMp tWOzO!U1^oSP]8H1#ވwfip!9%F2Eɚt}˶ID)DoyGVx>>!Qt }^$F3ޤ AJ\|je Ʈ{DebR3{F B+A,wD7+QH"l5SP(_w͊EA,)Prm5_ևlf4+KݺMUA߽<a||svBqѠ- U4waO:~Y+ eܪ;4{kBȂ7'Ss #eL >wzgX0,)xOӯI};!#AG})  ]AhZvh4SOTnZiRB4pW>l"4AL/E?@8%b>N&)]K w|T$\* +h;ps;-gܠ{rOsD׭Lg3_>w' ń.3J8]uhKE)9+,s u6k!1? ^Ў-fyx[{v6n8G% tpV[6I rѽxQ ?W)g<5&a\5 34Ŷ\vtt,"S]s'E*l"׊Q1|DJ Ж2`oOo#@E]Jse}qnt(>iAU7u}_!qst ;sJ 9T|dA6@zW kVfq+m/M㴛`sZ%hW"L>Ȣ}=5zF$ Z.@`ȕ~9l*ڊ3QՑ#/NTN'Z}e~m%hh );Ǒ6oRY"1`6~M~x:dBqB&OiWZ@M=a%Y-^kql3m$8Wbeׅw~PtoCȝe:"5 Ψ=B27"!*/K؍ǁ%9"RU¢(OBgib׸σ~?{)Z ~/lפ=HD@N}7Q?$eu0VAAiDc!9qa;VEy`&</`O Th#W#0;]GUjh 3 -=GUaXʟcyʁFӏM}iܣiuq |it=4U.kgz\ ddO-ڨ\FOTR8OJN1)v(r[DaCwg a)oB+%uY/_jmx'9Cl OeOy݊_s逕] xYrxF/w{?()bj(#fYu!O">QqU7ڦ:X۞*FtM{ʣ?Z GjktIcKt-c`|Xu&shu w yE*WW )ʫ\tNZL=Ԍ!?7h[wĝ42+ﯭO{hk0p#腮o׉t2=~cwC*AU(h/LZ84,n;Cӻ<í rӵWBm?xef|g1xrDqMr6#˹)5pJdzѧ^IRm\(RIYծa* L%oS 2*4}l+G@ {ސk[@tƤؐH }k[q h%~^ *`xr`a*at:5j'ڛ_ ǖ ^_;mNOK[_(pPG;SGTr#OTF]ه"H]0wBu'029iFF ?#]k+fdB2e{Gس́ z((GMZ\!I- ɩ洹vz(UKTh]}c5 ~$˜dS""={^W()uv"\ް2DT@ZÙ/(lJyѦS[i%;ټ<W ԰Aq+9ȱY73J9Deډȩ pxVZC<XB$^lgLf$2A/ygk*uDf[$eC W3Ӓ[.)>$M5UyT{~J t,_:7 Fќꔴwl^CVwF'.Gg̏o!ȢnhtnGQ$݁d Hcx0<EP=IX^_ bRJ.w!@J^JL[Tvw/,lܤŬxr8KVXx-8#ZelYs"Yu 'jR]bjXC/۽Crʹ i3#s][D~#ef5@*>{zjh'+# ('xC:7½kɛB3Tmo3R.HȆu)"|9fTd^,)3c+ChY8KJ``^H*KlhLڀG=[8]c^"ڑgF=5,ʈ/?I> m=cSB{.vw!`< 3eN;(2/%oRkv!%a$bN]o9րPC($N4rk0=1v i..ؖa& lNt8OS~&=$uDxF?)*k7sfדQ=q&_W½RI~fCfA*ӧPkFf *i1{ȹOF (D0i-:蔀Dn{[vU0?औ_%v"]wd =9 L^w-<*,Y}:\s]H^?F׿^*"'Q1n\(x$(?[/NwψCd6 VB&g{a>OrPyftE iTՐ,3:vX\ >U: .T1QDhO)Gio7J3Q+BJ>FIRT@O 6Xlqi,3ѣs(NK@*#t/gӜs7T[C.r񤋃XַRϐ ԇ8݉vm K<˸f+tPMj?^)9Q2wZ*[ o俑,u[yY&N`γBoVYd䳠•A" `g"ծ1+{VaW5Q0Bc욣-lzbJ#,dP'8x9wՇ Fo>Ly'[;qN&2mŃSE1A>"dnX۽갥e|Wh4['B$rKi#$G1I m*J~`z.; }\48Ilk%>)cf 7vR/o^`H9(m0t,,C iVGn_#{] :]UXJ_JTHS670{)ƙŤ~ET#[Ǵ:맧&Fe(E`B X g8:;)V`Qopawߝ&F%V^WV(_U}-5БM.F4`'AMVoHߑb4sydR"y%Z1CАݗqEF:$Ѹ6'G3i,h\+Z+j"pxQ )<#|a~Y? WsЫQ] Zy 19yEwfp+7'9r6jzy5C-jH3#Fޒ;q"t'Ld_dQ/ѡЧ\XQyUV2B x#oz}B1!K7c_h[n)%K$f9vU t=rbUPO-:?B8 "x}0SL9z?m5chRTV"wiwC'p<$k]yDmz!>]y>BV2Rtq/^yfTMc@G{;=JϬVĄ-A ub>fm2U/, /=CS rCЛ6=\Wܘ@&D t0jm3ȒՋOÍ(s>t0}&yS( Ec<1Q\m[MR+J .o븐Z&߉G?(¦pׄ~mx])cf7 Wc.ӕ>^b$(ÍȨLdOW8:\ 5ɔt&YT+ iα]n;Y-=&93_NC>01y+Gx@46y3xBrȶKNG ‚ϘtoR 0M;unm伖C/ߪZ^{05F-1OςIǴ;Y& 9JF r9C [ $(W̔Lwn|1L<0a5GY ^2]|àz?$6~dž/)d;5v >@HPO_Hr|j1 APnMwR׬N1b:x`[/C.~{#1خ^4mZ DaVȔr'ȋ qQv1[1 r|g$5vY9lagX[G#dG\Z'_hT7Q ?PYC9#ɚn>HJ;xN*Շ^7ŃMXx&Sj/{ Kܤ6LAhP܃ThSD2l/"L+n>caT||F pk] d)շ:'5ɤRV$yQAŌ&"CӪ.J\g3XoJ9bz@,?*KP|m9UKcվkVJtTζ4͕%i/{WoC7 V>_-ԔRdW:z7a˲MJ+X56br|ȸ6YۃF5pD$G} WF li# 81-.ˈ>ύ#XLvL2kO!QHߩGӪgMDYOjIv@=cֻlIK"\`= F' m'T-7Cl'X?zvƌcu8̿Ŋey[&: w|S'RXk`I+kogN_!y/YKyA$kRv 2/_yPed9nNeY.6aOzcql)[O O `}|.0#M2fbI'\i{El ?#[c}Yl:rFz{7IK=vBRh(6+7g[V! URL6"z>' iNQ1`>4s"1C>U|L %cvcv͙ġ'Kl,E6),Ҧl[b@0kpP ji%':07WM D e>8UiWjQψ|l2^oN;{ KlcL8*u+ns b caEVy#"q"k*2R|yeЇ:*,F3YCXNt+g΍#$CF.`|eI<'GrTAbnp(0jA-e1 l|pɊQ*r*fLb^Z3ǿr˵nviBN[C$P1r T~TujebI9r qX+ 2e,pȋĈ!Wa2xUgmjZ>X}`'S@Kf+x[$^f|&by#"Wgt!ɿM|}nVmN1F }2pX3 WaCWMqpr8} #! uQ'/R4 }wB ̼ q})q H9P.T,4H8D S |/5iхe au8 SjGKr9!Y|ڰK2,^fhP>Cnuɐr9Dẽz_ِy k4LWM@X(gŊpԷ/Dk4'Nrک659Ȉūn}m쎄+~APYM! RCn• x)_8އ{-0ߝ垟AUQB ^RIF$[ s9>qhnu%EѨʺb5ð)IsgM`FG,z*)^_Js; 8W^U ][PUr.6{{U/[ ݯw%gAVVOoEyKmy٫aNU aEmEcs5`ѐ5}O~Łl@ ;w<$6\FxG%3KȝŃP .bU= w6S,uTY xDW &Em, KW3Kz?uWtT C](*KČ뿡ےlO3Ou&^1sz|I_X5wٔW$`B|EW{+K*R%L'ekvw>X߭)V捜hˤ'9dl9N={}ӑRV|qӾ'zU[W b?A]r^g>< V[¬PmƴU"]6I|'|m }θٕ(W'mSIfGZ1!R(?\1sPbKth.V WbB 3hk>"1  ovՀ0-k.\coq \jJ:u.+E^cNI'tԕٯ׌I@G VI; ̸dOI֒Ѿ\>p+'3P[})Ba=!h`;,=!DR| N|jUq=PʖSh&GE)p(0 'GKQZ<@|bd6 a7Mǫ ޝc9>'J3E=pq >[sKuul$SyKJrlBԥfNħN+zSO`v+ T9oOZM*AJł𶷶eZ"õޅQqmtPԛpw2 &9L]2kBd0uLh1 R;"+Vv[X"*NԉSix[p>fc FXG%EpE1:AGY7/ޱ$>>*sY"3 |ֱ@HD)HۗHOe.m[/aS萳㸺z_L\2["dGuXa|HZBșP [ VFV`9c/l2\Ӧ19NOq/֙10HX aɍv+ ͤmTxF+=0;pej$v(Wgh> rCPL%vY]mzw:6L_'qg?:H.\r[w/JS֘\('gҙ7dl-#GO!,`||29 KL{஋SzAU'DA/#~Ut双a\UY9/5'2'q_]@xo?w,Lhh<\x")őm|.qY0HP%ˡ `+" JڴoX_Abvg~ `_KԘc\;<+|Jb8BGdeNH8E^EP/UPn4N@o5CQd(Df7Z<*|Ef}z D[ p!AƟ_:&>X\F¶Y{AOX.g7(楱@M"l ZpmDa=ci$Sp(&"BpwM](luX3hZc<&}!vMUC^$zJ Za|5!?1qOYy7f  Et@SejnG 7MWv1pZƫX~ҀUM[ڼzy<dMCfI~,ΜV mAl盈pL19`Pk:?A[Cv/~hҴ ܟAB:HeV_W/#YCϬӜdhj|CM0 1eDi_*"iE֟ hR00@P8AZnly'BZlZHCZ9t=?QW!Cв.7Xov7`ºŒF4W3,= bO(C=>heC~ĸh.ʼi-:>)]ߺt)I?kEXxTw"5z1m!}=}Kw9H1c,;Ȼ~FXNUd :XgᲜoyd]`rӧi_3~0(%;G~jice+JM\y -5wfCʆRUWi1%T^4 R q{G%@z-_OZ' 7I6tCD""b -Fb=c{݀n~$h8 wx/~wrO9Ӥd:MRG{[c9JiFj]w؆ Yrf5X',`*-ϔ'b,ԅzt˰r")0blY8ۧb6Q1Ƨ3/;#BDb!q$.qSF~X"KS4VJh#RM"f8?pzz論{ #E4"ÏT^|xp"'5޻R(Յ4~ٵORfTճ7㌼AM{4Xc/Q*2|TIE-b\Fr~ҥG?\~ ӼJ.VEؠ/ra)@7 ޢ >1EQ_:;,l*o pKZ}h0S*//xt>k&"­W{jď9<՞zaOUS-&FK gd=99\|23X0]zB/WlY%tR#N0VjEĎӘCbWr%~b(217?o)WS]alsZ0"}9hy S>lKFỆ.8k%vf^bqǑ[Y-?w ;t%06 v+%p"*_h6n?M[R-'h^Q b܈>$bgOMȴQ ´DSon&-ԁ?6̡ڮY}4V~!h. i(w&,^Rf(wS383mL1P»<)jGby4gWi~o2ՔrX9]2' oɌxum0J 7kHٌaXe8IYwKfxCrN2,6R#nb1#)J%RsZW排 ?6;!g(J<1[#s:߀ J×\r NT~#֖xՃ;W|aJXT=CZJ|Kƃ2W\2Y=0BT3wnz'{a#d8~6!_.*pu=uo:Kُ1>(v%xDC>l<#*Xm_:$Xݖl[ :bTហ +}[YDMXPsNw+8lһol!DXjNs<<lji#*~ %}jz}gMouQwz+:K%n)cl?J<n"E{Vjd~_eq$^_r,}lH{=+NzSB/FM!?GG&$a^?ul 쓷\/ b9iuQ^6c= t%f k:UIH~fpS#3JqMEnֵ- Nfa=I%nd$\zB=HU';dG_,X.n 1` +eV>:%Cygv >'(PQ2 Xѵ9F@ _; idf ֐snꓒw5N2𐻇q@/~ڱ ;kNv9fR@j&JlF=WPT-ȯLwBRiG(GUz˞hXzI GԙI,c.\A߾NFS%.2CM~ qd ه S'2.=P:Yl={0BTƒ+0GS^vL}K}ḷq7OxxwcE+kK6?^j<%+Z]QS`JwbEeM\cs;iy-5}s:O0[!}#b[ܶ)JhXS Q"x)I 70ۡG3,^y0Ȇ U_aplF$Vć=C Y]gyK'~;%ó? ]FGSUCTp 8f8`[[9^ I&a! l$BeڰJC!KL72N3~GRaz # Wfkd,= (--ʭ??3EAz{6d;i%dMJ#jJPaFX&je(/3ikQ#O/uOX#ٵ ceJv(hx~Uʼt-g̑Xм6_c<Ru™oOQx1T呯+fά#qd}7y(_g%{DOMtU<{ZBjy `4j:1XeĶ/&+)m;P\.:6cR-bWkƊ0V&;-IѳWkGppPLO_M037ZϕEG#5t1?]vV*n6yk@saɻ2ָVOc=>C,`WV8~fitp?xN{3ds`R/B?rktjHb #;&Q/]bDxڙltGjg0$ܜG% M4$pX (.'rN2pa/HKϱ]E]ŒCYU3W Ol)lۏa+:6!z5[d$=;,V;g~4(E`ez{2U+Os*\7X˯,Df&8e uȶ ͺ׺=|#2Q9- ifz\{^[&ݺAL~Z3nVTTлTcйUbQ HB,  xD{՜H͗ةqΜao@RB#Q#T[tʔ h,NDX#.)v*:b~:blط`s#DCRŰb[_W'sbrd'${z Gܐ;ϐJu9EQdrZg0]f#қ>oHp낔f?3JA:ZgYC O5\}c"O!bkhL f3r.\|uD/B!U-\a6FMS;kn|o7 flT}L:RuCQ.^.q zڌ '3yta[CJ E%ЪE`T;aO^$/jw>X N L6 #+)UUz?$O-pq/!&| !z~S!@Lo-%(XTSUyTR>/H*lEӕ%>nX.J[R!(j]{<u<:M-餶随Yo`b~RL\) [;?i9팖׿؍,,k^[|T+3K[& ʺ@ci Z_/M*bcPӂ3ljqÌtᧀ!z܁ؕXa굘(ϡ=Cslc_G6(ȉq@1Jl.a<(߳!F-(txb<})9{yzޯ$X\֥i?te[J$.`?]n ¼ a-/,LJO?X'OjUGϱ T/K1/DPIJq 4[۩u16o.J [{en7NlղZI1:fU~(7yB@ d~α/?h[ ?@zmEO^x)ZZ$bh_oIj -M1=aƛCk8ȇ=U=LPTSFؤubp-#k`T{@حI"G%~I 8*LpgɊ4@R=9Kie= %9fpXV/BN0 >ud ͐8UW j+6fCΊdj[JBSeH տ.LfWCP$yv 5D?uA4'} IUXQѳ}J_WhN|@Rk{3A+{O&,NE+flg Z{΀-Dq!DƋl sdm?Nɂx=$T҂Eד`^3N:q\D!HŽ`lcd s4i(ǣ:a0?uO`h+9{?.krNӎpkHPOs*Jc@J_a*x$A% ϲsVExsccd%I=N&x )R Rac63c#lq  ;-6*-܇>VK FPe䳡\d։J;Tny}W~͈]HTG'j֯=/eKQ2%{SkAl4s 4@Bs$5t.iM+fZk^W=65J|{䉃O!}fو9]FvVDk_oXyGZ^}6w7 _}z+u ®P]*WZcDB9QaմX!9\,VN㴮WPm~jy4*Մc5LA~j*`y]efX徻pťX0nʊS2\.|IaԚ3S_!n\~NJOhKnyQy:(%BZ)#^D;^˖wK,(J[Ea/ǒ(WMo\:VYS1huy9A 00 5}seIVy x7U ;k{fԫlpb MR{:|zMJԣxp Lz8Fg6PguF@^%JslWF^-0C]O١\:Dd i{J- lYBH|P"$H p229Y)_ 1G[lj }U{KP+P?} FXj@^7Z2 \HB ;t6ƥ}ûJ:9728GA\yXU63+߮AE5PM^"~ؙNVG+/#D:@P q ]]U8: +t5==)݋ڏ"*B||w>"/dV.( Gð4ۧM%̒6 xCNSl$k8H>?fNMif܋5DsP" bg LR4w33a -+Ts |7'?p"'k4Cp_s*z`]^fafeAKzK1Y# ޴6 nYnfp̋ȸ;HaAH2(?B53@ed@A|>YU{!D.(y7et[U(`|{rapk(MP UwSYg4B-/Z:SQX6xe`&:D?Y⑲uW#J\m!@ *P=1r26*h7[o3sI)*`A FLPM@eTF$,}v+1jpgZp}OD%&y*lV#)'(^Vcʔo!RfNV>!awB!F:P ;ؠREͨLk ܾBL@ƇY="$ˣB}Dw^;ĝBN~I#Еa!{ '3Yyajc!TC*@NL(=BͮLa9~4Na3ܵԒva2WIpJ셃ESA(R ,;FKF05iNp9E -8 aO+T:\$CzHs*!SOɃPް4 g9gXD T {V Z빰kE,3oB `V$vzHTťtx(mKLȐo20I&n[In׬OO 4iq%r<){la|&D<'Ź&WλG 1ق@0<.$Ӫ03h2jGRN^%/Q/k.}=^Q/dJn1DQ\$ݫE\?҉OI6]C| _϶ 󧅏]OjUn|z3MeN|u|SB2tQa',]A {agWw2s.`K7P{Ǒ3PX&C[MqҜoCd2_C>[b9~"O9_c_1r= dT?ۺNa2ࠨq:Fj "D> yU LPM /1Gɶн#D'x{scYlbS`_┼PȪ!4P&G( xPܕUž0xr{ A|ƹ` [\i-P2BeQwVt@;9yFw( qd-Q)݊ }x蔶]JIx\tid)C9sα($uPeɖgYlj4vH{-1>wәTto:Sc 7Zn܆Bм/I,Cn: =tgTt30 \h'FG=\UI kDl据9qK9h0z(PDZwj_h*Ҥ/ A=:/Jb^rȅ"T[vN8}<iIV+|T*[6[kUavOƲcTmiyqDY>Zsee<:;g_KzgK~m 7StȺiiXٺ ;68 RQ7 T@`H Rloz!֮Xd`Bv[GGW<AT5l]><9;PCfHNiK&q-kxXTNQD؈LǍͳy<w;u`<'+%?^7U{W4{?fRhfqJJ- S`SӮS%!Ae!lb\N{{6g}Hܠ%YxElWM|scQ*O$"vua{Z:؛J)[Ah v$ e/ <7uIY5{apREfU3.O|-* 399ڪ}`’!'4f:o꼏jɳ 2MfB.ROLr$rKC3r2=%F JB ,z}PNηd<m>9ɣȤGb?N_{g/(UۚΟopޛ]//L;ASN]e5!)}qTӷOu2|&ة\0n| m PN)[[$ac3K[]瀓0E0(AmǑo4ZfӣΑn{ E@..r6#z0u7|f oD'n]0p a`apm?0Z I|Hsԩ06,1p'GG*$6ϟzQ|W 8dMӭɓ,.9z(imtC)!tK~5I>Irw;(n.H:@R?mwa/V`)/$ϫ)%*V\'ŹYFX/"#t5D{F++d.2#<[y]}3 2ob9stZܮ_T 6սDK=H#H'x0^wv갞ewF.HuƕTIeQ ig8Á3Z1.^>=iE;)C {:]K0K[k3AMVƠUhma}n?s"8h܊rɧ{N%Hw:LMeqiB^K{V+{)ԖY(A{Lx(Ng̟C\ԩjSi_=R=9O&3U((>.O,7_*(yE*c9R@FI)-oXΜ#jd/CQ-7e/L7y LyNN҈!ȏ)^Peqcw_ ⺛ ҌsQQaŠ5:\s J'u*)ʣ},^&.=j@8k0zD"Qm 4621]Π0qWn7~怂㲻Ti8ό[N58QR;z9b}Nk VBdY?hTqiW8 Hb]rf"R~MҋuPaR}\r/L}np'O/I^3Rc%t!*?S~7{IB7 +`H_k:VKSK?c )l/=jrTQ ^=V'qNUy#!P ':4eT2kדD KR$.M]_Z=yCNJt;V͑87abW}*3-xPhCoގ*Y}Z()mI׬d3'%ί<>bAԸ8?c;k9D| XQ94S#"q KFÀ9 (sBj7eL;Fv0w /%LlleaZR bn.EٮATto/`6-JzSgݕ!4׺&PȢiw|]g9^7TL˼/>r=NUa@gq&Qx`lӗ_O7#N,O/zG  0yG:v#SYӝ3QnИa`wv`yR`e?sBRLOnN]l[<$-wzKVsL^+zK2Z,@H;ùT#΍ @O=P֙/pw5i5U\yA5)Aԡ@OAh'R,u}2V=!/E 1+M2`c)뺕Jz':_Z|&3$"B>#>|IGt-Bk}}ry}stiU:op=pl+\D[]2fhS6_]Ԇ?zw*{θjXYujWGCU/i\ %I Cmbu&]"jb*a`;WX |yhdU#URPD*U1b5DV;TC-Mc;ZeY=C&k]yy ViQ w-FQ&eE\.P7PScT_sh, Pm3&Q *($ݲ5IO@V6{xz =S)zR¿窖uTr!̅8(]uG4Bi'`~l8&<}eR3GL_~ڮ5iÖc<ٓ}F&dOc:{!-@K>"%Q4'i)H Vq (f: b8 ̩[ Ԥk. -TIʌ݅^ kI|3(畦T]\bxg(ԴۜywlB1S-rJ 5/IEm%s@~뮞K{)e@tXP2*]*;aH) kG+3umm\8e?;.'IF3U) n )f1DG*m@ ;hph](W?y/ɀ#)FT2YoV) 3 r1u ꏁf&UU u* ɜ&T@e;Gt%73w]A?J`=pՋ$L So3'|~k& u;k)n!p2Jc>[XĿ[3\LB8D|w}l@gy Dž/~A\]T +1_"ʓr)J#\mʄqf\/c&QR$ ^}@ O)#Eje>^-M5OןHFta&`=TZ;B,;ꭐ)[񡊕ny~U͈qT<{!ӥ>'K(-z$i\Yo{̀/쓊abTmU3GZ~@[( _r))xKUXUG2(z\ RT28Φⲗ a e^N Ps8v!dy8zj3PЅ1BKt&P']wp_ *M K~F5OLmUHiT L 4/=R[BDln_`@DWK"dH 4#W׿7Zn?A P-z&W^CWsۍd:Ck_U,âuviNjh䊸Ccg.uQ()._Xe_-!)I`_Fk[R8 K (O҆Wg7s |Rڠ̭^4 Kae&D@XXyD}@ѳ1I>'l R@M y:ZK/O;W{E+m-ؐ>TĀ-af<T#1|%P2%l&SvRTZZA6temMYa00{͖j9EZ范ټ^չ!ػ5,0JXnx=/jp՞?*ţٮ7#|ykX4[CYQGA QEf8(* {KQgokGlx}7ۑrsƜ^6@'&y}A;MIHX[j{7¬Ȣپ޸&M~US6$l"}aj$ڄ5h(rK1&ʦ+&]f{ _g2}S/+=|º`;i<f36g BvchDwp2Q#OE٧ \m1^!;{bU@pW=o3,jwFhgHxSc"j7p[E-ǝVL(#p<u(N͘5OKN~)Tse30VJNH*prl BdIvb}#!j6skw?M,}ȓMu U?MBAPpWIs  K YlI J#Bz6)ZJ ]n&a\ Gb5C ^WG ojGd3˪77|iwȠeQN˸3IxqX@$|:%TU2<,rۋ `2vw/Y8Hm4ό++2z95~B4YW/_z?G0­s?ZT P 7/2nfpy Bq~y3 F+S<kS2oTl/E>(C *c.w^3JɕZܣiQg+*\#P;]KzWS2ts-;+2uѝⱍFTC/5QTx7ƠIVmtj֝u s-'Je%2QZ, ،;tbdd'O{MƉN؝auǿ'!QA"*SNSOϷ3!jӿMPВw~"^.,H"^i@񇗎Y dIdT j}n B|j nAxЪ$~RSW x$'Ƣ-Y9V__w]F>~͓5x9_j3_NBހltDr6&[!#7#wd# nyhU4+l (A$]"V90<{?~t V8ѯq vh~y^ؾAh~-whw@Y"CMF_p*n6y6fG<3?o n7Y,lˏV1Bq"mj tI5-U~48iǺ}X}d2R@pCEM$^4vVH/5t!< x0 2t<I*>r1Nvl&!{peX4HCg@A~4go2aTD9RGI.Eܲ5DtNp]x`*p4_Hp w]!{')rF$GxN_esHfXGB:*PY2>0{#6-^ߖXPK<Գ6Σq ǽVw)w7W[6`ȟY Mo]\R/n(j<76.KW|t:C"#+` mD>\:SE_XU9[=R(|ѹ-tM@Ǎ>)@ve&gKr:ka{`F*7_ԩD/[Υb/$ / (Xa%Bw)W<"WK8WhRxLHezA>j~%V?;i`iH[)tRoVۦRw5~`>74ܲດ? y&+#qȟUΙUYϱ),]Vk@_z")E ;:\{Z^(H2)$)C 5haC >%>Uȹ| 8) #rZv~7:f-IO?DΣcmdjz0 #lݵh,+Z¯x̮U)7+.JOL#Oq5#M)l㾁(nŻ_4n)lX;T7M[ O&|*F0rԷ_q >"#3Fd;Q3)gjYC+!lIiw2aEף#RXI1Tx|cpY Q 1+mL t%=w ;ҠDubbuh7<1VIojΊ l-%SsҗKb[ 8wyn_Z}QQhhPDݬ[,˺|RB 6L%o;-]KnkYsބ6:r0(\/B G!97.CCjj4d gJPW4%.EJ*ыF7Z/>ݱz+Œ|O[x0 G$ؽ(˰+VFup49Uqg {hphq.$,{$B2~*+DW)FwWΙ%N+'yd('JTR|FMl ΢]~JT/&.`x rvH3E'/$)5e.hQc2> ] ̸"O}met4v bŴdDt4_K)ȅ\~QvA~ؼFG[üI BUlmCE1k%2kC+WrUD8h Ԃ:k]R}xc>VXJa X9,e h2}{٨|hfWb%׷h|p& ۊ笁(S:B*~ IV}1-jyn(4Q @E O1g<[I>؂FB77/9Nx,DOʋؾ [AjbtdVTep̐I@!'7忻@dt}pgNFNZ4Ϊ+70lMMu$d$zM7v(Xl@= ϊ7lbaаm+-X;j'aFe7~lG08Ќv+{ .A<1nSɾ4?h'A;qJM%*P^Zw<6ֱ̡^iCv˼@ޱB6) 6M`_PʭoB +b;"HY Mc:8 o`мSVHڣE¤2912!nq{v"ϊ΂5SG "&s3U>ZZBp\ن~ig 4$Q ZIeH3GA)En/d)/duzްf)}%L$Gn37G0~gdduH]UFgpwD@P~3bܔiA·9yLu/i_u"%̮*`*˾R^7߾9dPEw8QϷ 8%ǩ{4 %.|4KT[l@Bst5` &`+뼥djLKG?=0̵)S16͈ٿ%Lufh_3@rϭwJy G̢x0 ܆)A)$Є*)wI>zleq.>X dR~}Q|t?C=nvvT0VktJ g>iKF4"1G&6NNS ,}6(LKe3Ko1]Jʱ7"shu}Wh?.\X1Aڈ`RP'xmܗ۠K'!Z 2G=s# k3 Cu@t_WZm _߶lrmo \qn7AOgݳ8`@ۄ}i E@_B[%*Lt|z%O:n.29G¡$~ N] Ri;Psb5vH#.AJM8RKULW ͝.BDȊpV.1*ML4āzx%(QWO꒴Oj'H k7Gpou 'T%fc3H{Ix:^tl: 1EL-4PTrEG"Kõu!5(\TWN/NmZ͗:íe9+}N,eXXqrC"<,>6Y,&#2_bKhsi6 1h>F 9j[:q,}$gYP6[CxpXгXЯ@]Iyrn4d`yu1nY)"@9岟nD$WvKg m#m۵ߕV_JY{m)`Js-o鷤,MހJEcȂN,ţNw1EOr~z+R3>zmMJHw5*_@#.ma0v,r˽](B+AX}^}R\ h,z^<ԹQWH \L SrB̵R/Hzu '?Iz HC :z39 2"̐, ^27jOO:wh0DzV";] /kt;IXTo2uz"k_p'!RG!C%78 V5r_tǭ2^B֐Ҭ}8xxY>c/~`%>[`}r8C\_B@r "bs{iE_U<̊}XaT}9-Ab ifS$:ji`!r-hk0 J՜cv4p/ vjc7y:B5jTdF24Pj>^PU<_ڨice].Nӟ_0~+pN~?z]}f['XM5~3߬ ۞ !YU5yߦ$:Vl5 sa~$:47~0^.ّ"'IT_2&uJfƚSdY, iQxQ75&[)qvV) љM`7(.po9';6.ʥcLFli9"#4b"Ǟ=6@Yi!2?FR\$dbuxrrk~ o*\Av7Wqeo_7Dq5ȑxj~s|Vd 99Rk{iWcGd֢4`]_ b#R7᣸䧼ZtoW HB|(zYek>" Ai ,/e`/ڃ±._fE3MLʔtOE, ;AН+W+[/Y(j$sI-TI̱v-T}Z?҈WRq圜߷gr1cOp#|qD07s`hBYպ_l[\&ϊ]HWylڪ}Q4f; rJ>  p[?PQ1yDjoYr//(_d^Y ַNF{E&2kdՂyj< %BoP[S(bЪw0ɝ%B>O J QhҀl;oe/3u7lu[d3`W c.pqDi$PmMQ2gHS"|A*.)籊;<=Q.<q6xqm(_7ف0n4ߟ%^y/jP-fު}\ӗzs;"mGegdčJ>3Sf!)dPYpu@* ]rԧYVq)^80G3ֳ:`wծKgF2o f۫QDNH\[~~/?zG.޸4'M|+>4(qS]&b夼::;S_e!4]~Y*nPl {oQN)>+v W`..V*fظDP'=wMVͩ'|^Fz+KgH}ؽG)B 4nbfx| fΨ-HH}c蟱0j#窬,̝T-=xTg$a#{QH}-&c<a5} ߭64vcM|`^Whge%cDv Ao5l ["niirF<o 6|s&NZ; aө ̜{v> Oy9 iUЈ`m%r;V8SBl\o1%De'2KFop|y)ysfVuwAB-f "Ky W $ vQs0Lwysh< Ph}z7+XX(gV)'m31(*7C\&`c P4%bf3{P!/cGş :IZA9`R-"0YD8#z428ϙE035fY.è#y= ̔R)ϗ =jͬ+:wOH=6Syigv ;{$7:#Zd8<İ\;n%GeRM#0.߾܊]qelOd]_O o,dd"uK6pO@ZZ8Y(lݲ4kq`zM3]:  G%*V2Ej-rb m 5IPxvvk^MfN]a\5\KKr*RIrEPAE4}&FP.r{wcX~zN0t:w'{Rn$??m_i4eS$sZ4%NCgad"C̕DvLhqVVl9la.;1υkӶXj"a$(2dnQA.աZQԓjϥhq+ Ŵj2,1:8b"VKVD4SG,\9!"h uA`ݺR: f#=s7kPh;U):2j-cY~wj]Aԅ/X{ŔMaov<^7|Xa>V QIr?#T+LAn7PHt&: 2URN,E{^_}wvS qBqpyvNžuksZ>'ͼ`{䦡ԚHkϥ=#(u7ml'mcr([fQh Xnq]O.3d#/3ػ#3&n@ecN8=wv, t-BGo":^OxQfh-Ϻ/bʕRx#C)~ؼI(~>0_6p$clRUv#(DܪUM1 \~ćcoMT[6N'4N mFO 56ۮd&5 po{|(yDY%` F5sS"cj-רb?{7!߄xn'm>? cf}M^SQ} ҜrlbO!&l2j%78K!c^Q)4NS:b6&rZٌ<&\׹6GvA \S; "[2*۫me\jHWߛX'rO ;rȕ2rANTꚧ0B_Skwaw5[lO}mq,!^꼾_e{mćnlA3"baAH ƈ-ό O'-IJecftX^KVU ijdM-%>>d,>(_CnȰh^xSf8*=71‡ԓQM-j'^) Mܭ\+nƑbVkM(GiVYnzqiL92rgcK3ҟ" ]3g!Sqmڟ" mKN`o^YoHT 9֠5FjRp;96)}3%7L;A\9߉HrAK`۷/@턍& m[XLc_E(oD\e"F!?Q  ko|s:SEII3}EälZ〣tIn0p` _76HQL-dY~DOÒҁA3ƣ~s).IUt/-ċ 3*3p}B*OGh}yZ-mѯ`G G0$w8ϥ g^ta$t{` E~X|t'Y"ꈝtQ#F33smȋB,p-ГC{X\-9N%W'+CNhM@ +)xM6Yv. BؗYD-!9203;ۦLp"ڃehՒܗXߵ P9$ek6ފI[fx&TKbyjMsk4kfg*5 &U:4+蓃^T%k p) Dw ? K#bЃ̕&:P{Z 3gSmAˣG\RIY\aT84oQ64qK\:=xcy;gW$-ӛ!9)ݛ y?9 k'n5wdwִwRq5jdE7~r`qQ6O}ӌl|ؼb<q{\2hW~4`3ˊ!iFŭ)tO9: t4藑q"ZRx|4Qiզ.yB.ʼn*QBqGyc0I_.fg|PRz޷Fx}ظ \_,MoAG2n':At%:>#.ŸлQ{q@=x=h?L 9b A`^Mϕ$gl+ zQv\?sp ZZh9 {-3x[[N˵ƓC)#>2FGz{1 2'U+WL:OlUE=r`1qlGErױezv(&Vvi<ŃhwS))Dp1@8-qF}ZYw|%~j8Pհy("mKu!=Q[I:dJfv2%%ב* Ѧ򘡔w^$Y+Οp<9 F(/r#} v`S^:z264H{W͙"La%ER,oC}E*s{Jl^yGe~Է-G;dWӘ-xFIJ>͌ ;೭6 Ь 14Գo@0,ʑaEs C/UmjEv=VU2[L=6%8袮UBMM..yamk(:ryj-xCJaƗdԜ>|2Ɉw>KA.Fl6}l?l[mò SSp>SX0:+Ǣ}HewmL9^Y# ]:ߵJ` 3L}Y/: !FFW@j}(X{" f4+&Fڽ.XOEjnHe+@Asؑf &.(]nRAi9vi Ufv̊XH6cZuۍXCmVPM_A־uzYsAXkb$ks2pARĿ3n.~sMQ8 sf- f5Up6(i֫4^lِ^ss̼72-7ə0 `!k£^R9ڥn3e[2.@܊6tE+p{Ѧ#ꮛ›lԇ>3c\Bj 1s2"ϕ7mGr> "FK\bAww,aZIIG ?# W,Z}ƳiP/>36O~J#zi‘G0%ӣar 5T?'myY?>u`tذc8*&6ͅJ;;KzU$IJCd_C?>,%#b.Y5>"|r+h@a#-zPVv 3DFn ;Éb_Z9?Pu=K(1]urʉ+^ļdqSB<aDKcYNЎBU Z,A`+'!5VkST8ƸXL=*G-V#eF'~+|L䰾Owc__VL. ˃wm|z,M.E.@"$¥Ah' o}97(/D"+p5 0@HŐtrP !ɯoBVJ{ RHMx"Ki@QFj_oLrPH[ *~57gp@; ^=)1=he_?-];1ρ0)Ѳ'O.Bx[}LpKJX<<[Iy*,:& f!n6G-AՊw2ft1?i7 t~}v/PWčD4gvYnhT¡(棕6!/L7%j Ͼ'kwۋY*ru"ҏ>\ )jͱWa2^Sx}xPMiDJIgvXHv?/,rΘK뚣#OO&W b+q0}|^RD^aЖ:_/x x{~5bP`ʩ*#f_h`Z//%Qj7dZ=rv;pnNYAnjbtFb|K@Tˇ3=Sě\h6e r9b@Ȟ,i]=H<9^q@Yl?3`#b^v -YjRЀH?VYݮ >dDri6UmEIrgܵRqpP $#7oݡ+/^ERb$&|4!X5jEa^`xeTdF-Xq{)17&;ꅵx˓>f} ,O$iKnk!bozM5ѥ 8%%OjFO;Wt7y\w\.vw^h,2b'T9aխ;䂛C㩨;{;陴/G;p};R*B;+PZWTSp<PkW`wZ*P_ h?.".$S#Yj% k KvZK$Ui67K}xg>yOζ.f\쵡6p(AH:>C ;3rNa:֫_#C ~ F"_"rxL[{Y`Np, mF {OXsݵrFRB'{[h7D_[PTqm|q("~G$(;d~7OWY-s35P; 9sGDmF7 |p'Wp=^oyFz|lv6tzK29Dעl#]C [X\޵oA?PRd9l Iֵgku1$J]^*ţ{ǵWl2fhF"\![Ύ(C&SeGr~ޝAt'PuX3eZ4."^`g?`\Ǒ׉.Z9 Q:=;SyQREd*(i#~k@GN R 2!z1~ҋ+=F~g"+/S{y?sp 2䢭?}!6asqŅ7&o":&[Cs> "913Tz}:o$5 wԬ9&8$5Wl'$bqrblOB$nq&Zed3{hmlUjAetF;`JX@OOP>F3,hg|)aWY F׊-BȲq]_fvJ> :l榿6U17 xŶ%)ء8OD-HB$G\#Z\ y-f_9zPuTo{q1u| jPf? 4}a*vi AAGYI.!1i2/ՏsyZ{Tcgwy ɭʧAl[ ƿ" A2\bQ_4㣌\@;%m3+է'"7w(h$۲rVԈok[̩Z8؂H{tl\o>Nh TH w9A!vCNd4M,pkt8 O"tpDRQ+VggnUwF/32:n G9`ᰅm j&:|יEB?! 6J' O,T-[m.X7si/$qn.7fM.5y}[y0~#8Z;sכ 8wL Ƴ.MO눢+_"ձA ^![)߳ }gN^a˒|U)K)!X FC%k "(!#j;e0&t$Ƭc"-,8fY=m|.j:j}4Ptve Ēָ=jxRd$2rWÞy^<;c{USoVn9gd"tߚoM!:3X.KS6+؟2Ƴ \r¨(ߙMxߟjwc u9ʔXWqĈ|gdъ>:L+UtAF"~+ lGX IJdȀetvŽJxf{uEwECٗ[_xoqT$r@L#NU$L㺉3Yo4h_g$MأmrΦS;ᦐ<C*λe24_U@i2#昼wvt~z6qNb8 m6 OtޫU R5PDLK 5KӐ [,3-m!}߅C zABCCMkUb=2{tsn2٩B}'%+w5TXRj3@ɩ ʧ]SۅGxjܚȭOIBG\ڨS&@E\L FxT:* Ak:d'-Օ|j!8n*Rj&P|h-Jl:;GN)ud:c<6 >Fzg=I4ơiF 8P>!`hU@+qs*fiKq~h73'}V!vb#(&_=g~F^1gmk=bUmNUVg-EjI]dh-\!2\-'!+Fp(Ӄ083 ׸󡘺jlIM?[O/{_⎎(ݧmC#J6( l`IVt۬eWq5d#Y a1wMz*f~뱼ӛ<*~; V#VuTe0'NXt9Lc):RI2u Fn s7)u5~/8d*"Ѿ ;Un~,L.OScF&϶ؓZ-| %F& Tz 0 !7>[a :EmDregaV^GKy8J!`oQW*,fIqŤ?H2WKwɻdHjQ Ql,։:fvT䌻5.306!Օ\ɸai9)c,ˎf#j6%rí!fhS?M@ J)i/h "HKbݝ<7B;j4Z@_weh:ItQ[H[ G9Qnѐ+w,\8r6(J,ӳ֩4mkRsiQza6}Gt}"t"? Z d 4>0j1Z+TZG?,mI=z HQl^%S}p;[?uE]2 DE=œLszwb2`̹#H4˳p"tžR 6/3U$< ol+Þ7zY WJV?3xStk+3nQ}S;(m{X#[;d{N͵X)8eѲfiW\w; Ь_W.L'w9O<2BvޖjJCkS"dg2B[₣h%>;ETzrϵ4Y"|R~sاO}P,1[ x@}Tn|F6Ri1s{ `i}O } S LY1S<{V*4-ڈ9M.'T[3 >5<%(ax֣2yW}k/56ua;%rF:OhVTu{ J m%]+huK$ec<׹3V:6C< LnYDz&2}p@X-{U4lKJVQͨ'Yt/.j/և/cP ( $i-!Pko]V Dj$梦ڧ0{୙7 v(X2jiM(=wё2c/E"PQ!J[/ijj*[χ702Lj͚-'fs7\(V䉲D_ <]qs`蕁?,+BȸpΧVģ IeS g',NsmcFLRS\+5ַUǚD\sf'B9{@.èk䬐7wngY;ScdT](3Y@L7y-}S4Vm؀˱I^,5Xv֓uɆ«e1oq" &MyQY_+S虠aAla P01aE_aJ Cy+?"H*[T&*:Cq0$A6ss^1fr j߀DQzcA^[-1!.hzr}ujWp~*H6BՃڝݰybq\^ &=\*;wHor7AQppr2bo락E_wR}ޮ'm9PO$P'1k#.jސƞH3Su /c=@B/tk d`V["? xHʣtPzE.Y%wϰ@g% " =Ca 0rQP5{pM (O>5#K}NSʼ?Dm 萠&`Ε,H:_]Hw0m$e=@Xè6ebvzagU{"wt 'ŬrI|A1D93"ytDe(Uj?Zͷ{a QM,5J:c~Is,IdE>X+,CƑY#ȱ{G1.r?{#[{&29zmNYJ%M@4gT@mLZZP䍒0`A]4Wy]Aq>/X Zxoʿ0'Sah XC_}}6nZG 1vgeBHwQE"PM5!E!XgzIj][! cKlZe3b5T8$v'kFRiּ>iIO4d,QXqvQ4ԯ 70O*o0bmm 0yD2"] Jb޼Hk(5˓Oڗ^ Zid Oٕ lTlArzhJ]$&|H'D1#$c˼"/5lڑ25:56-&X ctPEԗo\sT!"{vgzzʘڱp6Ӆ<#OkRdq6!CBz-$-n^ם^?Khxq跹nvwOT(@-:د|i5@ui=# h,!n睔i~L ĞbWR+ȭ8T"Nvlv,ybUS>&pHDJy&mQVO5 9U6G`qB$Pti><'{!(;,{$j_,Ӏ]Ngb3d0=7"ϐs;5}np2HmfxY/ޙ \54":O϶ )tLvʓA2FhW7, 6ƋӌRz =&x @NKm}t?\k. ?6NRNxGwIÖet1d& >%w3 } We8{Wɢfw7S gg/QS`ٳ9'O\xN55H!9Έ-$_Rȵ>ט i۸ ?$疯tHU jał.|OUPu8 ͋g`6&$#]nxBW0$yY~l}ݡY$Z"^vZnZie'W(5/SS2ypWiT ]gӽd98c.&6tyC:͐10=4exzQ#$+u u(9|-m84w~8зS$+^dW Ftų3~ZIm%91>V.7|A=WU@6K |cUVYq^gkL7@8"HgXL3Eeo},  `J]/kPM#$ĀQ2^7N*CP֑i&o]q5X|֭*Dti:<&1"O:SWXb͠ qѴ>bɌ$/W_yTXZTTM:$Nn=QZvv S@x^#Vlӛ0j>κAkAly̌}6>x h`&աSWsk11} (mQ%Dyl&4{j\jmd%7QV3gԯ,.YV)ALVlqvaʭggHOA0 >.c #r"N1(rCp.d2t@"^lsԻShAy0d@Ye+U(uh7|U:Kw Di3!0X1ߗ!覰Bm7_QS zyUˏV !ј󲭍RRaB`7 w[ѸU%8%KSVqgnڌ.XcĮj-oA bmV3k ;_\O鶳7"(kWDx{ e@7u1[vw+-T=[ @JstaPG_m$``Zp=Xw JlBȅw_9jնtTp1f]㞅>1U9x0*A?B3d3)aO(QVkg3cs7cvnUZ:eޢfck)qBڎČr/vl؟NP꘸WUs ؊a?Gs Ǭzt9~$܂p1?'U]YxG~lc#Hm<`ڐ{O.>AeT6S,2#Fy6S5&F?!Aa.Ce y-1Jv/w%lʉ.n{F-?˭ۇpCk"ǰ$gr o&4 kpݥjpn̆\b Td)Ϝᆹ$MC1ɛ0Wf4e`QdOްjY;Q@i JPQ'7R'a51/?4Z NâA7PO/.:@ٓR"g1MEV./'\|$gY]،"u9= yk@ ,aޟ\_ n6Ze$+! D=Ȃ!HZjTXلoz,Dde&s̞yQnJLңe[w %UWtPP7W >-}\@vr}6 utӌao`ko=S[Z}}LuG|z6 P\sRw~\ ,xs-qqCD.Mr IڀC3,gˡT¯ߛ0Hٖn$%2 >$_OP A{]c?ՆWa/aнMgQO5bX{ uyءK>KcAci2+eDP ݘCic+v/z1/dI8cH|+u(VuI S0j=WǁErѰ{9E,t96,.^#V'Gu`u )qR"V#C7;~rP!? P+݈zgoQ?(..|m]ǮU<vArJw%߇`r_Џbg<̊ٝ„ije+} $#fΧ5}k$𫿉`Mkj{s/TRkY}\ \'.~֯/ཨ= Q̸K. |sL6Xqd/P:݀/ݜ4zp궭LxoS/zNM*"0ްU'cmS*$9b`oeՌ,$^tM wo/<]_)__Z7Fӵa; G32WsN]+N]Fyљ INMĥ1fF9JͱҘXo1 YJY)\ag\^J60E. d6aKw42,cimt/|`@zRjY:]ב0jɍ? H \ѣG`t[R23PEŨXyxRIV8X[O:[=S_:%a8.Nݭ hxjyפ{t=զVVNqX8L ?s'\){tZ5ʆU ֨-g8XQcsUÀe,?TAD~LdÀӒ3D.BV Bw'h@aܤ :_HM?6"g!: =St0W{&qSKwӺ E[fWH怅nx |4թp/~qo:pE~T"XmQ%4 -RO1:Y]AH׬6WT`A2[׷N03>e\=xC/#|ǧE9<} ױh`Ȏ |R0W*&ij @b)gqhΧV[NG]i_O-bqTњSr[4o1Ws\q.-+1T sGX᜴Rϓqm.oNsrsMUPQShQϰ2f> H)fKQUA'^07c8,(]36#}-ŊNdQ.7ON[CŲx-k$ J3Ыro4}rLBڲв/Z.doT(%Rc3:t\$c\K44FP}MҸ>q_~hG[t)^[u\'``rJnըK%9/a1myRMW[,=XһoV6vS+,uspW6]B4j;x<ά fF!ȯ*2b_ތpͱ;yh́rS$^o;hFY'`C]w[ w^Rzܷ|:>X"'@i&jpi}*)WLmpdiAj r6#tPn3~W]5]ӎ턲}/(x/~g?XlM5#& |n/U^(mK0N+L KGn w!}[R@0/]{Lb+?ULAuW0wN@՛,sl@ރkƘp Dlelo9$ޏQ5N)5^Z+%'4}͑ݐTa9:#Z&I`2H7/)XfMkax㊙K[LkҬj<]`w%UZn፹|hz5AXȆ3vJ}B# -c;3%V98AuU4wJ5 ?jiIgD*> >ӯy@&MhRº0J>A (Ȇ DX>i 1{^4<-@.9mH7#TajZK%\1!VLdhk(LKwP]4A$=}waBLm6چSN_β ;LS+>wK^+IٱّP[J0Knm pui8ᦛk9mc%VI>t2ߌ)R/`ˈp2*eQK*eEJ~x}m]8qb ]>cumeonVfA.~#9Z%LSvt W#x{ (([jOX#j{,!3DZ|ti_Ec1h#e:,lbD%=Ǘje 6c+#O}6 60n%64%;Rւ\zz{F(}Ekhd z`Zg]{ 0>g"MR/E[9h`eGAn.kuo}ek@`-wo$tQ/\;%IS {NN!6uȽ~2٥ 9ddo; ګԕT?M[}YUs^L8h]vbUPg*R^ \B($"XZ48iҴAa. ){/ay!=+CVPԃ)2ۙA_[ڿA/hUMo8-H.yE~\z5Bo.4#4Z VZE ‡۶ &f8)"qi,Ztk%Yxwhh0=>LQ%u!.!,L؃0y$5g/u+_ŞX*%ߊ PcShqؿCڌA|婱i * =P Gf zŦkdp |ڛM=lA*0K$-&)L;/.!&ݘa5${"R=uB+/GB_~8[L[S%/-g(`&)MCH؉77UiZ>萧:hosT0I5 4*yHBDk̲4`*J.66Eȥ'*ItgA$eΖeN:]@7 K#2a>IkϢRzU8p9`X[!'ZLC^Qz\N/-qju?r,gRg֧pa4k9oU(`>#Cm(wd7v}$Ý,N7ftsw 2d~ZG q#]_>eB/ʁT=6:"u7Arxw @_%PrPZDJݽy ?*<QOu EcW>55Ll> IuɢlxW*G4]_݋r,,ZxKh0H%Yp,KԠ!hlcDҊZJ=2dZݠ7/p x9OkEKD!1}-L(<7v rRy0$YISN.tMRN6~l0 5^nOm^]ZQ `6|bkPj6C|\$xC -7RCf ]uCvJ"Hsn=TE``XхچN8hbq5/I!8G=}"QB `"|b]f8&P%jZ`:Q2'Jz)\E^(Mx5Q8K#91}wTcǷ_|i`;rfD ѭʺ #gZV.ۮHr\Y,aV{_AR8SR[J_Dل[ؚWJS\; $+D]a.U'pBQ;" `\Y$'믪Z/VQUMm*:^l勢 j_04dNb<@ahjgu2бЧ",!ksf |VL "44Pi8" >DLlG9"Kr 2 hf%‘zGr@C)t%6= B.Ǭ5O@/*I$aDQ8vFPy#ȳKǽ=/k\n6LdHJPY,ۑU4 [dhS-42@OH&J3M iuFqZq+'zJaYlHs~,-RkiW5ulV$wgaqIzcF2~$D4Y&7+|k|4tI)X@%DMȘ9[LjaxG#)S5k+2ڻ5i= 1UaWa6EdG԰j-d<8 TE\l7gHih]Ne Ӛ2zJ,(':]t5ƉZ_N"o A+gX[WNP5|盭wUTpN7Bz:Ԉ_b񔕴&aIB`1+N;:/ϛbE_|ͫjO>>`1$5&ѕ`eh^{5|(k"'5e5I0r;"}35$LEY*%Q& 9G[(Iϣ75 U$^)`|i^x .p_[CeZKPepPP~)XT^YWUX3qU(*&$2BHh̥QD-Əʤ]>$=usZո$/ U5@l׻c%ZwPȊ/3v{~c1t_Ln(F <3nk}w rΠXA$BZa4oY8Yirrg ѧFxqudbdZ(xXL[3c?܆-:Rb\)lRƚ%Z=|ZR>1 ^:d?+#wgZ0چe+1h-wrgؙ,?//ݗWUP6MU5kÈ-v7#oM4H&z<c ZG!u,d>fb~u!ϭjLCg3e0^Z3qTI3.`2DOΟVD[b&P {{X(^!xoI-t\-Ġ\0:j2+nJ'$[otgMЙ7J1t0n̔jsAj#:;/B[FBPZ@KY !M>;= X_ˁWLɸb\O)Q'8gdž16Ȗ#WE,` '`1&2 BAuC$5ܲ]t⏜cV!ɔW8;ØOU#6Q8idiS5o&;$V'rIc-1y+bӬTn|gc/i<e|IM>7a0 důrY\jڸJ /?Q`/q:1*Ccr?ITϾ& _X03# Q`,6!WjzWya}K̔QH[Q ^l wGgb[7dyLOpc!o w&FTHhaIt{)i nv `'$yG#-:"w_7 Iuh^jZ3D81V͂u,Öp$ؽB.-.z٣KYl9r1\1O6'oF|;),.{J+?vd()!A!`={J-[~ ~U΋iLOH0j?;Q)6R<p]6[:DSrU%`m-/K> b}g!ձ=^חkxZ28˰.!7#&ͧTJ܄%=Kcm/k]\+d5I .[cmB}79~^4^`%aRЖWqvG4}?:WH0}=Om> cA ?&?vϥT\Z'Ll_9cljcBjU*;_^pRX쒤ģTl%+_%Mv>4$;21bޒp˟(=L^?๧d"-Y#p8Ya^]u?aǞ3+VK[>u{NjuHo{eYͻbw$ˊȷɰ9@.7@nIpm f%$ÍHAe{#g:3niދ9YdX;߲5 M ؒ;ڛSTw^5|@Rز;LVt׶>QxI} 2_Me;bsT\BUnaYbxtʉ";+B׳ Z4xH_ldԺ:Mpɩ?͆%ڟ"t|okWLCDrT(l?&¢QQ\^ВRn0:zhͬom六tojHVK1;L$糓d%Aa:=p^U?,!sQ{X2z+D2AiW-14mkL9Н 4615!ԧ)=EkF?\ 1Rpf %n-i JvOoLኼAs|6ܓRdMSz@JP⥭$Ȟ-2~ %!?:lPS;E>Zn!~Rp|hpYZ$}T"#lۼ*GfTbzu'1mѧ{Y:#)9IΏ¿|fjhA%f5y '*ƦI#t4kX.Y ξ!q$K\t`kz%x!+bTl>—K< 诣Qx.QNeOb95~(t=j64-m> ɩ!4ek Co¶9U1咺?ø38I&wDjYB jTCNBɳCI^~S[U٥ؓdşyglb=fƄ*+ˆ9'A!ت""3Q(3E%РE#N;SRNn䗖GjGɖS K<?]`jPV6P(Ф 0fu$BˡB:v$$%ng*gN72@ZFV&{ ;;}ZCh)6<^13 }͹+vQtɞm.(kdoGw+0&+MyL(Lh:1b{qٓ#n۞>ir=Li=[|2PjοF4vחX~@f]]# <׳SzBchw@5i?2| Y X_6 <;84\5" RN$;JФS$,Ú}D̢qE?հF S>t=CEomv63°[5Oբݞ;E6SIiȉ3 V@cl:ŧwS[QEPQb9j)(H2&l'7. D޿2?4G:̴'rKX[ve=A2QRt2P!I-["paLI-aé8ڛ(]@σo٘QERW*g."e ӹq55$}ɸĉkӘ!h n")Phe@P#2Jmzl3ҎDdozp FQ7JXˌȾ#[NS2hiZMnNZI+¬L Wcr$Ra<8X#u9=1#Q$v<ުMMC7w" U" k|9弊0\GS?n߃@23)̭T f89#T]M6 WU;JA3FM'KBc<ۢ{Ԭ+O4.nY/O3ϫBF7╭ݘ40/ST?Q,I 9='[#ڤ|6qO]%wF7\ӄ(^ICUiEc? Xӌ|3WkRvB0R }һ_j+/mrA<^_&9BK &i$uDEU5nmAJSyfT]i':xp:uh±~9ٛAElRSމ܂T^`JWv}. 5#/=.!2M&ߝ].)($Ut{Ϊc!<b`-&ʤfaP6B51 kG m>pa 0s=ӤueQK]֮b x`a6EnN31߱ 9)?} aI> 3Ffk) +,S$,i|?vmZʔ@J4DHQrlC,凃kt}˲u5i "Voօ)f-)G۶PnL4DթsyhyR,F%lW'`3>Z:Z` IHeo-B;6 Xmu\`OJS-6z7B.:Inu6eJ~£'uB ȟ$" g$% 9wMFkA o * o4mCf\ KAT<_`9!%O4HE"l}_ ļcuRB<7N 'f}v܆"mlTs經r Hf }ZgFj RWVWu{}E*:K/*r%r>prwb${|\(y.ON-F~(a؛9:_w橁7lCJDu쨓f3oA6#LZ0E7 o`e3i`C0VA)e- MNgw Ԗ)ߡ$3< BXY.5ϩΊφIBHww]_ǿ;ccntqJ4SMG_Y,XMiy=WoF$c&ܻ~c % W fŒ0ح -#}/$|v s?nV_ |oe"Y{-!T˺:P_:b#.~ 1~Ud6|Swm$im^Q܃RBbD b(#}Wt f漢pSID Zk&_f8W<x,Q8Y{izA!xFsPRr=.?,&h:X“rY$|[~*CFlԧ\WPM7NTj𳨟 "@dZfXu/?BuF2$?27ɸ@\ .{`phnf^ʾ٭O'*QDf@S1xU{fF/@tjSg.T bGWȸCکzOudZ_)1OE "OUF9b9R>.~ZG=9?8g}"Ql^nE=X/]s0#CsdIh- ԡ39O']8}Hܪ)*+xeғD卢ʲL:/.T9K3x) M\ޗNA"ZRz+{ͽc#]rWk_KI2l%gDQR8B#(y EJmP "ukW5y;g\y?%yїqV¦9?pN: InN5y6(; lFC(& hU=CUɫ]V5FI㗖۸p·|CUɐ)[C4s:#|<+-U E=fĿH\K I @]'y;r`1ڂ/Q+'XqϠz%veɱhfKG$g_A-"ֺL`?_4S(6{ ˠaCpؤL.ڂSFIsMYA6]l\BA^ioK| Sε6qS4WkdD$UoEbxZ-{A ?8UX7bxwؒз;#ε}Uʈ:QA_-ʋR@eP'ٖ/춝{U{ "tfkn~8ZAϓK[=nZn/5yo {zq kgZx t"ǁC|aYaRٟ,Km=dOGj &'%VK,6 塌1B6j%/X _M{ 3)L9 Y-FC@m0)2%.Tz=5^FdkՙL`Z}n4G=RlQ*B:Yr*\BP$x.+\=G4)Uy7C$NOK0n>v`KEMFxJ_"9nW~1 OE/.NŎ3%ȽҦ9cǰXx}^a=UH/*Ed%U'Q5s4)z,ICa@Vshs_3I|{ RFdѺp QM<Ϯ\>.ܠQ 䠎σ׎[g 2Ui0hec5nZ%PA,q'Ȇ1t^Í]Pn0$!5Fa`JԤ-I^໔SWBH=يg"7#y/>d)@̡aI>nQ/*!ln¨S ^O\xOte3 o !g^zHd%*Ahl^OwM_\>}qRoD8CqP_ְ'@w-J42x:&7T*WP@M@ YGsN>1B$ Zd-y.f0'k=ipPx}`vX缓L\Mo_z˵NJX4ZػD斡?GKwb^T)K(+~%ŞM~E:QEnx)(hM 5k*&?#Ν[yq}Rb2,>q6E{ەqTn<$)F\<;Uaz%S:4kF7{HǃE4d Ts-MjX;:|96|Z[jE];bR;>/m0䪭_jALK=\ɢ)i-qz!nM:8|^>9 |WZwN?h C~t90)Ӵ#3%h͵QBf zC]ޜz+]:kR~?DŽ*vjdʄG#v\`=v/un%ަ`jl0^٦"-wnI=bEjvV1t㝶`Ƣ|uV-e㜩N'[ƫ\~)/Val<7@{<@hU/" 4㠵p6pej*˝\OieVa^S\@t Ԣ.^<3#Pn;zxhW0lyטݿ ]-fbgQ:uPݎF-kOlx9*S6ϖ-@ -1+q?F/2a[Y<zR4_x_Stu6`.|H,\(\.eF+q֟QX7VZh&%|d ( {~8RYV b6̚ZNg(~ͭv`/{)Z)gcrшnZb׷P3 `.e&)]OTs2 @Zp[%|7M#~NduZ'**Sm{۟߁ 2?ٽr5{p& c4D#hM]3,T Y\Y\&n&TD[Y'$7)?ʸUs3w_ k+j0(/&i01'QUߧmFwjiykU[GNtm+LrW:9 $ u8< ~_"L} B l 0Gn 8:`kQ4Y@hkHϽG{Nf,F\(  vNxXm;¯ /8%7B҂KO ot8=6 H? Ϛ 1Aǣ6@}q6+QBe]f>m&hÅhDHk *GXv$sM#ZJ/T@({jpE8?#<]̨qZd2'n3

!"yK'h@Qf~>~m,(yT7(!׋<0-{oyV"3 q{5 SQ |Ⱁ1:E/٠g&z@ػ\hD ˢ`H1|mke[%PIYxuY(06m[B- b**= ѝ T:gxq'蠻 I!P4z#Fj1j%&лY.Lv,\/AO|0淺`ɿp!=3Jz^ ͙@oRp%04!Zǰ*Hwƪb(VF2J(F0#Kc\5+k=J5v cu^D~/ZȞ".C>j~ۉ JxRR3a$1ljm_W4* 8~u.->>*&S܊$af׾Y/&>+F!ޫ4T 73'$+7g@ڈ^nQ: JJ9xB bD7y #ִ*a,D kfdAJ i#vFGZ4' Uoz i;kO^źLk'Dm3ߤK}]Ӝ.9RaϥCtŤiI:KܐtJ ޙ!:˖#/Fǯ,-앧{uRL*"/:O_>/2O#`܎}cπ3EYqRTIuIà68g L<BB"..AH'Y1wm/k3`h eZ[{G)~#7s ns\\GC\,BF4Vs5!SH:h(ES|Ai-pi: Q-6[ukt13 R8v$OYS\Ab2GNbq,Ϙ ATtQ.[ӣPC'|T:\^5ٰ /e4ҳ!vj3?Ձz#R!GJ(ymԓ!XUP+o]Boɨ(\%ڝ+V0ʸPi(huKٱN80c] 6늻Q.^ HU#gwb oERO7ڝl;^ &yo: ?I{x-ؠa9 V P;e@ڞ0$Z3;BIߧ[>יe<P琨NiA'=54 8:-wAթ0#jX0b^i;ϫ)aZҋϔf`!OtR2R#V` 6_~_0\MtA*˞ b]gy A5&e2bHc{Y83<]jPshЕ[ s("G=^j%x}661z: xtA!3Xo ثU*{qe/ |pKoi@0>}j<8~|*yyΛ5QRCE1U7L#c%S|Sc7^]Q-]aSn?]'*`Ŏ,(N񳤱ku8,77IT1+*oK}R%z#n&rR(bF'>dnR,ΕQMjE/,e3W@|\@_aMIktDtma]F]W-}UNЖY]Awo~շ9MI//.?9@hmQUGg4, ,VÙ`T/[u83Rzklk;4&s^;1 7De%C- @u{J虬dIcD r3lӾO8Y#|7.wt-6D]wxkBJ'7PHܨ#ƉU18KiJyJJ%`@oZ03I1cT> RR)QdDcT r>p,Onr/l/?ӿa. c +Eh ݙ~_| c$ܙ!Mh*>YAm4#qГU_7b)Tl~K8$mѶD _lVv ]M.>`|Q*`hNQg|.n%4`G[Y7'b<_F;s]d"3,㶉g50!W@6 19%@w="QZ6V.YGG%(H^4nZJ>Eu{s 'os&IϨlZmߏ2FRFg"-nߕ,o'-$T>]:F# ZPc㖀s!?HT ҏҴ0lB<<:XxzU˔^ƱKuo897j}s/'kk* Ke`+TE:jK}EPY*i&֨ 17x22#o;mr"tAŠ"=yZmwxX^<[l"EO3| z8w.dB8H J9./^;Vh%t$mb* K=[z}*\6kg!bc0kAc]H)irۈ/$ƽB蘠퓿 K_GC-mrlSñ?e.Ѧ ,RH;^"XQ2ASѢ P SQp2&tt7$D O7.Ujd}qj9̴~YWVU^Tw ď툪p ny\1K~CW4ROfGj,M`X24r`e2M jRBq+ 5 ʬ1EG~xdY$[u5#W;XBqn*vC=m `uqQh%:bx^gsj)Zl:0ӿlB8S/j.S"l3!?XYM*NzC)KCPmT@LTQ6yKb(sxյӎM(fo1Y9kѾv*GihО'Ej0Ȣ֘DLdž;cA|Na~9h7}㧉LEe;Ҽ*{a WDobu1A~^&K~-G䛉b ge}]O|˼!><ƒΊ{ն]$G39v:tybK3~CtKkl)f`Y1AR5so=]8? ྑ,sȅhC= >cFO3! IbXՏӃLd0s5q6!\y_ =M_ҊS6}p!Y41)o}[6J=bZW?!dB졃e 5d_ikrGpeԂwa}%$ H[D#o ÍoV &(ڒ$C륦4|Wxa3;˯>gN8IG{^rĜ!K=A 3DtQue=K5Kh@ܰ?ro)ޓ^#t2PL}:L-=[ؑ[!*tϛbuEzNsE־uMO!gغ *«$TB|`'Xp⑶k;B£MҔxH:Bz$!`ͼ`ɍ~J}dM۸GE ڱVAldF#@ӡYQ4⢦3ZV^y,t !_76>"KJ8iVu۷cvz.:sVs nAҕ]w \d*+c}lH)^އըOnJuRQHx`&[GB]|(PF ߮0Q5Pe:P_Uy.)!w. I|SEBDUx=4:[~~my=$SPZe'VC{xL. FXVJo$R'v]+<9N tďbf 荀l.JuQq_8|_֊p"}RNYvPRpϮzP< XZU E=ڮo TFc&Jby $ J˛Sh: o0dηIZ(Dg(L )пjrͯo 24'I]yN ǫ6u&6&fRАqc}3 RI+%:oMi d*Qc[bGgB5ÚxkNs PI0Z lJbImց)_|}1_+) @I|dF'lcAGΎF #|t<ߤ,hw2?rM>p/3f4W&ub W{BHUaHj Th%ib{j7E$1ELiwpV7'F2/OّwJS@XF5 1 ~=F"q\ X59Eb=Q.CTBFzpJmK}^ÓNd-%Wk&5Ae|h ؘNe:o@Da:{ m4&QK1JrH3. pr`]TQOB̻!% EPgy]c&7𛛋xC ?tkʞ7 :9tǍlH%Y[%ypαf)VD&qRid o9-l_z~}PWB2dp,fE(ןޤqQٚ+7 `vןOޜZN)ʑm-JkXfW܃~kiZ2Qt`I f `\ RqKzQޔnYLs*‚S1'>gGOM>8 _ot)?فʔ1fI1#ESO"JK1}w4da(جRWv6:)S( /Oz ;|1Arh`֋tՋ']wW!{vr=-d E !;5|mQr,6Z!_̆p}{ Ae~_H{聄,H!EVb*X q(GPX+MIL˜gwU7&%InE=:@.푢j5L^7uA˴%%Htԅ20E4ZÊzEm^R-osJ;i#rd.Hiȝ7!7 (+MKTAf&,|CfyM3B^N2p*'1EQ^?c@˻wƀx|x4^U-GUw?x=M:1;Ye-CLPJbPl%3v\# 8v~@>Zzd1\2O}߅|e>" H/dM}f` q4dNA ļ낎+2Xҋ W=^!fkD}MI4ʦfӓt $=`^twcS~PJVjڀѨ~ě]ɏpi̷~Aȍ2q#7f05{avs~UfZ4ݿ%n.gC9{~"_DA49Zvxai'ł"Z] \.{[ J?{];[(Яkvy1ܸ %^?p> o{TFwT sH?$G^|)J\D~z<ÚҪ겯:?e}B롑>ܴg!2k~1,zDaW?d4OZ๠=p6=&8[ #9KW9DW7"AIHcDb4[Hӈ }v{ZZs@'qc fJ]빑'ZEJGմe?!sU I Ec*z'V#4ht/a<;87lBeպxi5Tn }49)/0unƈ0F7|nPd'_aRVɶۭFڨL_[r,NƠ1tU.|2 ub TqFl Ih;%PV WSݦs[kvTeǸ`lql?|lP夶I|4OLk5^-[8mk~AJn7hlh#00W~ hA MW>wCFh"H {h9V⏁!7iBJGOI!|C=Jj\4[pLzq*+(!JOHsY>^e8:%5,J1\2j1rboSmGvI*e'*s{n`e4Ԓ͍8zt[._.RUa|0xwX9{Ro|L0YfIg*]Hd[ ksCT^'7+݌֤'_/ I ٕ/'ADh J Ѿ~p QUtet{6i+ ^7.Z ~KxƂ 8󒤗-;/<>m0>EN+sLpŒDzOJ=g0C=Hs2~Կ;.|eԞ z*\=*yueԫ2Q[z\ACbU i :!i:b9͈Z:d[cED 6 XUe 5Fӎkᕨz3 2aP}a<[m5ʡoMF~Vgg%xP*`$ZT:Zvh % ZX:v\W0wEGh3qx=2E؀M`v9`K@<_|_+EV ^`rï+mEjIKC9wʱ~`$w[jog.^X8X#!JYYy 89}J`ym_w1vMz' PKNLo\.v*@qp7ojގMOpL_yhZ̃D8w 8Iѫ"ͺw2{G-٥Q'țSB禎۟/2K KwVDz2KUڏ75DfxԖ~J$ 'y4f:xPdƽi| 1EF^LOwhY1DzQ?b{Аti) jyp(M< (z_#4p]yqS]l0\wMz)}IT֯fˆ 0$"4?HU[vi7 BBt Dǒ!e"UqtrglH4Oo NzPo"~|J6^Ołk}.8$93i80M4v7̃*I]^*tҞ1lCx Hc2Zv.twJz$GX lZwZuhdd.N+ƼyW#Ne/P;"dg.JϨRq2s q@~~*͵zA3EG93^a1kSP/\*n~JEm?'F t. Nʍd1A;]bF2 "”+&# w+? C3A/>RLZUYfxm"+:^/k;U8s(~e^и\%LV u]&ʹI%|o1qv(t 2yja cJ G); %?yolcz၎c/uh\џ@&п&m (|avK6UfyT;piX㧍#qPW&z)!v"5h1LU>#.O7h YQ1ݴЎ0(Bʺ#5mq VLIUxrɍRnXZn*;в6nWߡSov%ᤋ6+<]bHg= 玫D]ln,Enun3nnΐtq,6m$#v=dgdR:WIFӳ2UY(yi6e>ˆK]mG s)>&犉`e^̈́̀jZ#-M'Xq"\PP P,`~V%f"j s JaǪt{nԋ_}߄ &5 9b7Kt z\0VNlR= .%@g2/xL4Wn&X;*PӀEx/ voV $֛ CmBfb߲!=݈q/LXK z[Va@*$['Isjp{AP#no1Af ?ژ3*z [ igZuuU|¸//Lw!Œ :!EO^\ua(ˤ;;)_bnRK3zxʱoK`pBӽp#!m-.+te SᬬG.mt~ku-4In}9YQ9`#eǂK7:'nebҗl(Px毠ι ΢pTE%")" ] lψj_*|9O^\sY.VF.>n *aOldhCv5t ;{]&rd0ض iitQQiaP~oTviJRdhݪٌ0#N!!5" bkpӤ{by+LFpK*>Zp i @ijT#k-Ǯ(xІoLHO=& \EpuMT|x5!ɳ"x?vC/jA%ٗ[%!MլU~*Yf&w2QTVǙ$1@ݑ߳Vi4EHoJՄs)yD!1+qo:sߏ{VubLbUt0ZLvJ mO<.53Fy3)"t9n( CЂ$EcߠִWkxճ!bsQ5LU}剏Y`PjخGWN!<>:HAQgqcqH+zd)+h/#&Lee=E<_g1Tx,Ix9ZKgp^p q;d8YDߌ.zMYiLBZEvՅw)m A2mU>!|AGWL)3(ք6 t*U9;l9 ;#wLfmY5""0Xv\'&rIמD: Mp4!` ^C {^08Mv)9BX3 6uӮV6LEXcr1e婤w<ĻY_W><@uS9|0'~^9Qqbنlcj*RV&gdot'S'eȳhÙzE@[ŽR_4O&H`dVpQ2Dz U o%oD X!/pŜ'Pbz׶^rxH!/dU8pSκߪ=4!6cv*ԓ} j! +S\<I(ҀfXծYb49gs ;݂4@\0 טDvcOaB$DHEn g@O"h<3$v$o, %A%fRV{&|Q޾` g$'yn{KZd2ֻ:^9jv "sD:Y]"{,_xo03e6utVXe|6p؏ Jw#w#Vr > (?Wsߔ #\u X MpU7ϴ!lFJ0obm:K-RuëiG]E^</\br{:&"f+66jTCB%8~ iZ5UڀeOAHDT8'e$E=e(c(^IOq@!t A@6!=9+y1T8C+a&-Z|Yt+"kzi`;>cL P:34} vd+h=wd ?DoHYU*)!ƱEע]7$@xUGֽD7Ca tqt d*xtGp$; LcaX2`a=FPE! ݚ˹3<8m #']/_bz@CˁPuyꢽbIt&@9hu>%> yYqDL}OʋZeOm\kO v,Es'RlDT\ct0 \k, &a'oӾ_UWrmw;;e8&D=$>s!S@;٦.CR5a5ɔ8 r] ĘPp}4죪 _m zy;_e_z&0P`/L{JL t)ji<|Q t4M"bgo, H F(*Pf瞩eS%ԋ Is(.e[ʒ,JD=kxkHxrlNNAXIrUH(q IJW_7e EiZ sho`.E I%џ:>.u[[Ű![!]s^f6{ү٧ ȉ)LuHXvs׌@.}kzC75.c@z_ׇ6Q)*,! Yn~DZc?T y 6S~yz02a0Q F囨7DQYG2ث<F8MW $\^]+M?6ya<:5rۆε1A!by&p֎3`՜Q7.O`r@U#:!* *P#څhYBklǵKfDJXgA' w&~r;9 \Quܵ E'ZBdhMWA(D}9yB)Av >^F|'@`.){ƣW?xOl'=: ]jDm3@P47ԮLEܻ,AU􂦋g@KhGhkE6ӻƜK dd;2kt#k#x5Q/65&\3HA>~n @KxpCԳJu#Ea AA郞ڌ C:nxVn8?~3NLHLz.OdYQ௺ c[p "[f!}FnMuq MDTxz(GEk8bSm Hz}j;'?HRH>z ^L˞ѧG5:U?@l$SS_w7v)Y.J5RR0yϋR[\BITNړM cZD.u )8j/ 3)t* ܫ tJyczr}=G&jx{GjV9zhx̡D\4Bxofufe6s!(Z~,W qUxpJDV)`Nj7ԶVl Z  8E酻xui9cCor=vڷ 4 Oܨv/VXfr?Sa\G"[ 1aF{$ņvV/$H"+.Nu3C-piCc㟬s;2a9Թ%l&MkCH!5wݔp:T (_<*Zr [>^7-^ >oTUZ'N&%b!CqV|pYW(е1S^tZbk]/T  -䢛5ȉP_Z0-q;}8FqH_QoPpLXZl;jBmô?/f0"~(5S$ =jV+hQt-be Mf-i`=t_: Y*y^Mۘ! 9\ʓ-9} E^|34S`eQ)= &8 fl29Ů׳, U8r!FnZM8FS}4C7P8C٫f5gZI Bߖ_);Yaj= W[+|˵W4t Y(),4?2b:idqjTV.lkCƋ0ωRK!:pE< ]XZodX?s!Do0a /7xSZ\͊ћj388/=92=7P{fxJ]*" L;g+F/ 3y %U4ϓ hcF8ej}Ek`]=gÉ?5 lX3mK4ce'oHEUHJ|38sD)X߁V@v;#?z y'ǑƐ(i%ni$g4CXii뫕G;Õ9 *\y-aCBUOb0[)~rmH+Gk4jꖮ׺wg wE0ʔFnOUM^6kLW/TH Hm$#|.ZGvG]]y-#PXȩ5&SL -D57bGCjYVuqYJVJZ '+KH6gX M/ڠ[/PЬ. 4!U~1' 1Vro-ٜ[P/N**SC% xOs:!'Jx:1xH_݄uy?XA5&^H@ɝ_#UdG`tu MkCãzQ7W=C;|(8նEq,mh77l^^k8k V60b)ĐZMmu|-Ƌ==ؕa-K@l5H,Sf]8W;}j-CM;soz;:yɿAxDSPG UŅwb>bHCU2RylCLfo@8-d(C/ [~_#m_?͚Ic- :: V\E+Gǰ^ҍ=[ FxEľdvxht}sE8Z4F%qTƿ%ZE7ݜ!N7Ke7[|[/*`d#0'專4= ͧn/^=%6;?oj*x*ǔP6p6cw<DH[ R fy v(/'GƕJq8$cS^`_6L̑N!4x٭N}w5ӀG_{(KVr0> R3!#;:TBCA6ufͫ}m.\3nX~}etk#L%eb=ԁ>A f[ia2\3^yvL+y ٠Dɹ/YBr`pt}ܚ m:'\VZͅy?=8t)dKdzpӛ<OӘdÁc<⽯OXb\aݞJ 7͉g\@QDR,ܸ'd#qj^Ɉ:`3F7_T*}nDvx_*O^5x/ 4D6DD GZ-:֧|ojoD|2[j2Ң4}[4 H›jY+8 L١u"prL6lMNd(vbr6hgMUyγK=fV?8{\nL1cL&2E_| e W*j fҋ[}xD e"tҢn]REYV$h#g~Wf#Z@ tHMDzԒk0%4Ln.W+O+T49Ol]6%ѸE^6I⤺C2- ;p`j(-\Cm[腱_*X؟L*#Uyy1܅gC58AF?bQ>ګ5?C>Tb܉1հ>"#FoÖ U\ښڑA0!8ėI`2pͺkBK[k<3v|" Vux*܊ -p4&/9ú3s ﬇by@Xd`1rfXK ˧-Y*F mmh"j(s4*^>[J} {;FG&L$w( @,y9ݛFީ567UYqo\5b#n<ӗAXK2@e+x!:.uY-ki1zߣ<.NS/[w1U=OeeIgY@iM ѰEW-ޟr Ŗ31Lkma'eYtm:n(il)Uyx*5Z$nHNZWn򲀛w$tjUN Cpk\G3"KMh[ςN޲BVυu.`Z|D(73˪KMƒB-gf<\|瀝kKX=+ևO !4  c9ͤs+~,ט:Q3_'VW@Ҭj<^I,#Z q3OEM /h$"`gq`_OA i@;d-."sm)4&X d ]`$ ^0xŝ-IE|=ho{6Z}ܪi~&7XV[9r28=N:J(0fٶ fYڶ qryቸtt70'x/xoYϻDi8 a#,F??MKB iӜ<|nŻV3@([Õ'=n8j?d&uۺbRZbݴvzgEǻjv!˜E QmR1fERCh))`;%$jq"5p.z;Lt_Ao9Y5hٓ ; ͏~fwbWEU Gjguk DlJ֊#¨ЊBAkFh³:UP.$ f0UʯKp6!Cecm?ЅO4a^&Z7IMbhέeHŤ( GFv T/Eo913C%.l$!fet+9RfH_iȸ#p͓4[wjܑ/$0Ν3Se9S}@BFp݋Dx S\뛢QZA-?q]V@le?q#H k aݱlr+TR1 k1N 7u^^jc67$)3[JUyBJe'kH]= G5۔W0u\&Ϋ3SI E0yq?orXGQ,*N/3eD[T[tzI%$CJ^PXT>E(;ءWih* ^[gC5%ۦ+ [}\^Y\S#bk͢wdy;jTwaG|z/ěO֬hXm^Y'R$i=ULڥ.oѐ@0-K#Z?WYFZ`ũ٨s`MWzi,P栢郬YH~he Rq~4QW &\(u&Wc M[Hb~B$UX[soF+y+gc>%Jq2C&[;}nlhyo ~Mn SDQM׮͔=Fd+X,߂=gʝibH2ӌ?ZrWEBϖÐ?a2anXOUJ yiWz¤[E#Rw#zw)1!OV+Mƅ rff1%n z նt!Ƕm{o?c_ކ{zOp4ºM叕B%!Z]NBLx,b$V{ Qϋ`d׍rU &UrVɹOE.docA py,&~U1:7:@/!kO=5I7# |y+S pU]_*o5(yOY;Fn5 xtGc~~d#`])8l E}0ZEMJ= &䗤_M1tM ʿ[^_~A{Z[Qr 2mo%0O]]giY3RN{|1Yd; g3ڹ_\\D-W騌LDZ8W֑b#ђnr=J ՕPpI_r[ ynlP; ~2J= X zk~<+\ƗT6졽ZfLf7#TԳ1*tc _&SժrI{mdO4V\]s`1CXG{Bo+B% T"w=iNyKՎ 4- uSp&Z.kO02P!k4Z?[,Maӳ{УU1tSf6̓JPO2Ae7LRQx҂x_p10S} 83iN*uARZBL3۬%:wZ#ΊTy(nFt7w$0mX 92Rq9f,csѩ3p`^Dx ՙUݱ{G՞l,;J: %0f:뜑x7s_5S (Tkζ89aIvh; tk@+E5XTUM)̳4j::I=`=^ 4l橘K0U&1?dno`|INҀ'&%XSǿ^z"cW!ʵɢâځØWIM׶Ctz4] ba-.*+DXZѫd|]wL`?Nns[:l޲)EbBD~v#|wv>T\39}e  +h'[_5lтZ ݎ2:5sςƂ&}klbӻB)0GoG`ٷ;97ml-/cJW.zFg/jT(#: >'g Gw7<Z9>qtego bFd#'fݗʇyXUƟ;1CNweH5u}^ %UO=TШ9 ]pAy׵3_V ]ƇUZzMTpc+|o*>L3iUgd<9oMltkx8o@[`HkIht*٠/bH$)ʐu&OB|kg>8Iq__f!t:Ėk*GTɾN_AŭxtlOˌ>-L lX|rݻ <}xq:[3[> bB*v픂:ցut`ܶk 6r  % M+IJv##~Vs#\ vt7\!:4uhLAl2$33'Nc꼨@Ey9 F2XC<wrSeSg[TN&irfU Ts`?ѫ'/?s1~􎯋v^Jp7=Q52m>yurQP=ڢLoWj^Wo`@# 5Që:^UmuJ4鲘_a ] SH,)6u O=A;!c\"El-p7#]g;]W$rw5;)HFb5 -[ ଦ}Fۤ I7^"2j y+ma?T/Rf=ut1(‚A4QIx2 2N`Bu)/O7{1֯%yᅡ6Av.;Lhpr8O-6F}R2TZz~1O,@p IT8/dKTzq+]IP1P59^2ѪȠp+ܶ̎'Kr+$5GIȧ'idAgu@7ՔMdr@}PP$1qgW'2U7cSD7n!{ t6f'bYR"P-H6+Iʼ([InE,M@y12m̭>`~ ?iE`N-@dhΓS |0Gtu 7x gj@-Dv$"-SuZ{o)E<C}lM&hoPm̚m^0}Cs y nK؃=i-+d%e] dwչooEQHЊ ^M X{ɽ@۴mұ621 00J*dBE ƽU -}†Y3ӳ BP3>1*c5{~6ܬ%;\yHLL@MƁ5 *(b3"t*]stcL0 p\6Jg%79;/GHG=ifԜQXߛmc:#efnQ;#zC <-t"EH\ '<ZvvGrv} vMIZ2ȴ-.ƦOyd7h4u CCkUA ק/sXEPt1G:y/3e,.s%|1)qL#5% '9sne=&VH FjME^ϻ3ʌοӿk{٘-dDj^"G$rꘇ78dއ\'5Zl=)lx>O40,m+p ÅCZX4&Wqswsl88ة\BP#94€jш$\NٍkMaOt!z59}ذ5B?T"8 (DB_`R ?UxvlcY|>AD'_wtX6PXR|"_up&hg"0£RLT;e4\A+?u1Ť7KE ܟhפX 4{>J%{~'PS_x#|e3uo{޾0/U8'U*aiռ=ZΌGR~/zsj9%ij4~}2Vm>e}6XQM^caC7oߑq|\mWF_! ̺"%8.>f1aյB.bĮDq}ʪ#fv=}b_"d1&y #<5XoVh^rҁt"L|: #()q&ެFJ@_@Yޥb6̪rVY/nv0Z1 'E5u SR/ʋwlPZe8]b˃xWbF5pq拜c~ʩ-oȕ,a=;QYU`1IIe9+ay6$y{*-{FX{՟y/J4UX&&ԊM$Ap  ,}A)bl< ݍ=%`sD2h\eIQrW3k_LzTLQ }i!(ɍ!<r*u>xVD8'S:dR\1|W`ԍ U/F`H+;LDjυؠ!m1WuA̝F+V)GS[=V8ޯ[fLdʔy35wsă/~On0HUg!ےp 3xySs,ǫC]vsKΟ3 Ԡ\GH>Wپ7SW_#CPC ?kLn٨D%Oȡw. Oaښ  ~s!ql_]VRc{R)ḣ̊m⴨☂ _!0 ^l:u "+ J|QQJ ~+ tgE.KoɃr~XEF^tHv6=bS+(ܬ\ ]W7y?eFN;˹⥼̣@AuQ>T&/n?8Wras,>F~R_}[t'o\4F뎐/-+P ǰyJwqrr@DєqrBtvOVh;! DDƎ2oM'<8 nGcۘ8P`xZ}VjThvCl6BJQ̪[Hwףģkcܰ)6C/J]d4TqSt.3ٌ^(o9^V'M2>!℣ĦS_6gC.0>zNt G.[  ZlHOc}}.,(SIz1j.+F= NNy dCw+D=D0x_E1-x X: fIlhʞ$6m$hE|.WUέbvC(fۀjlD  +TzoW1n^Ёzz6rWk?-:9{㩜p2xwǓ G 3|g)PwG7dbܜͭf`_MzڧGl#0_ZLI8aytr~]cDX^OÁJ7pXBBJě u\,:3!RFn4NTկK _r${?m?/Ӝ;8}E;@+Y.n @+'Qc}c\[iNJe1VJ^OOc7]uvKS+1?s WW~pacZ4Ɲ*"~Eոgؼ^lB(@bv;b ̙}<Í#2 #ЇXYCWJhVgd8,ݜD<5b9 kgx/6IN% T[FMa73r\wܒlAvI9L3UjM@W 5o0U9am7CxZ7]+| 9l$sC6Bwsp&Erc*5D{'rlu`~\pK+ o57F=ni8φ~D)/!Иf@'8R @"|O>3gu!6?Z2 ,* ACwB)CƊ̗A᷀cybB#"{yǀڰƁ܃֎|͇ҿ3>P9DpiN.7(^5Ryъ򫤋>g J5MijFIo{{[,T%1ӣ&WV49=4<|CP/WDd$=S;^Bq翃H-}r慩L'&W:P-+|8d4d=k\)t#X$շ~x bh4n׀I]0Wj0(PIT Vқhg!>ZNc2H ZWMޘ' ;uC+?aљ\UY1T?c0X_4\j&!iwvRKD>If+Jݙo{\=<؁Gdݲ+z,79>Ft]rv¹1^ 6:UJd=Z]KMYGLRS 4=_;U)@yջ$k,v|*jKYTRi @Zn1zUJ׌z69o0q8y[izN(x{ Xu s!]&rp|HͪMlq{w I谷K dl@ U K݂W5NxZʗi'Vo2s\ύ+U) ȋ&XW"O >?ğ7Ȍz/ ><4Vջܣ9Տ u5  'H(!pilbV;Tc#Pl]%T:%Ń3 . 8tJr\DH  ᏩN4"&XY+Ʃ"'#L14DP37ϳ-O N" Fp)tY%sk֕k PF9Z"s؄bw*)߃?fv|@yqDkJd|MZ3sCX˛PCɴHb??XFvWȂq. q{$S5sg0 _Fp <" ݪ,I}GWTCՠ^84AToB\zzYY~v80x5%_ ЌF% ٶo%V,PW/-*K:Iz>oF־綡ՂSݾm?:iKs-#ݙ.B?FTH.Th!z׏Rt& T.̺ƅIV@~ҐُvcﺘY`+‸ѣClP/B+h&4LU^@z0k,LU({DTܕp*TN▰RAp+64jg%㧿J }bk\O[3t{ Bz*&qn#۷)Xd@~kkH-m:~š.&+ho9jѧv˘+hՍi! ao}.eM @ٲS*AvֳD+%Xj(JqGZ8wGwKs]&nLW4UG"bˮĀ>GmdUȡ /<C3Kw SMoJڲ8lngA? UMu#i-+2bF  j$8@ Ĵy/,fR97:t!v[ m`=jNDz5REKVsu"p9lZ(AEQ~B|ݎS@Gek :5/سoAlTv?$F>zZel7QDl_orf`Ԋ~*Xھ0Ɛ$`qO,|ޅT+GSi~8cC 5ǽ(\|\Jo(``j @زZ;߹į맾NS(]B.C5ǜ^ZS:(/7ZVeR~ˀcaE"*`e;檗J6w[=+{_Y!y&sGpc^Rf⾩.[pu_` y?Y76z o\Uo_G[r;wO9 `Yザ|rm G0&,r? EMѯ6%4E@vmP)f\x+kEp?ld+Qfj 6PnStRy~gZ7jz^[WH=U4.zN_ҟ? vA5XcƟM@n#drpsI03{X=X;** 9xƽJP棳h{?Ԧe;/F8+ǁ%U4oxO/q=;QZ.` B 9Ox{LM'LEh 2֟Dv\45Ү5V-wd,ՁDh792&oA_{J0Ub@meyz 4կgg{c'1=tBY'C2eLtc~M K]7͇Hls(BaMTNOؒXkβV"˭+%<՛ͳܑ_11d qgDCb}|} r jyG'9)Ns@!rovw~#CG?^nT+٢!u|%5GQukh. >E1f2WNUdnNf稈񴏫As.-DXu^qAtĨ]Ψc\Rŧ ^:6 {b{[:fQ' HTǼm;FE(-DySY7ZPWsſ60Q:gغ]x P/xc::m~'oug—Wf:Wr2{rd:DcⰤX  d/_-K E@ä[ p_K;8E0j#{#X?Eͅ(x,{SF{l߱ZlYcu3bXM  *$0_)|_Ш9%X yQsw,éq_YlW?OVJvZ} w%phN㾿D0tPھ t\S+;FVjN"&;cQ~@udl;\Ήчe1BӢ,iix^"< cǩQkJ.Ubʌ II 1:i .GB'>M61\ɰD '-(*C[,u΢!MMPwS 4*jUDۖ")M`^7'`H?3~i.׃5.G/Dn.=9|]c[06فXX74]s@\̖G]kյȦ?ʬz4\ puuqb@x>ޏh(m7QjO," G2mJC!7+j9qqH uah4G蹖|!q}h1kYWq'Vjb">`9}-2|ji! Kà6 0<.%v_TBiZeKWo N|NE7(]uZ~iT_q3 (H΋uޑ.e$Q!_[KM6 V_v~K~&TdkV6uyAOasGgoT1]9~nFZ*km$e՘Ci^Uҡtq-cWGƵAwB &}h'瀔w, T$ls-1ύ h"eG|8$=?d@s?HiF޽Vr"NZוrmB'xe3V%tI}궒x<Z`KS :7ϥkXd=8r!>h >QaȕKd e'HLJ. um UJ*5bYs~Me {Nj4 cK!$z!!3CtAFwEyB0Ϩn9)M2 _Ph\?o)hR8.R@ebFL N2j멄;ΓkbJRJvꐜ5(ᶔn a8X(4>jILϣPlfz>o8 m?ky]TD" B.F>C~9\-k6爊3x܃x?NyQ)J(Ǧ&7A(;[)\F#bۛ')Iy \u]"UrXz!Ȁ  >dh}3Hܻ @확}͵o_]f̖#D*?[ ۴ХfKv L*P~S hnXz;\Օ#گpm .$Z&͔1EW*_ʴI,JZVDbx›l40Ib?vC22VZP큏Hy 1mS1hKيHAwE^ /j7Z1&2ݲw> buR*=U)?)@;-ʃߞӈm'R-02mUt=q7L&AEWx6E}!ρ~x2r w8#Mv@Ax J/M'nDd^LUD"P y~-ĄFK |`Ih~pg>א}{*T78`7 Y,6up J^oNgQ"1҄@Uũ:S荙I9"5]q֗_ϣR|_Y//LR>T`et2q-*W nN{*'֡]B3P>OΟ}&  Od5J$^ᘒqqe="@!`f1|&洇 oV[og%[Ͼ'bw1ɍPlPtUH]EheC^” =E9wq6i)֩kOl̵3,(W"!/aYl(҈Х R7>Ҥ.R*A >t ơqyC_m6גFuS|w*2Qz?BD@! gҩ P,uK@<:5 ,b % qF8'S(R?$$*e-<1B׸F4QVi*FM}T;#eP\$ <#>]o|D @i:2\1)aG1Kn r;~`hYcAixP]"ǁG({(Nbc{_x@xZw_ jԋa/"m@DV@?y7 U8o423k - "YKjaJ KJQB% _#$hh+~I8WC>u1rur{%r~+FXt/E[o£s^ꝕ_˫͌>p}nsW^ױ,ySc7{O5c\ BEV_}\-/V曅VΩ iWیwሂ8ܗKレKk^gtsm0'#8wW$$)fmcF¨J_h_tD8/;5UY1=ɛ/Fx0uc{z83,d Ze);]FE_aLb'XIfQd 2,Ki[ywnQd"X`r0ǽ{ %W WNJmUbd|2knE/2h߂jMJh/E̡rxg&`GS2FgF0fI8t^4:;"fR'&GhS/5RP$!+ pj#lcXܢ{1M<5^Cq4CiBeG+% 7RNeUTp5'$vDL"w`j[5&r4 ]*]mv:FYSsoMXŚ~mdxgG̈UOwS܅VPqFR[=߁j#lxڙ0S UOg1WσtuI> 槡:T%%(nii:qk)RIЌ4__/d?qiVV~lY Ѝl|&U)iR ~CMxT <܂Z L$ xcshwZB'0Kϥ5@sJ ARl7mX` *3zHUk DgF+sObqLTMɴML B]zS6l%tHPu fmJ\̈bqa{w3 aNQ4+WE-<"6A19cAƂ}* S&?=lq۷d Ii!gYl1 hRBЭ0oVSG&ER\Bc?Pa ܕNe@GO`Xsty";؞^s}>1m.!0qP'ON4]-;̹5n̠Ry샠N,L, Sek1I*"`"ˮ]-!|}?O8U[A֭jeAtN&6 ϖm*8g|ު⡌P3w0lL!. Kz!Ȳ^e6I QOP&"oxQH]%YOTf:BȖ|zQ6=o-Zqir]%JvEGJHqH5cF8`r5ޮ|}@GCmOiv>lB+lsmPhkP2CQzV)&Q¡6i`Ã'KI}'徤28K$].0w9gW&|#Xm6_ kK"p^28JT'(X"|bI-ؙ)Nc6<9_ULN4p%n+&zTKh<BĘᦟzn!M8= I^E&dcPv"Ipiëp݅,^AJ1wZŻd~B"tSa<~5.'t9% ;0Ѵn.cYl-mS0Hיm4Ry㉦3>^ PjcjqPHR_Fjk=6[B>ҬPp<%(IR5rRaϷ.\cϯJit5BE9;Q4Y$!+)*"$1A[ʿ>)[CۢJLKH>E1X.ŔkAGf_0ɱq#N@tKg:e1.C<7Wb{ʮ6uq:O!gR$1u̚œ]»n{(з3*vh/;LT[MIGp|C*kDrw%Xdd&d{Dw fV# IlfZIw+sg`4$~0hC$k=i9҉|=$@%4;:75w!*y=BɊ efƆ@sMv:[4g^uWwH< )/ $w {֝&0KڪAJyv6tGW&1t"SVWuC䅉|aji?/<A~0XIv'P. [xi_٫tݤ:xk9MO0'.3 U'( 5՗e M` K2НFE`q24f* #X6|4ZXN0VB쬕5P'<'lgChخ&nqvvlU>J jN%,M"yW"-..dy95F<`_AMV*!8Dtmr_@kf֚tLhqξsSDE DTc|?YȓGKS11t.c$:<,-q7`MeQ5~vM;T xH^` /T7n3 1/0dEk`rl&bŪƯvv{mo\Moʔv8( Y@h"rn]'`u9g8:Yh(L sl]gfs3֨'eQDa/ AIfEf>u[9ELg(U8t69yy|y&wM##>qK<NJ ~4O#Zײa[X՘y}3[A*(bkf~@ڬ<PQ.ST:Å2įmFC´˕Z,:TǗfll p LT!ڥT dYAZzK9jy(E]XQyDSp>4Nɠlp ;Ǣ=:j?9hg  .GzSl_uo5sW_]&8FIw&3v4`^kӔvT}&s;;趩ldp[Fϴ _p`ASKUM%>V_nIa_UF"2Ǡ dke@B.h|O|kĆtpK2U{OA5g E-Gv4bƞSR5 frp-`gR(GAz~YϦHEBGKfY,TeO<sڔ QKu3*qNݤyNaS|0wRRJ ]ٰ]=} d0h("i@ RU+=ȪܸQ@u`QZat8Ilh~~/]\Aڌ+aPYݵ$$]=6EYpДT}_ wq4J^AUg^ aB Rd구0t%Zq fha*qp75ʒj 7θ)wpbTPH:d2B>0c8StLToP/g,~.VW㙡٬CvR9:J!B|:Q\CW)G`U룏YڒGe<a硵01'"ˇ"bǓReeP~p,ZQ)^YṁyG˅Cyzfewta/,B8 4v`7M~i`L}gp[S3!un8|U\ء2MVUD*h9#R㩨`4+m| {ń@3e`b fR6S@[BT-΍S'Oxl0e "pˊyhݗҾ  *Y6AaT}Nph|/k>Xq%T٪fe+ z%63(71etSACu3ՍXK'nh C~j yJE8~9W+L0s,s!mIDu4Lj#|"!p Ng= NOG Vusqb'rƘ]Q}j\  -릍5ĝE'c1qꏟW?f՟LMgg] zo6a'[~TDH6Q+=(h~MHTRg Vz cZHypQ,Ũُ w5/oŸ3/^֑kLN64%"sd:p]?7r_yעR#6tf0YNhڄ)+'3*&lXmQHEOii&.Aƚ88,yy{%8v ȗRT̜3wFsoʨgIeݼsy}I[!!F(!JW;$F܆t&\aV4ǫZ|$͛mkǒ.} ;GΖV6=偒g$'^;쒘І_G. wIGn#͸[\=v$8mb (nm: j@Pwz\q{I•#+lIH\=KSdU35^07Cs6Ɠ M#,EOv* y c~D&5̏߅ډgI.yg4_Ґ@#%%UoiyGB">'Wup=7Vw_UvChnU>EAeKe퍿LÊFȬq+yϡ緿l5xF~nE3@6_md6*( GJR, cYrNDo'}-iL0] vBsV: Z{ly׉uZTX vzV<*TT1 ka t{,OaCP=^~N5ZW>.X>g^rhDH.a92dT&#E)N1nBk2-<jwօf䓀*6Դ+h!H/1yfz͐>#`f k^0 ?2S:&X]0rӳq;[OrC#.~`+nn8GYrը߶s7/^Zΰ@ڪG jW? FQ~0Kr{J_]6T .*^_jFԎ d}dbU5D(wO̳K0e>ꔩ3/# kᙩ3nS|@o 3*le%&}-\rERL;{rI\w#R"_Sn3ZL̞cL[ k=--F|Ĵm['}J=]rˠĐrۯQ3_bgNRx=#[k;O Hy%ݥE{ *yXQm{PC4yJBaL]Fhg}:q^bɈrdGSCQ$. N1sYU3]nH4%8 &z,'d77JMjYeԪ)t:hròΔƢ38:@v D>YrC? |")Zp+' nJgRGCY'ls%o-=2Bt!57(iԱ^=_]GQ=,킐VgŭBd6l\Dk-#OX@W}B4S7$D'<ͱ7ut,loBq>Oխ&Y&JHؒa$BnYDS,YanҘ~Jtw?GT\{ T ^:7;;L}qhX+EO01z06dvJ"NDhsH D$ "OF:Z)\% )Ph*|:}Ȣyl[OF\nɷƂ-}\RboPp773wFffMw&)Eڶk]/@+sv` sI+@ -1Il~л;/OI/s=LKiս˚s|an@ n/Bݭs2DhMi2UעgfkoujɅ'U$\8>H'$$S(vn.k!/.DY$M*Hb` iH )|yb:H⓯R;t N{!pIIפxx g\Bh p0hG6#mRëII&f+bW'x*=5\\cmk/nsKwfk u`,d{`g.6s})A ajAʉsm#U' fφX`aM>xmw4 " =i wFl[O'1{4XDKۅ]}Wqeeי=5+-Vߘ@+૧y&壄)š VAK[ʬg/6>rRk Nh6"TAh*֦}KEeX2vxi•WP(xLȼ-<^D\(eTR 5ي\s8(L !M2%yRD6/~ h0ۇuֺrAV*=C\fFSJg6+ G1*L$ψ3ѽa 2#qeh9.zYDxzO/g&Rə5p|jHC#`W}ulEݐel NWI?Ď]qEX௽@8h-lB5ZH O+76`Y1FVɿ~P=Ӫk uW_$.dq'I 6nr{.Q.uLsi 9*4ign3 7.5ؿ.dC+BC>!t^83)QuK* 8 [mAhqd>LH~ُQ޹Ny P eힻ`'_:1r_6 aE3@G:lqJz-6 Wh% rL>/enP b9n8*k.\dxX^K9jz3zIhce ߀džy@N5H^oSĢ})p?Fto,xs}Z}yD4R~H8>ic_XnȀ Sr3/&hYV"l-YV?73&És A&wxX>$M֏(]9s< cüklRw0r|;wD.lj7d*O])1?|Wpy ZQ=HZ =,BQԌ}̐L0[+" `T*WLNSw^Ę~,Ǔ2B _J"볻i?dH؛mN|)N'Y%/A&bWf0|J̪<ֲT YV`\U۾JjaEṳ>Z{2o#tIs~D>z5jHL QO6GʩmD谆aGF C1P & 8rx-)do+pMd$^h S]$[w>5 8(7+Fd0VΝQYD3_E/~UQZ-w %eAU\ɬ^ ]0ٽayC?-;JB2vF5mP1h`t? vɞD11px~yx٢]` [ ; Z:dOPc3V4%S i@>p噶Qf^6Rqfsim wDkU$}1qW55y %Tϡx9X{u|wڜ[ALcz:F9}Nw 59]6yݫly%@sIލp!:>,Ev0vQ}\98C:@I9etM;VKw@=VF/A4,s'WyOXf0 A®Q#qB!Eٔ4s_ϥbr4k!X/vKuQkVkSoݾR܌lB]E1/M;s^}H9!KžVT})/@C\[Q(wSV ZJ2UCVPp@8,0eC8\^^ͅZa$&<h>` sʁ5 X7 6dx;Sv`@7#Gx\gg`}&3EKXD!M3 |/#y U;D*c hΘt&HORL'u}sͯ{^>mhu^js|9Yy*D#tpZ?(%:]C@{议# ȍdh&On¶Tc|חJ#;E9HMtL $5*> ز߂eЊ|֔e 367( yc@6NG%: k:G\ *p-liXnoMtߨwHsG"+dOjXGoVl$ 2W˝ж|l_,/Ԙr^t {? c2z4qHE0ϖtP;$',c5dHۑv(?s\`GPГ s{:F}~bb~'}^{WQ1E߆ HB,dHʜTKxj_zum yRؽ>x%F5*Xiz>9\|#{j#S3ͫH bϷ2GwZpB[\vFBK.ςΚ-ĥvR_qٵָL,GuF"b![9BƥT`R*dw%G…s@7zB t=ֈԀ'+~Z9%j*Q$1)bQNNs n'8e⫂H]L63 }A.((f7  $Yưo+eу(QС ,W _Z(5 {YTNzz3z`CA0!Ry+9ONr|=-B /.2&VrZ2oJm4%kܑy!h |;6_ 1"e,ʆᶶmEFs#^5> ȏi5X-\3\5ڶx8Gol9Vq7L*GtEm@=eu-S)vU"gI}YԡjC+Nʋ4/P'%'G+1xFN(v16o(@7u: Bs;{\،y8V\ӿ@m_D5*  %m`9sL`o<j2r fզ$tԝԒx/tC aYu[N=W6AtpcljeTaewL^3D&.MO\lV?8 V {N?a9'Կ@iJ<;3 V !0/&=#?\zx(Ew&c"s3wѝ+(qFwmyCVM`h)x&}.i{6Tl ?[.M-! VjZà3k6MfFl}Z9d/=F(GkNX3g-Xrr_O"TVЀCC%v6$*C/yS cCʇfY47Zd CmRΕT.ZwQqz!LۻZ^{ EA5L^ԉך ~MTՎZZ-lGu"e2YQ6wxA-U@w7⶷g$l̽WԥAKV9tf_#!.rkQ?D )uyꊺmQik}ZH;Ϋ>RywI䗫o ` T!nN+.'1f}I2j yrAkP/|mox/>W8+mڊfd˅v깒v oLK@p*7P5Tj[FV7JvJCNetA#_AWI50(LHzN]:_{"]( &>  =kS>Qw_FH#M^ftpG&ƿPa$Oz)BPF{K^ S(jXPrYrJ\b1rBu50⮚bGE9@~ ̸/J6|g1N[ʢcޙo'[sUkZ?åeOg@SmxS8l~P_)l)JChg+mLFXF]]*O7 2U**bE} a6\g*BeL%8ftX^Gںg:d9rG["J"9\73ɋ`QJ%8T/ץQ=|*W̰hKĺF` m^^@iHW<֟J8Tz(pfH8A6fX=&%! PX }MSnXW#Ψ8u2У,ĭguukò&( gzc@ld#lV\H*HV4̤1o$|F0>;8aFy=ꊁ!iZgIހ:k }Al_<)KWj( 3g7[_ c# g~jnYn ̽|:~GQ{ &J+=Lڡ6.LRԩ-^8U|\#\mi YXr =7MRE69?G~'N@襒+8|p:eYTAYNY@%F-Jj N1SnP"ȡ!^z9wT*Jۯ̊U)Un>Ӑ V'&.2ȗvFX$jQohN|v~FJ$9-zŨx9n!x٩1K”(͂{BH1(1nlOݹw0J%l)5[.2pIbkyX!uvhf!d;ݙ2I''= JlJ+xcXW&>UTɤy{;kK'܋[#`gHvg/G%N>' ^u OT;=/G5Sֲ-uLſ=\C(PBm*ۿ33LǭuAotZ6XiVMr(dA]<6.sbuvzeP1B< QH`q4ɧS/BI!1H\ƍ4[ NcRF8@19KF$畭b~"10 pԙ0 ~?;+?PٕwXdEsgԐ 鵿v!h'PGTm\FȐGOP6b3<7T#VVMS|`䂯3_[! Uuu0n-邧5|5:yԝ:]Wxw$_*΍w) Q8W^_}Ja߿[}j=3SdC:NDýAeX&KĄ}83붅B\Wәͫ[7h߅6.H*c +<ߵ<'P,'H:(c)XrY ݼGZ:z˕UrS'Ё{պcgՠH֫v D&?YZ#j 4xi=yrUCYd2Tm*-ޛթL_`ևG~0wtyx :fqPlI`3b{vNVٗ6o)heEԠ@[3)FǶ*x&GiiAv&$C$;ȘT @O#y]L8yoNpQ46"7sAy\A!*\}r^װCV}U kZLLwCQKk]_rɾqlwxبMd?yf$nx>ǀ˝lMևd'J dC7f~BlxǶ␺)5ݾΦQzl[?{ .cx(8VΘ,CB[m .^qw2=8Khbq"nM(;]96v%YնB ſ k,EU5;͍ ؿwL:gZ;!uv1ɨ_Ue]Hvcl^Zh$0VW(>8y/ڵf .yЖ.U?EvFe-nrc~ؐ>u o.D8I3~]L=c #K|2WMƽ쒀@=? +%Gؽa  dJuVWxޯ/7 . yeIhYɴԏ׷? #s*'Ўllb40ӆ\*B6xIWa YB)XMl ,h;n#{*'w5bG6/?w3S\jnͽJTK2}9 !R 8lfj#rQVb@eOLl]x/Bv^Iw+v }U'a0Ń䠁~U9mlE.TX3 5FY^*iڠEDΤ !#e@RQs&rqo&vb{| Ԯ?`PK7%rQTWBUp՟F*׶ xL샼tEѥgp[< 35:b8!O 8w|c2_1y\utDD} EDO3UvyloGn-NO*E; ? kOƱBzLf "D6ȇJ]?u,o(+{K0NLj8t&T+e~(PjíL^?JP">,]g_d F^@m`zލut&.UUNHo71l8%.Uu&(kGЮ^zڡ9e/Ar26tPd3=\@Q!""(n s u=dX|98eGvi[d3{I"s6Pё CɅ^˼'wM$擊jrLSϿt0oiTb?Xr!&ȼbqشI"@t'|P"h9mE6c:BP^>SF)4_}f:u$}֋I˄7Y[a#X@犨||ڄ  >28Pa`e2suiy,]}ϫDGkC^GGbX*FLbQs"D羪 9HIܺX%E⦼cdP|Qz qEuWET(<1Nt^S3v|Ү{)0 mށޜEXc#K|J$l$<;Pq!xQ\Xfn+4Hs4˷joVj.:Jg%u2 )NS0RJC*ϓ|lZ)X@0:[NSv_DƊﮭ0 u 3;v25^!$ `QR3nMZ>Z|l'9Qc 9ô>ĚĆl~]p f̨.ӆ8q6~A1Ŷ0Q wzWipbZE ˢ2_LMk,#6k̃)"HuOv*a`8l.Te(R@7|9DPC`0HߚFg)k^$ֹA=́2M wmXJO^,SbNqXZ˴ANRݪWgS$Q"ƽh>26U./ |/{Px^@ QM>wVӦ14,G'˷?snljfipk/mhi*-jFRNğ0ڞK$s;Ґg;h`!R$׆oV`TgJljzv@X qc(9\tfBAiTܸx$3<.DHPXTO ^,{, F: Pv992"ہh1wYgh?u޼;gll y@E7yHrCʔu7hnl])L_U?X?=FZboY罂!Y4x'I@ӶD%t-}'S8p-j8J: ?"Mm%YJP9rFE8_fV)/$@QL#%72Eu乫 L^.`e#-FaRP՚:b+:_xwmwOkZPʵy"3Q̙*j4Z֤$ʞ%4: (ⰕL@iU‡P H|>C<;R|a"%0.P0@ K%L1LaϪ0 -9G%Q.Vܢ}mtТWVnԾxXoȟ@|(8| }rHim'ϚPp 5OG%Љc1{K 28'zxQGB">fSf'9hދ$Me8*<Na,b޺kny'E}DU5.QOl]shFx*nw[eXlc ?l)A,s{T [6eYE&@8GC)D j,.v߮K^h9 [nS_:\'ջo`K3X!PIE""Өrߺ,G,(^r% M9~`|{ڊ y5_ ž`p~#;Wy 1- ,]q5s$e'oI=i;n:ZU2tּ^?qd}{p:%0Ux(-ו c&n :L)4#g3̰BM, !c_CFnY5W5<"G / =9 ڴfۊ9DD>Pxuh:8&'m̊\kvZ\ 1m+*T8]T4PJ8~jR2<ߌ-Qv' \&f@!`J;/_5dV!%laiuXT-nÌJlR}hS/KV$EEJAcQb@6~kFBLl6 Su )h*y$N;b }R*Me|h f b- (F}ƴg S?6%V[$ƾe X(NZø(#R|kR}w{' YQ' s-pcU{4p *w [6Uzkzw_X,Hb%ű$vѯ{B@;URp!8\v$ bŋB/7+wp~-X j:y|ƦA4?9PkAý۰)}Nota G|lA }N.}k{ZkSBlfE)0gˉ}&w J~.Bιy3# ׃>NcUx'6G7{},g3<؃˙gdJa וC[*=TlFXKfpAR /iKӯP(^nJl5~fSyM _ gاtU7~sA[*ӊ{3OWzLTnVu6y+*nXESϥ)8e4 T>h.JX[ (ٔ.I^Z*ٿ-(]Jt״*KI2 ULi2:VmҮ 3Ac{* D|ך |*<0u;lד胍UƲܒ9  ZNF" <*3WҨB6c_;`B|l ݫؾ2AL.y{p0E( ]|Ua,TY{f+T)cbh ^cjkJrG8c䘿.xF{SEuDVZ*aWEU5o/4#]꿉 Hi-f+xvѠzw!}4[f9?ES3ɩ(.|:3yv(T[p rTϳBZaŷ24d,,hn%|[ *_g<^[xӕÞۨSkfeIQJo7gZ36 5Gul}]T-Y,CؽO W=ށdB=] w6ڗɨ t96wX,Sz8`WM s ݷlA=WgLE'g6ç{"ɛXŽ5v>96x].HQVD!!ΎBQ D622[ߌ댌6}#XtK_($A#i1Q{e}:cHYf7XR ڞ {5zOg1@ 8a =Av)5:Aayn"VB#R)k]Ә!p(Rӥ~t ԛnxzKrw”`( ve'RCco),ڝJ$Ssb-I+2yguDL^Oa^ݢS&*8\oa#By72R?ѪPDt>HޡxjkdWjnʖLީ pzsK7hQGkZOLq䠭b[+N3; @Sri>ׯ$cm (KKR; 1 tdC-fS`!% D;ɮ-$A= ~Q GyW1W"E/OH rOlr?\WU B=}7lG.siY9l2ĨwR>~ì{N0E0 +^Ad*Bϼ;eyAͽs=Bܽ} pR"sǑ5ӛWKdf[?aSepV4:*"54#8&GܐtxV䡽4vA-* i>&SNpǽ% vW n5T#4$ oYiSF֕DCybcQ3 WU-Xb^Z2Y䋧c()з >(`Nc5J8iEC\ˡ3_qwjQSRWN (ӓ_lS, cOtnBJҵO/zR~7X n \J"ZmRnE63"5$@¯|'̷cxZ7=ȱ|6lڏ,3WE$ _נPHn"uGoyn6J~#fr[MqH|ʮ4H~U2@dtz azܑ#O8켲OgM&=Rz3RzY쨝W$t+ l2\ȻF$!fV DghF`9 :PN",FkK`ƠhuwqaU1$*"_6 8Y=Pf& vǭHwD F7KE-:kJZ Zڌ>tڴjLZ]I ۦHib }k ĩa1xoƈ_ Fk!9U=e ONGcdO.gh(7e,tgbʸMiQ,e8NCPN͒!wJ{mH`E w$>} l*ٻo{]ktƭs f=+.u<2. = j<ɊOPc]r}Fȧ3 RncĻʡeM/"rehp$-`Ԥ(U;.0g5Z`fjvsM3Uc|P"fy/Y281Y "7^'(MD[(Lf=F cV SZCr1BcNx tD]?!ҹH+h01͘UJ=< PD$7IP>BTX;/NRDK&2N1|k9+>olNϯUG\To"uP{Q͈ ' 50j?=t|"zt/"cP̩u` f Rud}KJI.S ^VҦQ%~ 03ˇc=o?tHy^Q%QVۦ:#P ЍO4F&v"[kK`ܿ }d+6J?VؚRyMu$҈kLQ} ܬ#e Z!_`B]59;sLsO›Tcft_L$ xL (O #KFnlc7iq;[Au{V1S/x60zk 0v\sPme@<ŹN=Ӿ=?.TN i8l9Դn"ΰ[^|w߀9!D N?Ó`[~+&,r=Wk9=\II30\z\q%UÅ%inæ헩?_PT^ƕ-] =8Kcױ[VMQ9ߣBZ31J]rg>p mZLO@_g-Z9RfG+Bifzp d"`ݸPdxŎ9?WQQyRƽDӠi%㊧ [Qp%h9 KiAK *74mo'񎑩δ.[xнxDHD_?a*aK5V|mS|Fxv?OzN k;yYܽ RgtX?IX˪6mZ~6X '(C6#64{2$@Cŷ  9 txJcN7ߘ6+/@e)،GNlhOC0"g 31>'r7ʃ|ܵrr1|m"̓ןL"XÞG Gj տy7a~cocuu|r騻 \EK 4<3VYO9Ofh:B',Y~s ox1kftUF\vGoL l;?|?^P퉐6Ĭ# ג*/EƙZH.,6\._`ԥ7ݽPo1,jLd%ys>uvT[xvNЪ>\t,O cWGR>Sfܽ]!Hul3V*%2JGI4 mO@gx|ZX(UJN@/<G=s2D \M%FBҫYAlEf]ɨr,([2snFF%YyX@hgѵPx:vkoKk m#7l$,Qsފ]uG( x:!ݩm\Yyذ߀O6?Bӵ eG'_D>oav?UZqg|ʥٻsSݙ%a84W/EɲnNZq3kd5N2(ƲzĐ (!̫8sZ5G2\E:uLS$&*E%i|潲uZXfVGiK퐺)OD0 OѼC-EG]5|٤ieE$S 䭋9A1ll(΂e'iD<8U9lPS!E<<1x" ,J7&l%{Es-bc!$Oc|S#B9E#{ևl^,y<5x%@KY cSVt 2=4OzE&Q LrgM 2$ @a1fU!%l$-?Z뇖HI8o (+@t=s.*B&o,|mZvUɋm:)\:'&HXd3Ay)2<βQWCW3@gf'\qd%ϣ|~{!q-y&#5)N:? ]}D~εd7R/l!󻋩o1ufPMmZőß*XQAY| 9^+߿J@--3qnMuI0 b%& vpH(OB:|oCй$d= @X;c%&!;ZVzkKz y!Y[kwx@cTeyJնgWD\HtB]hNtHժŌǐʶΑ:%~QwS@}rst3l4Gq}I$,e2z%TBM! ae$ o;]XMSJxFp*]4[C4؁:w^Ž^ e,$0b%y6&"VאsljW|r 7 *uGރ뮂AyDʞ,숫@6d&Y.d.1WexKC7ֳS0A~9s`{N\;̞BMyfwܬ>^j/""Z܁♼GB3Y E_){H>0)KEQ#P9xzN3017%[+w ]V ?CX0ۏ '{#Gt ldHfp-4=s)P7~q,jsPE H9!w#1/{t &w_qLl9Zیa!`T+- D";L<'ƴL ]'rP}-۶.79!ˈPw\>Tᅢ̊B!>_,!P i[y"PVVHǽ>u2Ð7?T>ˍ:.Aeԭg{Wq]l UE*.w?5o۬&CϪ>:6>RM`{G1!ӺAg%إ4Ѫߵ}&~*]j}(^`VLV`LN|= GY02ޫD|Q;=er gFC'#$,wX;^ūVn9h = alg{Hz-=V~7ː;4gh7³=PZʨhc`w%?U^A%fMF6rn=TWɵ)@DՆՒ8D^@z+xE 4v}^ZŨPzL&ߓݢ+}n%Hk39!dM)E]>rzTcy}(C-b0* Oy:ON H^k!Ԇ!BL ;(v]Ŧs9aU ƃ s_&̣zcOTpiۨ}m7[“΄wTձzs&|w~ilo韯J`lY9]X̖ƙw"vi76$NB>0qJ=(Y[|ρVnÏhf5Y%<ת;g(΂3=Rd\E[,E\{ 9X'fw+(JcA3">zs)"n?~EsM`]G"t<2to'ZsUZ?FVwZ3.0rK.*;fk?\l'A+ww3O-ysT'n%[o_+ %69q@;zs jSڮ1<7E;JOuw$lYʯsoӗ~mZ{΋.Ǻo|[Bg -5 ,ZO4ȇCjw#dZ?{uqh23pC;jV~v|EFLw3ȸ"!;2&wpZ ֘Q]XDX6TKt4}*iNX>tnL}C*$.(pMz|G`݈\+OßMGy"ͫ9r`xW.*^hvIe[%֦Z%#}qk΀HQ{2ƕ5msӭKqQadF|*'$,98i&{!u/E:|3 ̎\ߒLfn('yy=ݕB0%reQOD̴g? ޯJC>)4vUmAջz; 2WAZ}a[Ć); 7|d-T!ꧦ!t2 ^ h,*Ѵ7 q0-A0˽ԶaKi)ʔ7DD~W3$ws;K %"-8* /[06Q)U/]x d\-LilDxmp'"?F:}z*#ކ"9jj});L@tp[{[ÃA2ϱK`kʨt5Ѽ/cȎ("݁)0ٝLNb[rpգ,D2xOL6gZwhlZ|bOZ;͆5 l-|j`KJďp'T3%@ɰlDYr/4Lly9:Jg=؝mz"٠%vda&WP# ;fy$(Z_v~I N#=+xBf?ؙ<]`Fv6lnmVۚP~s9*Emh[MnJd)"r?yɅlc ,/Q_e?r~678'ǿڷOAtOE𶨡j^oѨCpR6%=$qf;R wx{%[_q>8ey(9[G$k;[/ =*lSE4շ[ͱmܹ S VAS_vykpi{cFxYy͎Dk;=fo jXBqrrN_ ԙm}ƞ?-6C1$) FyU9. ;ΫViܑ,آ B;M.1ݪ0 pL(ۦB}y3ۿUT2蹦Edw)R<*,`A1႙R9)<ӻz.˫ҭh&Y *OT+f |GV仒('P3ͨ%>>C>K*?]zy;E#NuNK]-Ra{_?#͸iߟ/W0:4veHo",'{7?T&)z_ЭbZe=x vhCCT? -z\j)z8 \ <\#F/.kJ%~3,wdR^  qƙ{״ !"~-nA ۀºD$!@EDN0u\C}n. .olȔsj򩻷Tx3v*I: f<c絷,/vҲ=QM#]4xi4|ϊjV<9 U1A\{Ě=) MS??r`_U%9:1i>2hH&;;G2:$ 4UǬJ*H>MD?\`Y*~Pou?#WX-xtW>IjjUSeh3cu# dRb[dϥC1dllBq1qWh?攞Gc븏@lmM 4qUNLj>դ5A)ua[XYS FY-aBmZNd4!{)׫{I!3I8!Zy jUb3{`QZ+~k?_EU&,c>0UTa"k dAacšȁrb .EB$LF[V|~:A3э}N V/w@Z\eo&hZ}x,[ Iծ}!o@*1o%v9%ݠwa*T~{'-VP}"*k٬yH.$bNOv'!i*X>sD>muUmZDW'>Sy_f{UxTtda2&҂"*>i%Ytu魻\Xl`=ZiqrڂQQ?QBl& ,,=8;)D* zE1XdZ%{-oj!{ :Q=e%*7۩-.@Uq%AnݡQݚ_UPs"h7%<ȒS^b,g:yza@ME7ڛ1e&z'.xBk<zNu‰//A?qHVȟXSEIv<_B=\ eeYbeN^Y2Pƴ3?"uT[:3_inK*ߘ}mE-@@7twC)rP6&3>;hu`\w҅9\Ϧ?5^Wsj3u8/S\Ϧ&G֩lIJ-In84L]~o5zܳ>%<8= Utz<3Cױs ݨ{BT0rWlޫL+p$"InlHdyC.fvE62.܅|TuT+=4+<~"xAYg5/Y8YCڙ`XeH~L;Ǧ@2F.&g۞0)y&z^%N]/q%7,m8FiU<{K~OSvUC<{ JK>|VO薛H{>羉M⊸.|bw*'%i4^>vThrצ@s^J ִ1l$$RC;/C;|i>ZO#jmij c;+%"^ Gop76S/A\e#jFT3[pVݒ5hV׻k.8DB"hJ[Xk9V`-a!+-7Ť B0?Z:.T;%s3JT/a Xϭw+سCA&99z_Xގ),i;(2tf~G ٣BB|T ,)F^PaIFȚsnb$ _8[_꘢Յڢ'!qx(hZy(˔]r9$įRa f"whn\puPXndYYwVen+\ύ-kH˄΅V=o"hl\#YGTްW턗tI3g LpXB۞.E GO9U4mdp@%gbM!JN<޼(g 3;!8^҅FQ)G d:,njt.Zѕ}E6cc\`M| d*'y,ewL@!vG5d]X 'T (vNB^]R|;[3sSD9ԭͰ͸9ޥ HP3I&vi,)a~eݛ!O՝^ zJZ0IUְDEGPfhne\L8fD yoهNfڢK2?fi)9̃ep92uL` “4 AAe饜/Wc@3W >cn"el3O~ތѿuV}, =5n1oץ[ܒ4@>NtF⬟&fX Q}ҕN>z qֺ HTV.O<оD0'>@%t=&a#ȠlwJ2'lvdECf{uHD@jN ,K|PI:q~lGExsIax8HM%i6c\=*l.M>@%TbS(H6r8n-8` sO`^ʑ<#krGA:p4yAc2=KfoA2^phΔKM-@8]f@;$ V%ݵ*4b+Y1=aZW),cW/C(8*>2SgP;^vY/6\=)E~0yԢ2v&'4]mS>.<(PP~ZeNL^^Lުj <7w:/؈"wDHkc_ iώ*<'Z^;vsy^/NAWGbqXKL䋱6l$V)2[c9i=Tyjì%HCÏצoqp DtPrt}J(Ӥ~$ֳ WdfqRyZ7QٹN&4J`#,XACzdnu<{7&x0{! FEJ_w_uY)Dv44%}  2LW4pݬ[I0m Mi&Kx,FA=tk6<xv)(boi T{T&5c("A~׉uEn~_,L6|YV~1gTJ3&' x0? MOfJh}$r|SiO؞ʸvea*0i]I3 jhW'2B6UβD-NsD`Z3#)R(]26znNz,Dwt_^<Z-v8W!/<7DR${_57+@L`aW*T+Ugo|bHS&RLdݗ UDDmQ^'_JSt:r֩Y)6;2xM[hḻif,~?U oٽ4Y&e0(kg(,Bj{I7/YYHA9DH^'wb"'c0N:pjBqﰜ$nmhnOγ*sIhW,w5O0P ht< $x_Dύ#[w&(6k[@1E' ]ibw&JQ4\Y-F4ȣ>^gy|S,9eȑ<5"1]P_Dc"5{&m lf^A?*'no,6`:NJ*a Ҵ#8VYMW897 <>VD>/H`eYkhDM8D(̙u3M5;̇)u.NrYid&lhݴxB1J ܊R,9Wt*-/ %OxTQO3,/Á\'Fv!>ѡn?3 !;ņgě%7?x>TYW.jszYmyUxL'cLGuLJQ'."!>нS2nO=GX-Y: =رstnWU呓O٤ZF8i#3Dx4 ]>?)Mo0__BC~kuϭbę[$ErzS&`l7kV@\եaG`H"D5ʤHe?W[Cѽ(~tPQ{P1gmw{,C|m:9s,-5c{z(? 1.,} ^9*:k'Xc (pj770'M-s*%nYR4҅Ju:cJ gm_uG\dԢ7)t^ TCя ~q*~-(p(&EA ˛.GUe}}>uڊ ]tլY.B **NYIDf `쮼Hw?lb1DEdL?`+3.0(;Dʅ3Xk 9[o?hl~]f@%7vI t0'KjU' kF{JQm#DoSYs$' i˷d(O%ƈc{j;ST{*aCu+g iEIY DH¤m] 7YG8 ( *x&5p+sХQ}[4w}:А! :_ wZofigZv%<֮ߛSFQw11zK6EW61ٯ A]nvOCcP8Y_ۖ5_^[AQߦqBCas{0/] 5tbHr̽fF?.9gǗ_N谯(P%ea_|cb$WSbDa>HF:уqORK4R?o 6##g8JРDb; L kEH>zrl8^jΛsPCᨀPl܏H.NV2RLP WEۼ)iMy,]P[c?ytVj珳\RJ'ğ<Er|EBMfRBy?}'ڌ~}pxcn&hw|4 $:lhZI$B;ďTTkXcCx CH;l.\Se&oW_|Q(t?O3娔!DB|A{pӟMH@ݒsPlEsj<,wo%qS3, 4.8J#*01W47O̓Ū)r9$N9A}~pJB=6gqc1湄ޢ\_\Gw~똍Z(+!D+^:p3I $*߂Z)=L)5ɻ.j$>-@B$\?Im@k'g8' V0}*IRlig79PUo&IBGA"\h$D!`rw6*Yq8.Ֆf ;ԶMx3WeV!ɉNʕ$,Dzկ'ʸ/5`t{5Pґ&wFl|2&"J: ݒ윳G4;!LP wI]X-2WX XL /D^IE=)paR0rkgwu$ר@՞M;O"s䩔zF,F=@|V P>k/ UОBW ?;zi+1|l,k߽8΍߳jwQNV&AZ-Eւz)1Fb'q95pMñC|Ux\ <,%=륲GKl#$ 4+%52'#щ&P,6}*#]aEb]vZΆ$$7S[<, #i)ONiz@73eh0TjP-U½_LPc˦7)ɄP=(1T]:wkLs3v@?qg[*>@;.MpcNօ(&u7Gy86>ڶ LCjN+ebĈ_Zgkɡ,Эh6/z/8}z[%8LYAϗ2Рx>FgfߓU%T<N_Dh X֐"=ʱ6T>3M-!SɌ. W/]d )X5uxg#Z1 پ@XG."3@\5A!bYYi]!V=К`gLlVA3(#/A^!f7ĂoUj=}Vu=aqk+`:-5YiXf{4{XSɋCL|qi_3mJC_a:Vv Jm*1z0N/*.o#RlY H(LVHP:flԯ@Tp?ǟÇ=hR-'jKOyaZZWSegAFIfX?C5^MPmb`[,y;~@Rɪ@Koa*[8 u:n-x 蝋ѦsΫx?*$xJu#ޅ *G_䬉Z%m`(6%yJ>pۖJڒg0X8y]etP#T͘!f^@_Nkk~8l9ŧOo>Ca63# Hnt4htV[ } Vsb\T=ӨgyLpZkK\4 L-œ!#KC//}w:m{.j<%Xk}<+Y6C*ޓFTFLq86p:pmXR;6PpdO3 5mC J=U UyNs$/<ىȀ!drxnƝdܹ fH7#@_ )r6re"fCګ$j-5_ᇧF,۷OO9M대"\3dj:({THF-և8L~s&H[@>8gr.BYҽ^laPpS jahA;¡g\\mz -fJs/Ḑ2Z'-ތXl,N#=8lQ9U= Hffһɓ{)!/[:<̞cNBv%n?cZt/{HZZ^ [:DtHb췳kDZ2}i:Qt'jGEF A:ŭeJ‚{ [.4Y,qhOLaD29"q_dZd^! rlBE^<| 7ck%t[~lru;)3,C(y.IA}}p^&tي?<||-ڧ[<|cO &u\+p][re0.4˜L[[X8,g78A%1br **NQA./k V5*o)Wq14>6cH_1#ߗpC R rpahRĠ.scb(u^F+gMޛuʱX5XQ=Y l ,ra̐r fYGcʌ bRe~=qn\oX*uq(ȯYWY ,܂ķ,KAIG6k 'C }wsYW ֨.li *hβx[$uLr܆nGkܚN6Z>6Y}jjɈLCWIBErӉS6]G}Tetr "-y2pDڤZ*Cb@Xt+zl/'z7Z_*![vx3-WmRuq— x ANܙ5qXa1!"vdJ>nzkj -f0p*&FiAKPs*B0| ]5RMh_Tc _ 6FsQчH Tf?Y9,oH,%9g9_ _2Y_17xɖc.>'80q,o7w Q/*oG G~u:&+ZxdI`mՏk8R+F֧n~gLj u*/~+;c Ec1#Kl^8i gcV<(狉U'PN#^ޮ0g_~$R#t?iWi..yEA/7FʙG>.x ~{N=j{`F=,$݃f҈$v^X2M'͈t( tyᬕ|)tr RrSBtѷ>j%[JTvWUH;_cna7qP% nKcqZ[q ۣ.!ķe0HY3!I  kFLA;J` W^=Iߦ7X{*n45f+  D,- ~T#5Jwu!! mNK OJzn4JvI! i y&@ '\b hBA`dSrRumD&Esd}k3^#gcM=9faS3iM5 'DBFv ijQ*CeKጳlp=tk:"֥kpl\,ݙ91VjQ݁i.۱Yp6D[ABVJ 9CA<. `|=!*O>1 =vi[ ɯ`DW ,wE8dA/?VHV@~EZsZQ=yz#W/Y&z`&dHXl(\_wYT.uTABÄi1W֣: $Pɚ,7fniYdV%"Ev!Õ~XңԖ ؕ6m &H|/$zIN)DGo  gC'%,@){l6 (]vxc7`Tܡj-~Gc5X^ө}.L[vmj$ ПňLoK頻JYZ]CKϡ!%CqVqy d< LmxHEc.XL˗V6(=I<Q!gS1 Iڬ Ye' PJ*f\,j&mPf_z'k?|Lull25un>87m׆V o5d@:pQYoUKVEն5S0w\y"ӱ79hU7ʿٹY_3ғ{uG9RT LޑآЫ]OMVY[K3K4YR@ 4`N0^|Xh/jQY WR].=WK{$U)[rX=-h"𜪦iKn@jfC4 R)=00xLu2_&S$b9s.V(;M}Wڔ#w8׊(rޜ;%ZEL4zGQYx<Ur'g0hc7mM}{g6k|@!![|MH?^ )]r9u,ZV`p_έn{ B =,dl68%c{~)Y.J9ϰ,I0Mr#ܻRQ}tnG']Ъh)6l. }OJ>[(Ů b JY$&Gm` =1K&z3kyƜE*,ʬ/Jek*TY '9!p&U d\S6 !P}x81|OM,9N4AVoCXhހ2MZd7/[?40a?Y%~wxs\XWFpȳ](,.gX ɿ_BS7*4Nnj!.n|ZGI>)!ӘX?=zY:o`PkU$VeKj KԀ|=nצeiK`>Ro(MU>&E2؉ su~9LETdӦ~rkKd"ھzĜXb[O6?Y?[ {V5r <=OQ /%$s^*F V)'K@ka/oI掺#^ CE]LLi%A!&CE! 0ҿ L!ZU"HpKu|$ڣx^B˟Y>G5w]u#=#5-/f.6Ƚ */,nN-'iazjV^l+U }ٙKQ;JDXy@73@ /(6q 62祎C YYR8mA7?eFLTl=Y!Ircaf1rqZqUuNM45?zrI-Laүj2Lr}ߨ% EGgE(!AIXقNl;ɠ*V6܊5;3pc tnqd:Sg$A˓W/kK6" i_}T嘶̥'r2~]+ڤtp=\UM>%Aƣf=)v|ȒXڨn}l#%O⊨J+|qCUf#OH="m,-GƁeڱw};9#x% S9lm7a(A>Ih$V88 rA\GoA"9F߭B#nc?`1蟞# /F)k,4鏲~wgד@b7碢{/?ly=" y RkM` ycD"v-n@]9Te )}OznbcKw8HGNI*# V]iWk[EP)x vDYe 9@Gι6c{'"0 H+YzVbBAK96{=n f^ry0k]J$G,e4,GODlaq\]=CZuK wwue@2PO 7"ey#U22ȡ3TI[(4!f^mɲ^( +Qyz#<5lekW\0 3A S h}!`= 5G&DEt 5dbY.@M/05ˆQ3%|i]0<SKo%H8CIh>B V7NuuD}Ԁn_PrAdtΥukW(냒[4j$Dbt[LC0(rru[:O2oBv^lK $wkG {m@3\},^8uopXpx͉ɺ39TͲ@ߊCҿ쵝lf4vW?ynz\j/v4Wh%%j˄&yfd0]LGVV 7~3*e&1A-̧!ͲڿrIK(L^C`RWpK_GOz& .kj R ,8%Dllx1T0#%19rrw4 %rVBio>yꃤw2(D֕ǷDT_;vy>٭XA@&@cIx'Pf,R^t8M 4Bqù7CoCF' EN V#w0h@j)94"k76ۅ[9qhN+5$ ФšMNpG10S)qk4cX"v<|p514G<ޢrcxmR~z[5D2Mw,sӌ*fOERy$sJ]M3 +;|</(_8q/Ɍ]=IVG aho eag,#.c14C ? 1_ ?3\agY "!sAht j !V?\@e.!; f+gtf]ΘbJs d*=}HDLk&fx!DUqQ;*O1rm!m25ۚ6\ %s0l#l,4h !un/rU\<]Vp *KO*9D/[u5DKXD082.9K}x:Rx0'HV\zVB-Q^*QQ4XxÉ˰AD8~PSc$ 8.`}G2K`J R\,~5a}eNL Z=9ieV+:VuTv̖1FZ,dD=\f2LjK$*64_*Yf5Զͳ߻[vlg`cZQ{_x]r~W@_]gymOˏW84 Zf.` )Oe0 iKi.cr̅߻2jG疰 dgd\@`X+GA댫&77ޫ8-{l6/vgBv!"e ȒoPVǺG_ۿNJtWǶ)JqL[{/%@ٟh#BBZBF"(ɮjUy@q5A@}uR7(ͦj(\{yk._FETcSddApCjsc珢;O)Eй "1"\q[ z|{׌+Ϟ*BdJRi,_\q ~r5Wt*= 3,1w>sڿ6K0 u;-l3;X'. +9R26kC8ܬXVGS< zC/S^APn6!Dz[F4 wj61ѱdV;ь {/n i+ښϼbx!e@`[R>R+R17)qI{8<(SGU_e@0Դq%c95.&ۦz"TgKh~ M~4dzl+ar,~1hP:#/ZZ*BjjN~5ː#RHE+5J9,}gg, #.cG=ִyw1KV\ҭz5 3GT";aV„8.p\_ad^>*o(*bUύ!HCgu5k09}De!Kn&bQ5mR͂b@v〛=]-pgH`~/ap? p7?7D^49͂P% {ėV" בҿv @!#z$? ϋ_˼)x vռrq!Z%x>+%NDKe۝bt*6o[gFN5+T }'! TThh,6(}/ǪG{ペ"TLnͮcu㸟r \5a YMښQ▔餫\y,f9q:B~!5h]_I$jff4B R1L-.{ۤ%};LU8p8f[xZ[\n'fS{Kt2x纲jʛ0XDnP}) E<OV&q+ 5|2fa"tܮ/ù%/C~4ޖ;Y3+),IBg\(L ~"_Fvi}ڭ X1ǑnѴr ujuY(ՂoexC ~pfT_TǢvrI qO~}W(NUcR^ p`iX1(鍮F9"AD.mӓpio+yAaOŋquK^u&kn|/ w` |'5ӱFdHln@FJ4?b;/ir=8F8DƳX9߻Jw"bX:ʔ^c 'B2niѕ&B|'D/4kee:\;'?*9VZb~`{>+(o|+s4t\ .榿 UD+<mX714˻k;D}MڰQ/`WoK=uҥ [ {}Wv*'܈`,, t<@"e1Pιt:Xsf+x၍U)9sL,NCg?2RЖV~cm#5]hԑ(vB4%V\BdB_alK"'7qbqOJlgtR(];s'}@PBW|O>L$7w4#tN&Q_$X5xÀ9پjP )Yg1Uj[$h<j8*no!V9'[Q"Ior)qQmEl{&QB %sJ>8VۛzԋUʢTGfe#-NUW}3BD*]BzgCA-lSL)E`^o6MF/Ͽ-h,6_sƒv3Xϒjq&2RdtyS!/ הJ׳$g?#x×Oq}t2ϿPќ!7LY=$-/[6r+-DذozԐ/^@Ԯ<(']\g'F5 $I ؉Q)xgxJ"UL@6G8.N!:ghlC|yZ湯jrY(5b-f@FM~ŭw7I(j{S63G?*R;3#ÉxݹA=jm@qD g2>0 YZAER/data/Longley.rda0000644000176200001440000000067613616365122013711 0ustar liggesusersm+aby$(QJgE`ɶ3)7'98p e䥔rq ,s{;}105Ga*Ed1cQc1H)N~O;ҷgxIuCPkk~Z=T_gL#eI2#AV;>2ZZ_q)?<k[|~8kZOpDZLO#cYeF>og`pɈwݿIki} È쩮l,7n+#ĥZ5_&mNDXԪ:k"~Iұ lIr2폶WZd jo{\^(oĿ+-z"O,7왟žCu@ϓ9qbacz'Hפ*t!w;n|DZI5 61*!ADV2]{xbE!r%1=*0" G'$S4"ra4;VYpmd]CJ+Q-6| !'\'J`") B̸f۱C`Ək$[+iޭ!M, DbVlU6jbָ9h[26-صqɶ*ո\lkpAW98ț\54llFιutKf|B욍 p4h࣊c@b+vhv\r9+l09+9r'Kp`8hF7W9db#U({z=آN(JEԩӗ U_VՋWqbEV-Ѷb-FجV+ZRRj4Mm&6F*4%FcHlmF6,Z$mQqpi BcEdԔ&A(H*0IXDF)s&)+ lhɄ6b1-T$A!cH[0&FdRjLdb- db JLI$TD &СykvZ$(E1((T254`Scԉ&D`@$aL1ɢ$&aE`bJEa*  -jFT PY dUQDH?5?2$,T9?.%D;ѴRd)iUZRκ[kA@"$o݇y-<-ʀ[U,,,UeOܙfJFe@MP!GuAVDA;fAK H;hĽUV:hne@(~QCGvn0hE,A LP!d_ǹ_e}K,+WJJ!o@YU2BqH81y!󔐴7JF#w^qve BbfL.8-V鼻g\3gDp{yv0P[jׯ]u]u裸ImmmmmqRH4)$BBBBBBBBBBcנyL mnFFFF@9s9))J:뮺U.Ǻ뮸9s9s-MYPPPPPPPP뮺U]u]uz[e]u\I%Uz뮺뮫W]`    P)RT)JUzR*U)mh[$zR*U)IJJ)JJ)JQ&mmۻPJRaÇ '9ίFfmmmmm$5\)J[mhI$I$I$I$I$I$*Ӳbŋ I$I$I$I$I$I$$0`~$I$I$HmmmԪUP]mWmzU4I4`Tb( E_&+FFƉ# 0`yOH_hlF !-wΖ"as_˧mۻṯFLbDV0+ic4;s 4N2MܻuyW}tqsd(IHIo7Ͳ:] eP*R7 /G4]+m,V])C&KGobaE n&R}N6$##!HrRoP$XA6I LLIxEF殓 ]bSFQ ".$k\^RGx{kFwu.I$%Td#2);uQcw{+'w'q/w8E<.,M2#;D6$4dlU4d6Ld QIkr&3DTED%5FѵTmQxEh]櫊*j6qň1"n"I 3c3)446)44@ !4$IdF$؊%@B#c0$_cYa<)')rRG㭞l\so0`!Hlϡmᕁ+,{3m~ o0d2ٶ;MDh)h04,O3#VkۤL+aYKe@P H8z1lJEW7JYzsrHՃbxZbR3[No- _]b/LA wqs4Bv&53gہ4@oZcE\ʀbPjf%N~{kEǀfآ*:4Wfg=B ?5Z6%`dKECZ@0mɡZ ;RrE8P< dLAER/data/HMDA.rda0000644000176200001440000005526413616365122013014 0ustar liggesusers7zXZi"6!X tZx])TW"nRʟKMd[_;z/LԶlpCeVCɵIavZjx'ܚc7H}~{1z:H!At3 Pȑ53 6 #pZb- / VL;BȘCd7d?y=:O#sgS0ڥW[cEqwtNdL8G={7Ỵ6ܚIuĠ~ *8 .b!jn$e*չ%S=Ғ|଑ZJ\#ڬ]Nz:l48Nxo%Y1]T&`o? HEu"AxP@]J'_P׼1gHL=@]kByz*-# x_s ?@B]UT5Ftg\Q_|ktq>JhTNb[3I8c,J}p)N8w{ *Qf D1BqZ\{KPcr]42 qǰ>A%Ͽ>7 `xǵhHߞ\FY~}c1;3|" :զqL҉.f2UC(&˟B^3 rk83jɼ]}QA`Sp07{VâVz?)w@Ҳ*!zvd&jL@~E,|:D 23wg.JuN~AF 9\̓L[(VNqA]Y$Kjo}u#?7oVՓD T~@ fם*l7*-LpbUw9jA\??=86F8$MÉD'<~Y "E )uS=Q]43ymԨFĢyFL~ _Z\59@1 X(}+̐Grt0 |vX|A;((5zP^ %<j"v1MK˩EfXoy:byvDV&%'5.]"K؂_2Aha,70*м>3]$X~cY:EK|! s++XH\Sh9/pT$M8m`VRqUY9p;)u1]lqobwYkkR+Wyx Е<p y=\ VU,\,Cd;I2 Q4u^XJ!J5 n9`HOQZt<^ca̩fzg>ڦ8 N28%sቑ4N&d<}FP[ÁYSvM:ّGfe X6Ҝh/AyǟrA3(YX|T lډ*G-(J?W-a {;qæL,) b\[%L EN j&+»6iZ` ]~iI`V?A+|RN+-[M^? /r |HS[GGB妥̊>ӧ<o&1 o/ EX~~睧R7M揧Y=i \; 8w^gast^\#-j݈g$vqLIwU"o݉ qjN~@jnO1,(ifYV` |^>>7c{eN%aP@S /0Y\tq/ 疅Ee -j}THK[aXdjK@i{Bn[M_(jU!7S0{Z{Kn#58yB'6(F) +_vSbڳe{gXJ*>C!T ƀN f5o1;eAy 0+ .iVX%U8[ӿDj eW,^Ф9C&B4Y6IxCp-H>mic_ '2[~*޺{I|Sy/WAVɄO̫cE&壗lVTW𓂸8M,-*f`exZINhKU$ZG(6ruј|/$Áhি"Xjð]2+/5` QNadO?=̂S>eQ^Οj`d1ǖ.I8eeΖ |F^f-%@{BlTf禍)R\CU\޵dFଟ</$[H#:C~?APNdqO4ک!3u*/)5ne&v4٤d5IhQxZI%hmb15AJ=a՞՟YpG;<]N5݃}ۆJʀP&N#VC/%  ّ\`o[,nr]a (>r;FhQՄ1Qsn;LYYD6m9Pk %> N+ FE e MzUq 0Ǫ zZ !O꨹7Qľ:Q0q1!LceZ3*(!\JM.; f:pj5YmdM (5fVrS^Cc )?%Lп2&9&ߝ%%d =hP]c<*W^5 ƊimV([ǦIc !qv+EcTs*jgOOs" NmCP|Y17iᢔxI0K .tKq=XwUu"%;3#gڗ}TF$>KL6i􆋫]#*-6M em•.܀`>kBtL'{v8,Ɋ9E>⑆O2fv=xh!<*BE-+KOD0>UuA}viu1)D5Sh6w>43=RE_dKe} BHr= ibE\4G5kRzF ǘ/Oۓ΅ 5n%?uf#L :Skݐ1lz$ [*(!sz SW?O SJ_{cp*Yn4_2$P8/a&e*QpO- {ޤ˚o pu؍);5laS0.LR6ꢍ]ZyO6(q2#6sjF -m>et?^xꄂ/vNTG TEz|֤)RZsx &:8 uUO[]go_QUgu_RnfuFbbŷjVz_ >^fRhPNKuX =OD'\ٶJϔ>|a&!F6A DZUg;Z Ճ&^ќq"LU'bN=Ev+kd#@6ukC*g,Cw΃`ӊ#e0T%V"K33r:`oe9-NN:^,Io3.SDgӔum?\TRklٵƋ354qM+P+osMvM"`R }0k#u$WGa0fժÜ؉z/w7Th,C֠Kl"Iڇ11,~E<(7.[{ŀZX(X>zqxõHM7o'}KH嫽uXnNƽH+Tj+'rVx]4f{q6уl-* B 0gP&lx'N}]k=uM !vp$ V%Ӯs &sVd=iޝf߁{V&ꅨUvP[cYrjWL]yà- `^fpѝ3AB3d>%s%\08PtV$ʠ2YvRr>r' ոyFE q<0劢UpMŖdP!)cVh oa#=Tg)'h8&Az$peKѨfjZ^~V! 0G!($g\ӪK`:a0C (tUԏv8gǍ#/ԍ<`oᇘ[M`fY)UOb˛b UU>kYHH:sЬZn籤%j|LsJCLWT"ɓ:ÕR⾮'lkۊk3Nb" *V^)E7+oЁ +ב -*eS.Z,_WaJQGE8pM4PYS]_X;,$BX݊s6uV HyHF !3H{[WA^颹XaE mc7>ٮk賹MO(o :Û4pTvJϞ9v-y`NfZؑzd]IS#9Uzϡ/CRLWM*E3AɵҠV+^_n9jf@-N#D;he|#I0Ʀ[~ls9j6V $8afU?7L/Rj,@BX[D2}0Z7TU72gF2(E>Zצ(">mjT$ֵ}W4L3򋮼l[ \$ +04g7E1;?gM$`SD5ҡynaEݿ /ؿeN*1 \x6SY*@rVoVpߑn4N[BZy2:ߎ<ȭݵ+[q;"}rĊWqK=m}n7[O< czҜФ9Jl4&N8m.zU+PWza-inyCM:{'0!GtqEh,vnM :1.v5`c`X ݦJ27f^hl&ѥuO˯#h |9[k(K3X|鰛Y%AZ0aՋsXEfSI |Qc]%rj5RZx[y$#ҩcAHYIo,ddC=bBf_T_۱:P24.tN##=t1KwZ4Xg#7#`œ<8$|t$*Q/J(6#=Aj*4'RxEy@:Y1y"`yX lv]l?gµOşnsfƨfVQlل(!w `khԿc܎EZ|!G7>hu)|u;54\'ey}o~* s/<Ġ0-e_, 9Xu\=6nu1`RdhP5r*{ XCO-!kr{nt=+khw{%R> jHadX H쎗XExe%(8f3P̸qHř44t2 s>0{Nq  rG Z=;߻2zOB>-OL~JͶ$ }J~w>ֈJfH-Ԭ(R= 9ԉX r;c|̏ ƊEDuօǎ{EOw^=7,eۡ߱ jT]0Ej.".5Zb6I%i dU p*i#kL y/ܣv3m%Z}8PCJtJnherX*mNa'wcp2C5MJR{rӑ=UĜ\Ǎ1nr6=Cq% 7eXŀ sU`@t?ۼZ&Ͼd -d:WgAds  c2C)tޕCMrKE*t_n\ ,yxՠ 0[U Ǧu+A@9o1o+LVFaxV'\JRΤ꿧+E=ktӷ!ұ࿚ӐZ+dȶ8?SJ ߱-ͦ`QX?.])a(aobU#ΠvU?s<ئχVX~w 7i IB Qp@dBl"率`GY{*$&#h;Rp?#*y:ٻ ru?!jwm:&ގ8o*cv6+oĪl]- 9يDA fjUЏ a=H,L@rj'$K ÆM"1ʼ] g0-;G:˴2kDsF6Dt0(Π8&R»(։VA! ԍ@jȒ%{.vz'rN֥fARŅWM!#O=B{45c˧=^HVuiUmQRۑ) vU:[=cNe/G+?wAKQNB'HVH{?[(M\ /14ip9**:Z FTb VI%1-E&mk9 luHRٮRCB댠Q+DHީY]AR#/CY-yh c^1)*^Wru4ӨEe) 7 SWa"KjhRL։[ 2 oe} i;xclc]#3KE⫐]s7_;BRcO7!`~ґ18?OXbINs.dPJE_g#r$3U}{aKɧjD ͘ w!Hq7m<#VraE>+C%B\܎/?cٛr? &%Z$!g{jBRϿ( Bف Lgb;B+\:A)䗑OгjWpP07i*uv NRDz-@sczlG);}.X ]3Θ\W"e Q(15Ui<eMdzKK.4v\L1Z|w"'}ccֹ0hBn#gKq':E~ݱXJ1H;r(8_ю-ΧAHzT&vPK4vZ{Ȑ 9eYҙ,!'0n8FY ?V4:wAySYf 6ڈCH5zVAYKfVe/5W1Ҥ CLir/(cFo:^c(K90`u!~w`iIZ9x8q0LےtvOGlagEdT-~y%}ݦ[}^m4 j~>E wϽY+Z`"v*޼U2-1ҿPY<21JtbՑl; y@h~t EQbvɺ9ܳV7ASE8mMQn|-e"@U @sTIߙ[jhZFLk6f [IG)_/ɽJ(^Pp^LZ9DkwJ2Ȅ^V&dLR헹;n9B$rNBJ+?U8 yfWZIRrۤ&1M=cMbWFK] Bo?2FAZ4Aq`K1y+\˵٫@mbRNo53f e})g'v@[gb{.糒AJ5Z5%ԋٞ1V##}4[>dU^)fD!izhBq+4%9[iIN E~֧$ī@B2NQC! ۧ53k[֤S_k^Xv3۠h*.bƾhvIv4r[6P\M.(8䗗l ["קl/-Ebhɜ%?R>TrZ;=fL#Dt\zOCSAm&xaZC&-pڜLܪs7_ām;Y1dqq˛iqw#{ҏyo؅AdU D ~~ k+%@a /nR)/Ua6#%Au\q 앱Tf \DߴM|N ]+ѣBw끓Co] 3rRkiet\O<'ι ơo5H#|O>p39ER&CB̜Q-_ LJOipzs>d%gM"CԭJNf߅0.FI5P"wb ƃ5 0oF@ eA.p=k5B}31*;g~y(Uߡ6=f2k ~ȞTv7OE*dG +\b`cdB7i2'. j8t6~& Dm#&*A*G!Cϝ;+I.F܋euwuïao Wչ ?:nd:$ͷ^h͙ 0* nw?R@ǧK^>6uKLO,P ƥDkOoO?:#Q0@>>w'!'B:~V! 4X-3_j3 NF9M($ .KL$]Ac/Ts14P,75M$:P!ӊ[Ǥ_" Y߲gF5\#vz~ ó[P2%c{甹L Xʫ5:Rc!TN}{l \Qɤ>!=+a! q԰oý O`BEp)ر?F.+A悢U>Xod]( Ow8ש8د mX@O;&y,r9d$q,Gp'^Ӄzbd{Ӕnv&J`2mo)1Md٬( LDJLg)Ω ,avnz"̴ (`^%-}c\@^-^3kB<=9qG~2F321Xm"F@׻eJ,2sU]J(I;H3<|XtJ O| r%7Xwh۫^߯O tBW8ShFU1;-qd7\PW9`mOk[U!gXKM\5V 2ĺpC,W~?^ՎhX+M._cRhK鞊+ym8ct$ւsj{GiG8ddT t1ЩyJIv:p:9, 00u, ҃/gUQה;`~ҘMU#%:˳Ϗg6&njTTkj1](.)%)\r To=Ӌ>~u:(Ch=꿒O*rH QF-GԎ`m ALi6| / D_O3H7\1 nvELTMTդ|0wwVuX6S ְGZZcP8 s5,T]n'(L {ayY' wV;==Qϧaޯ!B}H*ĤRLˁwAT<æb´.nt)>d9!.e"A>JoA@u&SZTsΥu 2U9t%qD=w%OlR-9)vDNjO#ʓUջ x`|.ɓ~\2ul%A1[u5Hyom5]+nctg7D+7.;DGeG0*_YKmɼ%iYi8r[jwqU8 ,-3mv }d?X S򩎮0 U7,@s@lfM8SF L Tx#నFt݋~d-+!EarM9 q~w8dQ6XzP2X|Q Ge%տĻ8lC3XdhDyTIAx$kim&iX'#|"'YXC#w-k(*Q mTg?>&r<#fF~*yn=\hGvbhvkdpv9-/EFgS)d1T7e*k04 fHzI[6 w* sSh.a$hVHc$>K,^|!73O(IͼY"בԥEܞ!u) WGoDH5b@s+:x%lhd)$4,L {)2™2BPaoh2pAS'S׼9!OEɲ$8$ES'&oa/W:>0#@+6CE&r&EvTYVOH0{#3Ltw?v m1F F*9ؗh%h>UIruwRB kC7Lc..r_BNS,kcjZpEꐩ_f0oi2~UH^Cq.6AX5F5<;XWb<>'doʼ}kchAƝA]uѼ{~8piBYd;ߤcY&?ˤ(~+<7aQֱ9a}Ű .$pGP7u:)J)B-sWF!"A7r3)]P̥KғƁ΃R2_:UA2:ЕQ d D.ɫ *$;e~a ܞ騗z'A .͂aϰ*kCoxYRh/ H¥\SGD1a^}v'Vz;- ; X; Jdu6zk ͖SU0_rKA ߫%TZSVqV!x+1oC(IB[Qc@+,o!sS5g2ŝ0 ~-<ؿdEP! Wr_?JSǰ631s WnrOQRV1("tEbI43eWDd@hr y5) ҘH?V 6S c6<(Kp鋖aoGBt)"i؟}% *2~ ^x'ٹ&Sb"+9*re#U~iW,#=P֔!fʌt;X}h\B4J/RQh:|/!Uqˇn (!j| b;Ur22hzI3$#iPo S'[ȍGjdUO@W! f;̶ nS/9p `LoI?!N|tlGWٞ'q4V:4xeV[B_PJ a4nJ07\Œ)f y`GkeUC81wށzĀ6VZBbOy cKI.du7nS₦7g^ޑ,ߑ 2c &9>Qaz8/es ,&~~wհԿ&ЂIzm+$qi%(xekaWwmA=Z_k#~j.j@?넱B + /{CJw"`M_D)qJPN wamf_ VJ Toq ^7G$`?doWJ>hz7"Q-N#vFž moQ0[/d<I|{׏4 -*Ҧ}uu9\;*m7 #MQՏPT>SjaoٛNy;b:XlDKs^ikq<Ae uJ^L?;-^Z_ /o{g$W)h82bxO_dS|_+ܟ:̘5/ƒl"xaVD#\[!JyX#(ӖPR0=0 3WB4{αt/jmHQ5/E٨5-k1l2b`]V̛rn :k9'iөQ)ԞH,!}.>xRh2Anρb\K@vxT@X6Zv1yKb-ulG#ۙ?k5XzÒEɅmj-ٔ~U)\S /"qwޣ^,)FLo(:cGx.S  ȫ݌ FVmǬV ̝h={Rz!'4>hHrRSAsd)̐UCh7Šu''Gc!c_K1W"ۣߵPz(vO&0cV#KCy>l9|#tRGRN5x32oyw-7f!c=?tlo:b-)ܶ{]Z6\ .l8YwӞʖ`Tf+oMso4-_BhJu3Pi'^{Lt pe `52èT1EBuf /fqbNmYNCn}Ep ~?c EG[C,zt3$@c6lfZy ]@@B "S,s/hx()ݭmٲb 7`yZOr2whV%B29X8fLT4=*k;Z]} .q1CɝBt1Vt #d޾kΓf%Be+bf|mp<"!m/V@wnn9#k #;#6~8VB$JdPAD7HnGm~ HU҉˵rkAGyhv?NN*b\g+yF8'?0S(Rr=p̊ZMUG/~tDgV~h|zg-6_إ| \w9|7d dt+$o4Ї,y0۝bMZfJ,&qu 4^xԨ6Qӫwpt3l=y]0?4SkspuN^} a\t=n{>: s] xI+_nNgM 7#X&ae?m=!z~u^v1s|B["IG-Fr7ޫ39xCa.fo*eH8g\Aw@Wnm[AhLj+zIJ+5Aw~-I,//0Ց.nGZՔF"XszmF}_&#/W",zpĬ1TnBF|^-X'[3 h4}+l˦KE܂|3sSLH=lnsv%׭%"sdMiHke!닟ҧ`Tjb~q_6_?8wrb+Th9CD~XN쏼KlE:*\pE'hD#H*y&*LsZMе'?~qr~zـXӎ6N*ۏB9|/|Dڵb+ _!U AHV/|V>X1=aZ½ 2dkp(4t)Tr@\{P{0$7h658!2Rk+4..A(GI=ahSx\VI%-\duPiq?dFyOKo \] |߅'㷰*@"^X3\ѽ0~!e/ fn}YӁP{~dI@75yEM-7y$LeWy%ިHj=.; YH#} %{s*f? %ň\PJBM[mv8lK6)E{!h"&Gz!}8kkk廥m"-3ρ^ 4]ρj&qܻ X6`P:ҡK&yӃqӸsaS/Γ6Nw^X;ۨ5tCN-ʏ@[ZcIdSq [Y{-T`3E턈:A~˔Hh~@PC* b"6'Zfb郖WdvY֣)\rpcx9 tw6G bJ 0 YZAER/data/TravelMode.rda0000644000176200001440000001314713616365126014343 0ustar liggesusersBZh91AY&SYrEM\PE -'"D&tHM4SiF &#)4z5Shbh4h#)54yOM1j4zLȍiѐJz?T``*C`ɓAdaaM4`b411 b44iFLBDJm4 RQ?DL&4xڙDڏPh2 2F@ɣ Biz!df&iF&`a1ws d+E^B2ΣXDD^:<8єE43L>UϢÜx.|eEֳ80xۖrVe %4ngNHLRȴD4S$A7ryi%VT- PR" & aB)JbZ* U"DiV Qw1YYhZhJP@`+& ',GwLFϷ҈Jrfce`0bUUP$"hr@Fe*} ElFQó:)ƖA@!4⊑dj C 8m²"VAHMaB%e?<<'=%e&"}BeS{ 9dN]ˈ-0) &lUROQ8(Iv捕uWzzd(TѝL&`,ó~wq0q6\J~xK &4#_0fYU/eL- 3BGai}&96t93E3ի `Ixf f=BM@96gîZUꋐ@Z!4#1J&i%^kV)|P>*†o*tWo0hftrl¨>)|RuA2xG, sJ"m@93t$lӴW[NDi59GYYcdԃ%fgsNaqyV,(z遁af=DJPT|Ԩa1ϜDr]ZcIqBv[E$l_ ܻcjn˳\[ !IF"4";Gx2H*G[zGAæN˵OM@t]bNSeWE\UQU0$i@ 3VɾPKEp҆2&ҡ|#S60t PY3&lwkKԈ[ k[i Sf#'sq$(U99uV4*T bh1TFL}yc6g34pi)X;vygJ=:T+HQo5Km'Q tjU!c,N( \9>Y}q9g2N+эP꜈ j(1: Q\n[;'YYN\]fʝčIWupX[1HRSNxkD15tXq\4[6h-C=15LpD,Rjb*V* F pofA)Dl9x햠ֆɖ}x4Lmw3X,(\PA&" 9XF Y|_2U+JjzcĶSj3+#Aֳ' [aI0D5|3"A2o¹{?3^@o9AWQeT 'Wh3u4LA_I# M:% Љ3SޱG\RU38mJrfeM_oNuJlD<0~kxiMW5Ok-z/TyF n};d% ^lSKF#񭕟BBi6Z"fuܫ\3Ӕi^(.;C aj'HE"p.C;Hȧ\PPh[91#V| /Fso`ʺ~뜯][}~ ddwr15mM("-Ώ8-\ȿ9zy8gnܛf3J__YF1ökgĴ ƋcĻ𦺻xO4EZQVx=+|UKr˩Qꮬbp93uh:U;ٝ+/;SMJ_AA3xkK V|^"Ny?Yu{?>GCCbAݩXzpل]+ R.ΣkAnn۸YIΡKWHNڵF3Zg br*BrU\[<2=V9yqi1rOe-T^2./E{zRo=gy~ũxO Sy/^wQ)&2G'i$k䧿k/.]&MKL,{ Xުn Ueԇ[2+@`(e/וmI2@E6e != {RWLVAqwjuuAb7ﳹŸi򰛓 ngx_6d ?E|/lޏ#k XEM]z3SK!s:N/gϳ}aLkz=+v&W}e%MRv^'OKIr5`1 bRw@ em/x_k{'`~_)R/0-+JZ~h%J2JD~Rjt0;nzvK#򬤒}LiV`)fwh0\.ȎI| e+Bgvg}.nArAпusk:/g$P|4*5˽ ĜzKM8[>?}іR=if;6 ,) ؅ZLON)qOٔy B|SLj_uu-ʕWݾ=^@1IK̶鎾:N7o* )+} z&u?%7d9Uf~l~=՜zOGrþ{d(B88^@y]?Gk>ayMC<ٰ9B{([q=".gEɄcN)xýy\Ax qI9_nw8_2saSQ0(7 ͦ}dRusKA~Ȳڎ|)n5 ;S8 Y\ bX6p0|=emo*B*hs<pz:ۯcʇP'm7':dgG3Cω?oU/SMw9@f~c'H wÊnku?Ƅr*< Dߖ_ByNIXj{ܷU婬0iTPCBQ8,5_VI$!.2ESv;~Ea>!r{{V@S/!L|Zn"77W`yZHt#8oFSxlU3m(**'|^ط;2ϫG@ʘ⠀=U@AO?IW5I$I$I$I$I$I$I$I$I$I$I-jիVZjI$I$I$I$I$I$I$I$I$I$I$I$I$%xSw>Ux{ܩ B52%*H"13e"x瀨- Oxsr0!hX2Q<@i2gp%E&UaT2,Yeؠ(% R^1R TrLJ'xn..I$@8W UUQUUQUUQUUQW4L$Ip\I$@7mo[k[V$+@ ĐbH$XIV$+Ս۱n݊mjիVZmm$I$.ppppq+@Đ @$,IX$bHŎ&7nnjիVZI$H~kZ$,IX$bHĐ @$,[c۷nn\+VZjիki$I ckpzֵXfs6,UQ"EUFU"*0DUT`s6-6ZjիI$Hoֵ*ś6s`UQ"EUFU"*6,ٳ9͋lbI%I$ ֵ6l9UQ"EUFU"*0DUVlYg9sb6I%I$ ֵ6l9"*0DUT`UQ"EUFYg9sŴI-I$moֵYg9s"*0DUT`"UUQUUslc2ki$[[[I$I2dɓ&L˙|VVVR"*"*"*"*'GGGGGGeG T²'e1pJ h zs P6P H\`B)+^4@Q3AP:Kne#n85jyJHppq Pq+]FQ/C vFph4%7 #1 *,/>7|K|`v:l>6A..b1!fT"qKjpHxlmPF{o^Jq~Dug!y*h& ag IH2@ȁ車F a2=|ӹyϋ[萒 B@ 2 v8d>j; -X ^/p+|! ȃ9HBŠ(ȘdJ?|.A *9%3H?(}'b [ BI+22! c-(LNWZNl().(1;;׼`haMI,. fNnI1TR u?RBAER/data/Journals.rda0000644000176200001440000001255013616365122014067 0ustar liggesusersBZh91AY&SY0 { !_)`#;AvW@ IZ(lЩ"$h?M5M<=~O=OjSD~4 =FhOSM=5mOj$z7Pe:Sb- eo} uٶYz+H"zɔr@jT,]uR51A^B&](%5y5I 糦kNh՜݇Lq-I:V꫱i)Ĥ! Mid'9 N R j+#">7<*͋VpEMkQr[NgʵLN.Q @FYޥR%y$jȹJ2d97G"n؀A\]2-d$2H؄M@ؒI41C_nB'I I$`L$K$ '_?(@ *@w?JZ8"!$ls`"p`JT *TWhЧDuRiALi)9̿SH)j@%"(/%^NP[o;dקl|MVlkGaj61q%#!ԬfhPDMNBq1DFwX5ldB-CR[ 4UR8n|Vs0Q&)5H8I%5It)8PH !pJLM/4 VNU'"赤8_aaSX T5!Japz[QjM+BKuX.u4Y+:tBTrPZxz{뒮ؿ=Zfulˊ+H.Q@$+1RƈJ"Qe@Ԣ֎3.ۍ#q{驵DRJcRJ].=&[J+NN ؔsSb]ÚʦQB,fN0ZfePb.]js Q9疥_ 1zzj8dض/NC47Fʵ:9Om M1еUi Rv<*:;\`swHnoQ"idxqc@U˅@[M[2{4sjs7ZX1HiKN KG Yh)-pEĥҶ~J+ Uer@J;\y[j,Cc[L$ (- Z0m`qfa9Olt1_Y|rlKG=:=)OA~vo\rmy,NLr=U(ҋnSA^ĵЯCCRr 2kXW}v~)7Դ[bM)IcY5Y$Q0DLҝ% YYr,f m:Ff,e1yi0xiy'4t 4 QC稃~ 5]4鼲jC>!&K&*@ g"P"ba`q;S!453UN8}"&MܯU8L43:Zd6l cFh](j Rs_MJuj۴D ݚRcdZw@z/OM,ƙ0(Iٷy `,G*q݌J}Z/} Ro2 `2aphO$GFA4vvK|1hi,tL)!U|(=iL\ēD-9$5YyP2 R%[R1.L23-2 ˛hm×J,cXg ́ la!yb^6i^K23uiS*- N%4^҃woto ,Rєw7,+j&g@ÙQZS4$D% BVRI"ؘ 1N (\'Fi$6$-I ))e-ǝnSTC!"LiElcAb;7YM(@d44Fdp; )I쮥bK dN bKkeJsﰊ`~Ft)3XN׌" 4]ԪaAD a][6,ֿuL5S) NDĉZs8:;;-2nlzHhQ' %HeRR<<>J7^b9i0+B,殍h-ܾj!"tuN`#L%^IB6N sdhl^h )5 CHm BC4![p. mH$$3Vݡu)#^22lMM ,d!jjVb߆lѬ,.gV噆YƊyccd_aS2&i kiX@bU6aCi|7WM ehE9l Ez83:6ؐd7Lfl&X)b@C@ALUscq=46Q2DJLM$6ɑ1ˣ(m0CHvm@URDcMAƒ3yHM xImL$M5[ mbx绚J ȀՀ&Ȅ)S;>72r5ls;H!^[DJLmM,54g! y=31SF@0l.fI$p\:MA^,k` 4mMDNѱ+.qvI,iN4 4)ucJ6MÙf~UBЍ}KDIG6sT/ "5nFt ߌF)Kib*MBEnz;ZZtA®ƀGX-9l,9eCW/ <GМqm$ 4tbg PV LLBQEB+] V]Nk`sd;ב%ϛ*jpF'LNT)ęnp'ty9nYՐQ9.|& 𘰉[L8%K5EMd7J!ZR9enqɥ"@ƌ=ˁ #y4+Z Vb76#j$뜼r]/r3VcMY2R W\HEAER/data/TeachingRatings.rda0000644000176200001440000001166713616365126015360 0ustar liggesusersBZh91AY&SYqb<88x_\]g`ʭw/;NU]J!@M @SИ4F 4 #L4414 d @RoESUTTBj @C#@ 4 FM4 ĔOUO7h#F!@hAM=&hDb@4@4hA*I`F"B=Df52dim@MM1hiL2i414?Q4ɄmA F!OSjEMɤ(=OSLhʣySBf r9X5cNԥ=QIX)؍<[c+\A GܢͮB&,M8`6*g 4ڀAskrRV("6KL@h YI []zFWUÆ42hWJC@iRXbĖ&  `łABhlXѩ6-Q@Ph-Eib+nW cfXMj4Fض[RUh~lSg]`% ZfUhSQ$0 ZjV$֨Փ!%&dȬ0$ֲ:]5 ! $$xVgY-V]ed@wL;F7Mpp7䢊;U=.84j_ &mp(񕪍EQMBYx-vֻ6fЩ Yꊩ`hՏIqh@㋋i&[6Ch銍ªl7*ΝJ0Ms[TV \EBVb"ítajU; clF [ƕt̽gTժ*^SP&޵5nٚHn76ʻ31;&$τ<$]ДzPQH"HF(p t$Ed#c*6)prhvPzb'Ftˎ;!s1w];swt].BWNuqܘgt%QMJUau@ebsSP0,1q -R;<+.B!r&p+IxH$]Ƅ( ^Rq(M: K8$ B2aVb./ 1 ‘gT(RDU%r7w*d A)a@$Mf6&!mFHp ZШ%S8L8ro//mmmncĘ݈Svnꘖ[m[m[m[m[m[mmeYe2, 5LLj5MD4No5MSTը,1.컻˻.컻˻.YYYM&IyeYTϟ>|YeS,Ȳ$HI!$ݔrG j7""irnMɹ,,-bHI!$HI!$kZŭmpCSsc9cj[sLٳffI!$HI!$24Ld \#`(v5$8ua'tpiRe9:Ӥ;L2\LY;;3]zM_&o kBۨD!0nfn OĖq$fr5&C?H@#rOQXl {ݿ;o6!wf& oQ$5 Uȹ$M ȩ]n:닛p.WS399ȸkѹ'q!6NsWwI<ۢn7KsF--ݸ\u۝.\U$hmb-&شZ"65Ecm!d(54mIon#QʓXF[&Ӱ2Tj,v"`(*""D"TLJ#"$iI#3$i &0`JbH*~P4Eˡ.`Rߥi0Ba9f A!D hX 6UWM7MԷ ^q(ᱰ@4@6 Fy] 7 T&ŲNv%(0)!\ ,DNb.bCJOZD Ya n4q 4xmdY*dN|dMRt^ s> d0@Bq ĨdA܊*+D gYNkJtF!V3'   *rM(d8eۡ+Wi܊6࣒(PV\^ҀT`8^g /m 0E k욚)EgMRf>`X#0 x䤺D%UOmwƞ $`od!bM@XHJS ʬnA,52@MHqFrw8i<"6S+@j4ݻsޏ\ggBm.M"dY.%*y{-|yӬII{HmШ5h2$ Թ`푰tIփi7Z3WP&8w+4 ab'j Xi7 ķ@pIzZ$9({$sD@LطZkoe͐36>Jåޙ"΁oY7Έ(ڎ{z;hTKnÿbZ/Zk`'}xXs;v8Rzb0ɋa9LX3N$\I؜g߇zΛjts}ѳ:|6>l)& x[Ɯg.ZICK&/t{k2BjH> A **bƈQ sfj/|:M_Zmj9w*HEC*~#&t![lΓoԑ|%.4k(q#m64تʪ|1-iW>EMdQ1jQ ')HHG WeRyC H L ӗ uL`;.H0(<3k/i  :m}mw>KAl Y3ʮfU= @MMs+,cf,FB0vS;n7r4ZPl{\7US`#,IйtE`}01BLW<4ļxi|+ _ ] y$(ʯp6k'\H 5@AER/data/USMacroSW.rda0000644000176200001440000001017213616365126014057 0ustar liggesusersm ՕNjހk^(' Ei6ZA=D#.I4!1x4c99Ǚ 2#(((b$aw+>9{rE~Kdey/u8xάĭgx=)ђo_}q'ZN:<|x_ziSVZjq|Ae^oˡ-ohpUDYؾ/GʛM͞G gӟC߳:G1z/nruCJ;Q_g>~ @[ރ)?[X>-/س:g2~QEaz+?!vCzԠ*$ 2$ y/zBE34FO= k5!D| Wgύ1'5ęAƏj[yxO 62[;^RG/zkg,vl>[ok[?oZ= 3E|w[v;KA68c?׹qA'7ryZ-Ÿk*mq;_=L?~w:ߎg=GG ~F#w^15Ir~F/Q|CG~G"59T}c!ZU|F7=wLٛ}HLk~ SO李o:7zgPgz+?HύDʒYY➓9عzůFs!Z{ ynyU?o2H|a,cdg>~?Q/gq,x\, 鯅[qQ.BϢM{_r7ߢ_L-[H_J^Z77N?ǙSqũwe(lɵUH_xvinS'\Mk%'Oq%Q^'y!\*I2ŕOTvSEOjJuBVɥʯpœnz=-Z~?μh'%#ю\ӑ+{לD 6?u1=CZӓpcn4%_3^ͣz/rFto䭚 qUS׊{BN U_U9D?#H}gG{a{>{y0 yA99K^Y=:D2,~[ߕ×gQ>ᗟu=VG B>"ğgO1zF0OR~#G?:ENag(OԡK8s+#_v n6O/>f?"~slw.ry s 񣐸||*|ϡ?o5{\( "oA%6S=kϝs׏6W<+5^ rג:l~cPJ;/gefD}<m{6o`/~5 8A^G;0Gp;_=yk ^FȹZٯAuȗ_9~| }WD|Uz末y-Kzv 6KmA3<^c| 6K~{NM>{Ϟ/do9͋W_Dʏ%9QC+rKǙks=s1{o@OWw{-ě;zGYh`{cQJ|A=Ë|`{L}Qisx䫱f<QrykgqydX~[˾O=s,sN6mwc_v sh0gj2~qﯷyNVK6xQlQa3_TI~FK_jmso F7s~'?uo9D5v6?w Xeyoy0 Ma[?@۹S{X?\{l>{ +^n٧*}7_cF=䵬sE>}ܱG[ 7; {'h9r{ {/{mEC s/?zVwtOwh}d l=1s Z#? Tz=?RϺUzWٿEa n{^쯂o @e`=>wyѝogEOZ;|_CI=?;5?:-3mc.5D߷ķ;cZ/ˎ_HvVs`jٹ@L1Ui[P9_|ꮈӗV}Orw+O:%w`7-TK_roL>n.Trs~^~o{qxn$#:Q$\NLj\HLR_$R9|XyNѧ[d?yj PK6?q#{/يKyvs k)Ol+*v7pwśޭ|pK/U:jt;zs[e`pKi/ ZpFvNEA߿녻xR|o- ߞ&GZIϞ?~+|o>(M>xNVasюǎH?'Any ~6Cֺ|ޱa~T?Yh[&}|yN+;gMqK+N. l]p5™'X֔;X)\ο^Oopj;ί"|\7%X޴c{OqX8|Ýrt=}Ŋ';% .p}֎vtݢS{Ӣϻxͭ{W^up;[[}uw8G5d,/0P8'fOptYaN+)5}/)ՎXCGWrz*mwr:~,\N_M䬣k'͆k tzj͢^=o8ڹ{j5V*xZ|/|PA@^XNHVBM-{-+I꛺ ~G3鯻RnއƒG2}Ჳ5xC -.NU*W$|[jQV&~)fݧyO4P =܁-YMظ<)d, *5TQDoK&me\ލ1"x5j&̻qF\VW=0_wR%.|}H@FѢƋFlVQZ4ƍlXkFj6XmF*LF DQHQVXb#QS6#cFhэѐ2QbEQ6)+&P&H"0TIE%2B0 ,XeAXx7 QRA)R&P($P$\E,XQb1 0"ʼn*1%I2$RIdă4%E$ea 54I$F$K$dCARR! E4ęıh,XģbEX@b4)HPDhɱH)4lFR F&D& 2I)4MFZf@#A&$& L"d 4TRX53!$JI1L8c(EFCI#$1F0^|]_ڮJIIPPbRE@rkqD4 I@&HB#%" I&db$h3a̘ I0X$bChAH’2E%%!lJ3 )$44J@Qh)$)4L̋(b6lDe)%aD3 RLDaf*&MC(H $E&$1&̢& @2`0+ 0SD)D 22DTȣ!2EDXY6*$6(6m*e&LD Rm`hUȫkkVhֶ_^ԮmbSnMDvZ{8u}q+b4UNu'kW8i5x$*D±eITG x,˪d.M/T@[363T/S"C c`2B-+yۖ^4pqîhs&tצn**P۝TM< vˆMH~"3RH^Be]daˮܓ}Kb]]+EPh5u\eNXd@fdV}(3ecvInbJ)TB*Lsן"@6<o,pbe+*;LfUru׃ksN'[s烦: :%4Ȩ+d@e3 ǖ֨neÓm%b'36jZnfn՘bÆ\nHSt)fiT!A'HdD|䤈.bn݌:׵v}"tɖuv ۩^hn.`O[03c9Ŵܤϋt~*nHj}d>7O+w㻧35ZfnP ߤb*fV^B7RG I_XQis[dpYۧ,YtT=܍8돀Fxmv:VZ6lu͙k4%ll5'AUo`XCңw,5V:S0*d@_+ycSbxlK9"dWV1 \"E0IدK7u]e9B`{ !P0c>}[L)K*nydY v=̼="Sgר9vl"!!&Og[BTMǻLxQvӋ2DkZBav:mqdݮtƚB :fUp|җfG[rtn?^Sg5hڭ[.?6n ˘e'FY8M]>\ǫ겵˴15KD.MsSvV:C==[m~HF&)6*SO,F#7_yf^7u4_;=޺+y&'RfldSn"Ace)H&Fw @:+:8Z{vklmSL^(21Gp(D)C fkm[m4ڭT/dֵr  Uj:Qx_UШ!hAFayߡj2x^=i<-Ft~,z8"-wAlwS˝ \*g"HF w%bէ8䴠Zl͞[[[[[X39s81qqqol-qUU{ oUUUl,YV@+  %[z;m7iՐ dYV@+  ZzZZZZ` i[ UUUUUUU[ UPYV@+ *[{x-L0R>| @j[ UUU dYV@+  {wg9hYeYV@+  dY,I$I$I$I$I$I$I$$f,)$I$I$I$I$I$I$O-kw}v:ϟajjYe$^I$I$I$I$I$LI/{0l,I$I$UUU33333331-3<\mJ]FF FCeR b X%hQXōmcQ0fOe˦Ej꯿$[ ip  Y !ߟ&:@}zOzvoyAb}x愊'ABBz&[xH=/k0  APٜ8Q rPғW5jsVMgEsئ"eb]QL|KBCwqpHPZ dH+q)efhu }7囑ޤ±Xjs+&έ[L%P(c<] %]MFFj[,*L {Z = ,_'Fc Rw26CBLXPROn|PL6sHt1.#H^7voM2ߋ*a `ղGUR`R9T[Y1x.i|;㲌D,6a~jJ#[;ڙoAw$S AER/data/CPSSW04.rda0000644000176200001440000006173413616365111013343 0ustar liggesusers7zXZi"6!Xc])TW"nRʟKMd[_;zk.MZJrہb:-x, X"/?KGAdck PMR oFCR~G6 Ê0Z ؙ]̽a<'bOgh1$xjiBr =u\,T'nQ YΠ[ };#8("U>`dY硿ӫnadTKMT0:Z/6!!G̀lB: &1yr׻ID' x`GB=.Xg4g#ͅ4bzp.[-, zLhZ4b@\ ҾBI¿ߨ@H=NI l$ߵC$'dIpV5we&da،x{N߻f )SeENeFA< Nލtv_FZ0Oiߢ A8ξrVm;"uk{Dy\|fyp=m 6Wx#Pϭ#Eٌ 8B󲶐V؉ӎ~иqd#wy.!T$7"H 8-KCff ]{ҤA1k7YElp'g`0!VBD&CRq mߓ0'ԥnU0:Wz:mTHU$X/8QJ}kQ)!R3Fxʸ QyxJgk;A2uے0cr? >sEjJ;<pXt`KOLL5zPЮܖ_+͊@hDcH 嵇x)I~>^zO6e+ *%iLQJE hc"dc)⛙RIf& Fho UȰ/UœjbOQ2`bYO̠ 1e$(]QxFڞ yϻInQUeUd2|iTl"ҌS9!T !e0VTEH9$&rIʀs5.L}28nXAx`jiN`El YթTբ׌)oFKCˉvTɇiೂR" k A=?,Rh 2˅Yg+fC[YǪe49<|%t$gR\i(V#04QerÜrc`I#I !qv,+{]gv w0؅~x,aktZ.U_7L[Ү-tuī`!ݨ*ҨuzoA ]PD.'}O5kZ^4l#Eɯ<ɤ*a9^sp5?PҔkg/n.vawhfi߳Y)g*ws;12[$Cƃɼ>ZLY:E=ktIP!mL_$zC9.gzQKjSB+Ic6F2r]f;> !&"UXVi孠KCwu&(a X3kS&H"Z4ߚ1BR+8@1rY oX^6L6W'U0qPDOWȂH1xYmd}$U^EpQ]6}o' kOv%~Wd4ڱTcɗ#ՙ;q70a󤢖 My ^e:e*7BKO'`]xǝer3 $6TT\O[nrKp4Ə'ǀ(+ 3ҫ6퐗go!7HLJ6r84*4͂ x]]M#-Ag1{Ft9PuʄuJe$_f< odFb(1wls~50EKGᒀ B 8Ny`fMPlPdϳʰ(ZY„1;5&Çcb;fUZ9^{f2M`zJZL ҞUBRޒVJ:vŮ;<^c0ڭO#L9q|􉌜^iߟi:.&W"_AXHLb T2QFE+5(L~bق؅GQǦҷnw ?U>yi,!Ƹt("M0::^ww1#;ƨ}FaT!1#KS_J"Rn#F TcH6;i02V;SQ#W71EI+66ޔ-iۥqSrI˽Iv#bc%H@J?u) ,E*iKz3M4;PdoUaE D@v!½)P쟭AVk+v7>W c{HRekR\guה*π XbԦ7X~':QWuV?~ ;9`:IЪ_}O%;wج4h#79P8Ku2=P[q4X ݐ"PZaJm\By]prp Ez'R8c+e-%D,v=|ϡ7eqzT J[tNPڡ]Euel_sؙ+˛%Y.vn b .% zaܣ{%<r_'W54:zƻW&b7>\V*_b;w0}qo-yV~[h9v˩afSv&{%hJqK7۪B3YZ" -yJg:iۆrYZc_9!0C'iO<>{JAg4>~ d9FP[4o[^5v郚 k2,^SU$UA)S9mJz L:@*`J Xo:n7!RBGVC쑷Sj{v3ϔ ?< M! SqaMPrcI')t|"bG~#Z1y3BS>-U^7{'4)Ҁ0`Ʃ{dII%u GuZRBʱI}w`Ut@,$`qJn:W]^왈Sg_!a^JWj.qI p;A+4ĒOguiK8vAt5-zpqꅣqvm"Dj35FuȋnkpEig$P/KՈ' g_,\g#6:B`̀{eJ\΍x`$uק 9%tK :IpP$܃ ^AhwkRGmC!`a~{*Y*9ON'8|hHķ*# ijYrMhۥSmxʊ#N9r bUo`C==PEHrP4bЂ2Ck#,~B?`1.uҨ>b D6(y-Ki+?${ t#)A.0[IT@X!%{xwm~I ko++\lXIĀ\9IqQm;ݤ=5[u;7,Cj<ǽ" JN6旕* ۜ [Ni5w`ظ:H!Z:K>ŞC*F'"^OtPu!*`FۥTƸZ3N\ bwpǀM\p:R@qKh~Ih6I\=wxZ3ʓigEW 3QFQ N`*kqVE3tsL:(.kã"!lz<;ՠ[E 9juP5`vUyI+x_a3(E#{IK{k~p+n{hXΰ%z'_".l>yݵԘ [(Й5DKi.W ؞Q£DZFaI<:Pq* 6/ozP8~ /@lHLo˫+:g-c`*D6PIYҍj]@s_4oTtǣ9?һPDjFh8tG&3âTd6#.#PIX͎cޚ]06Kàz`Q͐}~?13jx(#roH+jhsOۢ;dK3J #Ti3/ZAkNbFN >js*n,}łkJp9 ؃o89ƸuZILATV-Gc*GBfhÏyJImqZ >G7,ND%D^HQ4'U;:/Db*G1f5l7zҖu.Iۆ[q v ">'ڰ.َeF'޳Z'/A ꨜ9^wd7mM$@!/r#D m, axKר \L;Wi^5T!r>,&1q5R)@'&}J:z{?'&Hm7 EW+@?fMOĤE'u}C3>D"OSxE)U0 9i$7 ب0qR3+;Jjb͗1cZYjc)BY +ngڪƴjCj4-Lg]#!Ż}GmŨ%Lm;2= 8ɑCG E<V܀XJ&c_$9s"(-I:2P/r+d:mFkт$,6A d-A-t.rom5hXpcN+E嚟mgV}srvڛ\= DFQM1MKh[i ;4ǻ +&ã=[ ' %e@Z⇕9V9@J_mqˀ6L} \ Yo#^(`.-?WvE~U;3Mm]+V9i'k_Dl ~ZX4k0^JVDz4K&kP((sCn"L3f:3:]aՌ"b1TܓJ:ô œ]AKo4{I*FWj <țo:IE ?B` :B HWOAH|Qm .1M0蓭s!Jz uN69lV0rdV28OQd^JӞ04Ŕ5S)j/r־uK/ǧFJ mAc'vNm;lѸF #Lyb* n/MW#+OpYxpfs<5(1Q+[%f8}4>nB5g&3=J'8fjPvf UVBc}B/eUY/h\:ʷIԸcTHUlRkGqb~2+[6eQ*4 E9pe!asp[Z寸A0EPAӋsMhHɰT;"r䍊HL]XkeҥyP}̼@,M>?@O : d2]v2#V5^J,]f~:%"cWR1\ lI2;BTz@qk@ NQ-Q%k^y%yQӷ݁uB8l!b!(Tf7TNv^ien`Ry8KȈ>Iđ 8졂=OG I:pg3C;lN`l++ i3%c~ώu'֣yBXX"PjრY͞дҀ&4#*GO%_s,gkc~ "AT*]Li\t V{tvK t㨄u%vV/ld<|2U{ucA3x,+ׂGZh6en$DfγՂFsP^ŃaGBM}KY@9i\̛,qBHɂΕ~Zq샣KltDzǔwڷ¦2E/ɺrN\ dj=\DZCUzê'@:3rܭͣtYǭ;˫f̨:`F3y %܇)MI]`ER/<]] rh+9u?S "50$p 5i%Ȓ|fZ\a+26Ⱥ~308 LgTl}4yR8g w"Pp%t)C`Ci834d=& ;Z3Nk i&E;ׁۭ) teI9 /RΒk,qt?bNW٤; ,*_=2Av&9<nn@ib8V E:j[!M=Tɢ.]6V4{tq=OFFvMrm{*>4D۞Lk/$KwS~Ri(f C*mxq00q [2~MC7[jW xE&dU}Owr"׈雲N`9'tsX؈޺. }%zW c?N vM"JF=)tڨ9~HȀߪʥ< m3 5c:=9ki_e?bRE8FV`B?_F[;_wdUrF]y[Z+b@ -lV 1Bh5y¹{GԖ/*ٸ\ ZykԭU㒬62,Z籣,ZV@ӰHAH' /*[rMWں>=kNLQ<#*A4U(@-&ZhΘį&W4ϽF[.V[a'lR~@l%y̦%dytBge^FGK ˿-.hXGV'OA^!=@n]:۩́5.:"$UpTu:tfgAG3H&NҿKk&>M=8JjH5sjBd7WY]El= VHrkQdJx oJŪnpS zIH$g Z_nBz2S_qv vZO$yƹ2]-Շ cT RxXp=6ޒ"%[#7.Q>"p\8;}6Z&9{*AQ8,MYbʉ潬"n`s4,ߖPƊ_~-Ly$< +=ag6.G4'Yn%t Sgܥ#jP>eG3E=瘴 /qQ>}e ImQem/1š{|+ܥ~OKF׬"w5_陡UUJ0 mSo^ bIVy 6eC܈bPOBo%S!ePMzZV8燂ET\IZW@M)x~&^/S+xYS~l 22D;`1;ZL(%Q۟# 9 ! '67e Xs73G5A22a+Z*UUBTĽLͣdޙj,C,qz8Ppݭ6 k>ٜ?*:$T5qx|XyhnТ>Ř+V \R_ڋ\#\fLNi>뭂B_$UAҋ0ө#X; !.Ŷ~H>L'2g' 3L_xVknٔp)nv#@jY9iz<$+hh'bzLNu2i;ʝr=ǭ,ن۝cmѴ[B،L3h[*ߤ\*bP.%(87pc]D :1am8 //ؽi:trXCol(w  ݕʸWWQ:+zjnc!|/Ical cT ^2/3ŏ8i5B+j4k\dS;l1wL Ҟ/uZށ4}7·4´e)M93OOb _I,P9BTw?Glc)a%_E%+Hm'On/n^w~ Bnf*ORq%sOϬ\;8/P)BN(]"I: #={^tb@+<И'CYԺ:z~GJZZo-p_ .ZW<ό ҧ쳠P]ĠOC|m$$Z _xMU;遚!ɮvx5=]=eH^"93P38+{P}űj%ž7 ` p1eËڼsեpW!ս!-u ZpY)XPoo?b̀Z74ױ-ԙ.HVu@̆_9 *_[d%x1=>cߥ5xH{ cu28 ʑš6݇Uc8"cAs'@Nf$z_zn2LZXM˟k1X{[f/ZA^Pv$n جa%IJǸT}ŠG0p1*\6 }wl`o#lA$Qv,σ͔@Ҟ+aL_`<:_m+dqݟ7>(:ف %V};uk 3OZ N>9s%ч(̩p3mq]<'nàm~$<~gx~B a: #ﵫq*{I>r2qW /*$iAN&p0/ԧ<⁐W9J/kbSM2!e֡2|ǭL$</Y_l,y&&ygK;Rd.V|K71Kϐ$D'a;£+VߧJ!JR{MCy ʌЀxj y4u@ppJ֓ l5 RCn+AtH2v;GN2G`ԊĝH[$;rC&kdϩO {nِ'4;6O!qDZ1Ϊj%У0^@r Aڎul6-DyG6_ϗ02QZnL4zM3TzdBLjN#X&:MD \n4RDzJK041Xjv=IC'(~YM*{xE|zjX @-! ;0]D⢀GړH&JT O An^t( |Q#b%MI0trcu-9v=s~}`C[)n2iԲ-II0lw |yn#T}Iy2 uq+* B"oV~M1:[Ց6P?&A*'jA]5Bqbp˝( dʖKXJ%A9;VBU⢯r*.}n`J Y[˪ m'z`?{w8aDɯSyy"ZAɰˣ?[ӣC}5v'ylly~ q p+5:^,p9ix&M& M JH_0իRAc*HJ_kLlV'%Vi3] ȾxAed $_7~D:Po\b6Afz$6#"&Zީ,G!;6Fu J\.yp$ zaSVѿrysN8A8"d6Cr>l=BLmCa.GH-E[ n9+)?@-)@qڭ^G ȡt>b~yBx=s!r?:vՎPvFԅKx~][L3%Fdln"5b11rmUfq\\wM+NE`~1Sמ) 2\QZhyG 0Oc>K; _&dޔMrfP{ [gr\%2ED|lpVQLf O+́v.][m~DaEpDi[IX0FRK z໓ø~9EQ8VيNW3wwP Pb6fXGoH&,x+Îz5" ,Q)פMϜ$AD ԙ?"`q>K1. ԰EIxծ-'J5mr(iQ\1:׬@y*.b(89VP;Z-`%ˎJC[M&wL sLghSyg"S~٥4gzعed&>wd:Uh u 2GEP +˾nx.z.(ZmUVoH_&"s57 ;) y\DZM$u4ZI*Ewժsȃ62`HS2Tt!\nSv={=6^r%gU̮[\NhrlOᣃ>d;DYa xY r¿efeDkѡ}e˳lI8H9 j׌DҁD%7%H%_,4aYڦB/lω5>u9[= 5",z9z1FkKJwJʤbrWrG,ȥ|!O"TEKY-'n%[8 ") cvDp9 <@uRĭFD(OS[0 &ipͅ>oЁ9Q,*ʙ޺%8R~xi28 KmEyuX0̾uܦbl_{0cMuDn*ao !`阋7CN>gBK5/Y.̄|59s&%hBXUgős/'2KwTUGO ®7/|bK6*ˁw(yUta@rM5}n ˃?2;1ZV+,{: SFc$ic v5LiA?}\9ƹhQazn@x qpX=TRU<й6)Q z9^ {ܡKUQ\(a'BQ6* p 5aw5d3pEZ.mŭ]-s:CRsI"t_>F۹zX7j_ݐdHj5硑ф ? ~JJ`UD V8̼ npV0EpwN"Iw%c=ڌa d k DINĚJ,b"=mS/c1`:!iZהgfO JS8\Yq)C^"]i'R @bvopb\D[jyBn`.Aq&1ո=gdP< W`t{GF%4yzwOGD* ~}Ro2MХj.H._ՠ'dl gf,MeR:MΖBΚSQHm03`G`ZS ȩʐ=!n |$FzZ#tg[bپկqHjG>4/qr8IIʃ]e*ԕ+ Ӯț:Gyl6ö1}9Ý͙,P0fQ3Faest3pFy:ZF7I`QvbStiAT%~?够x wjՋ :cȐ?q"]KJsiƠ@eжYQ(hE'r*-Z|΂[>M>cQ xUA4!-)3ʔ|`jK=0:)AU:3,Zgmln/ͨ<{&ȳpCe2H'4M[f8/ayHcDy6,"`0RTdId LS,x:f:h :W"Kw``VT0$Ju m[18psÞ0ԩt} ]n_ph_Ô,@ZI3ڠYٓJ :&i֌ ^_q|݇+MZ7[a_d1Ѥ_C u_I \+&8D%z1~YdHKb"Or弼 {9Sv(],Rq=`*x*n #~E1~}aM'X=[J՝ע|Fg[3;eYpEl nbCCd~U1G6ō[fH,M6 |.Ϗ+e/ l0gM- "yqLT*)2|nRˇVbS9x'?)pzĠr>EBe!J|tcq1X4y5OT_ٌ/W\MΑC4[2{]4| izs(aC;2zMa=wui'' W0끄k`ĩْ +y݃KvMi7N]>hy:1nR"͚ϔ c\W_,UzY+s}HYx8 wHx]vo-:c3L|J׺%z螨Hh Z8Ox}jYZG<5FCɠ0ާ"\Wc@bCW2WdT6wI u?WD^;I25k_[sd1Il$4H€SDHfx7t}PBRW;#%ꥎss_굠Ba(=ed!<һX H9^Guwe(;Y=3l_wtXl>yX%U\d q]SiXèQ [R} zR9?HDމa-X /5ʴ7 f@wCG~kî!CDyl^x*+`WD<4;-03)r 1CVR5sGk]#|⽛?ɨy?٦ءtfcـ{Q,౟b~hng(љ@k/,xx _h-ݗT R9EdHFaV+8l.Ɠo#epX& W_#dr<5)UAlY!m@U 9$0sG`c)6kW:Lŏ@tcʸг4^s+T<ӂվkVbdg7avK8=lqcˊ(BW_St%&&6|3m2LMÕ̿]宱`>1ShǏMӼTn~p#@څw Zrop(X^j#N쵬xkEE zk}26j_<6ՑpQEe3}'֜uZ:m^Æ?-y |:ˋvz@PUT;50iRE^@fe/l >ۃqӪagZ"} @2Q凹,H3ץz'UVvEppRGeE52Y+T EsOq{7S2 l|1%PGB -e$8TY}27/{ڼ- Cdn-NO8O!("^~rrh o^+5J-<oJ#]uCgL}s;ᤐҫhZq'DŽR(YlEuħ@}'<7#̵El{ֶ&[hogH_lj`A;@fC\Y%瞡3eCǜu twy isqz=fb ew_hxp3g:MʧgH{_O2yVzV2 "- ڇpbڎER5#;6>˜-':{WhzV#"̎ r427\?gJ$*: f-o *8|`aau `xrF\T8 bH&7[5EcLV.GwҫXaGluwYph ;qe`8[8ǻo C:d.\'~M*Q h'X{{$˺ F)DHwHKheEȔ,/&Oi6D"_M$V0^r`ܿaojhSA >x. SC~8ci: 8zL%`Z3Ef2E/d ƸI,@ɡnsA<3N_,f~P 13ƾ}_^x|50Z!/ó&YQlg1Wq]sQƘrto٨ms[[aYnn2욗n`fRUF)L|xLأO+9K\*S~]EZ@-XZ^04 ^U>ɻ짴uXBh2 ermLȖ-:vTL7 b:=rlp 2 CYXL?!툵Y#h`~o:Bx7@=M(VGXF ⅇTeeMֵ`iᅡ528\耖nܚ;i^Ͳ̴1(iqgBu4](<V/op.RpVML'W2@K!0R.ɥ)>ZJ 0qeyze3[%\7 8 ZbIc0i%R8THgF]P>灭P1!+hl;搆[iRHĐpwz9&!0ep $X6gt~z^NFyuE:^D'θj 7G|`jΝ e q;IiNum)&֏ٷhhαSLcYh52'J?҈cjl-4~$#W=J))ho?)yt!!;￵kZE)mxbwR\{/alUԥ#TF>@MŤ߻vFBRDX{"O–W>s1ȬdGWvKQ 5_y?e ӈcVtxJRHҼtpʹ4#.LU%z9L af\>gެ{+,#K# 3 L*D6Krƛa>hFȽR ZZQ:@u3 %'& ͋γInf@-֞|!wP;Ʌk]ɶ@-Tr{(*=V/ftҽڢC+fN:Q-0ӫ'n$,w^=L!>H7#4:$iѓ _9,1ffl7:~Ao튂]>O*si;:FXe4L˺uזC5Fnߔxd4޻4i]asbz"\$" p}MFWL3-57--IPr=w(}v4U,Lt +d4 БG ;wG뉛OYpFǃƲr;.E C3b'oBj\A*GL\}fd7Z01 E x3?zI CeP6BqrTW(-tf)IG W|D{ UFE.3TN)oQh;E~ V؈|9D$*K[(m.zG@:/cbbT`@o>o-utk/6LElD߉Yyլ߁W0B 2è(V(_8w+`KQiS°*urnC󮑟K NKbe)+/Fv:0v t(+' HDVVSP VCFQRs-P4YLVn ђ5SFDo?M)Jy/Zmq{poQfpno~AՈVF S™k;a`'pu P/N[`\Uc Յv6'f>9}nfF݃1OCtR/)>?,a>eX2v{ɰFQt;pb4I8IC!B,e.Md%,ؗ[=G LCN0TȬݮpz!INَ)<Ӭhڒn@l՞ r]"7=FZ#BG_~r;V&z'j+B6s>ώVwҾFaCC"R!n9h߀'JґT,긺q_nF0d¸5gUi`lɗ.:hn/_^iЭj߭W7*᠄\Pce02{n쮜!i8abWcA')%c$/OmsZg}0 YZAER/data/TradeCredit.rda0000644000176200001440000000177213616365126014474 0ustar liggesusers]TlSU~kiƲU4(dAVGN6 Ž=\ﭙlnsC7#܂ eAęI0.8T+[;#&;9yW^Q`n3L!!CS5,Ê_M S` |7Q[Ōz`']pAz v!?=xu3@=.ԵUG!ޠ# W3;[O<y>9ଠ*8tlp]|Vҋ^i8}ܾ-ӗ> q^o.7BGawg?| -~lX_oJ/Oyj5Vvyq^c\ztޯ#noyGXolcShoZxs 廀xK+mѿm(wAmLxĬ3N;^먴NG,[r^iftn}rr+Y9GN]KR` ݕXlDƲ}RW:DOu߻ieQۓujپlX;cc92BRw$>d(׫o-1 w9~zQWងLA(crD>da\"67ea;hRzW _%Or!C%EB\^T \q=t 둆.K8yMF7j$Z닏xϳB))"cjsgV^$(a_]V s{J$m I?W/y~5@|u͛CQJP(/cpc ȕyaES‘j~3J? ZFe] A]:345ªO`jp]gysbʚS6toMkzU[AER/data/DutchAdvert.rda0000644000176200001440000000476413616365114014520 0ustar liggesusers]W TWA\P\ꨭTmmu\:*\ihme\+u7 D"$$!d1.s}y+';tx|C>؝O7^w^.gގͳ79I!Ji?^ܥΔf.@_eR^k4ʽal&MY[Xgaafw gyO?)f>GV}~:TQvBfbku$iJeq{՗{4Q/#] ET= ;˓'~xBUWgX\^_N&oOm?lnP8I!׸y 폎};i/w;>Gb5"]MS#)Ne'Rџ̫ȟI'BہO?:sn~I1,~/]\z]םӔL钞Gi7 2GiE 9l=/3 <(M}WQcw i~V@Ð GQ:o-= x ߷Cav77׎AG͞˽0׀x@?sf n O9)p8%r,IVmDQ6ax#SGn#qߏR5- N Da~܍$5[ů>!zpC[K5Dm^|KhtQ(>_~,촞*jvJ's$}oQ8XKܢMz҇vsfP77!i~ܷ@u,閮z #辥A)u$!Jezk0gRdb^$AרsD/].Rx)JW=j۽8V}u:Gvw@Aeul+d+7'm`'j|oZ/E x\?=x5M1ĝf '|.8>Slj"y7¦ơ_}<جnN.Vd~Ą)5WH.e~>|PNfq^鳊Vň/={Cy_s3rQxUQG>p̧J*̝yQގ@ߣ _\*t3b8@7o9SNsF];V ~)ӟ˭&a[ =%bkQ9Po9gkcgV1<a0)z2U/gU3ʉpnf[&~E kW/52Ͷ{(>7qZk iSQx4w!k6yNو>ޜ[<awuwr'5<}?3|s17;f`S3uaDW; jOy,WnB);0j1wcmVq0t}f"xgygMK=`')*eqn4;9A_'DA1oY{;ZwTMdO2Y7oZnNHi2Yȟn rkqkG_T(~=wƅ E\|wk2/S=seǫb/.\~<_gxf>o<7z[%[摤?Zn A7\h ퟿8zmO+'AER/data/FrozenJuice.rda0000644000176200001440000000500713616365121014513 0ustar liggesusers]h7:k .LKM zUqkGic-N$TdӔB C@RCL ~H':`HѓZC0]iu"MR${wh*{0Ht$::u4Lb_&~=~qΞI$:'_q2_C_ Sk&^:6=. <2JPbqmܩgSxLΛzs _x^Ag̷ mLb]b_ddlczl)mbVe7: ?9Y0O_cvrvo9=+:gm*t79?'[ws県Ɖk$|Zf^I=&r_ϻE/89Q$:/.pn$cXtsK#~ύ})eZeڡ}UHmG,}c:02"HÉGcd<0>I(z4OpIDpꞙ/m }o|Ƚ%XDv8u (̠>EۿdЃ>bw/c]%K?vG{}ҟ%vYx ?+LU =juck ^gz=`c_C; o: +x/ |}ѵzؗv7: +d8!O: ;Ŏяk /n!:oӧuu͡fc1})>WsV1gygf834úG3E9:9-(r_y'._F<_G,/*:X{x/X=9:#<'q߷b>xoӏ~n@ƫ&N `>|tlx)aQX[/ZuqQ㶛v(;F[wE ]ɣHAet]yԇ^r3.w8y=QC+;%^P {/b/x}߷ %*%]GJ)tL+ 4(ORK# EJ+ |)M?g!_3g1 ̓Obea|Cgz'6kັ$$ pyھUg87@')f}|SAuë⪙?}̌O~ilNo7?g7<oJX Ⱥ>ᣈ:W2O爫?qyJk6?_-X$N]Icy["eVaμ}A_[ Y9_~2F8YE;SO='Bۖ7̺/xok\}Y<]>'_"_x]=e^._ zVU!oUлw VYb_O *k@_ ;O@^#{n/;t 蓀:jQ_Gq7F~iYtYcO }j]{gT:Сy/5FNsջƞ9wЭ-by[Y~w7Su-K`܈ē|Mk^x2/yi3:u޳ 8k{y6㛌??SY۰ ٧H!Bt 蔈qW^Ó c1vڳč%7c;ɗlj).~޳׿9?xO'/G??Iι_i&:6N@ܙF.ϑM[cՍ$Yq~nx؄?:&%j'$v!l4H!qΝoc̜u:AER/data/Municipalities.rda0000644000176200001440000014230413616365123015253 0ustar liggesusers7zXZi"6!X᣿Ć])TW"nRʟKMd[_;zkgGasGf_LoZ40ۀt2p ;us H[B5 N p;P8̅jZdFr<9Zt'd64FÛ'B,?.8V0AUP$ <$4(v6N@ u;vQd>3 aS4ܰ%j49C1 j3=Ul sw˿-]2BP8 PLnxYO37& g'Mջ09W gheU[U]˴弆HK mQ[hde_ײtJ]@/R9[(_l߶\2 W߅vyBԛXпj_}n;X]?6aYV"׃^`#+Yw& 94r5n|)w#Ȑoln*7_~!d2!v lI,o6vO2e1jBz#p|?9Q~ogsȱA.m;5uUOKP!5W| %K63+¥6z] #&/[ݍ6=8jh0 j`X]9I]T'NO`g_Gh%AW^` ILо=sHN;q}Qݵ-Ȓ1mق9>h#Q ;V!5v]#BrikSgk8vCzs,_= (ҀZ/[GSJh?$do0-oȹw8aCwct qviZD—9!6u `W%^Ԝf@vxB&>Î-v%{JjסpǺ4lT^٨)M%v6SG:A4&X`f2|?ϗ~yW\l"_pW@yt(:\_}VoЛ(|(*# |u o/GkdžB@^gHvʴ<8x/@ d*Da  P\@]ljw%͵߁i ҩd0_IH _s ]9^NS3x[IVNWB$'t,–VkA@rgd+ vM"}~hkvҵ`%V ?!3/x6|}aQadKw‘caQ(Y=j3SZȍ4 eB""rR"M|~z_9(}tN%^_QEmRaFې.hWx,6DG*"˷躳3{JA''\znsIʖ8#V gmzo{plqxح>!saP7dK9b v-Gb>EMQz<>;%o 5RFڱ2[+ϷD,[*lLn|}WO|@'pp*0 +.TTvl6 $'4>rCSklhn1$m$?{3h ӑhd%I~Tv؇54l8x'f/P V&&[}69(zxn3Qt| _Uo[JD$9(P]U^"*Fԛ=&0iEmcI033 .z!16\ FU27NL{a-I6tFep løc'tj~r4 //(vtjtgL;b[A՟^Q>eialPOkZ3r^Z1黀%5ht.u-6ixV^pUuwOKpVRWH ,AdԊODz2g eRw-űfԩ):Hg e<2C 33 ;uݶEmc/n3rnvtiٶ# |y'q(U.߿`B֠j%Ff8lQ Ƿ?+=յscgb%=FQQ5dI<N0D>x IqmB'0Q5e})v=b? WP $wBC6mm?k${]|\])7q6 IR.SǭO{vzqߐ z,qM^᪙QҎq8i= #ɼ-[F /WS4*>Й7N-6ʇT,o[mH՜8%`R$J]j߁j;;m ik rY, Apl.YSh @[8~|go&ѪG"RIRT/ldu؉ô3+7=;ͮdC6e@ʉn?Mնu o㝇7Uu7lG$G7٩Neٙ}Ӳmm1?F#Y;lrˆG-(!=sU#_:=Ol>Eϱ,-[;Hqϔ<P&CU+9Lt}J.$m{ ZxL'2>07[9 >7@Yb} z)VN{;m% 2z8;Rbʯ/ߚa05sT4C08 Ug jd#R9Cy(Z.J~[Oid 3P'[tֺW[ny[ņN¦~TIE%H'?>gX-X>3Gf-ť80/b1/1Y)LB]ټo?10}=/zŒ0>Qt 8mZmy3Z܌:e2,al ĖȰdӛ7S |~-!͈0EFe4cfDgkȴQ0~Ġ`rSuՀBoƞ_vT@a5n镴N[9b,䩚-wu'ZIT=jԤ F"`O# `y'i< s=h EX뭡c?5^J^ٷ|{/lF5YNY6Y} rpq'f;^*.f.D܁e$o~KbF^|y Q,O]ٲ$%ٟ_@  #)qܜ(hp1Pe @ܬH-ve¯py7bC8in.|kXy< j0۩t|ME4_cm/0uO'ؖ xxtH/Bod6WԳ}悦r7rsZccw/:/$t=JHleݺD@ 2%~B\Tȥbf甥V$󎍭frb,,MQ}$"kH /[X62c $= wxMƎ*~G䁇e`9@KK1fpKXi5N{˦0A꺝 Ѫ01^]pgqM?911نj%ԋf5!ڌ(1T 8+/}D- aD'YUɃAwA9v+f*J XġKh]0lͩi$2NxڧT:!E0napnK( eIg3*ay.ڠʯ2wTy]9h%ĉjxRX趻{ ԣ/ 5/." Q+>B&҄VQD2<8I&-asxE]{`ݠם +dWYy?(O1fFOZz"knJ\q`\dڼcRσ$S=}[t(hnŸ1i1I"BQZ6WCKA̵/jl^/@L 0;T}]Htȩ*@ P^g:5^|K/tܿ"Xl =- qZ9_R5Z7?X%8ZDftGAI$8+\TT m>y߆*}eNv52Bd4CLҸ^ZVL `&}=,[h}+4-8^^x3nj4?QԶ‚8-ԗ';8jz[cҺ35Yc |yKM]TSknzL8ZB=g!o kL^W׹NNyOXo6Vg:d&H+T'? /ܞ_}۲Kdʼnl{2uE?oL.^O4wl#~bDzixZf܃rm{yI9IVH2;`@"GyZ1jkFSt zߟ3Oȍ[.1Z v$=Wsb}l;X9Mz)]g29/|#ԛ"'7γAOE%,uWR+^XևLS^g;۵*mQCe HTMs.gNyO: qQq'D,cMД;GW}ΪD2%.+m_, YFfɝ#e`}R͟T\f췶ݢ7yM;8^<^IJM"a P]4AH^WT+/@/v#;*a:*q3~j(oA.+>o˄oHq>1R=x_ra$_=ٳY?QQ` ;b Goѕ@P AпWU5Jl$U(oF5aRt8C$y9݌踙%*Q3GN]$63:sx#ILEE},o4 Nxn&JxN`qC(ٌgm=$t U:@YaUM#۩G:\pƐN-~qYS$m#sXR( ?R:#1L*'١b& 8Bњ*7s vwZHmz Jh\8PZD#.TXng ]ᒹp:X n.K+qKy(/ jKSu20z$BػI]<^UCN a9L!+N(5uh{d-,Sw\x4WD&XwM!VP$Wlaz+=4,/fP̺VJ0]dOZѲ-Ph΃(sq/XvԇUg֣O0KI3{tX40^Ph ^g~Sja:w .lƧ_Ai!o1sO:RaBF yPێYn\8u3ԅ[ҝ nJŒht-؉y754h2e8P z=񏿜6VɄ71Y(KR hsZ?G)vψ 2̼~['䠴˽wt>% 1ED ]"T1I`uL$XR =dwn ~'104-`vM70v$ KFv`9q;dyFKn}I5yܤ$ R9KI]IiD/Zا/Um1+;cǟY &F.UBT} Ҹh])9,˨}mUm(69eVgSg9@U2 eU)"@OcJf+,7c$bThn0H[KgKGVR6)+`w[)^NVh7ǜE\ EGaUmиXKsNy@' 4:' ?YsQ x׳wܺ{]"ZG!aB;) "Q=1Lx{E p'#'֠DiR?s; ^ du%zn R&׌G;MM]]4ɥ>`#pyB(==҃0ȴϩl|ce u.H2 *O/(9Kg怐ZNUa;?Qrs=.I٦+tfتβԲ0 Lt3X9-{!W笮D5PN1eɆš8~f}& S%[*"j'2{ֻ/ t^QVR1cjR-{y2z4  q,mէGWXWNFʠ4t^>+R Pc k{K%r(UBvB tNɎa+-DS`_ۖ:V3 0Xf`WMDzD`nζɷk2LB5wھRODE~jg7F`C C܁lmi9jKi9ZXnW'ij^M}HC58?05GT13#@P O39km U2Υ?ϒ򰄥@Ww&1\E !)_ 2@\>зijiD1 Guc#q*P=nUe_or\8$DR)\];*5"myܺQEz)xN֫la#[al"ipKU5x@zUOے^GG0Fؒ~4{6EPs /?5 K 1IӓUgj6%IOLIA]GdxiHcZݶ)+&{"AF1~8[H8(AUgzm1p="Xҩ_x W e$?zؚ_5Js>#W\rz&N!J ۣNj1o|ApUV_iJW]|LQ3 rϏ xsK#ԡXA1$!@R~#ts}TRo%fp+򻲺0cs #Sholl@U0=4\Z<+;ĺQZPtk sGjeգځ 0*5/ENsS88f+%;X[AgԾjgA7Rpv|<9eS gQ;xw*|mpog`b"}(KlE\! v {xE?~< NiCXI3KZ3TP8\CK,~!^$5ݠ%kJL)Q*[&6Cn_/ɑpR>n3o:=Z)w *Z\fE!;>m/jE}R;HXȜMSU y%\O;"W7Ա?h~]N m_RsAY Iϫ oY)qHPВơ75o<5dRվ}##n.9Or3l=FMdGc LZKzkPh(JS pHzu)8 +o ѕKbIacyLW]VpmI LCzA&a9#64gS}\ \T!~3u[Mb<,^11^/}q9[ x`M8N!/*ۀǻ&0z)r>}Ō=;uzhOY}6KVC*y.Uw51u8~k̵1B&cWvTHC=]4ߢ (p_}x?o]˓/oiag0=!wXڅlg>Ejc"e$i~w!Cl> $|ص+rW/! R}rxXsS*fTEB1uէ6j 7^5"Y][{=1`2.k;"WRSTS>Mҹ Qχ=#uS{_?~*/*/gtUbqysH :BMypn۞ tEo_\ U \JAPGD tSuq%C.g ~]$IQ&;|m`nDΥ*"pбO(H/BH*U.AuW!w]>x1}w@\!9tiyjNH}ƫ2`CD >Ri-]CZoc8`gסS3,eYN۸?l ɝ|Jag 0?0RŔ#|8cUvVʀ7.'Ʀ5!bujR oS*dZ,6DIm\C|:^txQpl [GZjƜlz6Ϟ=eeQDDq%&]:)5tܧ 8[Bx2*}`qH FH$ehdUQDHh߈<LJ)=EMKyX"JQS2}$T}@W6-p |]SIx|]_\}P]0_3ؗuz̠X}rsu}ĥ6Cg%CG WEËA{jGjnMUbR_@ 63 ( =FEnswC%>gL@MTH Hכ' ; 聤/d+ 2W6_ARb%LFj)`{ȑu*|g?Eؙ9?8nhEz$/d+]#_LI=f,GYO_KaN|yi#8Of";۬[&Ƴk30j\#kn,DSJ?, "Rzi]AW7QȚ'Z!n)mWg<*MIg 3fgiRO)ܵJimؘ3v8ܺ~G8I%z^0i Q* eŧ)v_63R: ^fR} w;X.0Sw6po d00(V>oYӀ$D:1&6@C$D][t*F)/^JgojuvD7Ld@o, Erz+%ss>È| &7t`)&:ZEtl% N>H2Zhc5 P7^Xw5l_$A AI("ˆ* 3 D%q8$q6FQd'4ffz! \%i"/ÒvSІX{xq5/d#3zXnc xw3HE@GIԡDW&x%!YFQR9UYP7 U8v/@a3@$8SKK4Y{"9.%\AT` 2:\O#?c4Wo&+JNiloܣfA.&ؼn˨;?r.*_Fk#d7+!bd0O(#0=6z(hQ CRe: DZ!{ S? ѶύB@]7*]͛hid'_e}N/wזF3; B|i7JJcLumVPChh[P%Q*YxSP(kzy#A0F+E֤ ͇h'0kE>Dͷ Nz&gff ~~;;;"'`rϛ)fCCvz]f!hki߿Қ"J׫K> ,mMpzfL8glKOnZˇB@7'(؂c[7&aDԕ\Re~w^6"': l֨BM_) mq9Fr*rbP?op$B\7TR5?*E 2!tEÿ _Fjݛ1I8ƋK/bk[͓S`hYx;!N,F\[:f+bJ}M(,6tșl8!Q(^"Ŧ@Kqy {5xh(P_,\@(C}%[ח?z}DYH/X6nr'þܿs`On$HߞngջR=3&I+-cUBKXϙT*oƖ+S1v kyy|u]VFPNA@!z; L4q\;(iT-wz+^6P8Tin~V #?ȝeWN@|w"5OQ6">/ YKoq}qE 5u[D$ ť?; ;BZ`qTz hzg aA}VF S!_6 …X]ь5o&XYT*Ew8pU֞hS * %F1>Bp/:G.c:'Zg_Mhp#d_%JeqI}b|dT^Ca b3v I#_9y5  orYGnNcf_8WX+sj¶ϖ݋P;'Eb} `y#ɖh*Q+0-OYb4ͺxs%$TM(10:Q1lPgU!BHCmřX.(5㟻'.qwkitd^:|<ͨUt}lQ($Q=[Ld7V31֭z v]=Y+X t?&a!&jzk՚)+ GuEMI(FS0g 1. FG.K7=OYAŸهh3wJrj5q7NsoAdB:BrqQP UCA IK~˂&2aË!] =kMA=*дRdg TrH*8+$Ksn+'I~F\BܓSr#2 G=Ti`5]Ƹ Ɍ/ "{|(l *XUH"Ub.,AIm҄ d5ҪW*Yv_v lKaƁt(%2KYuFd݇a1Y=Z ̩ }ع[V0$oD-u9)) ՟$A `ӭ>^Ues!*kQM8 H]|r!j;*0ĕ7sOœmIp&Ͽy$HJ(Uǒ }ʮ8w/1i<~"ؓ[d+)D%bMcqw[.2W,v"2v폮K ÛRxbϰCfZac%s#.2b䯔i h][g KŌ3%}M'tkN^=yF/SFE-Sеp:5Um,@BvS"ڇZ2?lz&~#3cG"Mg;(݊,~$B >^rfޢƶ%r)m;y Sšɔ7kc$HP,V8ƽMRHnmZa'D.o킔]L予%Z?=8*ػB”O9P}n" y(%VPot~W./Zۂ?M/$Jd{\mWǁdb03"Vv6Px|o_$ 8r,p'P7j@r{"q^JǃC@)v P1E~K͂z>*כ` +a 7rh XyrKȤrc>~[0];0K-8U$k{AT|G1Q~ۡ0P0 25>MbIx1 Yz86S^$5rSYlqp߼nӢHk):.qR,xY'OCbH5 oȕܾx~d] !cn#Jy"J1\۷Gy!Jb pƫd_[3l%|h[oA *41}i_C5Yv!f}p4AC;GcX i)1 ӣ$v&s bZ٬29"-43ShvC? i6WZ98 waʌw nO q,SUB}ay`W!6OVZ$gTLU1j$$.>D~}\k]Kv(1L=ՑJ{p1U/Kf\ԥ ,_{A[o$՟T'sxg:@oX1 ,|\5_l4<,.KƦ:Tkw,/Ӑn.(,e ԅ_1VIpU_ˤv7|5@qR aEǞʦMGkC nr" KjsaVsp@dq0PJw:Y ~*fJ&Gɸr+ lh D>~><K2kT6<֟?7Zo ?z)#PQySBꖓSܱ>XB&Z.S@S($ 3$rp)S\wA%n5P0K%7﾿AAV߀4~x̸y؞U (~J t^Ƚ!œu)߼tBЎtp/w l iKnHO~U^p 3B^/)enT齠#\ >.%XWM<}M;^EX,Dz )R)t[&:=8B7,hH ai{GD4.sջY<- QwPKpTw'd^7񌕧՚#=],)xD.Aكe*%Vx0 &x?:ZGn}N5wNm)$9PɔILn; Ť)%$bpT:/\VƟ U9Ǟx?̿",jاO6]I쮵vry||o,(F`CCS`D|Q>+PUɀ>]rG&"oM,>甏oXIo.+,W{gڐ>|>„oFZUy4gj h LmS2nZ%oMANo̧oMDq1K jXEF> iב,0/xq.@QDZR!XI|u!g> +y~v5MBas&t 1K}O2^- O`'}X'r 1\sy]1i;9t:(,'*/Eon~r6#8z#ߞ8' MXYwHm|wx,lQA%SB.鼤%[? P#ueZĿ8.v3pqu@-"  L[Y]qA^?MEGpp ueqvHN̪3QJ޼k qOGyiv| ܗg}34n+E8.Kp7)>Z쐎'd_B'')HuI&nD@R 69)¾kVdޤ@Ay=:Q-e2zJ{ z=AFOQG#K]GAcͦ@y%8i)8Cm f4rha6 &DP/tҼ'@Y+^ .Qv|~36ʼ>Y9K;yӍ oF wRp z}F8/΄V:Cb={!M6Yeci QM~}̓3u\`_uG՛#X#GM#q0{2MHzTP`<~OO}[ ԯMch =xQb+\3 e.Sa-FSvzg rR窕Zfز-ԍCMCCeOr&CC5VQ=[npJuޒltzxgB?Sn˨xLt_k L%70Z# *jǴL T먆岋d[a9|\c?v3͖)`,g t:cŒIpex5iDOPe\/0a1< 6Q?܍ȹ] vֈ)yCa _-1<[<9h ܌=jmTk)ym^kMy@~7k$L+ ߔxw=2L[8_ăft;qZhڹ^(iRK\/l+|TcsZ&RF6_ND d ݁ TⲬ2P#Yƈֶge9uUÚT^'l*')\ѦuZA*+rŧBwsx L`}1<4Ƚ]]j]i 8V[kWki5mӄ1N&cȍQoY $5"r@31+!^ҡoy#7[X(Lo 9,l ɞɂNXwp@41bzYpt6a$X5<ڑG]TF*h\ 41==F`";z3tNfD9>y0px$ʏďWĎe& ST1-<lD8񎿑k[ ЖuD^UP]%e,.,I5$jM(ђ0Sڍ3:wyy22hv6ڃ$*H8 B6=SԚ|(.<|?ZûB`)͹}תܥyfj]~ӹ>P,Yz  ,}D? %_f@$ "CYI+Zjq,%=E7Vuux6`Yfg_ywzCnx[jʃFw&4au9<+]U5B[z 6;2 .qQTNEJ#ޡas4RE|T"_( w ^t;ȗXecGXC݌amvϓCRKZB9?p5Pۧi2^)v" hwڙ!HRV`Jյ( AsG߳#tw-&HOvlTƠDL4tb$zx7B T@nmKy`n+ |#Zȝya0 $!'7;[ݩ5et^71lNLw0+YDU8[GCe\'ԦF2Eۈ_Mz@>e]r'R_)Y&meҮH"uF{A.ATjS0’hkc 6=lXJ]W@t|QK*J\ęzi a'mV@ XZ~bXrlxo")LIBBVv US A SG]hG<2%bC`Τ y{wS+ ֐[rcd]"8^^!8J% hVP loRTdžf!\l0cWԟ0O&3 DʋXN}?)GnȽ # )KROM\r-&IW7.gKI\+X̥|xo_dmP$e'\6Qp.z` BHhı At5Y@ܻ{|Sp`I=Vuwc5=a.Q.y&vN>LuO:z{p LM'M.u'+&G6Bde U3g/, s@lby 29\)YslZpo g)fͥQ_2})8LSlYA4r6 m{ꕵlwڍ<[t^ԠeKzNٿ];&D >9@oߥ}ŸmgR::RI7bF7nV~CByR's\u-4yMUKi q@e[Q$ qF֑&iѬBb-!Bάiqosp5gR?GetGbkeRVglS$+& ܵ3$ 1?&=W&}L>`hC!clo춨fkp@4,7d\;s*TN{t\ehj4>!NMg:3mfʞͭ<1ss82Sm%MS6 ʉ$%;2ɽXG9 Δ$7Af!'3IDC7I[{6] kZ˒Nskw6F$!Iф ?[dnȫj>I)>A޾bAhnȝ)r\4c\bwSfThk: u{:Y7l0q;v@]PY;O>5tz/[6yVhaSh]36^wC$*20V{yQa"crFjddvi:4X+zI,׮n W 1ʘ+ٛIˬe\fMI< >QMf&|{y}0޽uR0u4'D6hUx>PQH`Y]Vh3$f(חy3\}PsQD{uk #Yٴg6G{~F͐&}l%9|@B/QQrq~4glFdNςܷle}|~K9bNem'5D& o24hdrFEJ^Zqi(dآ|pc_7k#*{WfC 7BFaIAi|f3s 8t 1wݻ/%PI6Ȳp[)RM|a J9$P%P,{F()\̴r0)K1:NrẰY69줻&&e'4Ir\2Hhy+<)@B*krwϭ\E}'s22 Pt4xŏ .UQH}_+Vǎw,- 쓪 O*S=wbOݓAv,\o(RV/S%n>V@`x]c>U&ӯ^*h,!""n/JlS÷:<9* )6<}^kՍw Xzo~-n#mcpAܴ?pٽ;nKTP V^ǡlq$rlR/w*JAK"n>2"ۻm珶_륄]]P489k SFh +pVT, "F<x&).7S, -ladx(N:Ӈvi/%>&UƣYiݮo` 2:?Uru')R܍8[Zsok3R#GWËO8(K 3q|vBԷ:|ӈ>\ڋ3 @e^ށ88Lnu!{.Єʝ?<*ȳZe˨%k0* 6Z^;k̉чTVOg@H2<3h3RX,,~A>_C'TKSu'|i_>t/~5>@gHd_W[ $h{:p~iώ|*wyqJ^Ϻ] 0vjJBS?ϞL.5{tp%! E)犓woХā߰4_AS.(%ӽ$977S~TpYFw;r$pt&?}⺽uy4`zq`U١C9 'tT벪GJ9$=w,JdmݢWGƼP}b| Tx_~$c]y@jr.09u7fn&~0D^~W *8:F8g5>̞u;taXwn# VM\{6n(u" f44]&;c$C%ASDiiz5o _ƤȲ[GQތENdtQo)xaRwvpav&ʻ1'ﴓ\,Cc#LVr;.YÞXE%EPEԃEO@^ =9g;rD+uHy sD+BcrJ䶇vXNp ^W09ڋʶ:cGwS1#'\2oԞ`2Gtz!`Ѥ&dcq0 6$pDX:4]7?6֙}ǑyЖ Au!og/5쓓3ǝ"^:B;Z S5&clǾm_HiE2׬IQz?U=,kT'򷉾N'h ט5o|Cg\K*ԌXL(dn1G[ԕS((θkx4%o ݉ $ ZE'CPrabzϤ H]c}=nc2itˉ'lk&@N/=ٍE? 1;2I=OAZ!8O lui4ƽvy9%6DA#C Pvl:(TI\Ѓ>3xUӊ%ZJ$ǽn%kucX!k]ߙ/D޻*^mc"JW:ÉMn.|uRQw YD(vhX:{(9sS0yA]SfKK߿&܄FU+ Q?+3!!HJ(g:P>45ʆ If)+maו9N u@@c;^ IKjcFZV1ۀTeG_qxc8ds԰NO }%6Nn|)ep3m  0~Z)5Ǩ]=i1s5;WHjYQm{gi/1tIc9K `Dz N"Lv2.LDxJ?ڕu[cljB\֎nrOAr{IZBq]h\ CqA .?2A-0⛌Ms۠%O}(A'Įٟ,!SZh?dVc0#YspkG8nb[ (᱉깃E!Qx$\OKe ;KFyߴ*<2۵ anzR:s%V,!tuyR.CδCkV@pNS(ׇKecl=tvM V^(ah:8SZpg$!^6ql 6,:!7AٶK4wm +J[\nqE$er9փ{[|Aq_n ۺ,H[sB$K &$wgyNV85送ѱGm׮n,Z2O[!ig|ui &!4- QGL 8.lΪ"f_Qߘ=IuލZ(-#|+Ƥ|z:+RE7Uv^7kIp7"QG'5h fî f66l(|02QjZ7\1N7*1b( ]'D 'U07e).l"͜d6s 'jY l#MmTS:$eHڻcz_kT[z4^ֈk4vlT^Ψ\4QApE l ,5_gI„p?+Xbj `U q ]<jaظN<=K0:XJ-º$IdtPl XO*S{e3qGucbMBz, nEO/NYx|IW-w׹9p_ݨ+0r;/.&٘:Aիh5:H0EQGݓ3N%évFS68I`9uIyAG8!NQwEJRe6B xg­=PJ=/Bc|?@k>.ט@*sy~$ kE]Œ1)x*l%ĵbWl2*ڇu jESe13hڋ'07\ =vh=5U.2~͋49Fk8HZpcCm H&̄Zrrp.z9-;6ϵ;W"xG.9k-(cݘ='q\Á镵``2uXΐ/`|ê7"ū\jĊbO)8Ϋq.:ʙOG5E-pPO);oiYC$D`~jgXy1;_/7gN`K%F65(KL)/Qwp=o'T&24eUAqvR@[/I~ꆁl;-.L?'Qp9p  C`REMBw0P:&`j mV@n[̚[O<*`ݟ(}-Pm/84oķVV g 1)6֥uliu={,}1q<]0!ruJN\V@/y'#:ҶN G'Qpضnt1J&2hXl;unye=_]cA^Cdp_ ߐ0nZxfEnRd>z[= u.Zd2( Ho PR TaqN= p9ZM!ZT؈t'wƉ1Y(EjbA Ȯٶ>IRb}S]jy!q!­WjW+E jS.=5b{FC)LJ<FӸ b80n k#4}[TGеb5œ5ݍJM|P̴+#M5RD w;[O&[K._]msg) |YZyf2#1!CA4!&\& hyNxjDE{5wΨPWI?Iqشiw/{ZJS>2:< ˍXW7Փ% Hͧ#%Лx\ͲMD.1j{2:64#^qVje`pgGa4/"1mJ\ ܮZkjUvU7%)?(qYtpkQ@jw.M]>r7Ӛ*o'!1Szֵ+ 5{e4 ('ъ_WV7?_ rZw#*3@#ixٰNkc2t8`AS|ht) 7y$P*dlREͦ1BtȢC+77؇H$Ӱvd) r9 ?\" A.WwZ%vrk2|#E/.ʩKb~xttĜ&[kPn<<%2Q!qT4#=eӱ`;?aPnU4DNq{60Ga?{X"J_VYZo=T?MF%h![-.oJN7xHZGaq$n‚VzEq1,!RgjtC=@ᪧrɥ_K t׌l-,0flf }e,-|Ka!fòt֭ߚQu:ID V "E>JԜߏV]c2`6:?.GM-1CG۽{2JC?JZf:q+w,)OXXKO:!J9[>p z A5/7Ƥ}=Ӳ rM,|d!)D@nS(Q*eBm@ڈs[->Uk Y.C^./䗯Q<[@(Ɇ;!;TWaD+Thwԏ~YX*kݞb*\2?wt(/W:dAɽoqU_$hg»̸8[KZO46;BRIEz󯲪K[JR_B|oؒ5]"9`Pt~"t*-gMTh3@rXY7gKvG!uTW@`[UdtUf/+fGYvÍ6Ok=R.=% hNve?u£# TS 낗,a.\jdo)Mb~nbnƙCo]ة|H|rs&l|;dB8q [ɡY_tC.l0&̨_}Ѻ1<giի)Yi1L}dk;ýe+4PƆMk3TCu's9SϷͭ WXGgT`v qX33L/h'el:g6/)d;'>UjI:ݘԬc~ QL {7pq$,ojX악ڹhN7h0w c\&wd(|V[V y3j JM1I C\~RTmU%%0̃m=t;xF΂l๋"vW"KvAm4^{cr  ?Tb>9+OAT45l;qV?V/ yq@('g+򱗋9i^zq'Ue?P6zrnY`$~<ѧq&20^6;Q K-!:RKl:ԣ?b]68f̄~+gϩlY*~)d_[|PSL<&5M(B}Ja@wΨQD;<|Δ ,gVfx7mnp`b^R܄rAg??O ^l!aO6I1)T|oU lRW{nG|*@2y7=P*ш:C\gGw "Z.+aYMevj,zo/:Mϭ H,F d;,+-NÊTb+-R%Nًj&n%Yqs]4n 5Xn%ћ 0mz]8e{T `8 ݲ=ٳ ! Em+bLtU~ThA0j` t;EXUeon"SX)/eZ[ݻx9t'5} vsR_ɱ#5QsqV1r+΁[sv;hAhAi[SD ٢ "l=yh Z$hF NcP Ԁ|!%)nM'h,!ꇲAÊK La9D)1.]L?|}RǟJO+bBI6>{׳tkX[YZ34Ķ~mبJuX*DF33fx .u$Vp-"BЃ^ g`\Mdžxq00IJ]J5gcw,SHGS$Ҝx*d'Oo6; zkUZzu/.Aڣek7y<] ,]M?rKBïT<eAuy{gh8#u?Q{f[oh P1^&ʣ' |~W{wF>~+$;55h4/1g9gk%3&`"|$rv?gD1y1(_~[ d82 InD%\ZS }~2DEMw5曳8u;Y~J_9t6@ϱ%U LWm{0 jH$.!2Q]&>| dobx'shǦ\#nh#4\ W5 )s !jqKgq~7I {8CC8( Co'[ԸtrQ5>}j*F,O=Gu ό"h۫g)edq#"ykvR!?};`=0d܁"?%XbT4M w졫5_DgSKSDRi@u>){>fck |INRBiߏ@/^_ e0 4}5Q'8ͻư VC0٨.Z:8cX-$$)"C ]9X.;oOvQ B1ɍmrEjx? ܒY9jkvFň&Ij{; 6X*<ؓkv ;r[;(,mWٴc=%K%{sXTJ]ws bVNX,zho}AcJ{]Iy_{*ꈊd5B_ɱX,oK|_؀ÿ"эu˼O{3ZSk7 _P2>D&Abo#&N Wv?Do-uj[y.˓ÅU6(B*=N],l^BIdQFS=*o%CL,0_f;wd]&Lת@;LYݬ kp ~b?ۅ?S߬5M7O$@1%saIFsOXQ/\T+ogרuL4m*pSIV3CS5卿)ʖiaS# z h7.>hš@k : .ƮYn*B +8;ĭkbb#RBv qɟ9'&S|rF:ChMYwP J*{c+X1!Vc@HФQ g7_'p"VjxV[. 7F:^+U`9h&xFk8?X0\ڗ@hi|d~mB=ZfF^a2ˉPɬ(և#!X>h #"FsѨKR.Ez?$&J9[G+Ukia{W/LGέ"T8q[[h{cZf`yn_*OF j$˃~ǂGż"gLn|ܵ] 9F~,mEF텕D1k *UQʹ ;ڤ7*^pPv'\0ٓ}ǙFQO*1PZ"IdSͼdZ굦w CR( y' ̅tHł?fQ;2Zj`ߠՁ n'դ;prO:$fsR[|ŜeL(;L5]ZZ|jԍ/摙HHFK7u<6z=`Duޙ,g<-',MJ,7sr7I⋉&B9Q'iG R7 %l|_KmM3ぷMmR ŐrADú-GKOu&AЉɑL AH/?cLχ4>|HHⶒy Ь!܉^<>շ-G,~j'E0QV~`U,C%@ |:A̪'@9S3eb`G[2*F;Z u5d}eL6OR%ʠyE>`A`7dcdѝu1O> ǫV0j#Kui v h;zՎwzj3d^5%<&F幸ŹЫ5Y;d]\vmi{ USqWz]-9r-Bhr: z2K|aFt}(?Ғxc~)@"0{A/҆{ p9+)h/5n ٝ(qHf}){|gXlHZMԀN^j ! %u}oT7"ԕmuS.fc@O1٬HTv 2@ǯyzg ':QYpNޓ]s.ZWt)( 9r>4t#Paw,nS"$1٧`5-ᕏslVM?Xd,7I͈ q}-[64>h1^$g ӥ5lupaKZ;KL ^"!Jq+tdNb,5Xt+pb*UI\5﾿9%( q c Ⱥ:#n5I'e쫜py5Y=BI+'9 Nס "[fVJ7LI` ;Ya/?>Z][QlU7ݭbL"d.2moFxVP/ F.p&V.A쎁kN=Ɂ A;wS7 DUSP 3Hjp۬SO:zF)5lt0جV Q R{S:0ion|lƼp]{z"xHmCz t4{>˶l+-))DҐbD>Wv*kAyh,~0>yPH P8SZ*)wC EZ>%` <ս[Hd~;6 QԲ kHSQɲ8ruٛu,6$@ CFgޢSd>b]V .{1j8 s'c '#۔i̽\a_Vk4GWֱzpF$)B9؟;΅!x"Ok `"iedHRB<5lKYh`VțV@9kdLdK/(W v]U{ASkv$ޜgUti h4%\B\@jz1KWG&-9W1pͶ$ @.b3= M,bA5W|J|Ĵt(0,vnq 4iWK@¾g~̉mB`9dp"<|0ᷣJZyTGx Fi: }]z5{.501w$Z]^Z6Y}d9Lp& fvdY򧘞y&]H+J8 SnZ1`&mmυ!coBJ.l1l{erYtBW|Yjncq?!#-H8Y *)jĊȑyƣ%< q ;d>Qz*{TĖT\DݞmÎoX(̐'ո Xl,DE9fBwC ed0S:+kŊT~@d:j.Aዃ+20 ˵.lc<06^tAX{4<]$U`+6@>4/)Wb6;簍 0x c2?oBQco}Y̟i)ٖM5EѶժ`2!^1,Tdo7?8:vܽ|;i*uYt)<0Vk `ʩi$lgR)ou/-z(iJ /ݘpXhaȨDQ.p [%y % &x=<ȎfdW({ﲒsYYp??rw&\IW]ElulChXNFVat oN?.$$ʀoMAЂoEy)k^- \ir 6r%MF͈`zʄ~JAS5@:]мB7Ɇe_B4%n|Ev4Zp+uCCXK_+=So ;n#^bU 'tVx_| .r X67V P鯗#8Chf^߯HW]9V(! +2,4sv鸰 HN8x J(@E5]9ޥA} -BU *MtN E~MeOPRbrbnYO$+'5^`g_Kjy{+T ]_1;EYbvlM!ز]Ĥ,7'-BHpbFڐ;2:Ź)3ИaAmz8-;]F8w Rc(yNDȋZc_'MH66VUkD&ŀkt9O矤8u}_L dc1;*\(?!Y橚wW~M]Ob Y>x]CHо&FU9:3#$S->i="]-ȍN@m#eح[hfsYt70]`6+6*}X8XV-#ogjOHg\ؙ87pr؜$}yv[·B@o8bn+ɏi,b'݄lB]4Fڿ\ek(aP[DN"0"lG[[:6* n6Z䕳8J~;XͼCb{6yQzOZ/mo='!oyFݏK84dYo^Fd> P+}}:.D_kA9xQizR ʫk Ͼ,Y o'\[LQzX;"֟J1v#^uU/;WQ-bGNȚ3 K^L͋X"+fB`Zq&}_ߴlKDHS 1zǛV" A\U4lNn%tk죬=?.ʮk_wvu) SKL5Jiܚ=={,Z@Aǒ|]mRtFBة( )jb.L{g/Z@}\ AZCQ'ք{zZ `0E樿 ^Wk7vT^Sʦl~3W ^<=jwuuh0` (0j'>G1T(Wuj0=7aCQ~Ү-< !|c%?1oxI+/Nח&NC3&ro>k)d{¿tMVՎU 1n<}1L*{1K{,"\ Okmw" 4@-ǞƉ u[G b`rx%5ؠX"%y&:JC ;@ZZx BCaq@*wqD" 5 d$RB^=bWőo.XDs"ǐIBeϱ\Jǻ޴~+fB\"N|ۇer(=w  ӧ$ڐ[e?.Ȓ+&U{$Mnap $k"&-ar<faDqN`SW`!yn*[AwoR :E9Xy]~U}OC(:#4c?dKB$ޫTfU_<s0 \{*@0fZPx;4xZLzEw0oad*jV?GxX4>6QADڮEHXY$e][kV0꽦_(LKzLj6',ֿy$sÇ VGef1k@\SxsLի2 1Lx1<.{q*c)ji秓 :t#%Q%zI=0bK:G5?4?fh$M+3دn]dk;%?h55vuO J:YW^c$~ԅ; *j] 2_ef-Z΋@_tIC 3 X @Q1v3:qh~ha9zM1@& |/2QV0U]d [8+?,+(3bCgW,ұ)Tn=g?cL-P/3r?+LMgw=Py+"ͼ$.^%٨ދF@77zfT SD9cjyZH|kku Y!KeoSd@Ò fj]/Z=Ff X!u2XBAĽ+bnɳ9CtLm<Ƃz%̕Q$:0c&x'724K ӊ:#wקMFgZc :qch߶PSoޞF+NQK!J ^t&kcRN 6ZǚgQC.(9FTk?0Ro61 >!?Rae0pD4D`Iiݚ"ѐ ks|CCXe'p- $@h-ֺC\oJh_I 7qH}P[S_^%Jy&yFݳByZ%+mI;[L[݆Lˁk[UDB6zQS19oAFA?6=OA.b>F\)X"HN!O>?5zėϐ-Gavw eMAAu##bkmԄCV _m(*6Wso[`h:kǐWK?%2Y,fAEN2xO\^sR\^o#%玅BPkMe< U[.jÛlDAMΫyP`r;Ipc]N􀹃Z>02 0',v?J9~HEY -|5LjA*|1v{p#i$\` p]s“"\"ՎupqbP;-8Ps|kdDn ixqjhI. P iV|gUܚN0r ʆaŠB?@qҮܷ4d7Gxܞr)p_abnlE#GU޽%DsTs=$ͼy >0 YZAER/data/ConsumerGood.rda0000644000176200001440000000434113616365113014675 0ustar liggesusers]V J$-&L{yFѮEܖ0Qr)J|I*m%)$KMH8|]γRG7;]7]i <&? ^^_;}%>Nb;i@>953\hϘ VUʒLUXutwjMP2jA}!rVѠCt\Pg1*jQ}_Ԯ j׶&afͮU'.MF[KyPYA4p=(=o :++ןU읫f'(/(n B *F:A姻sb2@3cvJb=QzGCIQ/$rA-s%EL[n?o(ܐڃJ~7]t_&Ni%AA}3({5V@~hh{>< 7mphY%::swmNs$tc(V7pzOE>oZ.bS"x>߽b,;]p ZVuӄ7£` 7 S}Q}t֧wVBKǒ5x]듡u޸P%| E3ړյ .?Hhe]=hrVFvCHт/Wb%?Wj^?>#q|n4M4u㫭H.z-]b|)u;7&M; &&])Z'(/,`kvi%d!YP*Fvo*tLX pMrP-J}P<-?gɥhyiTAU`.N[hlȿYiiN~`!TŧƁk7eބzO ^\ tg)PK7m0&s(99N y xڸpK:%0H7F?B* |w ]I9/qdZA*H-tN+ ׊"bJ='N$ ZHObs Ҏ,1/@4#鍦i#yiO gUHg,H07\<uK@z{7>IpO _e. F?݆tHy ׌C:>R^uQُtʙ7&yyi٘(ڱ;q`Fa9*G>t(t3(\Q|o:R. ~%(zN?= Q?Yo=tgP_~ަW҇mß!q=4AΆF!6(8#.7_@EPj~$&sJf.e'7HM PI^bnj1- Uj!j2sS<ԊԼ̒"syb%Š<U E krNbq1n&9%P&SI1qぎP(AER/data/KleinI.rda0000644000176200001440000000135613616365122013447 0ustar liggesusersm_HSqn?M+Jyձ-aokkCcs䞂z-z!( {=#uw{ow92ڲb pCȄUgM Rޮ엂#k[=Wq_8) EܻU"@ o ~K%,EP_>Џ.7y~yy\5U0AqI->M=3g`^f. 58C ԗ.} ď;}7}/}ȵVsOx55?7?u_̣>\J_fl~ )_,v}5/xk/dO ܋y M?i"%<3?~)b0#ՠu:E{8w: <Ƚ8K>ܡ<8D@? DrUHmԡ0ߣAER/data/CigarettesSW.rda0000644000176200001440000000607713616365113014645 0ustar liggesusersY{XTe5I^;3 p2, xVMUqK}R3kKm҈,)]SLStk+m]"/s^g?^~~wL9CR\T\T."rS qhTiY[ITEw&b>Z!2PA UIz"y2\e(R1!Qj{7 U o윮އR}0`c4W4U`qPʯX-{j2`{@H; g<0z#?lt v cKc^ ]x| =෼}5 Qul' ?:O)~_VކxAQۚ0GjR''߿..'7z+܀y7^ \:va} D-[aiȋ=͸v]_Թ~3}OS缷 8^J'JGzK*dVK|Bg$䄽(6O(0a+pY7ʫ<̷g#voKL>E`Nw.8!_5@~nfsDc7+=ij.WH',x s}sOt%_As|iCiOn]OP3qK/[ѷק4wӇ֣CyNδ,foG?۝؇;MD}gc|v ꟗ>!_+@QH7h8 Syēs[Is.it *g/s)S=ŜV:W0_e\ } z٧bx~ ׼kg/|}@9 l|{>Ƣn9۠iR`Ws#vy;_z حﶻZ:CO`cSSG~]b"k0g<"XquoهuOXq%/"4Σi?]zulOI[WzC][RJyk"W)DPPQ-2˨ʶIFBI!DFV)}7݋K%œ-i~ rߜz>Fɶ_2iWj¯+s(~ow;FKyɲ#=)(<m޷n_ oOVץWsɘ cǪvJѲ?IEˋT 2[KW9yT a2Q=](dnE s{߉R!Í `ko؅q}p Q>|^z KC]O? !?zuYx2>;M;~֛2~#h+h^ m>FjF}4WuنסO/;ڷi{ßB\Zo_"y=y W!7iz/Cd,tt=xqm1^p6-~;< "2aP6xKLpw f ^B;^K+x bt,<ʎbct}8ʹ/%'xXquStu v,O0ZG~>V\YoE/pQ%z_\90 9}MZi_C/#{=5`7x> c|LU9ObnopGr/_‰EZv#Q>=y4STMH?koa* ;= t s?1C/D|<9 oo@~\[V6ݴOzY#8ϔ.#|~B ^MGKv_x mk%0=T)G<Jy'sO{3}>hoWd F.w̕96z9/_|TQ- N!QT]tT˺kaUWUV-.wԖV.Te57 ++eU]ZȽEMTW. |90 HQԊQ`EQESŨ?fMZ0kZtY3jP39̡f5sCjP394̡a shC094̡e-shCZ29̡e-sC:19t̡csCz39̡g=sCz39 a`saq109 a`#sF029{P\-~U;TۊAER/data/USMacroB.rda0000644000176200001440000000417613616365126013716 0ustar liggesusers]Wy\UgdEfF@ḘH?򈚩i j ʢ$)* :iT- ~ g//c u*8m.w"_F9ODފݍ'1^:)۱3UY\-<ʇ38~'}:WŁ2_U{\ F@˕= zwc;vчle}ֈ3&7`} {Aa/>olEj WO_:" w5~L&rgUN( A\mj]W_ _a:Q qugzu'P`lCٿqOQ?HA7(Q(6=ǠGC} 9|JvEK?GF2B|Xaב?ÿF\#Nx׏:xQv2ʗ~DS['nZ{^Í(Z2QHKb8"A1qb^/^~ w?OqWtWņ(2&E~_ "AK¿̀>"> y=C+#8Vo/4ן6^VQsVjS}(gzJQS=9yrp-kHU~Iz-ev\'4&if H5 qZƥ׮=aEn}~rֆΏ ּ7+0iwm_;Z%fT=[k6 l{ #_X/_TdmSSo]]3֝riс^\蠮&ۨ89uasocw=k0w\Ǯ^y.sh^:N+w|/@2W]|>or38<^n?~o~ݜ'B=GÞǒ|>1yRĵ sp0ig׃ o{~:z-?9q</{CͺBO#[S9G sE;q @an1n`ͼ]>{{n_aagܧc63}nx|?`-~i3:ڜF=<9 =[~V/8v'B~įs:/ΑrwCBb};l:)Gـdwjs`]r.7^@Ky_rkQ~~M8|_X\Y: c۳gοJsuskzUawyT"j;F/7z [*!JL%RɨT (H "iDZ*-K######################################################################U߭>JS9AER/data/USSeatBelts.rda0000644000176200001440000002517413616365126014442 0ustar liggesusers7zXZi"6!Xя*=])TW"nRʟKMd[_;zk+Lu0JJ IJi0\6J˹vLP5 zLqA|<)g B2`A 7]6 ͕xDfb !;=*@9T4-t.qpYiUdU&IQV<ey~L?öG ],.193tfkeO*-eA UzE߾CQ6m~a { O@}pS&>ľO1H{-X{3u,u_ΝjJoM];?gfAda3*]`\M!d81 V]^+?B(3btlӠcIMɺx@pǩI /x/#B~9QNdQ۫]v=sD=]԰vnk\fbR8qrDo0\kǮ]yu/ɚWeMHpB6.ivhCx;r STZr4-0+[6+E&Q(CȋH_Y'=/M 4/!NS$~#p{^M܂QVlТ "ݰK'jm"QISxI_s;a0끋,h1RZA- ZY1>~t@]ʇ~㤩UV ~V氃nZDNBRq9 R>$ajk>y*KnZωhؒcZFdGTv .F(v!oiIj.ѕG!kѵv|*g#Le =Uh,0 ?9;Qym{Ń(?uU2JVgD!Qo 8 H3{,%1.VHcl;^x$l2R Aa7e +G#Z^@$\~_I Tnc!lC 韑0iߦ5pY1eP3Sv=~@ŪE 'erڌ}&rUB[#Z,@4oBx&[(,ΛIAEiJoV:`/oĭ_Yv"Ԑ~z;|IL@8yT𰏁RI!pNe)rK3oZW3q3V,Ј7JɝK]ۓ* :u:SߠO}={or1wgVI@qs1]2] XJ](L[k4F{a+XQ ̠/`N ]PqAyiJ}0O1N}UvtwKdܲWxJr"[ۂ ڕ\]Ü6 oy#hfH#yk;D]GÜ$g9/Gx*ō#UOj(¶I1'W<>a-:yPnIcZnkF>LWI_jn> U PB" Il?>^>m[Pȸ2]2w(IaH n}o? clPQtgJEj"<+\0^jdhU4؍Kv܁cfw1@<9G|i6;B\q h:xOo5+,xK3:ӒPCNc^v<Z)4seb@ `ebй${Y]vBc f O7pCU_TH{9ᓞ5S_~CI x˧_g WrĆ.(pȗt;`V1X+W͹/fDb֛ f7 F yUq_,* \qe$_-Rl>?HW(f_SfeŠ-ַ* !G fd1Q*^ ,(=Pϋ{sSk }7+J>Y8=~@3܂ $gL>57x!3Rt7&oz W]ЮU|$dќ%ρsձ*[3w?Mֳ𞛮NYt Rq,k5j QkJG?ksPGpz$+f^e+“$ fd  ƶU4dxN7,Pd!Hυ{m.u- N`!DNS"0y {𴃦G_oCxƚ,:Vg$xt[a,'x\;2`' UgT*}?m ZIUu/(vi$$&uU%ӔߒKM!+:mi :)r+woiG( ,|4cԫ_ sn0r ѡp3ynu,XVoa2\G ` WCLvnttD<;1 +#bxri+bBx,'Jn ˿7 04 ˑqvaI.Z|yjM}*r\fŚ46U*ic/e,|UzvhY/[T5{3M[U ɭiFwQ(',|9]5?I18 EdnTZqjyZߗUc & [ ܘ#4 ?[kr#b(hm8L %+,2,f(7?4^bq+[^\|]洮'4릫l^ex3EKNj寂 w`h=gz1BL<1kaq߰$1:a[ipd!8,T̏:vtێoOtr? 28׉+ CV`k ~bkқG{;i{+T5CfٻIU{AHI6Šk'EmT~ɘTD̀SAv}xM3bMj=|xp<8+M x?? sx7ȟdL 29Ȟbm@JcݗN؝.6^c_&/6 (rk GEجo"[Քp*3/ޣtwTٴ&'ԮX[!Nh ⿍ƭ!F{>.(&^N:wm|~!*FJ~L^x A>>Giw5AnڦI5;㉆ۺ5eN9ssNbqO> 2 Ԓ^S;R]R&|bd&sg.Mr,Pv@ZDv\`#,VϛB ZA7W PlwXJ}{ZEptX^v2ݮ}D)B㎍J$pK@h[bPwGFS>M59`!N+ ;FqVK5zgH{*|}[uaٍ=3j꒡*TN̻;qCm>,m&WrVADYOpmƳͰj0&W™߅4:和Ʉ" ̵l,zY›epi`+Rŏu3+>8B& lڞrqlBoRg_閏&KEIuXFa;Y2R٭8?"%Gڄ7| M`z/A9n\M3Gh({w&K(ɿoWqLE3TOflQ`ɢ2ABwuCgtË9_eۻhp $/g3]10@/:S{.F7\d4"8w(uתܽ| M׷XrǍ&J䩧sã[ ?NNJqGDe '7?{W1x;'#ͽ\w!]C#m`"퇝+٪w4j5.Saµ| ;)3t>i oL<;NNYqszEsٛ'4cra<U=/5g"~uRWeYtW_?X $vC0Tbhn1I$ zڎN5&˂ |?d]oAawX{B=海?+Ù[ xL͢!Jn[9x-1sn_!);amQ=D/GЈW$խi#9r7vWjmBV5)fHFkJc9Vh%\ 5ܥ\GL5Vd0%Xfs1!)k|~ڋBRFase2ddYoema5gßTw!l6Ņ(z)G 9f=,d1`u*?י  RY)|G] lsI8m+r1cb=f}꒪t!Bx0'`[ E "椵E4U2ɋtK_ P/L8%K~ZD@ʖ`|4O8R ,4o`η٦.7 s-RH0@<Rm'Bp;`D3*|.-0iȭ"SHiδQcߘ0jJƪTv_,I[cFfK~3 n@ap-l~x&+l:xEMN ޭ1yRdgiyS˴XX\:.qzLIv4( @9LqhN P-\d!/_%20q׀ 澢"L0d3ìO'xTcw6|l*8Ȯo ō y{vs#ª6$:cQL4NO~e\-k2|kDj jM,j,J(O6KLr¯zD59[Sfʛi<(}nPxKX$t>F7~jǃ $wߥdGyΑn[ٚMU_1'Wn K9WWИhA]q ,~ ә-K\~*hk)֍vFE61n.SM)xc>ft7$q/'C(I_X) w" }-Vy%~Y{ƮV|p;Z9sR}tKEzQ ;q8:L{I'9FοipM#&@vpZA;oBà(g-tX͜uKjmcb Qk7_y1\@ opp[ٗswB_'nqϽm\E)6$qMmGV -﹢  1 Om菦:;70R7]"f{@&A\_Vx.|9CyYj%QP{(7us +ُPsYÙ~ɤZ[?Iq8s<[j' 4JAQ, =JɘQf1fjD8 -&EgO1|S*CA-x:C{c/پ(EۢEއMXB3gF}S* ~'# &R,P?"E++>sO,^^ ɑxW$K2Z]_b"AJ!>&FˬC1*C*u?W5;Mcf w"a!:crx6[g̿xqr7hD#0ZTC!k8'|c:5FA=ȗ݊7(/(Ek8/c[t填pPs j0-VMgY1 )]M/cvƜ⸽o*/W :67$+"ޓ p"2gr|<"-*U J0%4Xg >TY w\UpTVl+WeMHTEm3j*%>Ȓ37Cwh<3Lkе \&ICg҃=JFKw"آ}EM; 7+3G(X&[)EOu>?(7OH'Vq;5[ #M /%iQ 0|o,%S˜TE .OyVHbz@Taf>0 YZAER/data/Equipment.rda0000644000176200001440000000165413616365114014245 0ustar liggesusers]SkLW]XCдR?\D+@qY-DW;t Ui ڄXjc#}G cZFFMhl%ciiH8w I9wι;ϩ]8?PţgxO:|)^% eV6И( +"=Îܸi+ߡ^im]ޚ_e7\{%\ @󫨝y j됥k(I ,8=P}!,ˍ S&E{SдH{|m!ksG`eQ,⇮;mMÚ"hGv9@m˯Aϡlc/ؖ oh;7b#gF "Xuk: WrQ'S# ³9y*EPs>Z{$F/!+OA 쪣T?ZZ+Gys˅IhI' =AԃX(&;p? PVĝL;AY>z^E(!sv n @ MO)3)Ldb 1h3(4 *&R*Fh OTP A@d4Ѧ`OT@ F# 4` 0`#)4# 4FOF@ `M`"MMMzA z(#) 2{x5@H}QPvrbM[鹂.6mlouJX A 3'>i26to$ 4@́o 0iD^QWF@(X3FHJh%{iQcbff F, +4L$G^'EoId!4(J"J ) dQ@RIJ2$SC@B(ƚL̒LM!,5)RPj$1$ i$S"M !"խ%F>=9l; }McU%e;<"D@P?$-h W@hz^J̓rnhhnnTv:[WiD 4#E.ncjk(Td/WQ|:)R&bjML|LyyX hGT5& a7/a|d_ ; ?p yV<6k8ajrJc\/MmelZ!\gܫ_y9;Ǯfdl@|Y.A7]kb!0661(>ggO(6Ɛ}Ԍ\s}:,0Zyvbߩ\"Ƨ5i]~~UؼJYlʮJ|w#,}hn6=]1̗H =r`a3+3lZm5vN//NF|v"Wr^2L%ɧwvVx$mKa•35Mmiku$9 ϳFi~AE3@ku:qM+8rDVcܢj6]]jk6>L`ͫV#!|vY1aJ NHǚkjm;Tv\vӶ0K.n*i G⧷)kA;tqctB6nVdFt2&AҺkJ#VB\ƍkQA#-"M;p#c qPTH)10@J21[r'MoiTJ58FEDVn*sm4ȡ"ጵR1]tBZSmˬՕmmMnH&3,*uaWt5qSbWDnJ^tlu_փx; P wX#( NJ%}*$;˸N`Р:aUEiEU_tp~'Go>W;[m1c1o̅ i3sCcP# {\wᠪx)01BCi$I3!m[ju.>#8}ev^+1Va2Uwt <w n`GӲ.f^})jqfѭݠU8IyM@ Zen%7W1̌:0˦6olXw%2@ niDw"JYcoAjOvr-6|BH#2QY\/\b9M> $hE"(o$ׄfh`L9EAO}vo 9sf@d&@d&@d&@d&@dm`mf@d&@d&@d&@d&@dӠKmޓ 2 2 2 2 2u:{mLs9d&7wwwwwwwwwwwwwwwwwwwaB!B!BN&0/0f`sj+\Qm9f/s "sbGxTbZ*(J@d#ܴ˚-*-b5QQF[\-FŴV*+m\6jFi~D㿂L Su0N +39Xf^'/'NО1PCٰ5:g\ArN:st }d P}#r:麫"Bn?ex>k.bnxE/hfcxv%_<$U~,}{#a嵓Vb65ulG>3E|X?Tg˳P!PCCC$jK{0Ѥca_|xD~yIZkZDD~2pwwgIܝw#s;w8 q@/u6c x l7ELZ=jxsf%:/i~4{)G}7##Dk3&J SGwۘf=2 ,) jr8.W4\w]wvȹ;nBwcs\vX]u\+.b%sJJb3#,"/r&a$"QC;(G0+gFNјfPfٚ;0\Ŧ ̰4ѫ3w,O.p!eBAER/data/Electricity1955.rda0000644000176200001440000001234513616365114015101 0ustar liggesusersuZx^B(EDH $ ?M67>r|0`D{\[@=V Pܪ;pko9Bn?WlBqk3d4[OV*\6w<< S6 >:'Cs8kɑFCނƼ6J qAٕYcp ĵ灘^CaE@X ZwS2 <P_mZ/='汀%6P_p9. edEK1 h(^M /',p qmwS#ycnEv]x?bADX{w?e_'5'łkzhU6Ti- DuZC ƚw.-_z<'^YpkB`Ag]cjl% B^҆2Kv/eOQG9 w?ò|>{#PI2?g"~> M-?mq^'J㳥f6s98cğ8(סD8R~~|9[ʅ8oQo1_Qh/1(J-Jʵ(Ѿm(eR@=|QFg{}7RQ^2iM(J߈2S(8j.KRfM/K_8)OOԑ_~,|)e9h~#\3yO`?QsHqNYvSd| w@w $T!_1m$.Ix*_MiGR>8r7c䗤q 8ĴV(Crag\>G|x Q8Ur'|e?#qo]Geő79C@x&8vzփ+=/xP`\}5|/5N)Jw(?W;~:A֩zUDuk҇{'SA~'^2<~$ɷy~HBϓ7 ) Z6.Mh\jl287].Mv]3?pϥgR0olps ){C6;cΙpJC)?t8q `Z݆2;áUWz{ɩI#4ObJ ݸքLjs(z4KG4ÞOFY?J~r vx_5W@2MIs7T־ N֫}'phW8+p6?xOpۻLqZW8S8u]hr 544isfԁs[ם]o&dr@#2#qBNoEH:L+8gd%~!~WոM<;9Ҫg34/)p\~C }uD/˸MK/A ;ٶػ%hWҟēΣ~׭~.C!I;@"|h҈p/7AH$) )?la@Hu6Z>^-cF%uUT 8!uŒie@(f߮Gӯ7|:)0"yH{ YJ2. [ѼH7%튕p`kCrЁcU(`Xn] !ݰUfWpغk4qll/٪vLK{ʸ-vx?^DqqK?l&L B[jHLot*w, { ^v\f/BɝNCF猻UAky{T8ݠ! pz є@&d 8 )+~{Ba? YiO{؏/O:^qn>g8NHw#I6x1xA^X Očm!H|IĥxLD}cp|4gGc{Kqx@vyy~y+WOD@ACB7y@ݪބNsiU:S2!_͙ѦFib2(i%{|5 !׎P(܋5{՘_pP()l|7l%{C$=v;Wr6D;J (AY{Pr&T [ *.r" xr!OC~\Dp`!y#H9(nƉj4p#O5}!=a{( y \=nΨ'P͑ m('@W_gY~PDž<ɏqsW̗ߗAPrD8pt,/sUi'rp!ϡnvnH ](ַ9xs# :n"\XWyb A`AԺYp׍<,G;vx6{۫x?Q+JN(sO+~ 5Kq"}Y9ő찠]p=|Pk㭗 G)NdGdDv(/*?CQ>h>)T_4^qhT<'G[{='{wZ?'FAH$M^uLxzK+eŝI~||o?ѾpmF^릇7c w)ю>397 "O! jw+~hL;exPKϟ"AC:[@xǀ*8]\w6o"p16\9P2ܿ7i'"B ίVABjރbN]S~)克Nx}W4Åȏy sˠכ-Yt_wj߁ y]FQwǹ3q: O}Q g+-Pr⽭S\7M2A+G['zBI@ x?#z`rq\ˤj)8xT"h((øPvQIpjQݐ>ҁgn_<%a] "[ܔ/-C9Y 8[l~SPo\bF#J$ =& ڥ/'֙[[T wlY p#r5r'p`.:ifZ}pu+'x^ppk/X3g9 8q=dtqx$Y.N^9~hy \3#&EM ǁoخ9eFԫ1fJv(|R+2bXE*):eՌ0QL 5԰Q#ÆjUˤZfղUlZ0L ä0L ä0L ä0L ä0L ä0 ì0 ì0 ì0 ì0 ì0, â0, â0, â0, â0, â0 ê0 ê0 ê0 ê0 ê0l æ0l æ0l æ0l æ0l æ0BFQ! #Da(0BFU #Ta*P0BFU #La)00FSa #LaFuczӨ7MzӬ7-zӪ7mz3DoMͨu4fь:QG3hFͨu4fL:IG3h&ͤt4f:YG3hfͬu4f:YGh͢Yt4f,:EGhͪYu4fѬ:UGhVͪi126b:B؈Nq4/AER/data/CreditCard.rda0000644000176200001440000005604413616365113014304 0ustar liggesusers7zXZi"6!X[])TW"nRʟKMd[_;zkBD ˌ-ӆ㊸CMю6}߲It]p4~%n i_'H㗶`-: H"yO(ן O&@GR;&nڔG##R (vJ cVa\5Մ@,"~H23.E`!"%*{<KѕO Wo~dFFq=wQ_1I8/d F$"6)"f&?:ф'9ƨT#<18ɟIu?G ~l$O,eb6Ȩ5 f-RKί[`TJӣEP~b[ OK o#7Jٯ>oٽ8 42/  V;sMڴk<1p{Z&o.yzX\]U{Ŏj `]W^q(ߜx\L R1o3)EG ʎPnI\.X}RhMhMKrpv-[qƚ5ap}Qx竇ձ@bbSJf*p'P$  RE:\sEGgx_y)a,߂Iԥ.l2@Qc|ܲ*Z]F6đRI%WO)"/yl1)fWIz&Ap]X~wYJ(cNڼ$ֵNWB${Þ ZR?.+8'x'/Y5`?ޒD/X TůUXS=w.瀇|>m}Vm1m}>C>+Zn‡lڬJޘQVov =ċ Bx=L$9jv+אwpy~UJv%^fUӕ-ˆbYk.* !ĀPY j< KZ[!<_XmAOExߒPW U!pv(58y"Yf@w.\h6e-* bWV #eUku=%IxObfT2D;6BodžOy#0[9[-_ NLG[J&mBZQc.W|^55 F}]V(m2`\ڤk s* _r/}v*#RJmsJ2MA_qhw mYי?NiU`H7m/>)nVsk[5J@?a!q!Q%^/+ʮ=Qz슶n}#+Ϝ/b8U]C,XMR[R&)Vk^DmcTw+S?: յJ˹mL#2nFn9Nh!/j70c-WQLt/SR",T<81dKyq 0ԇFE~%li|6SXQFn`7|&c5VԦ&8:@#R<(S 5TeWr`o+\hSCWaxO fs\++>CsDBTCŽݢ /_ pc@&`dI&_I:`c܌$ |]=Q-P*Sҹ?SR@XP7֤x+0 Jp0.J‘}e#r^,)c:؀]mܓɔH#W8Js9 xfc n*>^riA 4K,4CE"A=}6b1yn%`ayx=JSQŹ7Ϩ"lBCC rwCQ@rv0leR#%5Spm,u%@cao}&al8 H8i30\#}ꞈ#h_bdN<) g|VZ]mo]sT).;vWirH[G:b#MAz6Bd݄f3@E7,a,TSVmI |`=ԭ1{OHŪj4[џ%>I*Z>\]S7 `ٿ*h#v[Z_yE,R%2VAIci6]vӷ`i4S%ifMNI\mh=uv5:':Uh3UJ(k. AwDBY6YSKTgDwy ׏SÓ6 ag qiR  0gqDN].ŒO%xJrSbR&ff}<{L$YlIV|A;P~ P25!~\)o+ f4$JkF4k\!ʩkWhVd0۷ oCl{:-9(  =bמFa,(,]T*MC~5MA:w\9]@{5!~c2hvOwK64*2?iry!_1[idr`~~߾6o,I*Q la/>wltP{ )-:W r/do`!jaឞ0PلIWAe^U3ˡՈ՝AzV/vB͊C&;fCX$LPGX3`>c 5ާ+_ E4`_;w+uI-V#~H}:jvatҨWJuqo>%!)9Ӫ"dm?:9CPoTQ}ώ[QV7157x"E{3 RNjYv`h+韶* ]8\m,ƉLfe~xQb{pH"XZE<2(DNG?S&7y6uwoYIm}ǵOu㶵! izx(=QPJժIPFocwiX_f.srt}6E8`h'@کT^%oEm(eӊڦ!ha8]f ]7QG}<9q-?;eU҉I2 hH7Xys2ȃ-8pgFB cXsbU[-ޚKH e`({qЍ9MFwJIϮ#ԡ#`@6|90h/%4AÓw#P{r|akq;0oswZppb`g6f=Un[E!}V$fjAcLZ 9`pug(kT1AHl@ ]T@`=Cଈ. VX;d 3wfTx]uv…d`A^̘x4䅫>zhr|zv>bLKnst UJ%ԃd MrΞ9K"] Ҵ"!T*ԍ4n3M̽nD8]i)2~ݶٚH;Qc)sHSKd*_G _W-(&c<A8d 9+Ka_'y\ Oo\z'F yD a/[fu N8AY22L6&sQ*P~tl  n6\v|+Jt1Y |?^JB Aŝ4I휥a`_Fr#9 !^ $+Jk&I=xY=3쐱 q)DFka5Jum]4Ǥj`Wc]ƴjSTo${'5ARU/LE(H1$ѵ%xX~J4wUj !h;7?9=+][8/국P$ԓ+m7C@ \҉dz΀ƁeGhRe}8lrnQcbF<ZyV0o]xGDA'DFk%{} * *[16L/mƵC7VtMkS#̥ RzeVgs,(5JƬJHLJRZbOB( P.˦-``B >Hy׀d9p8ts}F6Ϻ @7ľPĊ2 S`m r H4:c *-D%=C3y[*DޓGK*3'?uGuolg-/R%,?1{ijwpfr2 +Maq8 X7wA`2(pӈ~ȟSY(Aٺ3<ŪӶ8|ڟ܎Ur4[xZ[gBAjLF#}r}Wc V檵E7ee6 _Hm7KqhHDH՛M;K\-m2`z3m 'ہ@yBi ML)e# nV#x'+z m-PZF07DV`CL{VS2{b< XD T[|ŭ(0eVtNj!Y(@_2phYmCc$^\'16Ξ#ĂpSH=k} W;˳nR}3x-= 8L {WfaFfH[xpS|-~P`8%N7C+2iejx(C(^nYuY's`qܛ ūcLĀll~c/D;&n[%d [3xmi]*3KtMOcS8cftN;eѭ.!` 偝gwH+ "qA^ gJmN0閷po?W V:!%mŏ>i~ OA ["A!˭}^J4CmS?(g-K7m8[ tp.M_>e> OMzw";(I6uE`̰ %PPP8Wn !2Bf] խ`Ġ4qB7ĦK皒d "hr7'2+Eqyx[ b\~TۈzblL6D6+j\]xR+zZ¯GBjq)mT}$Q25{#I5DW͓"=eΈ(#:: f4\Mjdz]]Ws8&ۻQ1^/TfKڋ(;EKpj k b^xǣo!9 0 R pyG >.`EJNl;`D(Z w>Y/!h@f-ç,gp81]aŷV9I[LP Ľ=Wf*e21m vvh[AgZ*ZTJ)pF f;35'&`q(=|zh6O$o0Xug!s6~]OgX>z[vGm*ɳ1R7z$KW n:I"~tsO"t$H9н1 |^qkhr;c r=/#p~Ɠe]e'|7A"j" U@g=0K+O`;1F+6s{u0+lYqoRIHwݶ\s^2IȂ%v5 #25&^>Tu3Ay[Ă"Cy~S#%?HLdJԵ\Dy-(<[(n2*w7.n,A-hY<z ~C蹂K{ wm.DX vf)XGv XZS,iu"Qj%Ҫ}~P,K) jgA+n*lJM$劢fP[8ϤF '|c\} ,jOv$R7Dkn :TfY+f 4L&<~cl}jaѱ'tKT`9{f W\@Mp]\W)v⮋<#Psf| I>OaEžtC{TT&kǾl-2;Gds2rRy%̊r쭹DRPЂR 5[2#vc\pmH ERM;L1qJ2Z$@ .ϣ?` '}?/f\.[OjS+Sgψv*"t#^'CnI#iVX&[%dǼ`]!!Փ ]q6\TZJ-L {`ɨ 5]Io{358I<VXNңR'Q3"CM!}igj t#~]9kaS$B۞D8>OM8ke6#0qU_] b_PP,H1&zR0x7!MkXu?$>(q,Ym"LίpΦ;%K‰k ]o;X/QXQN{ }ޥ75aacfKy23`+e XʪcfeXbk8PTn"+ŦլF# nq\Xͽ1Qnh ֕jZ1. VqDn94Oִ3gz 6d3)ݪOXґ;j`ed>"GN˴KF)*~lh(Zk7mZ Gj/%(Dafovnh~(Ok66TMʦPPZolx_7}464NJ[γL^Z?k@@ѸR|' 6bݸm8´ kwo e8z򲖃kNEX=fl9nak S087TҘ B~t`gaS4]},PY1eTjڰ򔨣 7X`@/Sbw m'=Xl(_+E[!2KTV ~_Պ2#VU`^ͻ>CFΡr1hZx{v۞JAPc;^Ũ  O aUAQR҇*KلIXݶLwijxzA}d"v37̸09yZT`/rDw$7~,9.4Rc@"> k7R]I TCH-ea:jF,"#h3ZT 􆒗p RB " aϏDrcqDnxکg9'Y X7Goň֩&nOWnTZK6e(&Z"E(bNmZI`ssNdjoUmpXJn!Rv، .yMs"R&nZw;O&(^|AHr)¶\hX E(D(˽t$f1Ak[om/UgA)Քg|[,W<Cv̭AiQ{b/$Rݔ9{{<=-2)--6gKQẸWQ%4㈀&' ˍʈ7ٯm ȲusLa|˼/iTX .CWp@"VW&}2iT|VkVN_n9=A^?NjK⦩:JffUFpɣ籰 DZMļa( ^?X qʱuAy|B櫡ˌ=gΤ KUXvm[`'튙XWۗ  ;̉ 57NmC8\hlswZ@Pq߰ Bg>l T].:zy/XlSX SRC -'K}W}d=!HAv3P ˆ" |qsqPH>mB7LQeW&Ch;nZ(J49ڵf D<Ϭg_mTX$ԏ+' 7#F9G!Dy iN`qQΣ'kfO;5MozM!/(uqj-gjDPqb<%E2[{g"Щ k}AQ<!PR[@OxCLGRC5N WvIg.e=,>;g0e6e jIܧňXH%! g:;rb]_~71^#(XecN czC|켈u-Gp_d@d %hϻWĕBnHnb%.[یcs:?CUZuw1Jؽ/>Ndj6r<]x:T@yi%>h. >Ft7 1$.%2ldݓ8~`{t3> vk K ^*|q)g|tt7A5\|*rCa-TPd=׋X Fn&Cy甖JS~r5OJ_]]meHHKP8vt~bwSo*Gvg8ćFͭk8iaau $z5\ LdJFkՅ#omBH[ "_#KGk@1@B[UV R\JT&ȒL%X= 6){ Zf>6yPу"},)rX3rNM:RΏ#5S$'Qv oμː-h _2ŋ}I iW) DJAUurET5[5Д>r+;ueF;ӏ⴮`{3Vt8T6gXVs~#HzB.k3Nl)Z1E:GpAt=wk@n?-]hyJ0NrմG<7ߓ .m?*eX2C627&a\Hl- AFQ) )muqH0p)3JݮRB5Rok/6tR1\c;N"/y^ .3q3*72mO^юL"$+-xopcN!8km rD,lB??/$~O_vEӄ%P"԰HLHYpy&'>;C{oE.V<26VO7G Oʨ7KXcūEyA/G|uj(-0О9eݣ9 '5=;[']h+Yԅ1%F%i=aZx15MS,S9*fX:=D4 xrꐲSI9>ɢɟ)uGLs{W3|_s2#eײnePk.)aUzg0fxlȾ0U,h1Gg+DTށ5O *w&..Yي "pW4䌻Xe+x۸ eRB á\^XDٌq@ -]+@% olI?n%fsN 4sOmd-P}LŸ'&\Jċ}WcS7j*v.$S )>cvȇsYoF#v?-np],C0$ؒK&LcD'~8?To7TCRL&? KJ}̒3;j`>}gSIƠ"b"6 v+⮼7G!16*Jռ45[R56GtiQADo*iSD@a/C%4cֿDp(aڛX Blc')etDhȨT'wiOVS_(wfF%8H&ko뢈Nsvb&:4CDf$@G| _KiOw~!?yhm=<D9@zvNH @L q"+J]Bce17f46<`tX G.U_/];{g  #A^3Gwymq57饴 2ZYo͉۷aSt %!^b?xepm5*/> P{T.Ɇ6i{ ^uDk żL,hP fC/0Aޑ)8|5U B]u$OpG3,'kh8(JiZ.sؓ'#)6AFo ?1G1 ܫ88Vj]D&Cڳ+$MђzUzĦXbkCtLy*D$%E˩R|<3;QzjcDy]Bذs1껷|*:ñ%Zv#bxe84:b3_@ۍN b D9.$Cvo 5O ,9u`âhΧOF׆A50e v'= 5n"_ &l\[S!Z L5b)kJc.=c5+ G!Gcnuڻb eN[Sof|C]ToAݨ'M. K=o*sAX+ؙ=ڡa}L7Ǘz&\A|k vt,)dl|w <]|+D-6yeJ`CD o0^kk:5 cvpPm_˟`jg3U8!!f7X /}hў@{%5lEFEe#"xw"SQM4#T FLk3bT ,EXw,-J&0aG!Ƒt QydnpG}\4ka`B #)ؓ-qɐ"# 0J 3@5`$Kp}P ΑXlQ-N1G)R;/ra*"0x5J**5hSY`s}BlJ!}|hؓ##ۼT[ ij,#ԩcsj|پt\ ֈNTN߸Ԍ 8`԰|+(QwYDW@))}K@qL /O *bӾrૣ /_wU,R5pp5;`A\iI>88K׹qCǵ\ jDO @֮f.j~ړ, AXUxwˀ,tQe/ͯeF&uQ#WG7vOG1yꛯ _D6m4jTȠ;Q;pD GZf'g,kh&MXIgyVJy^*t I;VY?]g<*'j?F$幓&Lܔ88+f>/L 4DHtGm/B jg>D|]`@`pXFނ&%@fAWUh Achi-,)"h̗F3ou#ܗ\0a {KX@O4Oq^c< GM0e DGsv,Gl3nc5їnIA~ޑ);apNE"Eb&T@@+bSAZ/~AJ rUrHg3KzkPcuhnjlxb?F o_೐_sD$1 6+oWEH3 e$-G7] 9Y~΢~QNU+GGIp6T GL! "s^&PEveurk?ya-";4qJvݵbо_"*_xô~2;}bt*Ÿ.)MXQ28cҫcbv)$6F:}zoM ķV5o㇀JbpB0=6d3.-"? + Tp_ZN+qRS5M[@Tq/2]ӓ!u%44(_+clPߛa h2Cdc%WP^Mf٫zULZfFv:nUj1V Ϙ͏F!蘨sI],K0/ y֮:ku}Ԗm$qO.7X5 Z"Z}GK gF4@hw%n!ZM3MGwfEߙ>[tI,o;){(HNzx^;8Vrefh6,ÁPYdScXZz>L? *ЪB+l vz.XGUSԴ05\$Oz{1&@v>Fj\s.)x?*SPG *8*_Y9fF 8Y3 {Qy ɱ%e/I-?ҁ-^CpXzDptS4fR$4 e -əGHkir@A+tl+ f/"C+lGDpnTH |C53k6~{P#/KHo߆hEgYˡ4J@xk%abłҜy{O`wNsϦ~tgVsb݋D" ]zh2V/n Dd7S@So%x6 IFשXF%c~(TQbz(^GC lŀyUzu:"R FN +NuBs 8wqQ})p?zFmMBZqU tVq?Yem t|7 n߿\=NFo؏zc.SHל=6p#f%&iO 3<-EχKlg涢yõQf ƽSQbl.Lh U!e0Ha8Ȏƹ&`vVjg&,(Q(AvJvnvZ<*5SA4pC whFUZc=hlI A_ܢW+fFgms7% M~ੌz3ZN:G$_5s tn`8+VbۍxD6jpF5<+Yn/0M ;/mSɧ5!3,d,vjd7L: l?ɋ!@l S33b-zUQFdtTz*TB_& 5_P@ߟ:$x;M$#;OaE:l/q+ R Gul\0d)$gʈU2 ӶRa#5aԻRpa uéKQ ܔQiO4kA#$yLL5W>ԂhY>ͺ,BCIqZv㐋 ע,(J5q HGyIv4,} %vRbs<]&R=-0S&Gt{zϑ;L͑z=6Yj %EJcw{2<@<RG8+I*… |wwN -*mm!*V-[xSv (lWesJ|lP9%;k8`7w%FEj1o4d2gp{Ϸ)+i4,Q]Cj9%1TЖ62k|+ epF$]h&W&*v)wҺ슀Lȩo?J~u%d}82 ߩAɹ2WXP.>tbr|ATOF@%#čXMgw,FлX qZaFV9t/K*ӝ2 %} }~"7F `?!YYzaZv pw&pk9}~ꜯ 2+CB FSKS#i7 =(|ay򛦊FZJ)Jߜ&+Fnf4;-.sǕ䕎=n![hiF3X*i3?:eЫeQB]Y޵܅\R\Ziv\"f% 垇4$wtbo&V ]l,L }M{TT7FZdPnrL[$%`X:'e'GV կ&,{mZ@kt| DJZ^m)%||_(SìojwD.lWVj^] L!Iĉ_S__]Ԙe]|xTНawi'Bcn%2n}L%H|2Ctvt =@&Q(]q6Q`)H6fsݠê{)SvFtbS׊iT"'zkfn*Vp.*M j$vtN4sH%x*!rQX)"Àq GB=~OnXN =db,0:Q{q~Xd DADt8s -T' ^[Ųt"oI6=TlGh{! s! [bMlx{񛊒1Ҡɧ&9#<<e%^8 k?a&>&ciu8HeDr1n>L@Tpc1[eL#zpganI) vg}ыii}v8OtAnsDێuGD!+ؤS~۫/^2mK9^LY&fCz6Ut_">Ҙx+߱R=y%z Ia5FG;iܬ+^OWIbŮꕶ؟cp537`{aւ;p.%2)W 4$9=7l8 "UYx12&k7lRkHW;F-u֭3au0e?-|GOiǗ'bK٫n/-|Ýuז` !KC=E|mBc~^S&ͱpߗf=gqexOҳ[??3g] ɌRv3ݻdyBW6q9,$<ˀQ]t(WO0?$ZY&P4͆.윝DjҀTYn@<*#},XJp@i:{;q ھXi%]FIrE %pgJ ]*~4w-^4Ok"݃Y}8&WJLV mfkqS _w!oҠηAQu>=Cnd̛'@MEc  idnz;4#t_Mc,MrwpB+&$~ƚbNRTOx#ոy`  A1iߖ:u|}m\@yhBF]S iB?#J1 [=n@9:щWkYJ&jf*;.<j!cTA41XⰜT/bsU!c>5}A:'pL8+t@m@.3^\YmRBRgzHG6ͦs R-Qru-i/yʼ%㻌] NA+'nY5byVU沌$gi 7}9 .FnЫqt`Lo9!v녴ИӰR~!AtK *#< O kbEc/ċ(1u1lv0n7^ L&L\( |Q[R&!%eS&)3d,+1ADZ0W1#}a&;.pzGi㞢վ,` ovIK~y6!Ⱦ~O1FpäinH\l J V}>0 YZAER/data/USMoney.rda0000644000176200001440000000311413616365126013631 0ustar liggesusers]W lTULY$Mh$(dђQ̴g:N;NYitP ta@YRLERCDcL9'{Ͻcx|9^7R#h0a0FQ&Y&qTVi%ӯ>_HW)!D1B9*~ jؕ\aؕ}KhA--xhq`veWq𖇠~aW0Qڑ/vϲg1;`>UPfz`_E*V̼_u 6kXē ZQx7M]]#e湜[L pC^^ 0v N5p~c6yDb'LSЯ] 7% ^Lf 47@_!օз;0X˰XKnM\ͣ oB<-ۚ}Tm\m!o;ܾv;c}9xw?o|{.GBH }G;a]Brg-NuvP u%uV{&><*dK~rtC|z1{lf퍜W,f߉*z;Ĝ=9W> {D Vcki$ OfyJ9'ʶ(ċrWI{(Eh QSaƹTb\a_QzQ${Q? ?@sL6 TDX >[wP~aI30~ayt?NuDv|WbwA`oYc?;tρtw?& p6@$!O,"nQ:Ƌ__zۏཅX(q1уż}__/x}7xr7  <K6A_'o)ȡdCef`9ޫgyOJWg#`,Y#p٨քuڰ䭭O_\?>v~)ӹyr;~#C_7eш|SЯRQ|SoR[zB{Qycj·8z+};GO %&aJөgRl:Di.I_jBvb'ط A6#O3@ !7a O~g"q>λ#Vwn3KcH9Ov<&cZ7]> 3=?&:deC>f3=7_@=2]:g/w#E c6A6yZ y)s=.b\Sd(r6{!W\?'!J΀Q͛yb/]~qQކx}_]49oϛ8@/ A~C 1*N>vԿ-ir |<("ИtF3vhn{A5<`ЀYK AER/data/ShipAccidents.rda0000644000176200001440000000114013616365125015007 0ustar liggesusersŕAkA'+{Ѓ'%%IvA/ld!mUăIM x<^D)yugi ޛ7)^cLczEcXCK>J`D[^'r.gO`(Si~Y-f :knItan(1v-7tb@'sl̲0Kٰ2YӰ,*?6;܊_M}`aI}}9X_vDH^#}OmgaHꛁ߁|x_] =7~"uc>u o4A# h(&}7@CFλ2QN K)sc/,8uc? V ^dsC/]j(j>\mh+uИh,4+hZMr-r &!gȭ#'''''''''' Fy;Vrbgy;JbLeGZj8AER/data/DJFranses.rda0000644000176200001440000000534213616365113014112 0ustar liggesusers] t@=Ă@݀ZF+Z#X(R RQg&dfBBfg&ƊKAK]UDE*Qif7vs]I8zʕeSʊJJRRTR.%u)aG?T3Gǟcrg6 ,hv[:`̈́OYutQk,QwVe#FwXǰFgn nqT^Fa[jٯ|_ o+Vt|Zw_W&{=8껀~'t<5 5Ob߮| >P3wcg{9i?* O}+y`z~WmVT':֒g;N;8Q~گЁU:i!^έ<}e7/+꾐I>pƀ`5֜w/3l]{tMڇ# W{)<֎UݏeN[5ɪ{^u8cZ/'βuBջ\V:ToETê^|c ,kn~c- V: H?Ϻ:GOI_kH{|≆sysp;+w ]ˆFgsh?o{97 mǿ9RaxQa\su\'ZB^PsZF~dq${+j_F_W뮻g7\T"ᘫ_Czǝ"Wouw xvcxnWwKoWH酟/q kWJ~7—kOhi[˦:ֺ 5kX-;?BކZ;A½~\ʾ(>FKСpm qίkgjmqK/hG뛔gI܏M7:MC>p7=mFCįZ&OTpyo7N;{ Oޗ7 γ?|?}P&snV5!oT;Oߥe|pFd/6>saoYe7T;t>-: Egw8r+~tma?rnB? }s ?@_*  xWlYY!Bވ{ё s_t<;>Я]۰~3o/zpWkV˜ds i>{G`擁xмҿ洋m,Pޝ;ʯR/x=sKO!h(G\qSS/)٭jp@Lu-|Cq\!2bN__`5Gh>B9O(ą=;WnBRoCzpz^~0M)]ǰ6e#o$&ݧtV_ ѻgνEhSۼ}~αߡXs0?GFj͓aOX|Ha8̄:7|B 37+\c.76_ܤnG/ !/ѽp8eәnVjصzsoWMbC s{s^>:8bwc_'M Gi[etw"oq" ȉ.gr~#zJq;-7Ss|\s] ?f0 ^3Bz/\ɽqNqg܅3q6 >{ I|u2<)ƫ4vLT0BxH 8G(y£yOW"&ܝj8/E#$Z8O ly&60)N!lE~ר׭ȟ< /Y{9y5>qY+Lb9W"u7T΂T:OMD'zn_\R#F %5T8uϤ~_I/HyNCC}?/k8I?I:m gW߃ΒCCnw'U!xHT O $*𥦡T}O}^3Ì2W !'v'vxؘFNkBdvws?,v(x < /WIuBxe],jTyp ,;XғpuE_o8UgÃ`z`<8%ANhRދ8aWH{qivmsu~rM?o>Mm}n>^; _fnڞ%j+n~&EzLՂ{չ8|S'Q= Gazne6s`n*FlQč6iA`ll(oar$bLc*03!tH:$IC!t(:ECѡPt(:MCӡth:4Ma0t: C}I AER/data/CollegeDistance.rda0000644000176200001440000005670413616365113015330 0ustar liggesusers7zXZi"6!X]])TW"nRʟKMd[_;zkBMѵ89Ԉ*Wr/l#!Fx _sy UfIsUâ7PxR|e@cBx.?&z)@k/x*ߏeĕqY-qS\'RXbo|D9kLcY~;jEim !kG+IC@rY'd='^P3$"7=˂Ay1( z΅HGE)An}rs?]ýk'xfcah R1ѻm!:OLqك3vq""P3o=|56a`GO(qޓ5^,#bhYԜV _qgs~MN_{Ss^;7I ZNp/!:}]5s)D&~)A5Eb"WB(:I̜JSNKP.w[us&>A]VFÛIe<:pij Eh*4զ>C`qh ߸G>r3 QȳlXoqjs4*0?$b9>A[\l9SbV!=G<<^Dp&.dě#Q~ȗ9U>ZH DᒟrBTȵMG0 ;ǘ<#ul$Yxp?}WHN\qwCW R~U9lu|ugeƝ1-1klb6^0ν/Qm(k!4͡hhui쭑&}R*B-p㠠+nNaWS e\]+i(C9T` 3{p J^\g([ J eJ3,TG T\9YnYAW TC'[=c؃ccꃡqd Gԥt87^xIijsfRCJ}o$%\4Wrz\Q{T7Ru$jF2(yWePN?czߐkPR&9[~ mֺ_Q^6-F6'n>TU9dXɜ8?#KQ Av7")#ZO:\$]ꋑAwAУGJBb6êmANR)wYԖ HeaV2 =wTg)j5pXl y+4~/*p<+W+1M&~G}4!\([EDd9ŌA}!n%5ڇb K.8GN{Y ^0UGIyDOS} *ū(,/lmlm)cUɿvE1H'g Kb9٪QQAchL*>ԑ4R,7ڡH-}7X@b|ܷ&S˖.H'&)G4Ī!߀q0lҌMERBjW0)SMF>SJh?pb OлiNϿBFCj3X 3S$rTKtRV'I:AU1a!b(j{VLiM HZ(S^FpcO;c c?~5K ܡO4$›@ZDoJu-Uե q/lc,8()!4X`$kcm4q)sUmC<\*$;?#Dd5՝6|Z . ag7רZH(g2?w\3;.Hv_wF Z>P ыKZCl zf&(7C LoGl&n(=QWx%φ 3VSYC &(8҈cy/s_'/L~|5Kt6B1 bs3I7HW4;q:7MsYvQvKTJ6q"RACtYwh)lOĐ!k5D&^+NMJ3BFo*ms "[s '[C'^yqcLbKVi0㦟;Y:mǔUYZS 6bV $m cjmKysoU8D\ټC^=XKt @$Sƞ|es#"K^a T 'BXC'<`<|8핪jK$No{!6g.^A\i -pJTwђjUF̵*o~c{aCǥ׭vE;`ƫ)\$WﲣeQ!ΒUA _cxqѺI1[pI)(jf|V?)'lHDHu{&wF{6_0iS ޑ7/Nަw\fv0tu]9,t7h,$SMiLv:U..,0Ztח4>rV3gWܑwlLeX r>!,d7alo  EC80q._f hl = "!B4\G<}\M<OU;TL@ `|)0O%nf>!PUg Y~;V[JË=3R& C`ޮup=䗁1hz $zVK`n;[e8|L|?JJ bL[!U8!%TJ*u7^G<\Hj^H,C{M%pQti jqfTzGDHKOWV UW:ޢoTe~ZJ^⚅N*Ol _حr9 YL"?saE'WOq1#jni3]rokA:pD # $e)&m2ONԀX`͹|-STQ{7fzN KJXd0UPOe'Kt4eq$Ct0:QCDe/W Vp.}slU+e,ޛRoh2fjWkY :D~HHc HAN~5:=QeH~ ~AL4e4$JiFӁ(h~eSVbk"YaǞ1t DPA'ҡdJ1 pY %U3&i8A`m*h,KZ=n˷oeθi1=w?6 Ɂ!]*OĮ57p5<0؎a7L YfmAꜸ{5/6hrH h9fʂAlȱ䁙[qyDkPlz^#46%is|O8]4dE-JZ2y+"0KEc,dU*4LtkHA։+%uk,#ߧP" jY*A.2D@㢷\eoE]b PR"LixSQf?by2re|TlRjOdpdҏ  9STǀ,-ʊ,7}Nj3W)=sւ [ȝ1x*f~)sRcX͈Y?iP-ӶK&6,weӒf}2e?`4B57tOb|۬Dc 3>=xwQ5hjQ1uLJ-xQ,3, EgƋWf'bPb18QbSc "5KViA9I\1Su(7mxc L81=];QBCet1I?}$ODt/Q8kqb*oƤ}> ퟑ7u8s?fRq/V@V+;SSڀ-b uhIIۓL!gn- VߣJo8­ZY#':1N-d`Fx)#ϰ[4UiMb&y4a:i@l3^?[k@"zNȡYlDrӖ ff=xv`7ER5amw:֡ uuƈmNYnl[H.ܯi?'1UYKl! .iԛ0 z%YIv?Oݰ\0 OsfW`ӮuJo\ۍ:G_^/\Oֺ/n ϜVr6Q0JQ w54?8='lx]niͦ7Ĉ&ZEV*wga Jah@żWErhKY\Q1|(#ǫȏޜQqFwe*. s[009qlPUgb%{^RS(&dhJf~!#Y߿BK`UQJR*v@u$Za0A<"ʜnEHOnDæ 4o+ٻ6:0ń3h-Z1C2!ݰEi SrD&|Qe&?Odoƻ/++Ey_Pjɧ#Ow:LG g;=kG0Ϩv9oDJp "4+[nplA]Wz]f&^19c9֦|]t 3c!c4 tBWݙ={rC^,ލ%IUR ۧM?K*7֜.2y(_ѿـ_{oo QYa!??( 8r%L2 5CN~5eT ^)imPߴ4'#d16::386!?IUn.n; }c'E7lbtF3s֣Z{~ݨ8~[K_eo5%Us v) wKbm - }Qd }@:߸;n+ÓՇ״v&AghY=]yl ,5૧2 ^-QʑT-Cxjc+u}1_KwIWL;ۂ8Lm0J_!,n2@}3asnU8IN}hRrӣky$<7@Hq[ 2x_8OB;{0X\F0=Y*lFfl4:چ x\ǥ- *|m$tC|nƐ)"s[og{􊚫KKФСoT>8wJHry>~}\9M{# F6dLJ ? U54r1vҙ/}XiT `' 14'XH0E|E^g?zM.,Z: VDM:R)Y-Zcb(TGd.Վ(8p\̳JD>: ,ܬ;X1.O3$Ue XhC ;[=V"[ Vhy}r<9Z`#_ r5̑McpharVqq $ e/',?-j<)ӕ("ixk)ݓK^-f@Rubpַ|ʄddXa2ǥα¨낢誫0ﴦ-'&f3+ju$ǀ1ZJ*~Ɍ?qH)hׇ$Y@B_ PG2h/;곍>=-CߤqRQgUGWt-:]~8pߐy1R f>2Q2=,K!C-k|u|N±B)0WHqtLҖZgD@A+EwkMILMbU+B6(7 @.j:|DhY3Gk&ʙJO2X0Gh<~R>MCC:p V: x eēMh! %.HV(9 6z]UD%xls{b,+K2|J ET;-r]ʗ0^ e%؞ҨPBR/8L); vuolr3[[?>G AWN \9ONTڂ^hpzsDS3V{x=MY}!)wc3rrGfNbx)1cϹ~۶0Ei*m3" (K@1xV'@xҧ&[DCwX =!9BFN[?Rc)}OBn v[R2!`OO^;Xuu$$ki9>>AҠD5@D;JJlRnنUyկW OQ\B#aQ@{ȕ-$pメH)\v[kh?NŦZWMnIzfOVܳ昞3BLQ+VJU/2ϐ cv @,+Ovlơ~:_1%Cc[t z.VGiנ9MM=}֏~2ЇewCd:>jiޅ3,YM,rlh% *Kk\ZE8E$hˮAqa(1Kjgz{ vn69pB&Lxu"7?=Eూϣn,*_2 ɂ*ٰ3$3AJ%aVh$OA{roCihHrB:|U*,9滾 M@5ieVd/;lb&VLMW D'ڞ:!dk)4uԏ CFHbׯ_6 u0 8dB?}Z' 9'r@/%׿[C\`3`fEfS(qq9xaz۫M8OMgu[)i[""A PgMC$$^Y[ke^]Bk.j-]ZJ|^ 项o9 %WU EGnC-\;b}U0W$UWgS9&jP:E>xa=$8PT6@5f7g׷=yɵ u/ 6KN5n3-\}riDRXaxt믌p 6ft{cvOb75Na(C-~YN*VhḺ a`?`0M=aVg9Hsh;6Zֽɖn8~PޙC6~cqLL;U騙kpN*6&\lW_+GS/60?H"_,@L;G)L]Lr$+8"rP/5=TG9(J*ׅw1k*:ݒEY\rZ&~u;ϥvݠc㲄H.u$Ec~!61T˧;22=2deZ򖸌_>8(uh. K1Vܜ) $D'EL<_VCjOำF]pxOG>I-4[By TҴk1,7-%9ʒjA=Z|:L\!*__ρH cG |/ѺJT ,Se ݡ6m1aoIdIg&X)Bm +4d7[ɗO̘[e^;WV#Z^M=̀ai!ኅH}l?}- /j!P˦J"%akWK R񪐸nDJeo! n,GR][䭗ЋϪjbn> 9|Ӝ/2KF{:=H%Ñ}O}a l ţGWow8.6:3f_*qQV}Zp%ES5oϙH'1uUuA"ק$D_z0)"wJ _'oސm9dD~"V@P*PX09^ BKmːo7=)(@9HlX5၌xA58,~TYim<~Vm}FRɪ[ GfA@+)t6]#V->1 X*6ʂwq 6P/N&fA$!sf?ɅlJ/Lh %0t6vAWך„eI)oܺR[l'ދ? ȭM3_\=7;s'ӗ%\>1:׷xl`}Y]2phps ):{y2Bl2gVǵv+C܅Aq-cxWmI ֹa4e7^=se x*{y<~t@*pGuJ#X`HlT 8,t jTAǻ̭e#& p uask%MJZ(ڧ(] Mw6M4,`no@P*BΝ>Dclo ӱ@ .3b[WOMd5vmQH 2;`bܦTpb)`SsڻT{|O\J:|dߪ$kTVuxQ ?%|:)q2 iX hMQ4@2Iٕ&ܝMKŵC> 39kݹ$JW57脳TY;8az3{g̫m 7JkTYg2cO׹UR0ΪcKePNо"6JG̣s-dNwddl<~:Y+MZ `07HdyO #<_l#B4<6! 7Tu 灜)Hk"w, 4kN"tq<~ó16ݞ :$njmK]ץjLIMeU ŵ?|(*E$CbКo4 Xj%tu(o~4}k=&"/L8 V$xπ.8n~QV\T^q}#\)v0$3j'Ƴ#3_b,Ubɻ3W1ْ'wi2n4@Bk] z2IRl>*{;CV˻IӎzZz=  ]U u7 †]Sv̙{z_:֞ `i.z@&ݷ3'qa׵U; ԍ7':7茯iDr&X򴱨Z|Ǭ'%Ao:Zl; Г شXrhK u. H^֟yp6&s' z*5`، Plz.A"dK|N+[͙Ia /m®^'RrMyZ TUMقO<_1PS+)U.r>nRB}7(RR.hv+JM.]G}k+ jMhbz15aE<쫔\hc$@tS:ǮI)7?kMW&|Gp4|l@UXiIX{a) R*H0<鸅u--u| d#|#G&HLא.|w*^ژ[-J֒9 qvqftjdɃiC U";Exdbt #ەd*(Ewu\p;JWG> uZIώc ?yq*G=m'.8f2l ̋[m;2j<^'!aW|=hs-f$X[;!t]rt0C9Jmi>^?\:}.~Oҟ};+ C pYQ5N+!BmG>j["0j͕c!6ܕh)n\ҘXR{QBZxo6]+so=TԩmzP>f.9]91_Qq` m]".Mi#+ P%5]p׊b}u16:1XD9)]p"kƷ72@D. f+ֳoguޠ[k5#?}:Or.N)e[q ߉79gyk tlj؁@j^/}^$^vے6bkt14ֱ*ZdA,޻%" |V#Gy20}lSn_!/Q{qÛ .c5h9]0aiv"6q$ũī_tB*r峷ӝx5S1 .D!98e)K'BN91xK}$tTJ6'6P54Yr,e43$^w"`zJy \f*pJw%L58Ll(V?Хtw&>~"PM>P.Jh IO O'7 #ؘ9AY>lDtR|"ĥl[y9lU˽w{blQ7˜(:7sw4ÉfOQj9aWe#pvF/1nVbѿNWRT[]]#0]jf2FVCH6~4qQ= 0h -ۉiWd롪raw\5)av~> Lކ6}>Ƅ24g7SS×R JԚ䆵4Ci|36l4dR[NQԿMOrQdC!|0 TGa≸3v4[ v3!5^N=dϪP|jh>e !ch",LuIW"t~UZZK~3t_& $ *j.bUyMt6D` ".fy)ŰnǴbShMER#vĄΎ`󸤖ԩCSdwb %ЖI(;퍳U%Ŀσ^-&(^uOZ`"ǧ@Q} πk*ӷ|%~-1d(A٢AFe9oLmO08,@X7F14EyZfBۓfj3NEU ,!rS3Gq/s -5Xm~2֯8RYU }[@5[lyD&zju_B,I#==,(!`[c-P;ELd֫7jёXFR/HMx)9Enhhm.#ps_ƅٛQHU(\->H1D,GmS͞dx5ǓޮR j,A\,%=>J:IpMNjŧ1+zQ-9 Lb̚d^=m(36%7S(nO.d$v۹7P AHy?j* ~0͟g`,ToPS%7¿yoH^Vy{~B {џ86,s!) 2GwA6tȗ:3W+K[n8Sɽa5Ɂ<,`Z<cn nefay?a䘫d>3޹H]҃z{> eُ9,r a#wSQ !0x'>쏮=;@ >rrrK,ȟm E6H(UGr| zR>Y[Ir}0`m9K[+m?-GK[Yj|44YQpI<Mॖ\Kv[Wߚ}6_Q~2ř=E3kmh;(ּYf~}3_I/ѶgLP*bݭzhub4 ٳl#%e=U.z q蕣!a7qkܮR_]G>o?(rV5^2PoP.@º |."cw(pW5iDu+]Բ;  h'Es "/[> LY,!|>xn {f=Ƃ)#kqwՐEk+&ߢ[AeHt ӏκ/>`Y;dbʑ+$ϛNM [6m`*ōnPY(ʯ~{) ߛKNWZqQԯIa AV'Xر~m$(;|ǒTOi杂 +a]v+ '-LOt(Qn0F o@J(FcMi}Nke|XhW8fB ^,pkWmAI_uy<}3Exk&>Q Jp4Yh߰R/g8 BB WK CRQ#胲4"*Xd_9eG<VȒ%.BAWd tRTKo@^b6uH=Nkkٚ&EO.oU" Qs}ITqS_WQALȮns4rKpjQj*ԓOx _fCVN%wy;ۦ,pѯN)prk{[@6fNm_`ƼiT[dg.}d@rd,/bAN&iU*ѩ'~vY"^#%E?-o +\…X`?-lAe.- V &hT'VWֻYQL]+&-V _[&԰? dcsuwOKe Wlj9q*.%7 "9;; ."rBq\~頾-/,[氼Ӗ&L\fkX7"'ERjOã9֥d*&Sb޻&cuDTjrD7[n\8z5AB<r>L %ad]a,g3I|x* \fKgdXzt?TDP_*pzSy|6ydP4!ՖS֭y{Eqz/>" .;@^ڣ1Psi?o /? 6'P׋ e7mWK@dH]|7elF̋lv`gZTP5@Y X)) o%q58^݄f\SSSlj51IQ)Dw,hι۰Ŭ]΃̓zޜ8RSfբpצx_>& 5~5ә:D{Cƅ;W$LSQ]bXAV(*o#N=z}}$s7|5[:5_rg+J? q w" }ͧ'4zU'Mmń˟4@A^B>DE u 7~kdD ;fLRQ=&q 8"VLj)7YoK)Q3ۉ{@IQ ) ƁvϹҊG{X/&d& EDN)nE+8p;i0ধ:5c5!|9 M FOQ Wq}8j 9U8Iug- Ly~]C,_'t^oWjġ,07,"zB%ŌW'8sit8O{:S^via2i7c8h QɜSkLO7_?|AWB^&T5]y|I1F6G q#\jl*h61U+1G!n'ۅCoc::3@r-j䌿jI5<Ql$PW#|8yc[W; :O`P0y]e:xYE9rxAH114B(tj$4!) (e 5H(xM3$R1utOqAH?nKQMF7uƨUr8E(/=/Bt$(1Y AKSⳀ}𼲷xk *->ˮ 3N]nq{28 ᭭wج̴_}ļ 2:##P0|eA?^r+ X10=[Q%Z/m&pvRѨG{YJ/nvJI8Dh ȼgZy6dJ =6=A{#ii+m+b1dv+٫[4HvAwOK.A[%JA AiVaFy/iw#t]ur< 4)2t*_Mϲ/uE4UqϵGQC&%9X7U G+!;b2DɶrvdEO 8,?zkpT!!ߑ^J]D],;͛_‚GJyS~<9 Ѭv'բEX$zjkKGDfN rbag l9Gg5"ZXa7xXUV2_EUO`Ix1.B\Yhs"t6Oi{pMVӼiktG7Dqu=${ER8 v&Q1Ba䩲Y\M#8{]%ɵ׃qNw-V3AE$ϯ,'xESm~Gz<Ǡ-ש*mL!X;YJ‚//lcG8"vy'X+NaJZV\ix_ -Ӧ]*'n/pJKˌd7w3s;nT ~V/;`Uo롒7Ivug: K^ϥ"'}'{a3ˌM&]9M/Gr&X`j|Db@GD [dDۿǢ^ASkR%c$7$³T7&WTjAte T@Vq+BLMcZzlHi~^jӟxۋ]vCZZWgG0cb8o:*G&J6^YJ61ؘTNUv &ItçNҪɵL7֟i q- l M}wзĻ2ݨMnƨ@iG 5>+7:'HJlב|  aQ,%2k:e(Li4J޸Lxߘz 1[7%3:,,BPb3 kfl1N CߓY/ZYG찰Yx&k)^? -)jWu]ǽG ߃L`'ГZ>_`9x]H&KKIZ+7_n9 ;U>Zӻ֋UBl l_ޓT|rm%'W]R5-ŨA jۀgN]qcJ&TbmOq_9/o@h&ZQ6|P!Ke0LQ Fc#֩m݉%rHBcq$jMσc\1ߎa_R&l!gU}ma;˪[Ȑ^G*B@e EX SaD|&,̢R@T[=A46)G&PGj-aI ڵ"^4%4?`i´WzK?G>RGbf:&Ѩ8 q>GBu3C'睈A3 (7SBVZ*ۀ(v5bp5N4Bs5(CѧzdI5:o љ;"|~<'l/zv1R㸿$'oQbm4tU>^e\͘Ri82&s<<1 ,c XϷQ6zd^ki|ݮmYnS[Z[AaH-p$`@/ m :MbL9M_"#T:n_xdF!~p0>4|}0HQ0?AiK{ 'C7jƳ6&}8NvɷN-g:D˷LN9j J鶫e|IJEMrNssĻg;D~%җ!&IE^K^?kD&_jK\ E$S۲@>>0 YZAER/data/DutchSales.rda0000644000176200001440000000114513616365114014330 0ustar liggesusers]ORaoZ[DE!uQYISfTXA RQ#ϳ><>ys;j< H);+kd[o]jvZ] K㟨^sF4Da~ŨcP?fLen5#wc!kE;n{:r~Ҭp8Z_cZ&TK3IO)HӬirL)O1maw_x42+ 4<3,BFx^g+/kނ0/ / Y!:O[!',Q?h,_XA/ %W^W*s9ȓeYr9nvbtwV߰mpf/o2/}Ǿ^X'c}ݧL]h/oen[dU7Us oV yk9S1{zȫC.}[`B[t#|amr"c=zU<wWr]6"6 ł^WWFOO>'t9iv%DʡrH7;AER/data/OECDGrowth.rda0000644000176200001440000000200513616365123014172 0ustar liggesusers]T}Pe8c4D QC0P$bFv;6=!$R 1pL1rFc 33u&s9ȜcJ&w%o{}wխ2יFbm@ռjA 6DdGV3(:>2 p e,(6;H]Fr6uXU Ѽ:KVcN9hF:jݷhs,݁taYÿiead;ҟy8y$N.\iHh 70q06ҁ_)c9b4t*T0_h/U= q*Zk-Yquoob$OP_sɑb1{4z~e| P4N.~o=-NWxTK~;#+f?OU1n7\÷h*un+?pSm Ց?/+*+:uS=Y׻Rg^N5;u΅op"حt^yM]?xfЂ3+?Xw_f̢B"%|jkucv b+TWVktZ͉rTpꫧ5=opy?4}%k怵EEE+ ^7#ʜ|^~Ds11+'qN +h e%<  EMLvQ` ApvnRTW*1jIմx pq`>Nd0:/0Jbt@nVIT>s(x/ۧI@<3ݜ [p<'@u hdAER/data/PSID1976.rda0000644000176200001440000003651413616365123013367 0ustar liggesusers7zXZi"6!XU=])TW"nRʟKMd[_;zkF~5?:wjt\d1\@j0i I`Yџ_a)#0 ːfJk O_s%'OB!* A#4'~_1ky@# t[i,[Bl;H`Ɲ+s' ps#Dt0-X#X~'uhiwVxHOOvpa)\s7wt܅fQ$jo9qۭy'3x N Hw3Ln% T%O}4sg9?ʥ?REokd~5 ytL߹N)gduV~m CKi*8C]}w&Nz3?d66KHӎ];ÖV_S`xp~*[4%#9|T`UˏʏA=yFp"DA"l縣tCYUCi$dYcB#o<;IXtยjv=)̤{1@ i#mx'ؘ-z#^ wD&K?\@ѓNg`F_5 3NKʢ0&o5Fh V?ɪw.\}ZKȾd8J n3'=zj+N9tj˖SWu~A@ֿ0QrƑŶT&ATqΞ٩Duj&WY$׸RP&.Mw="y4A ߴB'*Rw\s]EoJV@*!q*瞧}Pa'd bFDxR;6w eX=%':z)gϻdefOuOwzV0ת~:*9Yn%dȳK+DJ.mπԿ>ڲ ,,&`rXp G0Ӡ߾8z/Sw.kӥ>FSc%c"b'L1Jȸ_b)#D!{p3ԠYG6".`hjծy.y1$5Yc%E/>5D'LQj><5 ڒ kqM?{JSoEĺ8Cy@M?#9p 3>؁%`«X3j*cM/.j7>z&hnӋy^s4B~NQMTyBNY{ȉ_iIMg1.'#ᴪ?'VspG%޺k^V%Tq<鑲҅Sk| sP.=ҸEx(qg{F9.G ]$#'WpUezH%J>=\%̗)H+ 5=f3kx@ve-j2 (cQ{]š_#>?~U?ǖy1ֽKaG` ӃfXv!QF2#}lp6.awԲ?t2:E R)I&f7 0)6yh?b/q4Fuq2%; ώ2};~XYyGPr^cli~r/bk2"{!=.b:>ҒmS)X1 B6Ki%NhiMmXX0jk JqgY7)!dz4[[djL ߦQS13oTWU]ieSSH6ZX<6RƷO:ɫv>IqP&܉2M]sad ſ)z/|'ȭM݇)R>0AS9hh4+3SΟ:gvj~F07窣TOUM,q 5$W;@3^JEf7Bʟҥ3 dA2>Z'h6_R/X! .?X®RIhe&_S"$wi3īV,ɇF`wPj_=/(%(F9Pô 48JI턂.Fc+TWGO<ދKƆ /hfFsXFk5T'-,s ICW];x3x?p4ޑwTL6%r_~FGuk>_O.{8*cLse9lf8>+rIs,+,PܵDW@wMQo)|vdPCٷ' .ŷsVW+8E Oڴ[2Y$ʎ9`C0;%[/D/‘J\Gj>c5ud/L5)H0LE}mz2<,88 5>krni[]Wl}$G cjXGiofG6 ; ŕ+_!bYX^19M|aFCe*'d|K()CQ`覉g*b;9\t9 p3̔QC,۞sdO-*m,J|xƐ5[< !DJы߆W|B+WΟ_SL&5&M974>|_bKfզM:mz3Bq7i+m̾Y:#",D>ʵ>b}0~&xtN Y}DL*Ci}*70]%~ xZzxe1Y[n>mukI;?CaMswqKhΛ`$; Esvfhhp C&TY !cy `l o^ȪO%ó& 蟜3 J˃sb6~1x )q>Mxۿ\4 ۛOplZ%ÅQ5~*NFn]/ːR bU+ .VDP:/ J€A4k򂰷[kC ޻~>x)I*'CBf|yv._S(Oc/"2 zO?؎eɜ|PG7/,?Be3G`@^<ǃg G3nᆲPTtCVnU h}k춷%uU14l`7oz":iܢt=m[$}p)ZOYG>pgcABa۟=ŇQKa#P aүъ(q77< &TA'̦u+o"~u`5rJ'x&µMշ;ڈoL'ݠd" 7KWc~5EʈcNz,nC3֒7C`#S.FûZd}TnRBޑ .!pڨAS˘j`LPH]ֲss Y01SaŖf"B;dxQz2ٜ3flS?{qlԥm|m:ףW/XJ2I\a lz=4رc4ğn ̑X^v&ˏ:NA8yOa /d1hzxxYoi%)pq,xw&SH0~޵ {krV~M3Ld`OI?Ԥ%@]tE!rZ] w)LDoU61eX_fo?)5H~0{Rp0}AEJtV.=Rb箘tα,A,ipGh=`;y7+$aCLg9/A"i!)(tGX: `x@8ٝk_̦++T`Q%Jtset\ D1W> 2j̼X qN<" ~oLQ~Xef91R$ s`h+ D^wȆ#`r!vzO0FgfFPw"ǥu v1[Ze>E4R$ZLWueCI,3 R(}B_р85VF AM ؾ6J Sc 1eAd428 U[^لcG|> T#Ã-lbiC\ÇY!heTta?P$3>F_ *I?:w{cόH#Ӏw(R^^Y"sn֡BY[Ͼ|Vk*wuJeJ(/xV]Q]Y]hvj)T!HZEѝ<,AIt-%}6VW70/j%Ri^'ۅttF%e0sRE߸sp RXfC6yK5A^`0tȗWVԣ=Y[ǘźBJxJ(zFI_̶"X3]$^kVH&h9Dd; #d0*1HӽPW$룼iHQ\fCH1vx8 kcs?tNeeDr~pg^ۋ=#fQ"B6x{+2tr3g u\f#M0Sy9У t%YoĶ4`g*4_;h/'MfU78 9RQ.K%tp'U30ŧD"Wlb;L>Y=8n_UN[B=5\|O}W,{r$[ɷm;9okd:dC=;5b5TcW @aNfGKkmj`6Xа.(H뇣5xh=u$aqpLsP|yOC#YF̿P}RD l]:"Q~ْ9nl0w&JI9}s.`KSQ) S 9a}0YU>Z!Qyaj*p:x0A"Ve@ίj| ]X g.K_N[Nέ]*Yh]4^o񚔢Y;$/kavѠOWK\F#΄j 7 UW9lF`%N>4jPŜD[aW/qv{1gmF  Dלt!Ʊ%:2cuNB1M䠊y;@BFJh}v4ahuT[](*E9@~ۍIO*%ߎÄsT\bn}ɫdDA((sk֒m.^]WFBr[ASW%j'NNfh[4z/4B^jӹ}ƝkcXjnfe5(Y22%~3|så滤6W=: *= NP#8nJ.Ÿl" ߚ`Hv X;En)Mߪs޳PVhHIHQd]zXbjm,E_V}>sc0vmFtb]hȫBx2n b.~}@_!A@X,Y8% V}^;{h$F X$$kj^-~Uv@tB.Zܥ]ykq{LNdݗSV\K(@T+koqeU$َD8q̹L;epc*J*L>;Q>%Iȩ2X+tzz[m7r5Uܒ[i&-~֋rKn"LKǹ,SI!y5`YCS:72ᄁ 9teN'-o1}֭3] !c)=bAغqQѐB+2 &H]. XYo!:Ҽ8cpZuYy%;[1nXXqB %IP<8`c2._TSƆA!l Q~gs;l~1#j:ʶfȦ[SjOd h7 }WN?fL`DRdŧ^geB\G3g%YhKf1B^!@E s~`yʤk^h`}{}PEk}|š*_λt ;cnce>WDSte5"f֚(Wz]y;5Xڗܮ牤@[y`s΢lD'/6aqYG A[+c+[1%,ŜKL|@en]/l q4\ijoTd.2;Z3 F'&?W;),W[ɅqEua9d98i_!_(K"U>ENwXC&sB`Nbڻiqk&mnٓL 4DKjF] DŽ"K3PrlhHljʼ;Ek[JƨW 79P3ƄW_Fe{N;zvE-+--Ѽ`zNq!ԉyLD5"?*-p;ebIguWKK9|h+4,9p2!a}_;b-f6gXҫU-;sLBNCZhgB YUƺ;bYϽ0 s Ժ9ޒ8\砟ۆ *B p`V, -z&p7sCygfo:8"N P:508%?y#ֻ(}oPf?=cCmW[^_6)yhjޏz65>x.CjAt΋(.$u;A_z)'!6rdUP-#sdǕoݡΝeY.qSdѹ^f~חudTeŖF[ ./rhgĕ`SG)s%<.VV_O]Ih 2dJ-" MwnWWI\mpV$ M4ã=A7g\=lU۲$ !c W$mBKEVNPivʔ9qV$ui < oGrϰAβnꖶ(Ae~c _ejuDͱ6|f=&/ *EP;Hv[[nh9?G/0ay*s.7}B<D9oSpam.6oU;"|zfIkOˏxGXmhT&5-F W-X^^k,}.?ن4p&Eߗɴ{` $B-L?wq4.a4JybR'|Yz)9o 74P; 3[(*ȳ4vk"֍JvT$vgJ76tۧX|=}JЬNsa˖*$`Uu2257pL4狦Fډ`-9j0,ezESؼg_Ze8T Eݔ=x5b7ĨY%d'\,B\xm0q`6+|}iR3sen6A6 P3ꦰz 6 ~IvYkJu1|Pf=Sy2l$5p@$=n(}p7YL dR$Oy| 3ϰw_8 z 2m(&Σv-.A/@]=Ak!OV\N}"-}ibhte{keЃ2 f,UL YHz94l+h@׭1DyY3a[ŤKpTGU6ónVi^uTD<CHg;=%GBf9V!T?'6ke C꾈i1}G G- RqK?_UcXU:pg 3f @AB<~`gΓ Dg"@KH/le I>hR|y/^ +"5Y~o[:@`yN,>74S4ks;ڗ/EV|/ܕJ#Y+?PO!%N7 뗺$ous e3(7͂f)Uٓ<{Y'/FUIl|O#\YиgH SjM[%P~&Plݟ9w0E#Y[_/D# *V@:$>qYtSE]Y3 ) AIsI-I;{H"_?Gtu &\S,,)}J,&xyRΕk4~_i93c/'N)pl9, NYRČm^,'\>N;iHyo2nY;!M EZe^J4P]OCn,+!I xe6Xk_(bXiлI{ ʝ;7nZZ:_M뺵Ԣh@`"Bd5ɖG*pNlEKD/mmPC]t3>:VJye~O`z!Dx&E9IYZcb#v ȕp&=Rt%i0\j CH "-)G|2R9|nn٢%@ǩ9`> ar^`X ܂vNbz+`]01m5>+1b~p'GBڙ04 _ɠ`{=hT{r %ޞiX32DC^G[c$IuLUT88F ,kg*I^jP~ԡZC쟱|RXV^'.h%I,,Iͅ.J "y_,5eC׊٘l܎ϊՇB[u!d0[')T<ͼ qi+ȢI|- OW~3"lB[5+ %,@9Jn~^`&vnż#QSv3L"+|VL5v(b ƴ>Ta_*ZeM3o=ؔ~ݢZϭkӸn#y@-BFd{itŬ[k?"x l5=潾U@rL Blڢu.ndjpl9u$moF".dSBuRξ5- )==d 45&̕Ye{Kg}OMpŹYt>G23 -+.!"_p v?U޸fJP#eM:+{5jG|[aKؖf/R!Mw;Z!KX)gqM:l{RdHߵ#>i\EՅ yj ī8j.` `Fvqĥ0&av2`Oq- i1ƀtXuDp=W zgV 1>>$A R 4Zf%ؐH[D"kU!14E`E= 䁥|_ChV^6[ܚS /Ak .Ql4.nU5^(4 ]B+ۡMM xttZA'sȏhhM !Xc yԱv'L~V (zёѮ4`bڳ/I*o@[ad tVfHx!?P㛁BP/܆bTa#N4A'ӁwmauUX9)2D &[Y. ɜS]HgH2gsdd_ M-A7pM Ђd ;sL$3!BCؾ?}:躳;ƅz7K}AcB>&^q{֏WDY1E)mAՏ8tV4c؋RA:S!h~tXQOvraqeU Q߬@WRPd@yR'( O"/ 7z!+ZIWxȯ{%pyq~VKR/3u{Jy-^HI4TG >E®p: cNcTAlSJ_ٕ?s)Ǖ"6ndCF6Aո!Sf6G`IpTok)IfGKVJB9^ZL=:NZcSW0ʚaRjld^xxlW꿚k5$29`/ܻz (Vwfd0:c;J-}8Ls<@5yhڎMbm,TgG"v%$6F9}VlÛbܱ;Ւ$<<{`0J~7YM;h<U O%Ւ*զ=/ű7G൞zDZ8/FRf>sY!Z1qK.\mdNS&wS[YdR""2Ϻc?sZ /qGM $ 1B/^$W'[ֽ[fR=؉ mngv84ǝ0uq{AuYa(2:]AjFJwJ z%sި:]|!?( 6+w"WQ 4fxR굖Ĭm @";w"FVCzv SRz \'5ݛMw-0Rئ>wp^lGB딴=0u)ROC}/p?Uj砠i2Ûv.U-)qv%{=-PL 7d>ntg?5eh7ґ2ȣGwܧ' k[i`s - ħYJ-PY’64ER(f2r%ŶzPq3DPUJG[Jn^D*7.܋dұPC&7~%y`;hrh+ZP0pr.wY/m [l/; P?F[7vKOk IK<9O&dkq(7O-bY+6@L_mII1(Zmɔ<6ƹ/svݢ1%(>8fZ~zp->0 YZAER/data/GermanUnemployment.rda0000644000176200001440000000102313616365122016111 0ustar liggesusersBZh91AY&SYvebiĀ@/7`1@ I$li@.pQh4z 4AQFL# & &M2@a)R F@@M)@h 4hd,K3bg +!klV r+"j"PB՘-9 ]Fn ( &ЄّpqZibX|3S LE89KE-ĉ-PCKLRfIr^YfGR20P\ = KL(䩩M3$mPٲlIϩ&ES`| GIRL)&I!+--D9+L()C@ЉA)-Msa*e:rl3`Ъ\R] _7,D =Mz_T^=RXҥS_!611ϓ.4mtfbSÐ>T.>y)1mṶVT$`&)„+AER/data/CPSSW9204.rda0000644000176200001440000013656013616365113013520 0ustar liggesusers7zXZi"6!X裝2])TW"nRʟKMd[_;zĮ<(p8^ޒp.$_"P1 {bQz'JDj j䠀K3luE<'!t:ӱpu_3~S?ʦ`)mY"MҠwNe(m]{ʆ~/~k8wF#̤=d=iߝ'UtHggٲқRC8 QN tUc)ݛ Vk4Ѩrz@dWx^ڿ\i=r6vH$J0r1I`ټJ0p_J$e3͡#N^=r6<[zf^Hwyfg‚4CpT [χ#'LVRTx/5y{X@T{0E8I[Rʋ8LCTU 5im O t!r?boWB(`ZFOHg>MDVvQ}z>-E4Rج3o(Z׽V91xTG\g[ Upd􌏽m`u ɋ8C6-[NsN&wuRjRu?uT\p[R" .Ayy8&7Dο‡@¾gg|Թ9ٜY:b泸kW;bAm6B2yqoRS?œ!,[PN<&y:tz~uTn77ia>HjMW >:3GwbJVBa> r"_)%dK gi̇:=_hRlv6+AJc  ںEXmȍH5" 8ݵr=T,L3a*Vu&[Ҷ ֟gA -ld hA޸H J"ԛԔMEyV+s]|#*4mg^eo}|OZY`(_k2jpE(è{+I-sS@DgZʷc;ဵ<*abeޗR#G= >H뒨~m{z~ȿv;ω9n9[i"7X1DLՏ9 >~3TTKLkAJY˰RV1%H)/Z[hh TQ0geE(uO0BZ{ƸqWIQD|+ !qHb_.h-MT?Ϗ]̽ࠣ8'r~'3EYWg޺؎3it[증qa|b_Mp^"ٳefg;Q }E um[O,QU;ɧ XE?_5/`@ r^ V 'DF,R/6e1oyƄ@1 ؀>+eSMtS,M'}μ)Dzڛij[ThS3m vt+ +$A jX&W;rpʖ.bH28宾06a#כh#T=3fnM#1 >"brP@l o:.| utʯݳQו(;Ҷ`CXz%S2|*LhW#&sji:.*`&hOPZY=8| / 3`˕ JNEmg#Ra8i()Ŏ-O fhvqa5Y< DD̽ ΢?)ŋJ`!#J2yFv>lˮ;%B1ʘS2szFx|Υզu:Le$ߥze8Mg3pA 8cq&/)Z^N{Qy) s j|SAgyF^;:F4!GOmÕcqƘV 4XC% r.#iA $Rywpp~K C8P[Gxcee6nT"xǿ?U9;jB R ,*NLZzնG)2裂%|D3Mb3o2MdOSNg<5>ٛgK%.KsV-+狐Ш;~朳>V߭c7W qRu |V*/DGsne4.I$![Bj;HV*<M}4jb|܏FcS AӬYR9ݻ-=[Y@Q׶㱆TyW iHMRiˍMx.5= ːtFwfDz)4HsgK߁pU:yX&X!o&]h35KL./{l'[gCTh9Ȇc_k0i \R̸%Έ`{q=BT' ;.Pf ND c2&.  ig~1m" XZ 8qĐccqG0Xmbmqd}:(yq@9fWSIG7+JO\.ie#=ejqȃ9E_Lz,H=oR*Cʡ_9ed01JktH F}lXM'ND.ꃳl垾CR5B+롺\ xBPȣZ?acah;"r 5r +ꇦOG#)+lߙ m4V@|{pb9TYn= ?_όN3(.ttmdlHB 2rU1@kpw U_fu_A\鐺IfH87ﴢST.u0kU_~Wyi^-”{H$j^Cq&S~m(kfr#$"o]|,Tq?ʬ3^LWvHpuy1顟 <:gxm /*9Ny fcxl=ì2@{U]C+y7;;w$6/H8.+2?c}?؄T9Y$ ÛF #q ՙZPcuƣZ>~L'}L^|g^>V0_ XpC@ӎF7.AlU:7T9g*q԰4fK"jK-Wz7&ENsKOvXӓ/S G)`m*'aEZSߒ#,.j쬸̫e~\0# x ۓ4': )g$jj%!J?'c$_L 3kɪOU{h9cǸyqa5<2q~VFޅ.oa ͖آu+t0dfioxT%@A֤L;5N!)ˌTJZ2DUOgX7Pr@J0ϑf=3&.+a]o3J[b˃ٸRE^*ܟOgޅ9;?=1h|8Gs &a^->‚L+NBL]3шo"Iܷ>Cv_N3 Ni's*0z&P 42i}3U)]9>\7L*>NI= 0 &9iŹ~ ̢#"a% E2}W[4iPdNo}7>=MCߥ:ž z=wA3mqUWzd$cd:Kif 3]FAi-v(Gٞ퍱kT\W'~*ߐYx^E6[U` [VxX8mQahM.X,֙8,(YSīʓkpƁQ^[ l=4$Ř~ R}s.=>}e`) ixB씵6qnB7;&<=@AhEZֽ?aM<] 'GszJC[.fp܂?`*юl zSyaBd#B*V+@AW_.zFFiLȚB<"H4az@]2*:z$-L  =Ț(@'"dG.%Q.4Q9dv>r^82ow F4E*T7?wmn?/R+e 7^7N&NNxh+0PUb^:}{<^8;?l1{ىݥ+g,a97G ݂>}Mts2k/v&mz%\Bͬ[;xV2Nmc]=ne> Pl뇠#R1>ߞ*8ib qs2{%O3(YY>FSU`d\,s_Og{V}I'x^U{߷-Ć׀uۃ\#iCΔ1o~fh7宯 :S)**+gd4I ]c '7lzm|0bY1}M򤿞ܔCruw1군 SL:j!EMI*s$8j$C(}3i],6t𶡳maNӜJJ#Qƀ/7^z0b$qR)U%=}1t0q:%ӤپW2^dR|g/ ]**^"Qϩ~񐒪a6cG]ȶjKBݒڨ\ef_L]e.%}1ݲJ83+ 5G=POrcq9Ü fqO  T n(z3-5G7J5 f#7yM?3;O?~!'-{17=;jRч~%BglEysQ϶n|!9) 5qPltyŠ/],-Z_!̯JB{|e\s{`kTP$a>Ϭ݀avp[v"ߖ6pxG4deöy u-9iDh( Ա>'fw~zxӪɏǒ6QZ ;0(k,=O'^ $XŁ7bX\253' Q]5z׃:]wߘxVb )]-L{dj\A%E\D |x }L_&h5je4O{ZU1o=֌" rYYcckY؎Ix-MİgZ?~z9=. :g !o9|i;-Png*5+ӨSIqgcp=|*UṴg@PFh0UQzaT̃B+4yaCI@kpF+M{WGp&y]`008B'n$te m.NUMzu|iP#S -\X)r[-XZl<QZbu?ume1͆Y.vC7UdXPԥxA?g~6AUT9<F2y st4qү`bbĄ<.cLmԈˍ8UWY̏s p,Yv![2+y>wᬆnS1ZK6:yo(`qŠ>c::ULJl4%ۭI4[s C r8:]e*u/Au'}o#'sf2v5k\ąMJ{)ǟo%m|E 226:6]E"_|3U:SnY9BD7#yg7gv I];+1'wJgZ}3}cth6ٞmb& J< WmlEK c2nue]QYM?u[eAE,Yܳ)4= sAtB ⭛!T5XJF{I+i#"bTR׮>ZDH#S5D*\*\EO;S_a'j6k"u ~nR^(҃4ӥa.3 򛔧rQb H |Q6Dl}32%9fvgDIͽpM5D)xJ8AaC^]N|r+/G>=b^2CՖ*`G]7rRw+?9x7/N\m4䩊t^P1'XX1^4FlʕLyw ckM1Go4'E{ Dٺ 9)`'W)^2Rq4kB4i_[m#^%|3 )glvF^Sws\I|bݞ-6챋j[l]K߈ _8dvy&aqx lyK%`TOc:ςɩVծ.n rE<. ȈKI '} 1RV{%ݶovanv_c-L dZdi1H`XKr'#$qwPx$('#um0р %&(\TR=^ ovD5)z~(\( 틕jb;vr#34`!{ \Buy6wow%C00n-MyE.PhR.,ºS6_`—nr ӿf`wz~7p=%I~ z-q^W*3 f(a-s9AQ?D,WXG"zyk6-L*,;RMR0$b]GZŃ+7hor"]r s@uZT6٤' \ZjM%Qٱ|L13,{ =_="hiX1?rdY{oums/ȯ֢D&b#v=KK l@c:|- ,;EJcci49g-R+o|m)\znr>ۤ: Xp`ZjHI7DH%;g0Dp?rkd/pʇ2i.%O+*v3R/)^^~B+BTzX[g\k6imz+}@ %ˌY̲rxS,]yX/ gj]L FG}<]9N̽c̬gEqNBIZcXS;ϠXDי=S [d Juˡmt3_` \;c/5ullK,,QW@4 C]ob{v&/F8ɵK!K]ǔ ӟP~W;aZ/3d1ʮ^[+H^vt/t^U^2aIV  xԽPT?yk)ch,2=CZMWH'}üBz?b)wj[AD[dPŠQz%xثܧ6Z.D' ߑGp!CrCg^*sM%\_ %4*nծkbS7VxM(!./ -+\D`WǢRRoӌ3P(tg!x>&1Pԣ,mwwn"ϝMT`KPm<3ڏ'fOk4!״|`j=zLQ>V#&HYp@U%.x V/Bo_օy<_[2 'Uڐ ʹxG"5N}}Hc_ր|O{ss\_lc`ʤ Ջ63ۊ?nR{qoU" jsLw|uޚ}7H]zCTn:7ʹbH[,uϨWf;)BX[Pl‰d#Gڻ2%Ϳ :U("be- @D B[#gbuNJwPĊ5p/׵&9{Ε Wmה&j~g++l׈PmQtx\=X':+N/v6bI枊\RNKh䜶_ҽʍiC`st\Z $twх~@̕9Vj @JI/4ItVK=Xa+ӟISo #!Zl}̭V~5h7~q4 Co=ϭ`Q!t'o٧7mr%7Av;i|KI\Kjj{AY o dzVZ"_qj~[wY=&IX峀PSyejƞ؟wQ"< ЋQ.1ᮑ!~lDԇ jt]Mb镼Ab k0{D>MiRvadJuB"6(4rZmʈBhLW{O`Zm28 29_="0jʲٍ\ bm?q=Ih9B؟M?,M֚\8˼P6x%C.d)nCr` -8bfތ:e7|F~^>?$n>ّ]7yB85-L`F+ea)Meaԝ¬ .2 < N'$t\П4#" OǃM8mJynVhE8l&;N #d1!7lL9+D(+ꃖ. ͭS`Ud#~ @8Ւ g qD~v`+#F g=j drXShجؘջSj5bZ$L}-환}v502gTnt!|OP<"ORd06ba[$NyvuUڶn:ie":?ڙ)~j=I\dIHx1_|ǜ=!cm˅BmdtNVc _+Yڽ#+h!;F tkQviq@Bgx|"UpH,g,u'W@omWi݆^Tw d.e&b{aMr_.߹$x6t<~X%R~Wy-}@M"_?Rnd!uG wTAQ~QcpRIDzTv*ҝecK#xЖ-mQ )ec& u0?tA/ CDj{%|7 Wuۤi2k2QO@NWE|Qf=A Be(QZϒPχL5( V;: S{ 8@>MQ,P;iM vr73$*Ƚy.i'ol°$ &SxԇsK9QwDuC[us G.d'Q<_AL392X}ڱl_IE}Xc0@I74.pcTFEN=lDn|Y˗(L0Ft RzȝIJ9Ji4ifKhIX#d.pRT}d"ԸڭBP(qn"vD co c-qCPO ȗI V9HL(I_vaZ}юnˮ̐P`EV6V@Pz+(ؗT>$HK3[;;8tٓ>)_O{, RƐkŰ#߈80 2}F=J&ԸrDa0\&;ړ0bχÅ|Ub靛x@;6&&9jwV76ʮ[ƾ1I j&~ s"}{{ E3`:HF tMĩEL*wY&!w RX PݴZ;%0Q7 bFC|!fw9W$j"Mrq4yPtP Kij'SOS(WS.?vx.kϢ ¼fttVղ._ޓ< soF=7_"ƥf%7Fת:Nt9"xI:t CTV#̠) 5?YEt(‹L`%q3NBiV\۸S{]f8zpҖ(Uu^iOv!g רD9P:ݥ>0qTT½>#nEqːWJd/YZ:8sh$!?4V S*c뼳좍%RWf'EɸyH:4on9x>Lk,ba(Sd|i>268+F,0a+; W,Q :U)+'bJ*KЍ¹S*"w8 ! @qjFAHۂ JdIk_1P Ǜ-N:k."YbP[/K)=ة%nb:4 T锺T7!ɮ -_'[saM7f{`x8v1SB#HuK}:Λ.3iPkH&*Ln{,\7߻Crk),ѕA+*zI9Yۚ .M>ې}~Ww#@nj>64tY5+Cu0!?Wg|$+vNnKF<VcyOm1GXeXzOC"aᬫ% tUpf;m L]";HƓNi7ImXYQ4 -X<Z*|L7CN啴(;a{sZ^Vb'IDqH #KTlW0To&Y>nrԜDdh8ӠoY8'U=xۑὂޮdFr6JQ"br 0θlG8TL*j[t0P$C;JEy ?WvkS E)r;S w;X=P}h<:w[G~\f& q>.uEI<'"}p j s$uWiӟq vV`u09wqt֢[/(,{J]jr`(9iGP;a4)"ZՃƎ&EuDÁ6^Q4dQ,@HBUB;W[T+ Q*_tWiR6j,'{b&+]'!yvZLe#"conR5ٶhq-4|.086ZRJRE nmR.nO)LpELd!T8dծ}; 31AN`ؕGAf;|fBglzعGXC+GRDvTu-3coV!CrGjLP⃫!5c YGd.Z?Tz}*gNPT @ S2MKX=g_M~9Z[=|4q /R=N)%']d.{Ѹ6f4 K!2l BV=[9!!xVm G488Od lP(I;?v|l]ʮPiyTi]>_;VK)<ō//N`,/}ǘLNVŞb}KS L s A:\gV?ăݷnlX5Z"GGuzupNrҔ8$݄֛j !zǬ9BQ?[ D 7ͭb3y{Vz@ 0zJ|zfԤ[uh``KjNo?VbW̨sNP󕕌?GD;궰"AnQB0[O;KɗHbDd.8RL3UW"XZGֿj(?1N"R9 ud ""х ώlϑ8Hgwٍ7ů|{9Q誢 4+;x4;'M -(\NӨGEx X{/j( X=9jw%f,[ܔ*65&L74 `(gN#!FVufA!j 7 >Qm/MCvoG ¢cmTi-Ul7W)L+"޶8P Ȏv귢ND+eӒ4~ں!SuAq{l =-9'  +=QhIYPj*-B[UQ#QLTy~m3JICCIW𙉸Gn(΍_DيD ԘxJR<~ |b Z']@vܯ @Aq.>PF݇f"^"J74'l!Mwkrխ ¾64uP8=l0s5j -_7p Ad]"0P=cPr^2JPUnɺ. pz'k89)) \x 췝i=؟pSU@8MyE 5'@N?T6>A7uͶ,-@|BVfqOf}Y])L˞ub [,2膴,Y\n=hr'  iP0#`CiQh{ s v(xJĶSܬJU:O@}᝙}EA6`{S¸ToبYBc匑ᄜ}YO-T 1O/w&bNfrX5'WSV ǣUktp|]@s⇷x-;Ȯ5Q/ i@|ݾ_gOrϦly0w4Y0W:{K$8\cV'Úx=&7_p)kf5#LɉJ9LnsY{ZdV3?,C9cwwT,J+˦^(ߛ8=d8u}ih (niQ|ΫHk vȾ a`Y:0.c T$#†2Y4a[$DB9^B'{SXvjX ~-!ܑ71 c~$ sWc,j zlMe=7 b=oS/ng`<60 ?BqCa.9z5@'Ul@\ϝEl)㝂I쎛mnQ tnUy4;vBϰ ^w4qD\5oOy֘qH$FꤖWńfS5== ᕟIM}'wCr 8VqA[/y2go?ԅ#6t*.;Ų=Tȃq ]fԮDf8"/v@ܪ =Ej̘E?Uo;ۚnvFϨÕ#v!>V*7~*^j:.8/0]Rc8 r-ߩA_ M ؐ)#i<fiXN,4ğdBk]jW~*Lj#vrFz"ˮϥcdN88MzQqmhє5[5i/b@V=_'!j?ա^շ{aC4St3Y!$)SDUUկ;{d4TIj Ma}ܛ[S0ZoKCY&f4zm̮ q!P~7v#<'g쐘ڬM]U:}wXZ(eE2߼R(޾Ca\(MwaT.{堯F^V✂a|Gz_ɰjMwlҷ6'Zݴ3)  J@Yk|d>cӺ]U y4-䭡MxsUg;`}}%JNkzIVteu@{GzQmh6=)̶y%{2ɏ0w?pZݞ㶾ݮVZ±Hgbje{":}{PwqIWh\ƖMJM߉楜6Do9` 1NzR3-OmMBjpLϙJ^,E3)V' -/D?3eK$`^)AMz{s(^ 72L.G:ah`W]ոnb;өdf窛bR:LVߥgl#+CIs[#?m"ק6LLd"eh6-qk5$l=u&lLug]?ov2POq;K`[mqo uz:r◫o:?60 kz8Q/2hkK$zFTA°E $Isq5b{ƖU=ٹ ZVo߿{;IncؗFS6fkaL̔c1E9|6&= vVLk414J5?@\Vw= 1Qbd=aח=3dŝ|F 7C~^YSJ|mNʹj/^xCSϓSuO'M7jH9-?z´BJS_uYkClVcERMMأCQn,f}\"*JzhB$+'B7WeM{6E]rGճzxNV/m`j,Jpe\mUtbj[NK0|sytd01|3f_@[ s5̫f 0wA$QQs:Rmnr*qWTGCfx_٥b,BnI sQ,UF8dqy/Rߓe'J/^eg:B`UuB0nC!n|=EZ(( {tv:z<ĄjjP'ʠ(z`0w- e^]9d𪁧#ÊG>1Yk*mE[ Zd[W`'(=0X4M lIEQ|Ud@h- , OS6.g,OʼFLHzps yŇv}^C~U: <¤E=SS`U1\~BJ>w^MόBDΑF1.M'zNԄMnξy*:kIB%5xXK$qGs_`y3SOO'z>.Ԓ9݀QguTXRM9p6O4pPQE[3R<#з4q+F!67yF]9X+yz}H14;C:Q9-;‚< 23 |Ԧ;5ӑeU%9Ʃ#:c23wڔKSD"Rcg q= ^# | d ɮPo^Hz*iw~fbϫ YHOxf,Ei@,Z~z8 bEp*_y&YY~p&!Z钶lO3nJ~!.o=:$d@m7 JHl!,5W}cRa)^yrYӾKc$~k6эE=>4IZ41/ۛ`}k65.wNwbb1 X߽^j~{Y_ =69yY(9n)342J#K9p i^izzDb__V _ <,hZ5eϊ'H"Ӻ/S q=F+ \1]% ;oBa0ё~$זz݃`2hf".R66_um2#Cw#m#ETt56'WףkVρ.W+«F_sdݎOt?gqQ%QVzႥ ąbxD,+R.u\y~ߪ*6֨KuAV !jLo );61](,0<.;g.p1nbhe)^H3_-T u++U ;NF@,bA( ȣhVk<dzv&F2hYG}YrH?gaF1ϧt-]c Qh@v%-VK=;,uWZԞx K#)0F1"M +B̾<wAH{Yqڋ,,+Kk'E;zT꧔CEִb6 c7y'751ffE$OM |ʛp]d(bk0lLC@ :p? :C4o)aǿSPlg"A|E'NE{ ̀-XPV2`*5$] \ 'a{Lጏ2)W򓀳uL5n1uU-|du ve%W%+$Z?LBrVS1GWG+\$0ݟ0Fݱqv~ҷh_ʑUCk% /XffNƢ)r'5fd&ǎ|ſ穠vW2N,Mw )ި梧'la"1(~5q~3 /d |!\EϜ#?*UDwz7G6_ SF0yi^t+`$B7z>≟-K T {A5g1c 9&sR: {01 _J[.A+ IZJ@TBU&YeXG`RIn{YPh6.C *~n4wwm$`ͫMq_o ty LN"Tm. S X79 !!cU滞ջjdttZg r;vd& sUl_,"-f4Y;ݒT<=s?3:lITY855V*~>ZWHGc5 ?ǼV ]LQEVB@#W6Ї5YIy[O@}y%Ϛ|RebWr.I@/vaZ)&N#&ay\@ٖcGO@:#0qf! iЈ+WEB )֟`g%gPFr 1i4鋌Iۤ Jl ؇PtXP6YVlh/ l#\zp?S~ך:8[C `DY Wel9%SJ P5ZPsr'b@unh2QK2cmwDv%ߎ H7NLW_Yk@'qf.f$8BgElgE)EV- )w%s, sY;N El$gDsJk;vQS5 Y@_;wC=r&+Pk5*Nl.F:܄i[E<$]3{TBZvָ_o۞;RT `!HQYUeU@lJd ,w`~'8{СDU`߹~oA~8R}B|WU9Y*Z֣0o $UI6E { !N_ YTc/ƩBIe6$w>& '鷯˲bn| @8ͪ`볔eɱ~.2L{-JL xhy96'dT \03|}'6OB(2RVBtΏFXs YsOql!/6Z=3c!&T7Znr@ajO^22lEK^EX{nlIlNOm2@<HV< Sw /6䙗z,H' /bL wK'_jVMy5| hoVlNԈr`q˒ȁޒcmo:?[㗻[PA-]4!$mi\oD@[BNф9€Äʰ,vE-\BY[(='sv4e횖p6IY Re'-![n#6mgD(q>^4K+b*|%F>[Xh.d`'\)eIF?Y5(b3վ&n§6 "KlS+T:x֞ n﷟w5'YoSqzg_+7 [ɏDc!GTG#okn2_,4jx8ncucO2*;pz(S~y /o5#6X{;pctsf8:dpѮ_/ی*T˛T\?y?ѥ\}y ܮ+=Gg0FBGcS3XAW8[E%Z.sD; _c%1$= .30QQ K`yfwN#^SCkPD"%^m|AER|9vg R[3*›Hh,0]hueXq#cqBv뇯oB6hyp0R-iinQR,<>륬wpjn8 +~ö Wuˉ(K JUZN-y_֠=J%'D녒\]ې EѻgCF։<z ɒJN|.Ԗh (Y1o͹NY+?-vy{j`|xYň)rC>WKJmdf) p?K獭0Ѹ5tg',3ܕP~zILbOms^yLIJgsmvBj 8 Vҳ}Vk}$ MrƸ`hu_rhfd q,q (ɴޔhKmJ;'(J6T"G K>nq\U q%kgB#&vЎ|POw?4YAa]7 LJͳo"=RҨY #XsRX?o`'NC){/rۻ]? 9{h^D-s@h C 8?;C X{ kp%=.k|Z`,KBb}k@:?J`r|qƖZ4T>Kg{<_ w{P׈6qZbA/R8O gcx]&uB JԊB%_ /--`ˎ5vGiЌA✚4^IP!03A`z/'NGiQs.7`oM {;?إc1,E9~/e }V9CNv&SnJC{>0ggJ򖷥u8i;L#hNz9.hظuDLOEϬt)y,zH^WN`3@cT,ZeJFl\Wģ##fx^2;"$c*)ɹI['vWu;ڢ/·納&DK:ud~kc3I [΂/<pZn,C[l #ם+;5 RbS H+FbHu~eEͩwb_7  ElYd%+ҧclyeM(2TzM|7'7J6æ;wF)HC x%9Jט)A:[,l)} +%ǠN@njSdžytQـMJ|+xl)f )pIW+^;,YTeY苇U^|%/(_ŒP7?ӣ,}D]HdtGH B\8[_ q(ň,;B-*xw[Tgpi/^@tK3&m{1-kxڽ,Œ-l,ȬC 0FQ*Bq+u"7bo[7S>_FP2lha-%U/F3of0-5q(fmu]Yz@ޝj;uح)$*A]֤ńx-&H@Ӗ} o&?Ce*u"C^JWFx, !gxoVK_gJ+RqҪjY7rfLlFyB60SuQ[ae-l*;HpFU0 չ:L %r")G*eNQ&dןJt?Xa?t +f%,-k,WZrtk61T] qZazN[CjY8ͮ v{}WtSX]19.s[Q <ag,Δ|ithbϯc;/nI6lmɪ$7m6s@ʋdʹn JE1p7–Zx{⼏BeKuylcNq@`u*Eݯۂ'!3\q׭7 0e=ւQwP2Ӌ"/͐[Hq*c((΢Nw/Vy$ /WJB- kSEcPٞKX %^[g+Da7=k?!M!0H[ vx)j^?Y}gIo~lԅ!{+\d`e4[27 Rn^ZBpDZSaCeK5>'"vƅ?cerm$MTIIHn +cѓ8ٯ:=jX8IZ7i Ԫ;3yXT[S&TYl?52$*DNf3W*[XFfsN$aYTno>YN̂X&<2nY2D8@YzbW)]x1:y3 Xm"cA75f#rKMG^KvY SUajfctbC1~C *nZBv^Vzzh2]'UMK $=M?P)*`1S2G"Y4"hbP VA)|6a:dFz ߯!p5GfoagwG;5͹[;DKZ6|` vP͚o"!D8 ]xYG}Z 1G#p?pIlRߊK8Q՝^ "p`"1ҊSYww Wo"ߓЯiv gf ŞQcVy &dW,U-ږ|k W8 8:t3!@+ M .照''q189 HFniA[B( MP|ԣW,k98ӄ7ښɱz54tSlI}Bwswɮs%e(ud5Ez`gj_n;ICf;%Nbo e:~^hSkiB+8z*Anous"PTa 1 \&[2W?R[!v>#*-sxȹ,FQ=Pp;W?E2ss:9WXB Yp9h v?8x(7<6ȣGQDp{sW3Qd%h+qz^CHIO|LؘuYyw5x7"ci_qMb;U5|svc9ѿ:eN18Znxu5)sTL֒fwN?!" 7 ~z/:NGO 5ym1~S]c/u_,_[=ip(P %ܖ.Q2Ӆإ.?"&dڣZȦa @WӋ©,h!;pV@oнc}2rhZ= ܪH>MlV֑pWŽN5*Fv聈㬓hɻZJ!EyBd=ƹ}T?TIˍ"GEYVɆ~V{_EBK(<|"& cNxFegWxICr QpŇNuf/E=mu䃈jA9ښ]C#Ϝ}PǒoddTbt=J_ߢ֮UڼE\GZV>P˦fј |lzJ]'h'ȥ[AX疟A`7\ʙ?U6VS>1 I֓|٧$et[\ e:fޘ>Z̕!D.}_}V`2͝>jx*OQGOf͢Gh{ŝS JE&#UuXԓYy' B&gn6&ۈJo0Z2UUq{ q ǿjQx}е-l,r "%зR 3j!ix,OqFO}P^^ޣ.mhh\ZiL9@GsitE>hgh C_Qve'YѺ l&/Ӿw SjB!6Ns!+"f?#+0Rv_40ђ9ghiALF dIWiӕ&.I_Y}I>pզnT0-JT1tJH0=R’Q7?[2 һS&GEG":㚍7n:[Qm7>UjcH9ۢi|HCgƙ }yMv4Fc5է%3 3Ѡ s*V|gZ)xf9Qr6M2d+]qٔMF׆ŖA4QY6bcg<6DTq slg{%ڤ~Sþq0KM0ķF)%/O+^`wo`7'kqMEGZzQ2Ŏ6*k}C*,davet|E@Ib#F$uufnx[GN@n@}o>Ԕ5\lOX跘KxO#1yh9x*'I&. +ɺSAԀo]АKci1`Yfڭ-ę$ n# S4Y,tSߗM)]xzԨopP ۧ4rѤiq Vֹ{]~^,n3)57O0c37-hT uXs(sa+0,;+sk*31Yn@ =rCwx&K=,"r,(Fi#ĦxH3@ARCOوWeأ [YrNX-%oNJj6&ȝ!}dsH\7%?W~ r1 *gSM}Vw?d^a{ 1Qm8b~18aRJ]*@7^{s)y2՘_4:~ҲnAC{R[*ͯ=e 1Gl+V IC a%lMH blwN1 uT[uw9 V~=3$ՄEgGw+eduS9qN G-np|xb7++CY `S}^WCW0*D9дBɦE}S'P%liwhHRgdb o9&Zzoˌ=u u;R&^čyɊs-MEv;99``owDNYÊ:qʍ~4hC8]+Q?l"”$|I3_O*Ct/9:[Ch}w wQ8luJw`U%W]>s]MtēzR鍣$6^j ?9(ق {LH[yG0r+]1c7ߵ8x ZT$q D';)U3˳K\NҸlO> 7Io7Iܸ{'U[5b_Eĸc OguSP\ejBm J=)}r__t}Zɸyu@rs{RX pbE9;(h[7kHp6mD Q'".zwHJ_r9=ƀOXdvGvO1)Ֆ;^ J W9NZ*ғK6pY EZբ=nUu X%_"}/6IxXYk+b<5&rZ$2BJO^jiRٽ§z70f2E~1]5?vWfo%yyO,(jD?q۱l _k ߆_Qת%ҌyV6G`G0ߧ:ދ4,j@,nh+tfmK+K~=Ul/G bJ 66Eee۷1vpf:扆Lb*g޳y= "{0+m CZ5꟏Eluۨ+wE6vB 6ӣԆ5Vo$H[ROʓsKM_GWS%[7ΧH*B5]S,or,?Qz/?T6BeC˥I-9/H=}Vqfm9T6GgpW{|C$1oYui$r'Eo,FJ2cg)郚;A ,^Nh{܊MlT2 ܨ(FlFf#ny;9{dF*ur7(_ɕ{ޱVyCȳYdUߛ 779a@KkeBw5uyȑv뺇s*\k3;fW_kʼnoـS+:] 4pmkWj!ގ۴&yǶ-} 둅0B'`6*a3-N?Iw lVJ9I` )BuQ3Tz4'ӊ]*om7eKQc"RY92 N_/duԠ-xD9Hõ9 9wfT|P՟:IC|@\ hJ"KOGcl jUf 20zQIpбZSI _,܅N=ŗOjkI[m(%d38\h—elf-pT=c-E.Mc9m ޽jaRDHMYWkYW =hq'h+'R^QN  ٦AMˆo`&֍nmuB8Hk#>LRŏD8˷U0e) }@YQ \1Z"G<$zf?@˞3vUt&3N䶟s+{MO[rXfK`@OgW(=(_1ཽ r6?\m=*XhEEyG!G˘ytM?Hf|iWvt[jSW" YקMP6lqۭcZ'7d ,`8d9䌙q9ؗ,gҹ <=3O"pg7E ~Wi &hH-6}x*>r[18_eIPE%fhh+%#!".&VsCpef|,<$a=W= Ȓq-wT) $dD>y0-#%1J EnCƋͩ}2#l5V11W, LHf |Q Y9J)"$tQgUՖ+Ĉܙxԙ$8 DT& w|pFF iA dU^X O(d*r^^]Ԇ:FVH-EI-ayT 0ޅb .2[ 36ةv}|bF^i@YPvzr@Z%eP2O!HGȍBH6EL\^e(O+~+6I4:Ԧ΂(nkFGvv ]^$&܏J\"ᒴ0k@U@=c93͟믢nJ2٬M5xhMTU^xey?-đ}}(lUD\֦VBFi=ctD0yP1YJ+.לʔ}B &+!cZB;;&au ycpN,E !ʒ\C<\v\-ڢ;Msy YOLT -PSSJ\"\~͏^~ѨTVm]QN: {nP|rD޸P4i aߪ-cvF;#㉦7!Ϧ1DTokrc}52XDEFvjߏk"f-pm/xv`UiMNz_\KȱcjG}}$ge `$܁%}K~h0.ib>[9;o`43{PyNפQ} 'qR=8inO"߬$2  gCi9JEP*yd!uy{aЧtA_#QPáaϢ4w,< խ*AGi4"]2&Ęx+^R/ _:МjՖ]s.h^5`xۙnƀ+>LۻuW⓶cYWޣx!Q0x*]PcCw{iœp#yS3& FG0ޜBE9v[ MY\;Gx&@[a-mWd˼R)127|FP&ێF|,exw!YU."fktvqp ǾQ-ުg]@OY%o$ wt3 dȜ0yn<h˗l7@Q:RC?|*׸9>(ddրLuLMs+R:]qfe'awo{:8'*cJdcE}U~snc\uQE QqΈ?]̀Ϯ$0$PÜ[qˈ%|+luG 0xbLb/*Hԋ9qTPa,v_\ިفqb2r6X5OYx j2Za}$xF̺w1 ?$I kr* RCPـ!rȯWw)->I%Lʽf/I"ۃTOjg֭32ϽEr  JC(EϮ9Jљ.2Sy4:!ٮ2/kݹ_D0fc.l$1ׁ_'-Eoc֊G;+_|z3σ(0TKM4-л4%)O! e8PJIhJ; ".^֚"\ۛE:_B5bHCpq#jb$fä ;q%b6؈eBӘ7@Q9E5^G=7bNS-N"#םФKH}w.'˖jB>}PiRE"{:9(z 55',vr^ Vl3r؂SW~ݬ'X;skMubeP̕Ao{*Qq6Lu/r׋zCBkmnfQ,}߀nmKYX֪-5r|^4 \/C|nx() 8(VU0F34L o,08ŗa-xMhKFyK9uD/{YdfXSuG|lfj|a?,f@J݋c|sg\Ey2rs3qr2*U5>[I.IZb22v :iR{Wa5M-R A5SFc z>svm3׌>l 2 }AnQZ ̦_?9-ΨF4?c/A@ sNl.v ; S~^Oy,~zuxŒ!y"t,\&|CBʚtxD2)X,\t3}JԸ,Jw Mw>mr0%3Zm8ֶ#X^Ho?Đ:\ylV:c!2bUvP|n_ X=n9"mP84B?Ux!D ~UUz䔪21޳dLvhF96|̔Xp WzO|0hF L[C2 JL9Up qxx|=t>'koCyYAUO٢ {_n8X2ҌM0یGHP yDН3MT Ї-lp7 QÛoAMW2q8Kv'1|6xFSNS6EC3s 01t(~4Z7}}3 yo*Gw5?lDB"j˂8eyxM]_6W_2Ai1y;*/GJzmfdx7?+nEze~ߕ;j#/p]cWH+F/l!ʄG  kGM/1VdZ5绤o*w3oz{K2܏Ns+L? 5 ܂.P=}r1_-/)Km3^I!%]"UCMPpC%8&,0WkF+P'4 SZTd;|q8qF8'8a`(>c9>1y`*S6+n:K.\9YdCs|O/]t ɜW RM2ϗnIBH{+YF쐐-}/J4d% XvUUN1w>! Ub/k6+ұdu~ң~h¿oi7.=ң=~<:JqO+j6z6x9]5 a֑v!h{E6u_mp|2G*Pb7yBL<3} 瀴}6a=Pr ryQo`!M WQ/P#X&N0oTArU7pO\q~# [ĄzP:WMYhI3uCXe`!> :R>-i|&sǏJ /\i^o:, gBV &"y7 K?a y}M8j~mOD<۱olH6. yf;gr+P,+f٫,ZA38-~DZ}h=~c#!"fH>/{#!,p`Y?\W\3ZdѤݽ6&u\m!x0; fZt77]Rעa5=@Ba1Jh}?+𦬾#,:kAA X+qwt??bpj줣:z@n|f⠸n/IR5@{z R ygZAk%E(̭0a`X'=Bt=$H\=#А09xLlp…,IbI8"y?~p+Stf3 f"u$FFK|#J[C \+iq8i>+AH:TF1 D hBuzwpzAfz=KJ0Lo?RigxLilT/T:`"3+FN$s\>-ɰ"ЋnsyʦﴃqX XҿV2xYC݂6aR义\՜ʙlV/RV*7/3*w[l&%M AW$J4Pϥb-Z:*40P\0f cNE@9|ޏAwҋ &hpkl5ΣU=hR0~ƿH n JzZ*ǬÈݕ*j\ LK# Gnj*B]:_ʋ$5[821'*~$j?e1]d@-'`<5 W(.>,9́ZYd+s*yS\YvL[3-3w1VM 2ǦWRֹx86M>1u00RCn$+BA\}? tB#g.(E< UM&B+jԵ WRJh;G3<R}wrb z (3aNr p`DY*=8`W4 Lxam~SVb?r0Yr1` $߿K 2-(D"}>0 YZAER/data/HealthInsurance.rda0000644000176200001440000005546013616365122015356 0ustar liggesusersBZh91AY&SY,V/DU@?ߠ@@şc*E1dJWHuJO^>0>D h*%(HTofU*CmئͳriE *&K@JEADDTA@@z<C!?UT@)J%RR@z%2h" "OPzP5=FPhzjy#ڐBJ%*dM(Ս4ƶ? *߾+y0BEe B,DuXl ]8!xWg]m-1.LR͚juQ'% ҹB^~ǧ;ioH~;5Q߈`"GXOvXK.BꅶYJZ0SPsC ą„_"b?=c ڏr/:YؑZ >4W?qݿR#=IgO3̐QH4^ڬܣ\ϡ;ܨK+EϝX/u X(-;V7 wQ?}<ޣ _c|>)/9ϋWRrik?T]SBL ;Q?F[z7C>'쬐+;v [lb>.-u@P ة?[s՚jnFv~aLDB P5S1ؠ'=2qį{bq>9~A>]mccd^Cn[v{>ʤ>3Z6iێ(H_F4jCT D=̘bⓏ fXdY(6F$6>F# e(ll%fmeRsﮇl6.(M CjQAXV>ͪ݋lա+P*gkE[ir9oFUNVI)$O,@`=sy¼Eh`*9A3΂м[-sխex<J"q{ {QOmU|6W7+tK|h/0_d]g }B]a04-:ꀶCNW!mBEl9GA O?LjʦF.Ȳse]o]<0dQyThP}!))k6e!5#P A%T!=iHHp %My<ҪC2*&{=ByϪ21k_gӉ;ə'Ho˷^hȍG э=_ c>6NǞ2e31şBz1I58 OЯ3 6N Y6Wtr^[zoiw7~cqC21R6ewj4 -A%,U`"*ɲ),Q6;7+)L'q<45Ʒt[[ fD:74Cί7cܛ%>Vr`_}\TnOj?i)`9}(G=~y^˳IaDDQރAMrAeYyy!Gsjg4SkO\W-oqmE˝zߍHHnism'sq.omJH\p )rӹ7]t+Ű2~xHoC]k~gYVMߍHRPe=|l:}7n9oF"{w|vć&Lm\ۖeyED}e$~gxygeY'EIogp]J}ؼ q5ٳUŲXqHeH+A[Yg] w2)ϽJ.jOM6& DIc@S֞[t}$M[S^եue` }#ޡmNV4e\uTx-V36l^F)  5ŸYMOm<ɓ][ Fq|kk nZ-)橚DƗ`$DĔ/byj6*iZttjKx݃\yAe ﵩ6>gm" mmcIb|fp+j#?=t\GFKp,ipCr1i!޹{. };Г ǚ>rnca|a+GB:Pq싏c6`Q!6uW_|'gU}~X7I2X1y/sN]5m[mpC砩miq%նqc25f ܏L{4폍0 aC,oL}-O:w%'7Vmq,}y:!gw-≄{XX*1Pl;fvlɴoϧwnG(P~󟉕閉lK0̻pqسb#qX@yۉy^3n:kz2^Ol_^1yC90:`im_N;pҬ'9dZqeVykW{<=~ށҽaMp-3r'k}sqOE֛mxՃ,6`4A?쓹])ݬZNe:Z%ɝqߚr&-_Jm5[[C{d2Zm]k{]O-;pЧA%mphxlY͵s˯w8kmm}Hols|W?{ GM~zog\y|QלrLjd/_:n}]Ź_]sqѢz1XsJnl}VN-am[儏1G%M]w4ql^%uXĿԓVMk5X$LYgW:6^ﱇ_tu .vE0"[BWӚHhA;%<^-wY\J3Nw?gC=8\1XL5F%_-ڞo=lƳQbVLW,-m6g¼'H mbQ<-{R]ebyOik!'># Mzj4$-*^dF$Z em[aQSPB2g6a %_=]\nXAWRѕ[nm<%/O~sΑd:t,:OT.˅C>kfdS`OR9EͫX6ş9-}<G{.Y.Cmo|wW^}ls^1PMLG>j*&P20ޕ1 P} Z6ak8e;ڞ뤼OL U~/5Lz~Ӵy(cJzZfzhP->np⹥3\`G'߉xQ,mY>Npƹd<|ң|}ۿrL. kd,5o~Gc9E^;ږlѵ{ݨ6s5]\^g^SMsŲASMÐa%bUix-Z#]mokɴ% }i[~6-RזDzULok捅'RoenUCG!}a{5ZYL-zyi<<}1NxO%N6l[ 7,L;3wb-5@׺aWzob]Y46iw%չ@:#9<=osaAy6}ߴgA~01\o_R.|l4?tb ,^RZjs֜@c_}zIr7upW(,;}yMGLOMkKn˲l1^NsmGةݠ~.g`6_m7օӹx;)gy[/>Y}}v}>GZ|wroZJT-KRbyI#ZtQ]8igl}xƘ_4ǂ8J}Ǟ{8%{Y㎥_Ϗ-K ^v]eo[b k5:5m~?=Η#}yﵵcdNY`LP%뭓/8aO+swRLos~_ Ѷu{i,mPCH"4jQ6Zdűh֊h6V6-RZEcQjDZFQQQ4jF"*-ƱFlVDTƱERmFHXlUF,ccb* m,QQi5@I(Tm1TE X4mXڍFKRcbRU((((lU[Y5Pm$jMj+c`[,dkTkbMQFTh5mhERF(EV(4lXlXTZ4AhmF65 b5i1hF1EhƊX6cTb"ClQ),h(i * DAj,FHQc&FV5blkkJA1cb5Fb E+Q#V(6,TccccEɣlmhE2Ѫ-TRXLb*IbcEmDUFcj5KIZ[Im#bE%X4F(ƣZ65bڊ*KE-QbEh-D4hcmjhTI Z4EF(F+@pQEFōi5lemQ*H+ AQhѨTMh*LF,E26QQm[$FьDlTj(T[@ɱ`6BQX*+cEhI2ѵ(cm[FEcj MQXڈ&mHdncF1mlkE`-&*,jFѣQX+m%dƋQ&5EEF-&"Eb IDE cIA6LXԔQE* X,c"Owknh-FTl&FD&KBQ[b#&Th,kQ1E0b*FѲQbj,j*,kűA`ъƩ4khEF bȉEEIhFBb͋,< E ccpьF:zKu!zJJJd0j1b&j5I-D`5mTPlFF-&hblTj,lQZ1V4mƍQb")5hXƈ1X-ch&bRHŢU$bI"!bXXY,FkQI ITSAIfMQ$ь  ,EI$bXI  6DFlF#&%z/;wN'E](&ݰ:/67o"MQc,kAhhZţQhţj"EFT̓lcXرQTk5cR-Qb15Ѥ6Ji(0"ت,j"S4FJٚ! he#$M$f-FQLTbX")&10l63#)D4 (utJ4mFLF6fl 5̓%D""1 2i`ɱf)1h`FF&đ2QhLAF%)) HdH׻r M HAD2$ 3c3J)4&&E"4\wvM㑯Y$ΦaRMYwOV¯utI-{2"*Z=|XѨQFUh(55,P,VbE#j 5cQ4i5cXDm)6!cm%%F-j-E`ѴLMEX$*HJ&)df% {r#R&F&6FK1($$ AiF`I1I@a& ]I@h1b ad6B(eDi0ID$i,AI% 4bFFc&R, HBJaL6F  0!"HwmyQQ$hhՍ5&#j`*`Ɔ"M! H4,Q0"2ȅ&S1$DbH*SD QI$l 1IfEј)R"2L)dA"*54 P5a$2P$1`)$$`H0IBh Q9n4fHH2LX2BCL"QɉSfR2H™2D`bRAKhI$0!RSPi"0IL24J #d,Q""-]./.5D9&28լBy*䮽]񍶴xZK_o񒠄9ҫBVmWl=R>`yKR]-ϷA wS) y%&ve*AqK׷UaCٚ[Rф&5T$ͦ(l/gpY.y"-z Ց2maՆX /u#bf{$h*djLF-%EXF4cDmѱ"5lF QLƂ1cTQZ5lQQ6Q&#K Q2DhƤ5+F&$D"f[IC1"@I(Ր-2M3i5*Qb* MFLL0LL3sh*d[IQAhMiY3&"5p 13F Z bK&$E",N\&M&% R"1ie$Qb&* 2 LQ/v;-I(64hR2$JK3FhJi5X "b,Ԧe h$@%F(FRɈeb IHlD"$RDc$BYJ#LdJ& BX"D$dc "*6&(ѓHQbQ2)McF)M$Q"h R#PDlc@BZF%ѩ{s]^PZ-cѩ4Fb6-DH`kfJM#4lb$hDM!3$LiID$5̛IDL63lFfBDȑ$…DC"2!Dc"D$F 13a(1cD#4H!Hd!H2$1F6A!D($L IFIPIA1&D`QfM" ɒ A2!4b&D0Q2&Fdb,ILRC(3 RLFL d&ȒA4fBDؓ5J@AEQ]ܹsb$Db) 3aPLSf( `HfIE Lh4Ad4Q114 )6da 2 $2Ja2$B  `46 HDB ILBblDf & L$$X&%04RH)"Y$M fEDB& bDb"5JD$iDDbL ) Sh4a 0ɔ",%R((c#Qb1e͋!eI!(d)RM3 $3dL`Td2A4FI$Ia 1)I"DA%BdE3ccai%bERcE &" 5Eb6 [lPo#\U^hs3W= (U<(5,*&g^HCX&x8Q1\L3@BCzrC,(:= rJz:tՓlj"s7|3^O~c)D^sh^}csq7oǓBبֲ<* * I?U+Q)RyS> -d[(wFLC۵,&|/AGvKKz.l}y5f$N=i)&BDᯒhIOV𵭡9О$/&Y甒== D t:¹s"k9&E^UAbnG"/F-szdv#I nY&0熰(1 &$ݡ,q腌~c l:r?/ֶ_$ Qb Fd%E(b6$Y5b1EL-m"QlEADX Qce#i,/Deъ"2b`4Ll*#P[!)FYE2X5J4heTbBcF1e2h6l Z1FHəE2& AE2(&m>kDb(,Q$YR$͒-4)c4JI̙bE,`}K{dV&%#E4L13`BA$RcDH)$͊hѤ4ABeMe2cE $J-QFKD$M EbIccQ0)pyzW9>]Km2ЙIJJ+)6h 4Ĥa !6X`Hf!.,C#$!#J( bMBI0A hf` FA1dD)L&5QhQ 1 d)&! I2RA˘`J1%IHl labE)ؖb 6ɚY1(eQƨ3!1AEQ*-A&Ѩ5FA4hؙRZ5-cPE2zb>=yNɒ-vA'i.nF)4EƢ 1DiEX4F F$+I2 2H431$ 1 csDb"1#aRJF0# "LlS Ņ11bRJ,$B0 E 1$S%M,P"d4ABBc H$FRC203B`fH* cDI i$dS04†bQ5E$0II$(Di&e$JFcD &6% 450b(I0F4ll[\-1FB,`хE 5BbQEH)%Ia4ђ`"b$LCQƊ d`"TTHI),ل Da1)$h4Ĥ$ 2d@!QHJ)QhѨI,EQDUwqdяkS"I`EHHKAKV K"2" 5ED a%E- EM&Ei #&-ɋX25"hTY(2@@i1hѡ0h,Zcd*Ab4TEѴlh #TVJF,Z6(Hm֓lTZb-{M&l<"GMm ~7y>}^\b("&(HͰ|YJ&D(+rJ0fDlNkʕr0&4 jD4`ѓL ۾/{)( 2FQ)4BY%,!`41Y100bCh( 4BDPQs;yy D0ݺa2I,Hfo:! aIF%$TfB#$PRcP$ E6SJ&i2I0!`( Y1H!b%1!d)fI XEC,XŠ^8H %ToQ#$DȘHc$R$6e"6a)Q!$4"i&I( A0124$@)ab)624A2D%#@)H2EHbF1D dM!FFjeF@Cl)@D @c(4Rʈ)(]J!4)HiĄ4EE3f*J2J,DPb"m=i),C,R &`,&Ec`RĒXZMJhb h2f!&0$#a)1XF - AjCbHQb Ahبh&I%V4Xdm&ъ"PXFD`Ѣ[Fw=y<ݺ "JwW|{cE;n|s&fNL!$E JiEl믩$:AU7 Hɷ{އ90wnBd$2;.F1HR2.t7w4@wnLĊKA$wg.JLLΊ.($ ܽ==K{>>ĂgN#`-׼ -j$fZk2H=W=C5%2x73MSzfIߚ{$}ܽHL2\ZvZe١!ʓy$.)!Nm;(FEX y^XL"j3ٕhF_~P>h+w;uޣ=8**Ju_}0DBLā#Ćm&'<ByA>Hq⣳ؾ{^S˳@Oʅ" }fwȈ-u2U}O'/#׻81apRDbTDw9OKEr撋qst:zL /s0XF=;[ﭸKR7̚?ug/y@6%G!ǣz bzTp}`q>]mzrM]9Ђ'yԁ蔒t&!xmn 6jCkCp9f2v*$vdtq'(d L'HtЗn*ѡt] 44Ppv>.yI=!k{P&L y $^:_ tR44%v&݃F&rTj-6=VqiIN$|/YU3ʭÐg9$*L&xO~'yO];=md]2r x<  =ӜLy ޸YOz8Wī@Jm!>.Oa mĝQm=&'c Z2^%~ ] p)'٧z|{=]&mp~k<!y2zgzs’(N4W#0#P¥g"Fj=ۡ)EAt\Š^k=5i(R"wn4v$c4x'x9yO,v(퐦ҝSրt:ZiFۚZJ{Im{/䋺}G̻P&8BXR!meC?žC2:*&G@l|ә^!{&CCg,Jtѡ8/do3LmzAC䃥=+c,On%0}1IևhJS=)!P\uQJh @t4%4tmB8h(PnQ[=;8 6HgciuGl:|h DKO;!Ń#06k膮pfĆH\g^t/eƍEDV*chT[r;eOC+Ji(N?z>gt$Ag' $r~?;yR~xə!D7"NPob"JzMvE h횤th(j <{(j O)RJiMZ"VϞYMnQ 5=uyynyOyJ-k7BOy)4PX˶Muait.I1\^W4Q捰[-R@ ZGHt@wCHQR=qoox.쎎wZnhshEyVQ9ʹ%Frʢ8\rp44!@'I)r,\֍rnEF7(UͱP6*8hr(U*ڒ֍A]"ҽO%5(Cl QkA4ElnnʣAbѓEb-nj+r+\p6pX.kI,Z6(s\ɢheXF1%ƬkkikѠ6#ZPք|Pyh,\msXZКRMP84$ڻ5DwqFq #:ДFbE\(ܢѶclm65E766.kA I ֐(JBU`+r梱AQj rV6T546ȚV4HPM44 tz"G3xI^ |^%0r c = '*3b4=ss5%Ĥ9Wխ&$&OlOJo>A편zd)ib=lj!_ͅt~&c dwS-(- ßz \w͘Kb, Ly=0w;I 'r]NSCm,'z^myrX6CK*L|Ovj'OQyyP{Nx`++=bQTmg>!t&${I䖳ks$ 5U8=lYāO="IHVLX?>Ǿ)%PCZ^C+=Aq R,r:ྻ=xCי/'*ﵔL̨4[|X%%=V<dN})AOStavOR9K}s竱3"O3h?l4L>#=ҔHy솮^|Wdh@.}g2b,),==2R^-ycm8{Py$zbs 25zp/đVDXL$$㏏bM<ʾ>kMDH0L$F9渞Ji'#!$r2\5v4=srbI7n;qĦ zo]ik+MAO*p$ܡ3kV'#g=͞'mBS޹'f`GQ#ж;lO,a/2'g?^BM@Ey<{zR)=m;IW=eԎ8IMNiBRJzϸ=>|{M ֟'z4rϴA"HJ͝Aw;}c %oRBK+zy`=W=Fx:sI*b vҨOu`U_PW^m#);D2T!fOyOQ'okC/>%xBb4^ݽI2*#Ϯ$+3YM|ialo1/V\ =%mAl?l#֢exEЛZeΩH,^ !xHFCdO{I+=y>v͡3+WYQVLW[?sؓdOGD .6&!'/~uրǑkŁ5}Wo@/Xu|!_Pž@9I(3HBHj,|)/>_6>T"H"cxQ7k~;?k(}Ua ,jfMRaMZ6!p*E]i!#cͺ B5id#= 7Km9j>NREu)YLq+h]ae9g#P1>U>bCwiۨtRi٤I!sT!F/n7)>#5BZD(}0k.'g~4'd9 uʹ=z_ib~f ?=s-w1 "$&٘ŷ3N8ХdwOna'/4D^Nbդ!CE=Sy' < GYd!}+>/Ӱ{SKNYQ8>}ZC -ԐR^t٧w/=:Yqlr{~o /ϺR뙉NcėIw>TV G1DY~s&>}]dh˼iYSxfȾ!)@W&v|̍ 0k7k-yScCT*וu'NEVhd NkjY[WVɆ IJgP]8 ?v t !1Le!|Xmuū}?<쥖Sl,닩}eU;SN`(R`lCJuWJU\h *򴒄jCec|5O Xηbْm=e l5I:O" Tɦ%I$̀ ;:Gom텷@h-RydT`;qcFXy롺[^3+䉫4qHjjp ]& 5\~9[ Hx ˘;O009D-l%I)fGfGVյɂ9,qu8CǫGG~>hp{^@Zrb˯AU5yŃ£ƛ5fֈX MAt(@'5 [GXe<`em-4`'t*ETDIܘLAl a^*Y\)k`SA"*c?An)&oPȏW97vy-k 䃇4jzOΎSZ,oqwӉ8N*ڶzA ړ9Dr|7 7YL"{iclt4 E(-P`ͦҸc PN~R;#5b.z#$7?&ywe|LyRވZ[0hst6/'Ba=& !'K@<ͷ4ׂL`OݪdL{ݛU$>k}жx׼3lA׉0>*\ >`&b)3CGIXjܨp~ b 1Y26d̙#3`̀X릠_ >GJZG<錖z2?Z=PN*8i'FFB Ȍ(Ax+)z:hx~̦onFIar$Vhp`pԣ޹#$-94krI`xtL)I=Vu\LO6wРnwA 0p8ҪK@.RY-L2∏ IeBTW nvЎRΩA?O fo{AhxVi y\mHt?;B){,}jS)!s7J~D ؘ ܔؔ|\(FaeP7yt-3\srU ;{ՉwE%9Trb ,I="!܄͠ůs{bbINYcf^6 a,4`\h Ӫt|&[Y!Y8Ut=`ZCk`qP*3Ziׄ6A4d̀G8y:%}\xǰX.FJn;-xx#zlIyq K^긍07!Wz zTJ)Gֶ\P8;f/)#T¬Ӭ|ö*H]H}UoO ޤ}brFZ*8.c`g{~3}0(k7fM1P8/#-s?5^8HjE.Wu\~$#4v҈5.׵ ,$#=Mˋc4Ө|8HvwV)o10(Db,1d8i"~!qzxjrZPυ]8zzpn*##P(jx"yuԳBVٷড়V@C$}} ^r|^oal~%:L4I6DwpȐi"&}k坿qӧ߂(8Rf ]bAc[]Q)eA@&K+bQLLah'#NY4W3Vel^[6hn"%o1)ܭ>e8"ug[Wp0;I{z;cb_',+4DEIJ !8)XP0Z{s)cW:CWy¶] }ŽLXि& ?#/nu7&9ʯK{RBF ZUJ$AڜPR$Ԕ?[UE%_[cN⬲ֶ㈧7ð9fD^SSW!2BY7#9:^ EkqDL=i Mn:GRd:  8W"N$^i7v_/)qE u+.7;7 'Jah.i2QO#Ǻ;aX$B*D[WS]Ju54Nc$B)h퍂7RTöz,-|,f,j [ݒq7(͂ :JVcE9_D!DjnWR9XOD} 'З)B=ضOwU\33l&[< UIXOOJIGuw 8&pC`f={sU.T|=$ q;tRj@~F[սw0wl1^~O؟!?A1*zeubYћ9dB.NdB PR!ba ,\Z+ Y/\=`\L:ȯz!Rqgf5RKYܳ{r&S˛ fѶ"z()d&fΐ`$)@/YإX3KXgO}~5ϦҀcXmb/M}X^pkpVO7D`f+9<_@GWxGsPQʅc`W0/(]q;l֛HB=lrhF. J'NRң|ݣ Fdt \G^ 9W9 YepmC$z8=Ң +u=Iٱ8px.E*?ss[_/Z}#a"z13:7[1qo Z48>},cGXxEÆQۻ͠UGθ0U%pyҎ63z5Ld8t}@ǰTo }m3Pެp:*G@Q 䊯<rظ[ff҆CDG}ucScpY_V0Փu3M˷B^k:xl+7ܜ]UX쬀n& =\' DJ:y KCƺ[=.i* 9&g.@0(n?scF'%vVbQpl~"iėsj *>Z r|2*VNtr$ؒBʥƜyAJhq$U`~xHT-;Wd& ypީbߘXn5/зWഀBm 0=(*V%FxS OfG(M-m^T4[.޿)IqC>'@֦#uTzN矫U t8dSNJЙyڿ|fԠdJ)K2=]l[m_/^f, 4KxܻoN8Q%U;n vavtPh+QC ™PSa $ZEZvs"Bm y\PQ^h%r;l|^!I91 ( 8:i7-y'mG6sb۬s a*DWN+|kQ@>wՔk,EZOT6¿a$0й:6&"l_%p"ZYێO8p%ڥq?l1'ւG]ﵪȃC~%!*)$:}V5)``pU":i~vӋTǜPdUBN. kI2}~K֍p-1U_ވM͑EfJE2N0b6 =^[rT=RS$4]uݍiz|-H?rHY$7!_dl:n](^DQY퇃<,zѐ,]-3lǁG7"TGQgq]6_ rw$XB 68[ͷ=}܁O"]Eڃ8Ȍݗr@Y{kbYP[? y/X6ӌ:pBr IO{ҙQZ.)s]yz zja{ps"|] mG$ -ә0rXE9Ų2N"#tC76tsF(di\4n5WʽCI5,z9=VagO541adaf"hH(^ItvFntke]]a{2y]N]JHxO^& 1u\14FD|͊đξ/C1o'PPBdMR<K J-}*\9/JQ<*02iuw~h}B̕g 9SuM+(l/'/KpXttj_8SzM'NneH9S8~\z, B]Xt (#TzxAѥQ( 82p5Nst 荧HPJoR$&#O?RFWʵ+x$pNxrWQe76m͂(6YuglZʹRIW O^;kk>/I(йeYAOEÒDHc \{$6GfDSTY"og4*]}sWtCcQ;J>źL7L)օހc3TEr=`PQ7dΘ֢X3Ւ0D[d2dg‚2'P5%]0\HwsŮ$@2RQTU-%ӈn$CK̿+{#6PЍhż]-X.cM*3-Q}~h3O ^YU>d_+R"$)!wZOxأt5"eC,Ge&vn<BqD{sQ_ˡI D9Ǯ틶(6:j z=u!-c3 aQ}N @BhMYO=SzjJAJ =$B qqxPt&jJ]*ٵUbKBTh} \p5"z1&C=ط-(Lu._գ^_kщ-pSCMڴU[j)G_CIUX΢A{, ~ZԘo&e\:X7 P6$%tѱiz.[ E@r  7S㬝[Q~-=0{8.8bҖjqfX>ؐow3ꃜ9pU/ӫ.kiB׻Y lK,I(?xw7CsGE.CK$aT8>OHv ɺ-79=ѭ}>XVYF;eO4йU c'L (T\JClNEf]`SkwX;D}bYYEeiUu~CyEjHXʉZ4S(q:jF4k .Z?5c%l{inv2 ػ {/RbS}ۖlV/MO=̷EZ)Ʀ#ʰS+&(DHdAj^c_=*p_Xk>sㅜRe*M6G!]&+;Rh5SR=yJ5Ґ1O˷>A^E;a*Ś~LIyƞ^L5sB hP#\弓|`w-`,.az35[,jy%g'[Ÿ!wR9A|%CD-O%p+KM;CsʣGצwcwڎqG}ixQEt׌{~}VP3uHϮ6K.`A= 4 !x2+ϒ0!YO~ 6(kǺ-?dFIke*EpB+A׮$R::yg?qO(ZАxS^}UR?"2]lHRIɗ{>>WB35}@4'@5WG9 [Z1_;>a}JrÏa@>ߘ\ZC+Uf}Yh;0Om2P.Z(z?N]zX˾d-a 2?-6/x.g[L,$f}77lo!GNxӮ"s1Q/..Ds~-v~CkU>KYcno mDe(qnfj-[Ew"o0V*+ nt otmEm(_hd 3$G8Rw%ү;zRcܩi .Q%Q<4KNxaVr'-wAџg,N)ޠ7p.N\`Aq#*;͗6-b1WQԈ|xŀC=![# R!nB }PE ( f@z+G؜;&y6Tv齰L;"[keK2 'O Jl^ l= `XrK,knoØA 7 (jeX7#Қ#I hVjPsJ> P{k=8 UCsj_V2+RF-N/e@Fe7OAyùY5 a(nt. }aɼ&͋& =jqvm0Jwɘ{(5>9N'3(Sw:k1&wiC I?! VuThfu_OCdnIo Kp r5V '=%C0 YZAER/data/SportsCards.rda0000644000176200001440000000244613616365126014550 0ustar liggesusersBZh91AY&SY fd%H/ߠ@@W- =&I=FMSzL4 hUC@R 4%=BCR3Jh2Ҡ 4 % eCOPi i 4rp_\A8Ü5a˙5:ٛI2r6Qx{œރ/56%R=Rܶ4ǫ3o!1Vo a ̈ R A@HRRb!`XURL**£0WQ"FEEAU`\^0UV $cAx:HEuh*("KP*&S3RJB DBܣf09.e6%+A"mSMeJT7TVza'`6! cj,\1T&&qj0/F %M(^fJ$*ƉK b]'If&bөX)jgYo1SzܶޮiSij`A a+8NeLbttи Shn`a›Kdf@5|:{w^ήV?8/OOD+1Y:ؿarRSLxݮɗs߳6R:#:+~<{2/ǚC8ڐli]ςΰ^PW&Hn{U1W{j8&[&D@ _a\4s9eVճg2T%:{E)l´/sEb@`HD&(*$$@@T~GK @!"`V#tf   E"dN\aHҰj;vrnE4KF:hֵ\!B1m"&kw3w$2A3$32)JkTDL}fffffff}l*ֵ<9{Tvn\r9zo*UUQ99}|9mۏ*"$ T1UUUQ0 R,u)JRh!U P1DfD `XRz͋BɑHX$@.p!AER/data/PepperPrice.rda0000644000176200001440000000352713616365123014515 0ustar liggesusersm{PUǗ]u D3!&5)-J3C)S@f0L |%)e XveykH)_9ʌifԴAvnC?s={^X-2L?dd_T_V.7_&SGƳ=šЏUt:ve \f;Ue=TYy=ZLu<=:Sd%=Ydm5!icHziIJs{JU@r?ffk,Ϯ8:y]3k :!8f>cfX|DŽ'4plxOY@x߽|x^W֫7"s#¿pC gϒ0mIqf+%rBY I5uSMr0Jz"L'gq2ɾQ8G]}Tqw]UsG֯H2/n_Wkr3Af:}]oRjoB+o|h r 8 AF?5DG ui'g`,󉁛 Oly 2@oZzo< 5π<9 {` gs0^3WoHn!F3QwQsF'n׸c{cvlti~ugs$ܨnijGb =€tV{RJ_T"/EK*s!V=8Xiu# !zTf;Jpƒrw8.Ns8 G%*|Dt`y= Hm 0X?R}~-&蛱~jCB;e Jt爾]`ѿXUN}R:C>Q|˷_ B ^ī1C @kt[o : `Pvb uc?`'vE{*8 ܯy+hviùn9mE~bVV~S٥L0Aw$ wy@갟>ptmՓVtySpyND9' w?Ω 6{v/+?x?0㌋ẓCkQ/4 O֟Do}J1~ꕰ) zر\`~WmD!EPxɬjgM;FLk z0DKН0&Mcd~kו$@Q`^1Q ߆d IQXZOyi \ܥ2AER/data/GrowthDJ.rda0000644000176200001440000000510413616365122013757 0ustar liggesusersZ}l*RKR(("pߡ?uh6՘ŧZKJZAB*aP71ei,56C &<9wyoӮ&_~?;= ?|_^soZߴ|rZ74el7 _(_{)Y\lRѲgxlϓWQϓ 77GUnoп2PXB i}zt&Cƫ~.%ݔ%gIl*]e__2_w.ce|6w}?n;5 >`݂On Q~F-zo)ng߉{\Y;f B\pxvT ܉<"΍#& އ:^չ~2)F^wo_#:9&r& v ~1t\=G#`/ku5ο~h=$BW C+>V& }z/ ?I'*D>nG/o >=8;Jx?Fe}쳏4YD^p{eU* >.~:A틇g;O 3 3!xZp/PNw!t~xv/wgo0NUAّ o| ~ne/k=gDއx rWOgLwJ$gd;ÂG3>7] ːj=&!ܯ' ?/pDĹ9UBA܇ <.|=]#}{sPm.'2 yEO #yZ8I}fg˼ OlDvgeޝ6qK!n2y8Y|O}[yTgE 'ASOR@?B> C!xl.?g ~rW1_ Q>CfBx z%8[ 9~oS yB8 e oq:Bk?&ݏW?@^vz:gթЊq86Uf~^qZ#aS?Wi;}q^ϴ~Q?b{y>j}Գ$ߴB1nj9Y&u<'+xy ~sQ <XNWz]3/ Ash.*ZGqD/@|`>(a]Nް4 #ewXOwc!*[7->#V_)/e- %D "R`,A1Ɓ 9>/8xp?'c6fq_#@Yz>}si{KiF\Xq{q%?ݩ]Wa>WuޓٹY V>XD9yu?>/xaW ! xʀW[qOy>Zj?Mtfޥc}/~iW =P>|py{*%Ρ4ϕu+6aզweOވ˾60 5T^mxV η>DSeoŖ_@ϥݐM_,J +K--v5C5r-}{g\?w6ý ;[_e~%@+[ܿ[!ueM;-;0?Gu?F7k_!T_ wiG (pW?;8dNLlXRKMM$olHֵ]T Y!D8`tY1$8HoNPGB: (b:(#)GH9BRr#!)GH9Vr#a+GX9VrD#(GD9"QrD#Q*GT9UrD#Qfǔ#1)GL9bSrĔ#q+G\9Wrĕ#qH(GB9ʑPr'# H(GR9ʑTr$#IH*GR9vAoxÐ7 {È7zØ7{Ä7B[Okj[k׮Ox>Gs=;T'AER/data/Grunfeld.rda0000644000176200001440000000537413616365122014046 0ustar liggesusersYiPGYo"JD?x103 WԤ Z1X$W7$ѵM%f5FSQl~ȇGy^_~{.Ξh44~kO?}%6jhHrt fik_ x?} ^}z_5 xfൕzrWe^$[Njz] WX:b#pE7CyO;a1|10L|M_4-A`DF2qZBoSa=f@!%?{BO羙 c:jvm [qLJ %gCen\VÊ)b鬯jth"MK 3z͍˞>iq@/'R{!˾Ph嬴$e'_Г6'ÿr։W'ϫPo'} !З S%s\SB̿pYk74pO0b'Oנ@\¼.3gU%{}6kP0@ž/`~ ;MCKB0KN|{ < IKl ga^;tm!q1O]l'WMؿ̗}p=Pº"y܆W&Ƹ-bп] ,?bG}xSd"☮⛼~o G< 0=ό"];Ƿ|qI-&.pK7?Qo}qW?qJ>U2cݶ녴w~߽"hƈֳ^}܁8 X<~ݺ5f.9bh$Inb]ΚG<ɼ1gԱb#BK btQ8gK/O%]9x }2d/Qū[G_9 WG&`=kcAykx(&|ũ=^3v=leIoAsy=M08qGϮ\ +YgVsjWR>vy_(`|w%`U5+#ǼW3Mkyo⽵$q`5˼@C<;xB8 Mmor]N<*!^s,a^;鏓(?h|Old?8 Az}Ja(c WzL~|S r *㞣pݙZxL}hQAy";o%r8 iv@ᇏM(ά+RoypHĿ /7JCB9"^X<*ёކ'?牐{D\oAY$iwω lWQp/Ez.Y'@_#Gv v#&^JOKQg'C_ynw捋}6߁;%|ϭc>W&W9Ád&`}d|Y]?%_zz*j-poֻsxsP>|錟O䗌h@؟rGA'S_bYTt&[K\Gۍ^U[pUV2>•Ipg|5޾f;73auВb|-0dOo<üD />x8\xߛ0E$w2O9KZHoÚ<G~j& T%Ik`E`OչmѪ\Z=KADaDWbㄉKuq|;]Yq]z#Β?0Ygy$2~0EM&+tĬ׍܂8ĵy36uCF~&yD"<8f"^=^x[Qzd6i;U#P~w9P6.`PQ_\12;*[# q.W6xH F$qձ|(Xr2yfUe OӨN0-aF.Goh 3,bB!a2اaY贚?ȶIEAknncVJZ~YTTZ+K//8tb4ޢ`3 8s86ŁKQq 'AER/data/USProdIndex.rda0000644000176200001440000000144613616365126014444 0ustar liggesusers]=La_[$JHdrrBJ{{/wK"h$199NNnNnNN81199%+Bj|C5\wMͩ%Jchc<Tsq{`x-OM=+^tV?s|:8qA񃯫 8l_8vU0wz 7M.C gH{o\WC!bW??O.Qqk8:`ث6B:~ב r]vAjO?'9 /isbz=ЪvW؃2H?_ dg"c?̅}yE{B#0_)Hߠo_C>>?)sRC\$C7_i^]7e͈?OF=}g^=_aυO\9˹ ܍!!}r, bmecǺ:~"4GOJ":q8#r2rWw'p<=;.&#ѪI>sxfT?9+Ѥ1)./7 AER/data/Fatalities.rda0000644000176200001440000005050413616365114014361 0ustar liggesusers7zXZi"6!X0Q])TW"nRʟKMd[_;zkNpXOcq=XXp"CebDjlB!^x^ كRk>Rdp:6Eb%xG}OCNK3%0Xp[5LZ,O.g9nxPZI%tj- 4%.$(?x\ 434w -^Ĕ(iBQNCx,WvkG@64\vkjX;4?N+\eΪFdlI9W|oMyt'@2<-b6aRńhb$Ry|Kv|EM-&൱tz=o<_#Au~S< }Z.92<ٹ`x!>{|׼'PFZ C1}VqZ9!01ޡE-pA`,p:%)k6!j!*/ק 7̗rXQUO˼Vjp,MY"?9n4`39/g.v? ڑh0T .GqBny!QPCjc}f%mcs8QH-Q({8BW:bWLE'ĸ/⁶,*k y9;aL%tUdO4JI{;@RK. ̺1e\Yp%JEt`l ~CyC@׮ʠ`XpxRYfu[y68"(<˹.M#`b`Lx* F!{,Gt`06%PY bS}6ZSO fiw6$6 9|hX, S%^+4F"V"Lq 5$">|֎&zrhfk5!ܧ?=u5ix:~eQZը{GvO"TM~"*oOL4{FqAqE1eVT8G`oek.*UW%ש%3FҵO]͌+v 9xZ~4޻E>W]+!?s 5%y= .H1JhlAJH;^t{qyQ'5se鰷_4\-Š8J 0o F+pgmu TaW4W8_w`Ne,nc9Y*J0)|9@q4E=ޏeY`:ԓKj]߭L/. 5pz\H<q1B@=ieѪE\.`;1I,?%FE%e^R)`{ݥ>"#gYc^DQ hI&[<Fǧ&F~fgUG/e ^ Lv5ydSTK]oj6S@r~ 6*1<2&yk eĵXYʆk,F3%U|h:9L{;YZ>_\ 0 _O/dСWr 5/󥨪.@/҅B2S< #Lq1=mz"w~}ڴ7-WNS}RD2hma(}l 9A3!`w)V86*e=4{dW}i$&(|63FN/eۉA:!Ό35?ټ'}N04Xƴ ;?lRDe,rDO{ĴHKtF^ JmY=呮zƮM 4LlyKm"K}~l] !lg6)|Yo@32ԍA6X*S"^D3R;t%dTކ0u*RKjaV BQh>l&4qCՁS%S5 It\YST_{Mp.EMgDODWlUҿjq%xnH08a#\ 1nM AhꍙC~ V@!.i@?5t{Y?HP`c{p7gH%L]r!Hg$ģM:@͑\@ԌL{Ǔz}BSYRo6C7o)ukҏû4Ox/֙f-wiɪ HJivyݐ?(xp+nF@·d%ftoE=ow0N5xQi:*@.k27p6y6+P|G0(X]\0P㼙ĊC@u,*Ȏ3Y P lz)[vt1wR8]?}[!+᧬ظp )>Jp3ZJ׷uP : !oK`6#Fȹe9Ի ÅTїdb[U~*\uUjr۲IW3W1|%k-Wqj0 BYW>A^ſuX9Wl(pf!|@xxGBg궁5H;sr*fjko;RV >U) L8[e E-ڜtLJ~”]l+c84&ٸz.3Z[o'$- fr,WُG%,jTy:A;[3y8q7˖5 -B[d}t M| BC$x."(?6j gGÚ{JV#'^Ly Gr_nk¸gyxd氷߆nȄ^a#3r9za:NpfIщN{y)A s}ޯ))kR6'Qy0^2zrHSuPhk=uoH&3/5, wshƷmO3sdЈWE-ՔkԚYcpѢDO]/?v>=@+&fԛ'c$!> ksJQl@r4+d* Wq%`ّq▸Shw\)n7!i"ubt-%+?A'XGIoջF#4lcɇ_SB Oc˴]?d)N-jI.~aU¿`.XULߣfU\4 jQ{lE[0l=UMrw!;ԙ-(|Pir- =@@PC_wd Ń:=`{i;d|uFR=W'ʵ0S$MH(Hņ=t _4\Iq^ڃ!zustZ'gD ÖNTq,o/o:tkqi@AEo`b,ˡ-'kޓ*A?4o{LE)"oS`cbAN5EuCi O2>&p(Aȯ~eƎZ\`foi0Sڡn%zlntZڂZ°g[hBꅊhtԌNT$ƩEiԍL*,, Wm|$\vHkp(?OlHjsmSヤW9e%s3 F#~P0)Kǝ+>kH ]uX2΀h0Iw0_s) ^ xQ}QN@@}Fy6+sq O\NNg&rmUwR.1ea}7wҝw5v*6D6l *\|N 5~Aml6a^ø0kPgM3RPU}`ʸ/uPERF%@ ":+ _{kUɯa[G&oXaJf'pߐKO!:C,aVM?ByX~.=''%9&|0#e|*PC"nqڣ (De&Oz>g͌U#n+ǝVq xFFejɽˡc3wfߏ-k.fJ9|?ؑelՙ6n!T4 Gz&}oeKe4w\jCW\2B!cޘ`Pv G_x K̩ej `qXkw\A;YW{]pc"f8C~<Yj` @E f9={Uw͚e 5`1sYe˝B"IcfxA00Ѣ/y⥇ei^?K=4232xad|TZ4AF;J/^iNkRety&[ɚG"R2ʰdm٦v˚VuޒMȲwofq߱Ԝm (ЍCOw_z|E@Dr]YaFM"ª¾WhBiy|Go+ $U5 պ|İ9޲Aaՠaӻx%J͡,cCVWz5L \<ű4I.zZ @m^="ojc0~MNJ龓78ʚt׽irE, ҹS-\|^hܗjY_K"qF7@!击H'>B+iX[D̄IL&ig< s.Ba1BQ̄^y)X=6JH-j0$gtwpȼ/r" Hӧ1di]ǻ׎+'WIf*2G (Cڮ4y]01ܷ /G[N) Y\ֈu7P>jzA #}  %G FPq m3t9^q)V#w:Z#XΩW[^l06I8P R/Ta7Y l0-js ࠎ&K>쒫~ |mb6F&Q >,Ǫ_6Rèy!+@Ň=RW`Ð SPF&y|Q3jF$ Fݨox]^%ZCJ]BV2Zv!B#"6.J?Ƥi@읢gE9=~}w`_(S6M/vg-]K D>Rf*5Rqa[GӱS0S'`s& $~7V2eUDzHlP7&s(uX,P2f/,z(ѕF,+qpɦt3u\6>!@e< c* a%#F_=$-4H {,XwhZ@pLI;2祀uP!]G' Քp5BQO/ڕqh[sm 91:+A3Q !ئs+Ǐ\1Z*H,<9sAp50`0Z]͡!_ sO F@oMBza^aԉk9P/l{;l{`>-T74GIJǦs:\Xb8#a[U`obI|4vkhU:]lOxϖ(R|Ҭ skO0V+"wg0X@hGۗg[U-8$lZ]AJxH%9^UXsTMMO\AMVg?gf"x"#Mau Vt9G}V8q*F ù6r]!_ꙄJ-cc3^cWD lSZ*Q]n^^z7LTPM6ݑ;4.r{ ΅u2,2`9znBYdtd6ɼp8(Pfd+TwxY=Q _jCaK_VVkզ[c*bږM,hiNꍵC -Eק<`_/ H,4#$[T \ sχZGs `j8xn|N<(v&Q-'(Kŧw> eI\^J`k&D|iG< ptAC3r61zx7&aөg&vPC}p}U\bFY JK5Mks@b2.5JuEᄓ+i68jqqrث(kT9dkz#LVGdQxDc=4玺;yx]ftu#FEkW|"]5ǰX5FW ˖دOvYJY"̪ l;.l3jtSrA;Am`V{Ċ&r'DZFxX°L@ĝpAUm_U7󈝯Xo< .["{#?g2{w~Sܗ?9/q|5#j׷Igנ7Q: $'aغ~yk>֨$>$D$.ފ9> MslO),K58Dpk; G4Rц63=/p@SBZ{zƩ*(E-i_:Pه[t~a 6TM_tB@W>ț,Ώ-'M:D1*j;#Cx_z42l=Y5۝(cy_;1~+KtcYXtW|G rƍ<#Nqi%ƂWpQn`l" C X2G:.E"V5*!Aլy hau]EOc0vCxBYo0~*~ 2b  ]:Cdavی#QsW&VW2<5ԠEH8&r )cě!n՜Ȇ Z[ý  pTJt 2qXj;}>jf!p&ꩨR+eZӭ`AA}N1I\' ^A]3 GkG .GGdP'x`Q&&SݜKǚl)JsǶA Z.oZY]ik;sTܑ/> /Щ}FNE2x@SXzhPjSqeΛ_P`wkrbt5+Bޯ+pm8&p(Q"2DLa W/q47 GqĬ])߂ae[`h^FȠQ'\&N[I!Yxt%J(!e%UgԎ= J0@i op_r3H@۴W .FuK8`f0(f7`p.\̻ѓR좳4F~"VJQk:~E2 !i*_y*DP[`cWe;iC|.KS) u30((;\ç`3FQ?O?) D!r=W#lBgleo"'l3UQ0۔*GePғ~)@5I2a>-[J^7'Qsu+GlOE =?V ~GZp]!zhnj&*28}hñid^Fo?vݔt=?4pe BK85S={h򻣒&w[B4 AsNf1;NCmN<(ڸ B\W=ޝ]!4.}2t9P;5M^*N6-JK4VMM}@BD_65]w[:as4+j[.6ْ AIVCXeR̼2<)eytz:EĚ! lpl>j^@(DqH,sŨ7bkTY3l mnR\~#Wl84m 欃L0iīrqt3ኚVOA>tyfb9wzȞK:aO8 .\a{Oz Zq9Klj[dR :dN,6jo?tb2z6/ubA{e#)RƮT?Cz4CMك,d8Sזqr^R f"b?ԷI-2Rs*ul`MU|TaWz}i,r6<U')Oj`\fN=kn!" @w eid+ \?"',JezI"E|z@ADD!.Umy-QTBgu"ݭ;o9D4MotIs\- ."pv0a*Wc"p(Qg7Hh9я!W}SC.4[&N371O'>;K[~sD;Za=.NKB'qr\0Ii UD13wlnӆ5մ@u׎l2;782y-m d2-$zOE[`PYCJ~QO@+qZn7[g+#e8&‹ o1T3|Tqw6E 0}#`):EA9]Nŕ'L0p.rRPϼ|-nAɃ]b&]f#o$dKvsZCBc;P -,SlVvLAo~"WM7Ġ|W"Xţ|ϣi\MŇxi!28P ^C+]nZvr(w1Nfk*_@SтG9x q_'sC4ثL: |f 7N"KgT L:*r,s+Ğ:}. n0rʓdbotH9nKˑcwd!Wg`+ymFb f"k%]RUW_@ǬZbQ?+cR#{36-ҫ"vkNC,4?V67v %D-"o mkHBf-;#q̪W ç$Gl! Ţy947=f5!e@ADeRƯ!GKxQEE~AM%>{"3NخaRq)krIB|d"Aܛ l.k2MoYVK3 _!tQ*n2wyETߌYv—n Jιmef5?j)\Zwy[ PtaƦegb;HULژL~\e1 ξTg֨ :viz1V؀/bLCˣҊL<+ ;E5f~c(sUmԄ^Sg8Yq#mp"z;=-0[K_WIe+qb$( zB1]$<ElSjz*iLbʚ8O"{nb(ȝLߌuh6oZl*,VQ.'3C={: U^ W_u{X+i%zXhcf?UTiڨ7K&-al#M`[{eZ8mSRn ?8.E-`)i*]% B -vv $Va25yCxPZ%;9ʴ-JՆrATJe=ZyLTQJTnd3s|d Xs3  2n&?Чw C$;82(5D&sh#ԆX^9JG)]53ca6T} nZhRua'%-"~ݰc@_kAEBKo$5j'n@$*RA6?# J:+.,hoժHXj2#$O=c;4JɑH☠.bL*8I{Hzlr$(gc2RA/Qv_%l.ĝH&hGorA#ʞ r|Z O>/bx%v.r*4!HP[#v'W'pX=֐vЅLbaO9$i1)b3JaIΑefRC =Dl>eD&*JsD*tڭ_Qgo(| aS%:((\몹wbBA#;&m?b9vT܈GVS Y˨BaGk?h$-֜( 2)5Z'0hIrsvZAu'a@uZׅT Z۫8ӈ6ɧ~vAq})y9 j*<#Vg\s;wNrS[VT4<U;Sq vlz౨]t;MhB@ _s@\PfqB%/k3ePF6"SL^1d^j OhZ{L#݋b(Te_ 4tin(L&p3@c5!WQ-h{bt!KoIM)vQ{B@WMပEŮ֌x4s3F7\]t!ĚF0dUs%^*)`Fv"tuh,uy7)m%e\fragzepxl*kqInRAL$z^zWd+)v0}m޺ SSlh/,HO:t i)LG|j"?_AX6vB2Б6'k~ &+{LN.ao*¨lAugJ Y'L""*H!WZq~JWS'c#q>qI%K<3Ǵ-)8*|VP.zAչ0^A-\_Jڊ: U*2K A`v)6k0Wq`Ӯ5k]k>!Kz3Cci10$naaMFJg4 crxޣOz@BA2]q Eֽ7f|=?ԏM~BK [\@R.-lmi̥z⽬~n0x//x{Z2Y"qRw0 B0kfe$5?p5)4sb MW})IFH.Z97&dSpbE2#"MZ 83S!LʮQɎ82Ձv4Dʖ`}+Rd2cJsBrR\Γ*tg#es#Fjx̬f(]ȡ鹽Bn:XO-GoH)A81y{L,<3M Fp[xOZ=Nl]>p|^i}!~U21 T`t f3<ǭZ>$sE#lwXG ^uQKsga Kp%BQS&gFs m.H~-է)DՏ E>!_ˡz^^~ݤYb9A rZJJG*HFȷRʲʠX|Ha氯aIӦW y}ԧYGr/jhRznF)ҍ?N<9y_R/6xT3kW|+ I%U}ㇻK+?Ի,@d0eV[*~nYln}ת5HiC;4)YX?5$o ?_XC:M3UM> ]uEi;OeUSdOxi1|Uu mA"vz*5Ƿׇ۵n#E c;}&-qaF٧T 3Y{,8R@0Lտk%uAo}(<۷nvuRIι{gtCI gE&v\(n%!Eޢ$ T tQE;֏\s.dQ^᭬&X7a~+D!BE|!ӻs,Q &J&!Bwt[ꭌ ]|ris8#y3wx|i8f7PB; UKey  91Ŋf̬!Q\-8p :RUcw:=*B!Gt f7qik^ eUZ;5pnȈ"[>O%XdT޵sB[2 [N:p^yCf8NE1w\uSyRNrֽ$ڬӓB\oRK\Ca݆r ,`4* ~]xAFrDrԀ9fM^2VGϣ}>Wu  KK/}ZrC߲R6j`*Xd`I(N*Ȕu{t$}uqWBbj+0o'KN0 {ۡVz(BF1ԽiZL\<RPB4ut;RL$h9n&׶mhhLrAYg@Q\u"OD X4+!wx&q˿ ZX/ zEཤ]Olˌ1,?EA0̨1~~;# Kλ# +څcd$Ő&U5#Q%&HPsM"taWe4`3n;('vDVN.d "9|_pO~*XJ]an&MMYXVi$ڥ؞5X=,Ek]Jc>j$1c8Cc/ kFNFYbws`[B%u+F2:1N۷( b}FpDE?S$\P$P7yy`dZxܙq|[\?R&{ PU.[iZzDe}-HDH:孚~5fZ 2;5orN'#>*J=:ٍl je2Ò _#x#6]q\:EN[}ރH8Yl:)u6A S-L+eoGN[NO=ٖc~7JP)au J>5tWr~W[3]nF?10x_w&єc$)d žb*@R L[휔3d|VMRv@^YD]G <$ltP{RLRIlPw[ZKe>aLxnL'3QMuoeHAq dV֓L'cuc @Lr4>ryIح*9܆#$> x_ͲRv#0m3b 0mV$:XdE<5*í[CM ZbfT$O- z9j J@Oy? ޛd'Ctqވks,lHS9ʑOB,]vDm݉pz&Cl';u$ȢBͿO1/AG:K`hp"/Q0X(f}@HxQPagdx𛝧@z*!_;$dL}P#oA>,f92 㺳b^f'46}6a(pW7m+ve6Fxe#|Ŭp>,Y|4#A2F9pAKDHRD'xC@A)T$Є1C<へPEcO޿'< %rêPP8ƭr'8n㖳݂0*#K*8ғzPa2e )C|0L(^~D}L-%JjwԐ _~[Rۼ+Fvf((_&0V",'c(%,ܴ1Ε9Bі r 7>ֹ؞?3 pȊC찍|ZSa3?La9U0œ MlGJ͋Ap;!\]}pe0 {W0 YZAER/data/MSCISwitzerland.rda0000644000176200001440000003451013616365122015254 0ustar liggesusers7zXZi"6!X&69 ])TW"nRʟKMd[_;zk˸Qo+fR6,Sa 6Q_aʟ>w6$pzKKOsSğӘRUFa %5Ǘ.XƔ?^!ꡤbcwCM!t*R'[oL~`gk3t(7f=NxUŠK1en7v[OE\;SPSWnW)&NY_QkA9}g@Q$Nvtݪx>WCQP.*԰0oblx6FVi{siW0@G+kB)YS+vI/;YVuV2i @pЊq]`z[bR"+:,&Bl;d)~Η]Kg60NҐp)ߧ[ȲQk;2uGǁwW=g*4}M^Va#5LkZ+PXWqZ̆-l4hfse 7H6ċ9W6y"ئ. +i}ROgΊ+U16%bHPEnEMq{''X]1QQUٕ۔z[5|8АD7bO .z 0.9>S7cHUg"6>>j];c~ JLf[W{E(Z.QbBa(wH~K@XBo𬍅Y.lM|2ؑ{4Qۅid~^JaUYW!"+ Z%3!{vD'hq?s,wSTGV聴UKJލZuWKB,tW/puE~fUE#@?.gZCL7sE\'} M`ͅ'? J;4{aUc_;Su(=Ma|p vLak V~+lv z_@!RNOGYB:sHC%SqsgWp%t4Agڹ/YѾ2c:դJSPYHJaj?OyJ^ 6p%Q]cw °W P.3`|˻e,K\]010[RВ) F!)Ande$| [ 9a7Pdh|L/%.Nڷ.< ^0v\ݰS7c_ÿbsb$I>c}V8;% Ǖ*7X_W mR ZF LQI9e9> x~+a+%S *51SꎍW, HR'nA[Q;ן҈@ a#L5n=_٤-*q2& a]0=Demxbє}ndKIZ_7Ȳ \C!E;x+)}oƟ}3f/ ¦H B@ k1 Zm0{"ɡ?uFO.Ż% _Zc7ʌ IHEw둂Kٶr h" '^ {` E[%~]mnmGluΆ## >ܦy6[l 7m}];U,f4d|Fꥧ}/l=R[B;*,9ǟ k ek%lߜa{?bZJOގ<ĩ壌q)?!JmI%6272#dHC)ahB>8(dnq%SM7rRʠ 1 _\[NJ|tضPPqu IF;w'ȱ4Ctx46V 5?Yޅˆ#]-q3LѠ%`#hb;\\?diijtCaU>+5JEq(@6%h˙np (4dU# Qdك{aKŏfS(gHvճ^Yj^- c RzX/-(7e=&\r Q-V S(@|{ď66E]azP69k8 -_IpR>6@cI @++ ]A"*7lHڥ|hbP",ģå^S eL7㳬+M|[8<ʹ吧y" H5m3F=AtJ41b7Ay;=бJ'3 L Jgęae큈3}%:^6\EMi*Ѯ'WQTZ=T=ȃ ن)*2a0_3{z#C[)|Fi[kD̏B1Q?7s>[ՀZc3,q2 W ՙ)Jn_uHY4MGJx.Z &.h@fmF&t/Ej4#rzf`Ɓbub*yAv-\/JZD_BuNWr㠐bj{2 WT*~@nKRۊ$w^0MnMF3ʿ:&R;ScYwST}.{Dw:UT6<DCAgejet:d]HNY&~SUFK5&dr1))K 7qgU",/w&mms7*V#qX'D>{+{T9f7Q(׿Xދ+g۝P'J1n-jpe | s1^/>HJz-`(nbcVtAA1f_@HSuCBĨ2(4FAw(KV_jGn)964rP󧨌ƌ"6}T Jop%JPD؊C K G\,> &obHC/c4G+I h.]Ra!O`8n4ؿR'5m1qkK4}݄i}ש࢛% A1TPE=]KSft(V'#/!B;/@*:ڡsxjӵU~X%4FQ6% Sdc\m_6-U%`@1Dg۝ӫv1rԻG{5P ?3I " anA#4kq&f0*OtcIҧM&UǮ ;1x6 (e~$6WW%*=ژ_*ߜruB6}tQ4maxl/UKoHP+zMl١uǷ!,C'+^pT.H cx[#'C]vÑHMȋC9/S)s1ԟ|!Į|`,I e/jRV%ǴxjQ tNK! {DC?u._>Ō2/!I\[Si!+J\/H:Wp7)jjh$:ENqz`1> Y(XȌRd >aEu{+p槎b=@KHzȽdZɃS:s^X E,(+ńUNYQUKw-ȹ 7R$ [B?^ȳ5"ѽV|ph>?q=;Am'\o!L/nK8v@  ߤwJ B.HqUUPA\7 gYvfv*W#b c oQM V__coLt;*S!˟{5?)8#»DR6RX޽G{V4\TwBy2SXup'`um-|Z#YENVxqP )֟fQNc6z*1 S4ؿdK rQAvh<&eqHށQώK8j1[Gh>S]WFdt[0s[HSө8PG-6#Й\yԦ |f&$Ec*, @NI1$%,K ؿeZTFҹ/H s\l^/Uᑺ}Gr T 3\, _H,z@H)v!-",&l!6i; IJXM,  GU:KLJo'[,\*=Tx,wq7f mbp9EK PS3,hq-&hG_ZTCnœUh'\hX:IڨɮL=mz F.k)3FXff8mmqXN 0i{˜Z *2G=kZ6?Pj*}O '6[NE=*)^4gܞKͫ$E68`.\ UC#hSTgΪ7U(466`S{\'0f@GQzr $.0EM} 0Ezh7jb<2cԪ aMyiCl b^j!Si\m7:Ht߶^wIM #y_T:dEhS˓k)QO݄UDŋZu26e1}lGhboU 30T`=a TF]@#14IÏҐ"bsBGm$̙xMv_~bu4z3m10v}LƁmҩƮ.\kk VH 9l>aYc7>\lւNW(߆RMrv!̌ 7\𦑣s2֪Te ߢo+wMNsۉXXsU^`knuYݖ:ܳf򁩇-s˘?%M % O-wL$Lx'I%xS.og%( Jn6H&o=UnQ·cZN~n18].&1I PDFZ(Jgwҋ#TچLlbxAc>w83l_Z߻`HrۼLBlϘ~ ӯ&BET vcN/ݥ#iqRB _%A6?wA\c8a쳲ٔ?у10'w"K.rHՒg?Q ;|io> 7P=(SWuZL Dп媓PC>PbagA&(Xg ϧL?_\r`)];>r&Q6зC>+^dF$~ Jhd@][?`50\IFk@ܑ4=3=?8ggN<Vvض(pDYhȿŖ_1)Ʀ2e;Bw`t<EbUZU{pE]kʱ w,˃=#кmD?&1pDY_bvCFL` \DL|I_:>Ke Mo>_$ bVv5{EQG7c4'AY;ƹk&cRl;Bʢ*Vțb =G2 )$/8::jWVе(U6wJS7Ab/Cc5xqٺ}4g"tH-@nKM/o%nTy?0N/X˓ PA v)}'kMHPsYd<d]־z` gL,*AݱFk|^74cu?hn'ύ}~6.Ԭ؜m3#bU(~I~٘q5ͮF@販7 [ЌRKChT;ӝl:x'x`%e> ;(x@ uiiL4t(:BʳPud6#mZ_h6#g;)=k䞼D y|_Ts%NԢ+%1Zܔ=ji㲜[W7z&%Eh1}H+kgߡyC(uSUc7.#p:oo%Gkς%1[NF??KG떮dYH CɕsZjXoJNk&,{-:&niqd}RYihWߊZZiS3!X s2~7Kzf+8 ˬf ncIX xlᰐf#qWTk>4/J1MzI6Y$6 {p/srN.ҨиC8(|~p'J۞}6k(^`wh׹ hHxfm9kI^@)m?“x}}4[,Ͷ'863n7ytb:,;Z uhzshiT{P$#-G\n 6}Z!? 7M JI> x˅d'SRd$Jb@ܸ rn!Xif0m$AL-Ŗ,Mt~۷ժXʽ-} ։jۻRy/OhP_n?ZU6trhK-eC+$>n;U%6GmDL=*Vwu YtWY[7'=%ggE'xA,aw"e Y,gn"Ψ%tΒ0}l>;%(~ґZVx``|y7ydQgHI=waJjnjEeh 7"4j2Hz s`*&9a~!hų;cS5&tܞKQZ=VH"&͵,ShS>(T"BEjW0ַs_:dLX4,l7A ĀjݵS.Mąa !ψ| )(l k  5.oS.T\e](e#)o`0OTeT`m[G '| _֧GWзy+e?2K Bk GT̠oXK==41K5x$m"^O?\]MBeg_. 8p+ݮPe$ٻ0*C6^9ٶD.9 D91x'Q 79Jt.+;4 z軎nU][?jE,Y/fve_&\, r'"O_bІ=:Թ>?dav wM'ihMaGIHm\81q.TfZtyN,IfpDB<( .g9dSym*fۘ(o披GA=wsr>}P QW<6s)aaA\Nit<w||:dqor;M vZqY |M#"&l@g uE|+Vr t6Q:Ղ=/OF :1('_iޯuWϧYl%jepQ _OC_h l}~-CIlˣt=9K_ONZTMi:k"U=DcG=A o=' ķ7oCXy=buu!AWe(b#^7Ǵ%ay,%B2D}|CI-+ C oQ,=1XG gD8,6Մ>)M"l? ƎojÔ.@.xTi.%1F+y4^ފ~jP\np\եRcLJZLHYvѼ@zli3L`32?-6oD}6|rht3Poa0^ıtomN.{gWT[A6Mrф ZЫoBU(NJuP6 g Jn; cа!6UiU򱘴jXp[qf&3Fc8_H( `e; tTTϽF8GMS)'ΘKCuFN´#A5ӰuV1ڡ2JQm :2Q Y1z 97);mlj|'[m!Ұ"+ʉ_~NwdDؔ8RJ `[TW-/۳<*J7S+ *ٷod!A MW'apμ+Yu &"wLd$ d#iU,!u5sSʚ$HM::sjU,Y Q^,elO me9"(^eAAm yR 7sRY{[B] е&h~R7v#5#S~yVQ wF,:' "ϑ:=slIy0zI2hτiۦ1lϕ^ ":ۆGxcCZ 4\ޘUnV?ۋh+UduaTuKx%IW/!?'OUHP4ܨ5 ^. r;rH8D"`dv|ps]5>WpHmT> DO=>ReU 'VwgZ{,߉=?mX[wΣ}׃$Pc(c#0zFl%~0,0H>=6 W _ f 0K[2v v UnBSJҒP~|GTcE"*wuXr0)f2媎DO\ہ&`ˬx/{Ҷή)D{K/c;Q&|Q9qt10 YZAER/data/Affairs.rda0000644000176200001440000000650213616365110013642 0ustar liggesusersBZh91AY&SY'Yr@ HTE`/ߠ@@D@@ICA3xR@(ǀ}MD 6=M"H<@#& 0 *hCѐh0M4h 4D!db`C#F =UPѠd@dd)A&@i~~<;mY?:TBX>ջ Cù:|= C<}0!^>}.O[qŏD(~Cn֓n3r̿Ԕ3r](}g> ݀U-(bbWDUlݞ+.z|= 7|5tٳlߝ&1Xֵl.geIs r}wt1ٰZ.e)}`+1,l0bBgڧn6f{Yfn8WSTw=) {y o_2ĺwA&ξ&/ssohO|/VJ{d>=OXV0ZeYx4 R }<vUQw{2( PCןwɲ" {6E\,]aYX3hG,xu Faxr댚77n@+PM8Պȥl1veׄ[|Z%q,mZ(TX/ym5-K՚.%.i\g}#ç9%3٭×4LJ[I`ddIfSFE8NKZrATk0k"s0h'H t $CܑN$ \AER/data/SIC33.rda0000644000176200001440000000121213616365123013050 0ustar liggesusers r0b```b`fcb`b2Y# 'f t66f``r@|@,UnC}\f,Ic4vP@a.[ EߏzaaM)@J П ߴ+Xk 80 hK LOdaDV?*Du;V@ij3ArXl{Aph*?_'D]Y; /+b^딇`k|@PG >}8$)ݛUpH,qQ ׬7i qO1/.Y? 0C 9vKBC-Q +Ivx93a9V=_kE&px蛑qoYN 1gAȻӽ"M;ì)`zK$Vn_YaCܷj>GOy@8BKK JK`JrĂ̒4c8J0F01acf09acXBLp!egY&p)egYp#Fp;v0an9z`'$B&ȕXV < LUotAER/data/CPSSW3.rda0000644000176200001440000017350013616365111013255 0ustar liggesusers7zXZi"6!X}])TW"nRʟKMd[_;zk-m yAwA +H2pZL˸j҂FWoҭMd2lxjHT#QaҴq& UBj{uyEGǘ~CI9:mTl.٤Avz_\FdQaeZ0P6ZqXMl9Hiͮ b5\g!R꬛GϹ/OA"2ot׾?-ݷH'PSWߥqVDZx.EA^w4N4.-oY-GRq^q.f_nR)>Vw]?{rȶ /e(SBJ&Qj2$=ėf1#%+Y{qK/dp-uI({dJnf׸YՍT N†tF]bkkGÏLO_&q?7aX$~.{k0PB9^oM47f1,T:APǹ0]R}Sߠ} )_fR=؃Za2PWzҎ{S-vԏR]wzUr gsʷW *'NmNtPl+zc Z{l?/ޱs*g4|Ș x 7d`W<<P~ىI糎6e=wE:.W0?b yyLx6nxҶFf,yS_ Z59 m:nc*3,14':$?kX2W'()M$| *Z$FDBk.8@%~'J:3*{݌u9Ӭ` r} qnW! Ka37fO]H i1egȅ&N WCI![ǣK/ 9}Qڎ,a®_XwP:"o_WYVJ7 g ʪ]pŮN4{iSs sJf 6;/+zJ6C^4RC4QO[ޅ*Lm Gw(>E\+zH, I9'_0LYUP}\29l1k`"V9*F)cCE I瑾}Kq~o4I;х# `y筳<+pGT*6 j.̟ +!oď0rC: $#ul }|aaԟ`B Ej ,KFE!ErN?81w 4%=:&o@QRvWQ_kr& U)ژ[oAus5ݡ-&*`q}dƻbHf7|ij$ok(3b1E x8GWfDLGR}/+b5d*jYؐe17h#] g_G(Ф ۞F{S- 02Gϋ^ XAHN' ޞXj 1Q5 ;B)JB"뾋T 92ύ KnTNZ:[zP10 VܡV5Vuy_5o Nj|eU+ϫ PΤ)JA%%( ǶB)b\ MMwcѤFTgx+j-[Ņ[&Qsgé+M]!ϖ3y*bJe|O׮@ĮߚdV7=aQ& 1C ]F Ւ_ s hz@1!.e ۰dғCxȪvMpe#ER/3`y>u'VqKWf_㴏?)lvC&jC}ae-<:^k8<;VUo;Os`?`cwV#!jkO{Arz2C^vh_S{8S&zPl10dtϱ_/6or䄯lm&޹2.eJ/^t`Q$6KصjGck:6T-ofiˠtUd6ۇ+nSs56+9}ՠֿ&@']j*}m3K{'$EPBU z;DIv-ŝZ%4';DgaZ%-a6MX,$yZZbHZݑKg>5mg6ejKѲbk" F(1]YT0z Ji^WQ@~cv<ٖ~̭W qyZfnx, bى[=SRWt~V=(>ɻrj16Ņl=MMIgu-5M2#C x.<]ym֥\«Z5UvqׇC[o!8D%?<咀mkCja،b>ۈG & y`Z D$e?m3vo̭>{cT4ph )`&/">>8^A'8.ElZgZB%?7iɭl=X^-ql0i{Y3>z%jsժ9'$QI;׸3.:ijl'RX`Khqq͕ZNY)};^"QE2g}ٱy(*7(6 T_D!N ES 'ަF92P@ɧe9ʲ$ "&'KK)kBWQ:EЯ?ͪ"zdJ(?x`K^REr:c' ㉆ȁVOkH(7Ϻn`_Pp{BDvTq]w"VeT3cL&X i%`<{?f6" 7#H4@ۏO7q'o ;{uw<,8>0mv%M@sKQ4yq+Yn+4P B-fb#DZ,ު-5G"4eՋGY\bf&JGrԮAN/΢@zHPriDV?в>{Q|j͉85 |r2 !oPL)Zi8!`'$RSќ;,gDeE!v]/0p33 z7PYTVVbY |>+<Ӳ4_U#/cq ֵTNd0DP8Ǩ,$mRo̊:ƞر_<"6Y笅L= s+/Flz67NF h.H ]ꛍmDc{`qs o! ȕ:[c|z3 İNltfKtcɓISݚKq_3gMd;,I7s]v^fWϬ9+o:f}#m^&8L1 [DK[HxDz˚Q2h詹"ی.b|~!Jm_ChlLpV~XJN*%.Hl3k:46, T YJHK+ vJU|rSٍ{#y׾恡ޗ1J:X>O{}M{dX'f5cH`.%%˺q"]vkT Lh7?X`ZLCڻ0ޅ+D1b`?R ѱz}TiɑG jLquc]<۝f-``S3οݔ'4RanZ)aA 1l r1|%ڎ,d䘾F~hF1HC\wܢˣ?ЉEN{>G>VӔF,ƪ^$NnTqHe?OuDMT b)qp:=Cz@_˩]"O$w:7eG҃mfոMq@JJA \|/@CVJya~BhHD[J9g&s^=IeB I[wVOJ6{FQWP<1Q/h 8>eѨq0TH2\v,_J$e10 riE]=pcw9KFY[b i Ĩ,[p<4`J+ԙ`m\0=ȗz[JZ3!ʳOhB4fzi^?YGX[sYˣ0x?3B39 it3ѐsI6?DIiM6g+\^xHdk*UYOKpvQ+LJDr0>ٚ' m.*a>wvYx@4 o8WYUMBe&LOz߮z(314a3FNW;cyT>A>""4>/=-,/A{\l Goksn-lhk7o 9ǿ(HM=7U1DAsdii |ݐOLu/UR@!qM0+6>6~"$[We(&5-˜^#PNrcrsG)fn92…L w*ʬ|l,$<txDUtY]GݾvG޾U|3z5@J܅RA4 wOu$+?Pm@|/jҡclFxK$)/_]Nܜ>m 7_œ l^K hѬXkV3`Pl|UWt_c*/LpLNAaqGc/|OB˰447лn~₼ 6V#J+5;UjTR-'P9C[bZ*IvFԍ;QEoJGRPVр#UӲt'(}u$RW< r׃@o3VjGP 1*d1I.[AcnXo$pdW~n<6FqW^TVxmBYB.m.?V[H@Ew)~@Ev|JxJ oi:<'_bySP0r¯gkTa ˴lD'}_w{l%), Tj9e*ޯHBStS_T SAmwV cNn1VT ,¬EFeH![;pj7ҺR|]-sz'`Y*5{1)kd:2e+4IFRU{.ĚK: I4-mv^w4 φ4(/UOZ!3IDW{yKjخѮV[fŢ-#XVi%4F= 7)~(?TU@= E.qXC)΂f0<^bAVCXpTi~ңHXTIUղ~zZ4> 01;;`O8H xLP'qŶ : bG0$KSTϳv$JɡۑòKtc[il͊|S z)(DAEѝm&[);MOsWLR[NS<A_,o ۷ >; Wz'^)LJ]M>v4iJ:Âa(Us#˸ ikIFN}\зL [bH!bN v }I- >2Z ,}\˴!>^xQ@η׺09?hϻWѫ}k' 4] o{M=!dYTy-Bu^mTr=(c߽${-Q YYOu_goLI#b/tXHڏ"@nL8] VtVɩLT#9E׬`Ȕ48?&hpp۽ ĵ-,E]r:\k_ݮ.qJ:sf`?F9[C`ِu+B (E(߮_Jw1&Um;J\0i]ÐX5Qg+Sw4 cpvw#{6IC8 w=9̀,_#a%(X{= %$ߏϥ):c$]h>~ 3!ZPYVA هeaA,Uy׋]^ P Np FbR#p@ /n @[Ӈ@r DмvOI!μZ%a^˸T--Vè8ڢFe8{\ёNFe9#lɶyHQ:+~Nզ'm;E XVˀZҞnӚHJ"n>-9ҋ.5 p}:J6v7}k λ;Ę@d  K:u-3MJ2V'rwMܬMb)ic{=,MP#Eie/VGp$v?Wskt!̳Ţohp^ ё|A~MDw,f h0h ܔ LEȗΙ󢙛1x{D(SQajCԀ_cq[+8 rĈl} Oօ%BO"|V|6[CoONxBIk|JI(y{M,!]DICKeAb{- >rfJA.稞ؑ>QeWéLwƾC\<0e4 Kr tӄtlx1Lx+j|JSdlgMY xuA.~ ;IAk%IQ$P,e_V'C C%ʛ!o"(FxRXʦ "1VG+hO IocکFp PUȨU{wb X^<^|tw5SC@%nKW9GF='fbыϧWg E?3RExS,m?K$y (7[&޳!uMgB]6g*Ú ONZPyLzZsQGveC:};aa3I1:[(g.a.mW>;s!ݗ]*At[/Ls{SCE{YX԰kr=!5*Z/3v"<0;'y^0v Ke{֓KwB宣j2_%xC^ٖVi;jq=0:QkIbs/~9?0VwyQRhHdzAj4Ҵ + 8J&ZaK症p]6|kqZYeT`,(4"a% vTy'(n9P 8瘛uaTc̰]3V8"]D n'mșl(N&؈*`}[ )wf";e^]>GdhA=˼jt ~.>,;b;OPm6}6fB1q9wMyA{b҅HtG}M4/}YŌ7Vt'l/۱`# H][+ZnLc)&*Sd=t .yt$߀qUK4q\҇=$LP_PkDwhRސCxTo%Ǥב+t ~cK4 $շxNZ>R$fJp2!JKdODxIp+MfkqPpߛcİTupG" \ѢvdpWl,_!eR_92%L| L^E|vztlKsl2}<,nsV`!잕,lkaBd#46"Wz RCҸRY.L"~C3S1l[prUwaL4AdF izK `|=g"*uG8ֲr DfH`W9λ۽E?3.j "1,dsP%6J;Rj$u,<'bV֟G,lĊ_Gn| 䱛C \R):!Dzhc'NpnZ~V`9dώ.EE4_|0'So!p +c!Lcd \Jz\3:9t>6r+UiH*'^DB4u]Υ)rp=?em7gP/1w*TULA>Eh'͗Dtٞj^dEl6^os7a\T#3Oh'K@%(7n o=4`W}NzsZz5ԡ$JPOYQӀRkdNaTo 0>JB%[91TrtE$Z>!WnO#p*2("eQ] ؉"\J hQj;]-:/Ӡ!MR0+3LKҨ8T[M2.6k~E|,bG?GA:Nx/Sy3)}JÞpQ-jn!Ѷ }nûȱJL o̕(cPQ&b,ˠ됊]+0ڼ EiIq(= (ʏv3VUe iA'%+<L au 7yGK:hbtB{cO \ۏH w#poB#_Zʉ ;3]]%8gcSStD<2L$s\}Lb\ j_:KDMΕ20mPh/p|Ay^vU$R^A7Iۙl*"O@[ĥ; F)~=M'qaoĴvuȧn؍j/YEa`[FI͉2 k!⋷)\ yr EQt̊sd um`; : BJL${Z+0Db^E06 V]'^_ۥ_B ymc=VO.\1UXOxx³({fyE~Cq ՏTfLӣrxnl'݁ CY/'Qu@>t ~5Cq'Wp EY"J]!}dhY*kҭ}VsIBKcYA!% .9Z!AUFw)0} aڱb2i[ [-z=#Afᔉ!뮯rڥ tUXJؕӁ!WT1V L ]]T,Qmzۉ[dymFQLjhh o/]o+(z/`+{Ċ,R)8^P{{o @KVyfMA (׎;:6DJ5cupa#F@ ͳҏU*RM] x(9-!.9j&X[AYE_KR[7n˙+Ӌc@4FX ߃KlqB5q$zR5Rgl%lk[3B|<1GHq$fqܛ 2X(+:b zo,LmҨ`l6@a,f1?IW_TzcvVChz_զ׬[+:/O`% CDR+0 !6 0 n3k.VVݛI!sR՚ Bz<@?(R{CpàU+u@<&zo16-q) e:ZFښٍaX,hV. Z \&[JqTvta" ӛg1Nq 5Iɝ̈Ye9{D͏4M F$YYPl\]WV8~֥;/c/.B@e C\mI,QlJ'`7O>%1Kw Ѽ_@ 5 PHC ͝ĩN,QSjz»bq^ :ney*UM)o|FeJ<%0_Ŭh18!pX7W(`sA>fLBqJTqQvq9s#}P)CO@-'a< V䐱.^㸾nSA0umlubR+?]zO0V%qu֨j3il V0uW~ȁ!(Ir=fn/PP!4)_^&=I ~} +X+ IN]jV)$$$hmF@5v=,9 42 p~4UJ\sc`pkK[E{5 k1=0|%:t QQ1{ti3lx*/okKK6ܗ:U.fN).(qœ(쎠Z༗n# pRh[Z7F .<]RWR!Ŷ'-)V 4Ř%X IH!2Ie]+O rwyNc]37uڕ.Eqi'ӞEе=9SIzaZ22Se6k]J@i{.^IPEEA-+ S F69 Ll9 ?tGͧJ#8d{A);쿌@ygz?)y(kɫu|0Θ$R/O\up,WAuRwWܘ{/ۀ*;ɦc[-Zh{'~ҪE,38`x%^z32N v@n=\g| {^G+C"*U& oƝ^A6(ЉdbC7AX>oR nEE zFa*>8 W@c5o%w b54wF$߾`{磿fE(lҜ¤FUxՓܮ -Kf$ , 1}q%z=5u)[7坩/6/[ D@@D]߰46A VBۮӝ0{p|Ь9%*gxONع=/vy,pˋRO"B3\ z |Γ'wMya*ş+Ȧ rlEWո ->ޖ{:h:1d= .$a+j[vz#uU\2Z/,zF*+cXc,/{[qVhR&U f(@jf^jwaMsL(b2{,gM8iI9ITu[mpqsT- &{!8f{&MO@̎YqlgR&<ĸ}~yS"1C SB}WW i5Be+Q1Iw9a08{ QcRZ†'bM X}7\##P;jEj Bhfꭀ>i /Ķ T䭲vTuF-Ɵ,\GcfB$! YP rQ]V#+%9f*IuxWf@^A娀!?k k w,dV$4mvAJ1uR I/pR7ÜDt }=7 \B_:7T,/QE3m`B7(q;އz?@ uUAb< |{qB,\4Au%*>k+/cK>((=9l@WEqطk:IJ"WVpX\z!/[e)S) X~Mתt$?"h6ޓڟ[R|]R^&Ghy &x6O`KTAGxk۲)QE9s'L@Cba)M2'kݙUq(OVZ܅c(k陖L;`Q7"H rtZ}2mʹa/x 9NQO^ZqYN,8OV \|ınlR6 wPx[,/hLev K.[D'iߋAHxmp;=A Y(ê*ǎuG2a\gc7 YWxOkP{뼑MђN@Nq6! EؒMvU9.$`ұ؉q&ʧs.eL?Ix4BϵR! t^Q&:ey؃j[*kXoooT_$@ܗ UPn2~lC*-+8P *UȔ7]Վ=18ULɳpȾ4@raJ6Co: @Ez+:Y!dseIN0p#3P5YX2U*,GI.خ;0U4FWXfx@.2AN\~ކǥ%Iyghb=U[aazΆ&*PGe2=2kF&(yT蕚UlM%'zQJ O Jxe O44K&"'i/kj7xX*ڡ9Y#'N Ua0ek cϥt(;{uKR7(i6,Hme4رIJm(; Ul+6PuT 0"[<֕{6m5@z$՜ųIV6 _7 `71@͢t節+Pqjiz8'aђ~K?A9*pPN2x:2Q ɰ>%7M76L?~%Cꭑ2 3isB[.=ahl^^'S"gyۇ_ ʃ_Ц66T1gWfNo_7jy`ɣ䜅yҔ$j/ !+u%9 :tcOOo\%K):8 x)ڗ邍\ .1؏lX#^ѳZ*7o 3[~M2.GC7L;1xg?A+,sgoPtr9b+y}_*"BA:wCyJ*@A_rriV(f[1W$C[F(} Hb .Aw@~jC` XhE> ;3%@bUhAr+ngx6Ynrk_ /?,3ҚJvS6"Fڴ_g|B\)s!閸3_#Xy9&} i~]u75 8 XQɁgM٥Ō3lڟLxÜ;Kи8bKmx IZ.>j{-~̾q-q(ZzC?6ُ9;sdrwL7V FΫVt |GJcPל'Lus9qk\ˍE5SaaC'6* q(?(v|֥}/?cPr@jRvRF]*Q7jxp@H !Rs_۹R ,nK `AUՖ_ L+.[1Mq. GۂOP-VKqy 9 AˇXX:qIwݲ`8A iZ~J@9[VO4K-7( f.[LĽg緓`&AmhnASrH6ZZnWё2[[j(.YZ7,7Ǝtibus6$ KO})L(B'ax4̷doѩzyd(l0W} ISBCr<ꤞa40>4.uܾ2cJ,u_' Lk uzfc X~}fA14E>G ᐼ4*W>#e63S ʧXhdXU>pz=˫4wڏn}fR0_:;9oAI栐UBsp~<̈́Qd"Zc b ? J$64^8=VFkD'ǫ"?Oqb#q1}G0)2D̕f8Ofc}禡:42_^2k>|G͛U1ݘj t/'CM˜G A%HvB2gei+Q1a cu\ 3 p :sO2E>]鳉ԥ+XE@& f iQ xg~_kڊR\4Z8=$R8\OqBn=/X^TFw4U5sq{xE~P,o\' Asy [t m0 >?yI"NGnӞu\@+g;B;ߩSFO}ʉw;: tٹ"Ѹp4s0ö2KM,0olRP1##2U#fDUgt`G '?hJ* Z|r[9dF*іߌN*gh 3S{G95g-,܌ms=P2bL?VGxTǶZ6n!/*0'0 dKZߛr/W[ɯ&C,AII6ClY˥RݷȦ9@8Hاx>ԡsb; hHrxcXHVÚ䗶%[ ,)i-ҵ֪N>v^T}'ⴉg(ؠgj|cnpzW:gS_jiV޶)wﳎHZᄆܞWDח|JZ ƥ^f6%&+ (u&:ڵxV}BY~XE8PvPK<'*0 kg[ѐap.z :15_];:ˁ&hJY> uSv1X\eb(yi|D0N ]xjb 0yTv8hUL.ԌQTɜ'^%9 }0y3MDK(N7P -d C.kM(~HV/bnl9?w2ؼR ="Pӆ,tD^eY}'oY&}2*@Cͺo~?ufIσHca~9Yƽ|6neR̡ߧsJ LSnԓh\t'ӥވ}77i/X3.D֧ws&ps̚O*Nrm/E'r_Z2 )Hzx;Z㊹oIôoȕ&u򓶷*=р1޸$ 6B̦~5JA \Bݸ5G8BJcC}m@l% HLPF@|bxli%iZ#BX%xB˳>AA!fƲtɅzU6q#v#'/uiO>o%̻% ɫz|Xiu* a;?TE譶 rIpvoJS(䨳S@&1T'~B_}F*0^C |+g詸P0[0Bx,_'Fd >1d2B:73yԲ0Q^-SԵ۫7=:f  iȳ$խwF3IMI{KW0/HüYl陡;f{FCZs`ƨGO/w[ E:v] [wU0N=~u\JƸ[oIR;Ų4V~U-HrM &9Kbx[(š0sS{"6 ƙxuDs@R`0a<FdGm" o%~ &/\KY$UJohRyt >=fue;mPzQdIJ:sYX~ [Fءs9T>bwW4r| Z>S1ؕK"Kz h?mDݩl<7!n1&ednu'ᴈc28^(7j];PeWCiXo֏GՊ$<_Krk"5!AXwU{.$ i(vvjG>k'Ja?'5XA~^FHelIkY8UDCkk]ՂKpl)MV7bq*>ShlkQ[r`Q>kH~gܼ+X ˇGlJKL1K8y~X' Oԉt]VzI!(rkƺ)r~9Sb8 ֨_#1&ÆAktԖVws$WZ2f`B5]OΡOFa4][[!Y7~lAoKW0Z#82#$}O1gt@o(Ƣzc~XzEp n_ϊ:|C9cwޥkjS&8HX9VQ[@s]dFAY;e MږE_َ-ISlAF{DCEI>܏Yӱ -6 ccRd)BZ۳1cwQy_I<Ϫw/ 1@o}B&t˕-zZJY䒃::UCєnxk\o7B v6ucx7<>kr뗬>=6ɘRUnaZB]>|K.Tѫɉc$TYtX![z,f{T>PQӃ tZ'&t(lO<8ǭ@_Ա|3O0{#;yrJ/Wof_@Ռ㤽w0[jsU4܇Z?,XT[+wE)Z_B?Ǣ+rDdsE&%[;cD(|.30*nsKÆCCw<:!C^^/8MҭI]xCIqP3n+4ᚎM ^N,qvѐpזw?i%p?YAˠcXthS'iJ:ى3P.[./0ȋ"<p|7 C926SJGszT{ \Ό.!ࡎmՆr:7smk gyբH{ʜeO[ ܽrNF2q] &2`1&Of͑P^ӎpG7G6 Jh/jcA1L:c>ń3Z`KYji?5F@]>;?o| 2ILsNDXӮ?FIK7E/9Gթʓ]JfL~CC R"w/+NmX "pD=A]b0?aйwm]'uїfQalHH'B^|J$k^>z$Uu c\1W9@Y(]|=GẢ|Xt@պת\1 q&Jԇ.jUQ/ o>46 'ڗv/d`{^m;A y9zAўLZ[s1mz K6F:aO{4 Ok:ur_{sa%/gOOJqPI^zcQzMAXFňF&OX: !ZXN.9Xn?/,_vXn(ƭG{s|H!d. .<;\T ?%1sM1% zK/p[[wl[b>q>)z] !10%fo]-2K[F+`'̫ jcj7㔺.E&^XL2.3vb}N񖳍jU )nw L ǥUh˾9ÿ́s$#OIͶI\(CG*ڞK9qiW9ij67Ž:D#gImoܿՐ4P@XV\d"OgQ zc\}3'jI͍9?L,0MhN1@%B?!jWXd)S!'L[_5+ͭԖnQ6$ H tvJ;-p%Fބo0݈۞D$I8O}s ]IdNj򜕀!tO` l֎;#8rګQZ sv,l,( mk7= 87_]?$&(ۮ[йiS U]:5).Q F!K6?f%J=;ߠ QJXADihNg| hIiiO]!sF;18>8*:+OChO-2϶43,M^ ]wd7p~r,(H-@wL(4Ҹ\WW7s?1 XbP0CdYHf3,K)!tuqPi1XLVgc&K x`QѢɃIO?)1q˧vL}5z1{?u:&;z$+ s;6T.bE$#)0fY=(ݶ|e5h}MNk t/ ۑæN@!h X!@ S^8kO$ M3gqx; eEkZK_+]B >W} I^Qx^ 6wxwdž5=e5 8܇-p1Lĕ's;\ZjF}YIi?TM,m_&@`?M0X۟o)};5_8BO|Ó놬!x4+s\15ԟD)xͺ!1QX`RW;$3~) {$;Fs]cN_G]I*|2DfWLb2"M=@UF3!(;*O)>V>޳Cf,AĬ|Qdlr'mr=&yش쵾 e 8Њ}[Ы-ߜLۣͶܢ#k~Wx7ay\ w}<51"bwK Yߓvg6*riNWeg`Ga1' Bn@Ot[F^}'\<it}Pit] k}Ex$?: Fr:5O"Re C8iD&&F#Vq-8^9ZA=EGoL)KKR9;++b}x7E)+upeaJU6v,'_ctgyeKL'O!Nfh#9:\a}w?JI 3Pl3P>38h u=ޏ4=B"3~f'|.y՗Ogފ uFOv/. m&v4k^iO1wZ~;}֯;@(k:Np3W]d܇&o'RUq Q@'&Ub?ftd*ׇ4sZ \zF9sNׄND4UqFTf3tag2]$#XW;Fl—)$^8%rw \:n#I10*.>:H suZ`\Zz\Jw^dm5W\E;oauvBDξiȒhκ:/5ZnQRsr>ߦ [zI,2UH30ZtiՔUÜ|+^eV?rPkIlMzO2i7c@FZǒ0E_w)ϐSߏ_ԁlh#fw~Y@CoeL&-w1u B"nl["yD*`a…J^C~xE'~'ȀΈ(~3( t<py͚;QhGSJl1sF#^;ez% _bvQ G P K rzˢ"s~×g"uJߐy=vW)t|H)N$qn<)[F{(KB 8 >λӈWF|ncm|}ۺ7 $Vvә*>M>ұZ*fbef0 WuԋPQ8{5S'n?=aȴZgr&<_,$8Wi1GSM8#~il~|Tpɬ=| Y$L>qE!L5+Ѐ`&7퇔ls 9zJLTGvwaE5\뇕l: d 6_oOp%=gnxHi@w]{vEPuw21S*Ub}jч7L,'PeE5K35(*E+9+Afs:! d}goG_4 u[ <1SH H]x8CzLzͦUלԔ3ק|+f\Aͯa?!p|%K!xS8[Y11tp1z,V?CWMs9E4Q!`[!:lyοDaS*M-~51LSB!::_1Gt^JsoM )Y"^;9gFy8;EDB*~!5G 6`&C;MW9nK &Ѕ.ui& ))y9 YR"!*MQKl se"5נ:[3H.^IʥjRN=c#y,0>>絛-`OLd0O-;"$sD@`F" S )؟ekF<4/[ jDK@U;cWgCH:tNM05 Xncɇi4 W} C{¤IJD~QTx`gMoZh7z!LEGB;. e=sgL(Ca1DEʌIAjV=Y*aZNnCyR4V<9㇦&*>gёYN)bxȓEc<P(51<]^xRR hM<5`Mj `mYu12 O̫_JK2+3H64&E'Q'-`"CNnp;0~78<7(-} >ClJQ6OȰvni`8K]P#@>E6疇(fNaI9͈O90w5x};Vޠ4_U@;ü_=>(P\dM1K*J(ELk4Hs%}uo=}(*pVʝ=*,-,Ts3H954ʅU>h6?PPĘ5%;u,$Cͮ;h&? rЫ >G] oBsnr<>fIfQVԫ%/yx*ֱHR`m׷7'_t`fWiN`X3MÛd8)3n' N "T)o|1v. uLz|&,${q v _ĞgBbɖAxmLs(@[fJ>Ê8Lo $А\3~}֫lGVwv^xzGl7\@4"⮊tf5N\ц 5x5=/ @]U[SWxg˺1n p$ 唁7XX T+?!IS"#89"P`Z5R.0"M 18ORvYO }8#,ld?~C]{4(Ap ]]9WRH뾚J&U)ViUgdž4{̡LTTߪ`XP(yu0[k6Ji昡*ymny-+.ܸSxrOREYcCD1AS Vn 4/DPb+.C{&V҄rI#)H]ICRLE%s9y&|1N|H@p!9Pi2f 0;Pa R-2Ӵ`n;yh)hG}#W%DLӵI+| aP:&o(Zr3qp3@ $*$|m1U4֗9Gy"CRĘd蛊*8IGo;M1TYN<߆mCbu3t9Ի]N ƍ^yee@NiKwP?3+;/N4ϼMV#JřsϳfkcƳ4T: aPl~`~ثnaFf|eaO9`" *dro` m='$weZfZ47k!Z.bnRMf@VS``ΗqoN#Pr 3o󱫅Ò_i(UYo4 tk\jrR[֣:%2M_61]@{O[Y"3བྷ?JfVAښ2=v fo+xp 5M,b -awVDhnKoMo!&ۜTTN uV2{43|f)$}ں8x 9#hd\=S^1fON% YϠ1CgN5A^qФ!4Q_+?U෱Hy'V!M{х$u sKoEkaRw=JҋG9rh |meU,[3Ͱ ў*(^\v_tO+ݗ(Ť(3Y%b\ŵPJBQOdd[]xm54C)bw$J]c\桹v_j3cGᇇZ e-cTekZuc$vBֶL1׎:['"!jT= w 6pߑv=\ E4ן;W#y%@dScH/C[|H, #Zқ괝MpYfj[ؑP`G7ׯw2%1RQ)࣋"iKizDz.b4;(v'HMX9Z~a2 ־n)d'l?I(_ aP]ݍxpaC; &9 6'0RݮV#ά 8Zp(YdT.1T1,$zD.i{jwW RfOr Nѿ@VJb2=2sAlAsK!#G3}!U^ԕf#Mn4Qq҇jcHmEi!iTjUIv&$btȡ(-RB.˾?e{ApyR54͐ ?p!P2*ꃕhSρi|KݛV@2`.Y{`!L~Q`䗧bCn{BH -W-[t /6QV_AW"5Dvz/6֗M75Ⱥnv~q!:0r SCdu]w0Q*eO@`+Cy8'ҽ0 {ڹmtz[?K|HFp#mw*w.CTcwZ6MT3˸ hidt5)#2&@"l1n{e5e|* 3iZ4WTuon(c HO.q_NO.hj ix}`|T;?c;q%rc}Ueal)Ɩ4V0Mq*|14$p 6T/Ѱ&<,o3!a66vnQ-\uRZ !@cya ~AY4Mja}{]Y&F_^ZyC/ !P7_c'K8"tӌJ)j xGZ*8^0) *D+۞SS &y=K; NakgVx2f q]+]po`1)+8>@`iNN!'[飖&YT.Y~f;M<~;сo\%u{_t:wT-RU#1钧M"7荭]|Mۋ&Qo#+n۷A1iG9/ <;IL6˱|;>Z*oeC,:߮JDSIt*,YjވVvV4:%տK*HhG~ʻOɻ&="xj*A)E튞d# %KcP4\avnտJ(V\eY H߽jŰ3aЎG {Uv5,ƸG`.!fOAT9ʝ 1I,^#M Y֧l:.U^15> ."fю|lVJE&z>h]fב_Cp#4"M( (YގMEUٳչop| zFX֍SX1Tg{5#YeF0f䘪?0V:n!ȝoRf򽅖! 簣CdJna?ޯ=)*{}u,3VQ&/fg^[-ߐղtS$^lD>Y}RhU[1vZS! >ODTuǖR VonلK 5ܽ?ݒe> CA9IGb6U It~T⦌!/-^ + rsp'}ohUvbb{x9xkKOGCYcIy۷~[Vb͘ht|P 8w:?I,9.ejJClaD||FAܩj--Z5PY$E&MCB/c`},C4oX|ݝU):sucD?u:O1qE=xLT}~~W{=?N(ZQ Q)D 1 !sRdS.j)x sr{w6J ?Bn%/➿ZVh5M?zh5?#*(:}1=3;xwm^KB ;4Ju< ώL{Iz&]&>dpg#s|]]^uLisƀ[ 2BNLq1q=rf#)4 8pʔk^xtK 3y:=}kWKVYjy//K8eL ^Zo {A7L&1`a+=Xlea#JXKZ I˨LPmn%߷K\#*G7hbQIo{샢/c= Ztɍ HGZN׈JU5ScVn<< +0{PƎ='̺8#mbMy:|PCW~SV|46͕tu[Ϲ5 WޚwЀ~Oώ7|+•C=P.$m+ǎWyu` -.2ꃢ)JOs/A˟8,^*ZHZ'u%b&ER"-U.G?ʗRfnO2Y5&琁"rT <.H'$ -\؟#ڸTM:U&_cUN0?Κ@#kPOAi9S4O" $8yM}СAcN#Uǂa2O.a@)[vc>A|gDmMg2J6w" U|W(ؼƝ~<8 ߞ<|iZ\?J*pEڧyBC&Y1,+Pb |N>ڻ26A.JB!|zYñVbF 捳 H? jd,yg|.|w'stTqVBA5 /7 ^MI/Rko i8t+mIZClZ̮nyQ'N-MbS У@~%#zd/_aEȁܴ^ŃUk9M˸)CKQwI!9<|̫C5A&ٯϩ9YwU]Y<g?$som䂮gn5oTJؠl#|fgz2φ>foeju{4WO>pʻHFGl"%WmqTJh h`-u) .0{v3s]bGō*o&VjP4C;lҿ<Ǖz4bU|zQɀ8} ,!C|}5?_ɸtSG "@vQ쩮I퐕TK:xzD.#" i& !@ :e}ah AψG#A<5Dv:*=&lTwNJh?FF@qCzMv*½8J)$|tWC+6Cbo5s1p-U1V2 [ 6:(!dW {Go"5?"'1c.E:Xp`0:jxaR=iߙ()zLߑH4~4]nE[3=%9/0Йw23 Kh"ZA@#l)kMS]/E)O"`о('"m2~Iq_??]xNٟ!57bDaj$4//y42xkZ;3'5䄚$f~EIh|A/1,̆)x3$7X%1yye227 1]8͵Tài|n _v륩)(y:)1KQߡKQll6#3`4H%D:>۴sd-Itӄ~:K(ǒDa[mEmq"UIKuc>&Sv7ap6 ޖx S:~A+ 'ҎMrl\q yj<֒b;rtPd rG#6/M{{.f&(ڤVЈgEzğ}40m|˺lyp` ֢_S4*M3`qmr\/-J8Bf5# o:J@6(F@65 mjmmiLrD%WAإk )T6^V*Pi(z:,_DޅrO{z<9sd+#-)" >Eqzzm~C)m}e]=+MױLdPy[103q9 >[1]5B2ᇞ2tPltB6 da"[AŇC=%̗XjU9g?wт++B>[Dff(,B :(8c.H+R8?_I6NZK@T|sk.K!z GD]X]WҜ%:e כgYLi1{Vr(^ylIK)#tM0BRE<ήw/a姹-Dn|Q<+KH"K'ׁfׅl0ɢ^t{Vfz×dܱiAu ؂g^-7\B92:BI;E_<`y0XDQY*K/swmbhv,j`n9.'OBk֠#AQzGQ>k#|oIF0蟘߾TTgqj+ ]ӛɿ yĀ ^dʽ蜡YFO@Geu" ɕi 'ysj\MP;M,zTYeFԷAxcCs|5}8]n^(dRU4zQ`1@E}USQ4T G|sOU唈y.k zGW(?PVTVV yRCRgLF [x]TyIqUsb/2؟0i`c7j}ψJlZzp&X6`~Ғwq MPd9s#0PD'dXӠ2w eTrO!k+tIv^˂6*s_ffQiZ'Uv~5;77># ft'E-ft{TزJ*u96ZtaHi04-׊$FXƶlX %V|6|JF]ҊGm28$`ث.cp 0ӵJnR<\1 Fm41m\Ukw5ѸJS䰙tH(*u_ٯ?l{RDl:8eP]hIG"A,z!v3dWV{!lF u(e6)!Zk<ֺ2vv $6b)`ݱ~Xľߖ-',hc\m?EeiO=@;Pqx"DTF'NQKvbv*1\"w6lf:4^ !j#Pu*x{EF j>r0>럶+sRfSs*zA\f0|P?m♏gX+/ :-g=۞O$ת~#lEv9+5/qCSe P^_>>xNؓz*ܴn8ˮ27&*+f NiLJDKec'sqܱG{Q>bdtN@<|P؞!*pж6"i_Y^ }k+^јmRI:2<zpd'Y UM1TDrrkJ`X!~χref["7A%@S;l_"EJU1!z[Fi6wMPMBx6)rb:pEE&t5Lzۊr9bil'~Es^.5Rsא$jYO?:ڿs#LɊ{ @|gBDTX u[TXdTLW6BHK] (Vw>M  li:09hu;zwȺ_ ~¾4Yll'l_ŕHnٿ_֨u-%CFi=fMJg٥, +HWQ 0.)V2Bp WZC.ysvf bXA~tHQS\Y\/ =reo_4M0=@>jc4c#eƚOآҾ/wpθ(| Kuu8۽1>! ׯ4ǷJ:y0j mFO ƪ߭<<)=ʫ6؄0NvVERd&ݕ\&1;$S Xu\iCbyr\9zD^;v]Y*6<ҕ*Xh4 ʑC>Hg0[7t )a&G"U[R\*`ztzzv7"BFoYO!KYUP[Gy- uwjÓuT]}9Sc lʙ] 'G{AGyFrvQ;t5X]x[Scx6}Gw\CPk}cJ "pf8[1S0qħLDC0yvfN[}i:`xն(َ[b_#4A*ߏƥ<h#?b)Ngڠ԰qMH?' K {5y7Rʲ uda>/#29JŻT_\fVPok{ޅ^8Ul TG&5w̿:ul](*e?;sͽߩ' kr%ӂMƊg]--=5wO, n@f X[|biMx/|XְKtu[Q k w`t0ܫ@=E|`qK*ަ2)c74P$AWwfyAtQZ3;c q]NDlJPD3BODx'A|&y*$8L2>$P䴽BMbu%,VSnמ ௪0XN9c<<6Jߺ\x̂PLv|MtY$G_̬'.PezwrxV&wP8gwWzDYæW@%63׻%0/d}lR 7 Nuc|Dt@=@ʢܘE{hj9o 7{6N 'A{7˃[SCtBUo 8ܹL2X Rnyˠ*t-}aKJ^l|`ZȰ$|\6g.'}WJ\+HB/{Գ"x0dKGnSXOL GHa<] 8}̩9) @=৘yȄ! UfWRfZCAGgVT2qX-:Pb!B ݒ<Zi4.n^kZɪmQ P*9$dBdLr =׾*iUnVs9FnA$;EC_57v.n1䔴5~K+i?=יI{vimTq>$A4#+5=aĄٯ {d֥5:Rx ℗xӿdjApۅrHr+@K?#+%ĔW6_gA婝-REIA˜x9Q0ч\a5m?6<@ >gsc)Þ(\JՋhn_u'<s /\p/q1j믲Y<2Q6%kAςKwH,Si"*)v7IFxv~ĶECWď; Pr'E SaݵA݅(@Z)\ljr?4uI@n:WVwmFu]bbb.knU23ȸCʉn4,Q7-mCp teID~IU* χd ZIVJ_u(Pv\ 4{g |v̻E😀Ju8F͊\~C:QmqBW_r7 $;RB_2Ҥ' ʻidk \])u+[V 1݌~2 V].O 0v#]>aYW~G@;{n8(Q!=igt@/YyYcUgRb/w9"ʬ V<#řqcfw?;4oNg44[-nWU +{1m=?/(iue_uimJRyz~a.eki4E @0#~ gY9a5XT,p\sXNCbZIWP4JDEĜ*ϱ`Ԅ;UT3Y}.8X=>MGKLɱ'߮1Q~RێyU|[!*n EBbmנ}Ą૲5r i#(*k3J#m(/.ɝH0"PY]a$? ŀ4 oj%^(l>Uw4ip炵>1 !5HWo!pN=LdٵF/<8$2^=3NY`\ [z?>9b6Mõ9nטm~$3qʃZ̊wu;l ܲǟ2Hџ7}Hw/W$DP :KMD2R (\ay{U{eTXKnHL?S3R[$7<,&@wl ) U@ct/Wm15GEfVj羾$լA_DdRDdةo:"B4l^y9]ڹT]auk|[[6S"~?N(7Maĸ|='0\qIEɕhjiaf`k {,odz~?>l5\nwN^΀<$HFQJS!WPFbͯM+DHjhn t"?CWNQ`b^1j۹6[ |3Sε4!SLXvsEWWYFZ: tjdş&q̂:^v$8@^wqgs8x 6\|i;w F/3dd†?x9.?zzj!^@^@ si]އ=|{#aAMPAv;h¨m-Vy~6mbLʂqݲ8 p4uߣ*Wt"u}T-_*&%^9D3/H=Kع[{nָIu/>}jqpWzX)=MWw~v rү| 7-u@Cw,H`7qh՚@!ihx$cǞaUYW$I( TO$`82EyR-a婒onW< o$>go#%{PKtfCd}jX@N.JSwU2}|*zT[I1A"5VHҩZ˹-C't]Osڱ$8 z,NoجwFx ?S]shΝ)|y7:80EBM"t2[LWv!{^z?4>>I> jRK/:p.*]8U>Թ3x$qUu7+)2 sOWMT1Cvrv]7V\c˦>7^p;M VnX+21P9\+R,tU> trЗ)!-~Ǭb0RFq=~ fkzg.R4)"4ר?ǻҙo#yʾaB/aKܐ(9X0ro曙2Ul_-npOEIqE& [s׸4?)(v~:6zp`KoׁQ2}`ӷՃ>UzG#rZ/{KT[Q;P:yHϕw-?>y/"؅N3K ff%kw154̑)z*wzx+-E]2ȃz))`p<9\ -A|hrz~i`H;u9=CF*һ E?f*}_Г-gU&a{:F2MGG[}/9@h7Ky9M$NiyяL#AzE.2JMo @hNXdQ 3ů_̯P+r 'D?kAPBV#z'F-KA,_]70C|^a N׳\qE//O@F?&,/C9 9}*Ljg}q FyQ<-Uѷ͊KmJ+;oHcYv^T3;Ac}pDծ ˁOxkW~O"+6:PH;E0 A(G ap ZtFzf t ьJ|aNU[Wl1\ gY6C#|iI;-mZ3ˇl@(Ý0p:MO8$@qn*o0Jh}P J r0o%g Ğ. ϲO1~HYq{CbU$6TR-B$ד޲5,W?$Rie8bqpu X[: 7z1LE8o{% 6Gkc}vgJ ʟ3V%{ɬV4q!GUԇ|(+l^Oz ӎG1z2kY\Aw*W&C69xj~T3y'>u.fl|tT8C,b N{wnptx;V >Jύ1ڒNgMiӢ=>pPl͵G+$kΑEE5z{1C{[61-& x&mUrjŬ}E18 YBܩ-\4`iRŇ[/-(҇vDb/9j7>ڹ̑,b,zWkX-̷[4^f@E"Ѿ5:xeKPL u76TZytyL/ 6U.%0XIEɘ뱵(&M?0gJdja&u {%}4:+EҐQTyYV T@`l|NaOA._L lC*$wZR;ˣ%;3(Q`^C.o57I i[fu72bE#f|i 0 }9ZW34l ee}+uߌhX\\:F[;Mq)tYDƗl *:W(Mjv6R()FС]8RLNwHIQ*XvZr4T\f5cd6B!<bc*d,:3tMc'cPETՐ"l2`# 4[2'|5= $^gFSdH!yq8챚'mMM׋˚hq9e+"^Yu?aL=p'T2XuQ4YKhHaGMܭTZx:SbYl|C4Cg[UX:O7D]I!a(utdȶG*&QͅJ1E&@W(PB"zgdbSbh"PUF a6T/1JH|ݚܖ5 "WW.5ȉ JPEPInJQ$i9G܀݅c"f|ʪMt#so5@uQrM`+ytkS3c>$u.1>HSURXW0(DՁG.TRߧk6'}wdM>2lV+/w7ݩAU;6VՋ39Ӓ5:pmc^})۸R1ӏQh-(-뺍CĆH@GVȶtO3)?Ǘ3$O ܶ3z=݁VA642:u C14G|ϟ(q!" ҹAdGC,o͊!YvʒF-Kt$xwY {7osߚj OpЃx&4C2\r?u-߁W^{.ͅ5QoNa,_\h^ɟ 顁zp=q0t& _mU4(f{ſ >Y}]'1GrY tpwvR@>B=nDݑ=`C+3;(| ShaW`,0LJd^0zj3f %̳血C[R3lJ4a'HºT.8Q>yg(5zx@Z tJaC zTh 7gh@}1 ,*8S;niA VـS.JH)KT`v@ vOۺJp5cr(p>RYkzn^ru7'L ƨz|x!,QSeYLqmzRPœnt!GTL#rQŷ_+e:j\-_1)YD m. H`tkr/`_kr[/NjA b+aË >mA%72Е[ 1D$β5{7"h'N`49{S&t6Ўjx+?E*Wue^ Կ4/,~/IXFZ+0MpKP9+DCsl\7|}ݑ Vl&;ߧjzU8cCnSk]\ZaUVvapR]+[aS7&p6nyv'iq[ Єz <0Ĉ[W:9~P.ʍ:d rG?͗d 5b^˅ՒxLt/ͤdԾqJv1XɎ.]èt CEġc2L n/.r/9jǠ^z)|  ְTf!vWu<?(t~ ?txbJP\?$A+;̦|* RIѕRdU^?1?8A;8ru["fRXjZSc%J쏋cnH>x;"nlZ2R9V(wczry8$ k̒QcPQ.0vn.> yʦ2[yW{ם3;1Nw:;"U=sdV@!`ez6Ƅ[i6:ҌV9NGfѹMa*ԐM(tU"I ݄dc"]3X }Xby,`b=mfJ;߉eFgΖ'~8TYQg(c'5i+~m{B7աfږl۟tAo1i>je,-KT+]oq<)"QKRtUW$geL5'0ov4p*[u;_P<9/6/AZ8D'CI CAnNv"m/>@KIwX,FٞRQC"q pɏ:.X.aumI( ~m0TȨ4c"%vCOQ0 nV?^s#L̽ ?\s Vy|}4K*wzKFNvdTq҆*a+}Ca|?fx S9er{GbD.ajp hp] (f,h5ONY^MBr$iDvފ޲ݩ)ܗ!/jqE2؃20a"3mI[)sX @FtZF~ڦ-ib$4Sd&2a̸›3kjHdg[8HF5arslm`]T7'SGv`eْ`i&`$zY:\epR{<=WW#~bb*̔49 86lY]xe~ UXE=U x$alWޏ/ 8ⰻ.K?c.p rCs>2i!2QK0(!VM\PK|}5AG^9 K>Jd8ZmT=;z^"gWK^4//0S>pbmCEjOgVwӹ S3e.1gCJv!*W_#O4=A-kՄ7poƞp51]:WbN7JqrGCy6eē!TB W"Y&>|k 9mZ3}'=CU{U@r2I%CYݓ'Svbz6LO6]L-/ k2v!2)wEZN|-+l+:aolO=Efzdf[$rEWr"4[r4N4cߏ?Nv¿&L,{o@[w%M"k!7ù7v 885[t XX޽ueH䛶Mְ6*l("R۴vlQ? 9>5!J87=;#%V/]n"0뱳(8'xKXO+U!HFfѳ(qU|Je}b!YJnj¸ivR꒰QO/&(ZG Nt6}#qWkl}X&ŏ>u^ ?g9.X8i9*fpka2%<8>r/D.׍FR. CH_oI5@2sbUp!gy\d]h?9(F80PQLja?dJs|T0~LԯQkǨ lS|6q13; SA&*L7V-Sy >x5 20Vb$ :ٺ˵%L|@3ºHJѵM"ľ_2ȝ=2G6_3 _ L$3/f0}RL0̭*o`hUGNDwWPҘijxG8VO,n"pRZ.ErZ+QLO$Sbtd1Iov -8k+&_@iD:H?g\L0Fz1K> H3٥bW'zK3e8l|- r1Ub7C<.hqN@N3vN[pbVb$_$Ghs|&Dv2M΀FJ_A/,$ڪ.xe;mTeR&CCD3E]C%5\ð d v6~J߿*#W3I UߏeuS_S5fчb*'=` }>0 YZAER/data/CPS1988.rda0000644000176200001440000027712413616365110013260 0ustar liggesusers7zXZi"6!XK])TW"nRʟKMd[_;zkx,MF^Qḙ o( N`:NL j2@$:WQP*Kh+AZS6SiN@ܶu;*>x>xˤnp:[ Z751O93F" odNRfPKN7PW4pDٵ8'ű<#U਋SQ%W3&D~> j&f~7UIrk"e2W|; ` Ϣhib&UT`pYiWu_\ '` JFJ,1(˃V30^ztrAgױ4檕(;Dmx0E٤2_TY$QQTr.$P (SYx8q,1[rt4n-H2&f0#Bae(i P!*3tV9p/Ff:[k!VдkF>ICs7\œǠHZ)IzE=BrY$?1 JIzJfIY*緒v% +=u!"XNL6O}M>}޸$@g$[9 [/JY_tL!_;R+ox[&SFp|cʽK߆ 989[F+x.`8pn.gt &' z AiA+:l=NZЗlօ%냠8)q̘ ^'I 㪴*{˜(c1Zb'jȖ1-~Q=E.3LRFu8j}&q9 U=Ss^UTi6#PKL.CB n}h-(aI$Kb2)Zݑ(ctTYga^ g75"RCѠ7U$)a ȶ*t_S<0S7"kAkFZOUm&9By}x/TtU[vѯ,;Q/Z-GO7݄Hy,킞SB7lj-cZa b'[OFzflp33zR9K@@~i1Y)e߻#JtyПpHQh/Թ^' inNC9FSȣkOXs 1X纲A=^;7ѴIGc!-U;ԠTTHvR($yCwxuO'Y-ϗ"4]@#-/H*Ĩu5؄=-\e%!@v>qzNr_T X-(JoS">P4MWB^ ]&ʜZ(~FK6T+z6HFI i JMaذNہ6m :[o.4Y z]pGib|zW'R3,kh>9ۍ|0JXspL85yp(|uq_.NݮpuFK* CָG$:)]7ƛt 7$vrvG9,i8r>_B15(>R,%]~g(d1{8N9¹_ZI{ލ'UNOW+ϲч:E!0V~Ɖ2eQr94U38XU {(vm-|%\`/v턿cfLT-;S'(yE kgNIqxh:JX:;Ţ F.|/Q/-HjLT2BHZ ,xZ|Yڑ[ 8&FLbc\ I&_#tv|Nd'!0ic#xOVHGvcOmY͍Ҁ(J&Mw*G !)f\z?G{)9}~'=oQaBU¦.!]qH|E'MxCQކ[%$ʭZ9&mݶOkz]gDi1mIv0ךڧnkÜ:;CL*E|m\3 #)̍ڛЂʈ{IҎT(2c#% OVZHɿ2(+nQFU|jF@>ǷDFC"yg ǬLŢ\[,"SOOW_C ¨ w>|Hd|8 e&Y̆DnۡcvZo*%S\>ӗvqq$,v?$swq,YϓnzjwU!;9YZ׮l Z%q:BAɒʊ[ㆴ2 JMlY#'+|aȉh`ٞCR]\5& ܆A0fzwrNKpPΐj(Y Y[,lQgsHg\V:D*%jYz ]PBtϩR&:P L@Icbл*JzUDkv3PAvhL0$T up+՘Ϋ | M(ge>-&gVTZ%38n[8EoVfmBjZ*5LaϧGNԏ1ct*r*7#+Z7=$?'<餂(["#V.;AQ])O()D56onBz Os4Fѷx]z)U9.{PQuS_EH:S6 \WHk*h#O,T(@*DF  =: U%5E9CVh/6s+=gb3wхhwѵA`X낌5dM](tm؇xZ<\Q#4-ʜ+=MoeˀR,bZw1^ #F.~̄cFeҡA<7~>\K,P%۱rf.yX6L,z*5D0ZjVsٛ9/Wj#$>G]B |zv7iwUk58oy^hpۺ QX8%CI0իt UecS5Ws԰F$"N=c Mtp Sg[_ pf%$}8"NN?fhHTȹ"g | fD=ٍEe-ǒ>m9F󃁣ƜIfJ=ӽ$zHxWn6A 0틽+$R?r"80LVt}zn^WSop]8븣(A3ljsiwC>I[KjT+i --/,Fp*- xy7 S};*Z 1 eN2|:]C*ϡAROX ]Mc:@@gq ׫VMZ!"}qxZ`5Bw ,_c~p1eΉ>._ICH8Yc檒˪W> :$ v?w 9#Ee܏iX<$=&)֛h f^p&߀2qM\=cxfJI&RA)>-c~4T$y1W@iay}On91eAMٷaKTzZ\>暕(h  Crl5=Ѕvy4_~)[]pgb[[ Ng MF0 țR;&Bby3U;}= SU!Hu\D>+ͳt[O'rΧٱ*dΔ[\^pOڑhgo\{y a]'xc/BF2+Eg2t=KxA?Uքx&n@=W4'&ۣpdWq@x.Z_  ?2JM7{nnC?/퓾e4 htm6$T:k ulz ,O(Wy S A1YJV:ߠ m*9կZ/ wiA_/VQDxIiTiHNc/ѧcݙ޶[.Xtixf?^ЍF "MgX?YG \%#C`u;uO+æ<آ1VPSBqv%]iƀ|S\γ({ahj)[(Q/aRlK:ak BTKuܡNH^X>Լ fe'}vz<'S \KaDVqdfs71snao7ՇoKmSػ ^eu:8:@<[XKZ9j) &=7b! HrZi6[AYB^]W6%8/oR9&P&TGRLӆ$5A#bB,8ӣ<>Ө!cvNZqd9~X:MW=,U J셂?ISs.ƜlYDϚqvܨue:,y8x@0 $ i %#q B-UbWJcG2ұ٤;F%B%6f =4W`x/EEC^4/اJEdc iрje>Vɹc_H`Bm<&lx:2S0]誕7m .'c3I ͫ2^9Hx'p( 6?$=݋E8i:ou+Jkul%(ttOm/ L !1ӑ;z7kfsBWXnT4[LVC\ E.雊5 >pP팲HNY9AŊD3=zN~s8I Sadok2iR-6hc2y~jQl00yeSz&SKgC%B*%J:Դ;@tV}b 鹺sCg86SB/8a l`\ڀ ֦p5~Ɇ$^(qE /೼_Azeo45i:IPW* B As]`*A_lY($BXɃgunmy[r܈G e$ Sa&N QoZ"Ԝ&1_Bwp6~":2'(,ikͭ $*xriWC݉.J3lL(WP*gB©7cJX:t=pL5]MڲdZj%Tr]I^Sed'^n9M2fpg(2%,α PH?YB_􇝈P2TX_Aq,]E\F/mMF6-䛖O{mܨlsxp.zEs'5d\X-<"Ι"uf4.+ 7:pE5%_%jHi9铐?"Zܢ,przqgߖ/u+C,Z yb{6p4yD`y-7M%rxߜeT 0X;/(e,J܄6l˻s@\h_O޶\L1dQS p=QpK!< :v FZJ+ sԀb=Ui9X"G;餂uڗ$q>aI:m3\4H".oNU96K?q@f$P%%gn@VA&7C1 P^^oYh/ۑeS_``-AڊȬLdJSoC?_m>d 8AXYm{rli'ꘌ۷vu@oZ)d{LT\ID^]sK<پ&(2bSDs^|?QU"K@Bأ?y3MCiP,7Ә"IBVP4!PO0A*ιL!,VUG_[ LahW3'sN,LD%yarPK#B#'3uja{.+=RtL謹MNS{E=HՓĴe)1mbLK:Z ~3?\ jH-qwo,JV JS:(w4m=Gnn6 &d~ɽ\@ 9-.oIT&$cnM)g]'wF64μ[z|-*l^ &Mۗ0$ϑm+Y׼EoYĢCiUYoONMa ʗJUU H5w#'-u۝v%2Kxӆ8bk C@9 qQ2O!^C.649rUQBsG9)8c[2kiSR34hc(a+560|~ 8vb>N`L絖G&%j}?zD!3T{z*fyQF\qphA_xrdihzCJ2o%#j^ɋ=ke;" vd DܿK`pibF=_$^2-uI,<u8gv2,FRxx ;$t_^_ø\::MNiLMao@U,*2>3¾"VuGX^ß\cFZ@ D P1V:IV{ -dNǖ&>((*M`[̩O-X"Wos?``YʝpDmnİ(k+ Ȇfu*nnAio.~ƿ) 7+3mb;'s#N :5jz?J!uyz*RG j\lG+G^ !a*MJ{ $!wRYG|(eREqlHvKtQCj>h($jZ1\Qz'S=+LېqB؟".a&a/dɲ&nt u #dPD jMX dɫ{.Pe̚?qs's1FD2W)â<Q)AkIsIOy7Y ULRU-yPo[' "w":jUlu`-KŴtK!HCat[›ˋ5Q5شxaS-TSAuy:þ.;J-|wԲ4I w DB8loxm@Mߌgޮ )#F7ZQV~>#!JΎOү)(NB=+GЌId)O65Os$*`߭a<'*cp8-V ΋[q,'ԽU) vQ˃ZlqKXLwf}Y#D^kUUC-}}$7!iA]BH0aӪ<~j϶aH Wò TrVXOZǦ)9-[˘5 ^$ }p7׳x/TB}(U-Fm6D%zM.sk@ u~+9[_A T60 aVejڸ;Ew]A2{   Atju//j0J*_" G0--L$)y̤UOO$z TbM_Y6lgKa}tl=znj/BHc ΰ^O˺eS:*OxQ#,iN NU7dH滖tLJ5-\'*j/>2tq{RV`^Mzgxڗu&Z\yϓSl:&8R!809j/wCo BBR8zĀFlb|Mo<&Dn9:tz۫G#ue?FIi#9%_UcD;fR~G돂Yiڦ+;%0y]Y(ʚ DR@FPTWFlJ@ m#C 4(c=*Nf9|Jnm9d̑wG:>NqX1/Hƫ@F h<я *P[+|~bU؎6Vk Jx6h[)\š! u`S>ێT\_b>%kIN9XeDRp拚 C ,dNǺT&ru?D Ri׾&4/"+ ?( TPzI55e9EΤZaz I U:F74]I~==nLv祈dܬa4ƶAA3j{dyE2߳b1; ? AF0)&ϩg'Da4ɂ+3nHӦ. ^ED|ؕE0:- wY4EGl@³/֡ *NVYf {6XfZͦ@y@f-"9c%e/!P.iJau(v rG9s>#޹ݻ14T;hQ5نxHS'(TJ.tM_[!0s9-)]dDQŜQϢc;;*qr8>xZץRoL[{l8#zHmqBCiˉ*B~NД,N|$<wG~Ƭ$_fisgioazu_ [hEc9A>ҙlT0˧@%eDN/چq6f Ec+6kiмr?-2'Zm+!7oxK!j4fBq#xVd~OY[m$z*w`iq7a đxIpTJGtRbʂw$ J3HM\*dd3* K/6w֖DThyq?xb!o7e0Gzr.ڏK ##Ιx8?n ɇG~ETt|/ɚz 3Kdb*}[@Aq^U[<x7/`#NlW5$=c>.&ӹal(+_bcL.k+yljh -Շ&$ih5vI]SC ϜWyʳd2z !)Pǁob̥[iyt8v2QμND0Rb^vW. cYNs$x1Q|ƫ|~Rp?88HQQoս e%ff)OhPN0;>g@|l.tZRzΡ/|ck';50V +=֍Z_d]'"̅aƥy)?s_ֻvaA_7^Jܨ\CnF"nm4)sʊ.htD/D%jxz@$k(prYo7Q[4YM*,~{V,"0iP$@/YwYgϜ U3YGڴ:`OƏf[&PtrBc00Q v(.=,Q{opQg]fDgT"1Pڴ"k 5-L2[gҖUĤ޶q3EJTTWW"8}= +/}¬jSt혎֯{b ZPvAL5 +;J<]޻^ZrS乊V 8'C1xcYJo8{I[e_R|9)zXFʌG|3=knDʅ.&ZIuyL\+)(گ فų bDѐLwYm/ cU<'J+< rQc)`^3x1#g]?p)M*?ߋ$rAY)| ~ޭ4SL c\d k KN_U6 $CաO޴_aq4U*Kw- k=ȃՋߧ.cvJ;q4 'P*!mmP޷uT8PVrhO)ԋ|!ZxEv2/7Ys 9H47'1ZPC,M*~|Nɟg^/~)f1^tzϧ$am՝=ګ"?:L :MN b (hQ A1~ј(1f"!΀TfbKk+̋eڱ{/-Y[<](w e'ZRl3X$+s ;c ]QHƦgVTn 7pG VΜ^S7j-es88)mzm܀br9\7{=!SFǶ̀_xO众o<5$d'21pebEC ]z8$F,r~:cD?{dsOגT túnvi|(P{Bo/ k݋랰O T3xry]`~b Ka@S"4$:'P{㻘L1YDשylC 1v_f33PS :ByxLs Bd U5 .EʏDfip3.Rv⩎ !v#lS|hAױS_2{8C\ƺ!$ZyuEAsMjQce𿲍Q&6%~m0VMu$׃Kn+Vj-n蘈D^v\i=Dme,)8> x|۹ N,|HB{9uG1UgN]:oKtϰfM;z^L^.,6?`XK"s\C7GũҲѭzB9,R ֚Ef`]uP|f_п!V \;_}i{ z|QOvMR1:7kKkF3s{2ұuJx+J旤"e[ZudSe3/ȔwUk qf| (6y 0?7g=x0XQ-f t+$4*QN_ݮ#UC[ӻP  `Gv(w b6 u%0cJ3BLVZ,K,C@gq g&jU[qN$W)hvۣ g }hp:Ar*F9J?fe }V#* +<߄ ߓս=&vnGx]ǏW?1! tE𼕆ilI!tmA/6Zo#SY=Ig)Ja$q'?ħџyve v29zu ֧"=Ib} Wb$a(N)/ {Ti,!!fBx]*c2"86{뼬~<%`ҜȺdVbJvbX,\ۈ-b'C,CZo&]}+sLӜttL9Yv8I0S%u8_Wz 8ݶ䑋='܁-,.~Y@V>7x$bb\ BIٵ&R u 0,z}7 @έ6/9ڇ Mwz;5ηภ]Ty$ĩ$B^l};7۩}q[7v1 Z;e$V_iɵ(&Z||4^u¸Q"q;"J׾~T|<׼Pty$KGJ#{D)4XT{3J"zBV;3c9(EU8PLGn{DVMc-?-#ܤσH&y|E֢㪴ao. &,$z0s-n'Yo7i2UȵJ*}1%d7&;̫-֥RguTcpз慪{vK5z=2Zfa{_oYzuR'6z n5?WL[{U}PܝPH߾M?tU?T#VA֋u^*¦Awx|ԋD`1ƏY=sQ]aS%y<ͳT4C A,00`'a*Ÿ xREUVZE [ :wSooQx1 p >ޟ:,R+XkK宓tn%q*?W,?}]M3e~[ադiHfQxb+\*dw%e rݖ:ҏvaK?| XWm>\Υ-VQzGb~Ƨ; IgЖ릙ª\y Sx 3s7 Zq8[d篸oο d\p%6nTtrXkҀhD$ʖk;2@շk`acM |jQPƩWN䀕N eq}) (5=KA=DL w|CX1в|fLmj?Jn2xg@x ʮiľf?l~f4KѶTCWzCWnƠ .risa!Hq1:ء|<0dN|(^0dz#io^Qa)XtWe EC3H1\mYv͹zQm&FU+&8N(=l3̪CẸ~&ܨXMչc3%.n" ?)m-)Gt]/1YJro_@Fd9ol<,m M49wsI-]W$o4?qܪmׂNsǮd8X$ W_E}B@&jyhU t 7'q{LŘ* ;VQ86uHEW`-3F0<384u hsགTVSݟVtb[4zZBs^5N)J|hpp\P V)g aB\nv\U}IP@4Z )&bˡh/S̛2g<9X_$iMnPo$#eRKS0ޟ$_ 0XԧnF޺SP|[98g~b +&X"uMsy#V̚CP]i=eg,苡᪋\=cAlzHI8YD?g^>SlAb{`J͘Ohd܉֯>'6 8G[9f;PyKez3x}(JٚT -CHa_1oa97$ n\h{Γ%$k.̯^nISWp X`xC+m״f/Fzrr}*% ?X"j3>|B@' EކA7 MYM>n2"Lǃg޸^Bm׋RǶ4A# ;{ P|w V sHo0Qyq}v'+ Чl=mE6[N]l,PcʕM SM-nA:#c7@%TiLƾrv+i`{_R'G󹯚״cm2ɥRd,mî{lB,r@'J?Hi/)O9`ldAaGIrn]^_2D[d$) F2#fg?<;;?ʴf} v,|I.SK;:Ԥ.&S"텿 Odk꒓$?dY.(үH+M:M#rh5짃i c@+&nW!7K T p+Na:0Mؽ7*zXʰ|'>42!WƤbN>7 Aׯ;_v9tsyEj`3ieSy( #>HK_ԜE D$]I))椵7\/l^ّ8m:8? `͡?F@;مGvPЪH^ r;rXkOWGUj<kpgn9vӁm}&&~tn#If*xg3Cq nBiJJ\XbUtt)V ]P,t 3B= fd@tf=ʼ < FL+ўwK͕oF_X2 4jysOlC5n4mf$M3B<$f͗ie&"< .,3H6.A_wE[Fe;ޟ':lks3Pàٯtq]M,F`_݄2Ї3 +u8<*׈^}peRi(b3E:'S2ͤzn2P GVSWszVMxiGXܱ_z@Uo6^ѐwv:o_nlfpmxt@πGR`3;/'Y@-OD2X[;%'B,xoɋy>"qhٍkנWZNpnǝt=%oMEg-K_%^*LҪ0F+Ph+vpٰnN$;5B'ҜjzN^n pH8,UNoruH ;2*!B#.T_َdʽt\l/2Agg iņjoXK񛫡THɡ4%[޿LĴU%{.IL~o$ߴzlzht!v\$r\5с@8^/g>w:i-<1Q ԣ?/:O. rSQH|]d?pMmz*6,!I9_l4PJi:Ao:mJlp5}&Ԉ6n g ظw=F"uy'JÂeWLqcH0f=+{y54ǵ(Վ?睭)f8zSfC]A*+\pB`xVPKHj^R_߽xuaZQƴ[e͒6.+B@1KT< B$JUb/d_1Q6"4d$- -ʦV<\= Ky߮Lf*wrZ0 a_Q5*Wq0{1K5_{qêLs zFVH !XmTWyt/"ʛo BIF]ϳܐ/B֪LL̒ ?֑H~^֙kܠ) -84 0ܤhn= EJЙS==y,d L\p >'U5^HG#!HIN$ [024f'?v c+098+ӭ=cKϼ!B8h 6|儰XF@~T03{WXanr[\{}§ݾ/?#;ABjF)v@pN9"Qm^^z|}WΌwKݖbOO@ 8͢v,*cĻ|:%SYew !&DM+t 5 1v)_AqNԸsu:y$<6 Q<(Y:7m<:'+%ѨbcTCj8)/[Yj"(ju= r6Jw̏C}NҎ=u 2Mb+G5F @-PtF_ՕN/Hڴ쮞%ۻ8$[np'F][P7n+Zf9*GWIa +5%IpfF]lŞVbs:i-(~ _7 wI3% Kä]+( Z;%Enecs'xZtnGͩRYCVAч\4RGM( AbzVlNGY!NV}MUbzi-lG |dp7ShRfG$b'lÂN8$3 gh%5vFLћ/JC-)c@dpc;{oT6R;vw¨HN$[c!o4T{=RznRzcGNiח.</rF.nR,% ' ;%reڄ"j yKr9{U AFj[:gh_"V]gRъ_ufuoITx&M<DZ>W9PAlN7H&DaTUq SC/' {~6f M հ-PtwbOj %'Õ ٰې`}]1BqK\WX fdRM;E A=P._簏"|޹ PlٌQ6y@yֽB!ZF=7lҡaʩT<|GXK?[ z2X d'iqcMK hol A(?3J3OL` u*?^O}-!WFGF?[Tu&9^i~כ"VKAkSǝ}Hj)-eDm#kߢJRw;/_Ox*@˙qx}ih(W1)G.0]9{V2=`,ӋnwuCRDujR0:E#=C@)ھK\܆;%IZx7bz.pߠQ] 8 yG, yV9*¬YϢΤe]ƜkYĞ~НMѭ&t1KonUnhic&ٵz>֖l"$!p?kv=\-/ 8?76c w`7n5IŃ"^dV?lJݼd18CwC92w^SO7UKI}i{")նGL}%`pn: DΪ{ 9zܕR>–xPEG0_ºeJ?-ZγDgoƇY-56D#f2 'HRV-M[(’ڬ¡DaZY,|;}]R5֨^|"m?V]ن{+5>E >yA{!  氾}lwxmuIHV*>kͻU{-:sFߐ%N7Ex>RcrF:-K+;HVMv0q|[!BjՄ yT]w-kL-<w;gL>&R;2kD*-z-& w+Ԍ0ުž k :9a+#|'G,ҤG1ϑA>W逎4}Me"-b9O[24$>:5Aa[$]x\YrCox;6AI!]'H> Pk@Cxm̘7:( &۝BkIL_3.ijq1mm4Ŵ<)HZ&Lf32u/;s Fr-RR]9?F<@?c Jx{+%l!!R8vКa|fHJ$FW? pS.q٨Gt@`P<750K(TqYWn;< #=(mHzaŸyDHU:*`"{) XS:rƸkGB4\2k`mi}r`x^XAIۺmņOA8|gN3v M ~I\u6_*k8Lh'A^&SK}G? z Nl21VKI $LSC)i8bĴZuMVTԢ1"3Eq`#=Ms~V*w҄dO,Dyt2mmdt'h' 7gO[ Sb-ܺYذŘMJEKb4U5XkKw%=y@JUH32cGRVj'yᗂة0fK kuۯBp{fcQ)  Z7^Tg(dEI2ZR?v:&GЉ69h{_T Ӫi%L?Xe>9}2W^9B` QނlkKzjHFD8Š]uA}\t*[hKW}ϰ$%rS4ރrxUzUqG;OѶW]+B A^w(m3ogasYڝ>V{ MwK>ژђ>$P)(?CֵOk_,|Tqrs T, \J&$0iCU:d۬{; dKȥNBWtC^;GP9s=ˡw_KjmI͊I'/֯:0nVh8?W {.͵4?nm\A# +9/8WXD#~FD52yq6uQ~>!23?*k %~GJLw\wd m۠CzԐD-1t^Y.!h$\N Դ W.t&H؟u= E9$'zw6b&]QNgp/B%hYIS\*ffU~àp#2ICk_H]hf:JuF#e3?PuX0Bຊű`)w)Bggb.7b >\琒ra='[L[\DusKݩrD'LmY <&|h׬MtT` z~*H:%G}%` vAw K9[1f lmxw^_hݕ)˾p- Q&&w{7&+=6P+%H5'ZVnnu{[s T 0X4 ?yda҅NoTƔ!J}E#1}V_w E7̡;tA]vӠ_IZͷz 5su+Pf@\/N2t0U?$jxXc#KR3'WtH A[t]b.?!op׮<@ѹwߡ'n}{j#O.x Q@ﳇ!{ ]Xc& &`cZ2/5n~S AC <I:9(kJ}Xc%/:ao{|LӬ42Gy!H;x5"W-hCEX`4."׈ph 4׊vuCz+M}.Xd:<x~C`:ʹ4߈o1>Z5*8,NHv_I@(nΚL,&i'x{cK|[/N]lV$&)]WUy~/_jg{,9LZڄzgWq V=?cH< Sl_מG@Pȳb(XY A AWy, v7:R$-l~%5S{sFkow' =aĊ.ޝB0L`^+V<&*2΂&W]?6aY+Xd"xa]2B'@gy`Gy77Iϝn4:ؔ洿(9C0|΍)bMf"vdR>xssN 7j2|SWsd{-x9§|` Mj7WjBs?ҘL[Uy q3>u-j"JUj{xF|Z | &p\T橩DFH lDԓF6|]􇲡%虨Kq6}ȏ8~8e/Ep,2/ !W@u'C{ϧ|\"x3(~`, qRGkǡS/ޏz;fDƜ)FGd)<IPS$NozȄX#7sLAs*dy!8y_ϭk@P 2ɸBZszkmf_j̵M4kֵ4MN>G_+QW.]ʭJmo֤1!CB)N,̰t1L)aGOaM"O' WrB s/c8.(Q)8 !FΨ 8[gcKóܱo+8]em5"PH0S' GPݍPXk43]s;*雩|`&K0ڤ7ҸN֒po0e4 }*kٞeFTٜW";X…?Vinjݍ{;._Y @p1y)@ Ѷ“ p+4_[vxzĸh/ zwod]"ENЎ?,u7ͅ'u5ѵ,nb$cx軪_ !K%0˨ck|l'ߥΞ]r XUhIysҘR/C̢hZ egoqA7L?zj)|2fU |5o_F;c \omQ3JᡣAD/^RlV'@ 9*5Vʧ]#{-?d2sQd.J3Bo4N2*`Qu<"G!FRJPotlX$@؜?jphP޴ȚIYO#$ϟz*̏QbũXbrI@U!R,xl4u+Get~ѡNrinYƄis<c %~󚣨 !w[>h Pɩho;27> $l@mKV*\]dSFBNgUl\xv7z1Wa5l;|3}D<ڇqOIǢIC[)U=.q@o7i[Ub >>%w-@C,2νLDyoD1($%*Se`fhG5BIE@GFPAX};u 2 sp<Iヲӻ+8DUh ;Nֻi! ]\^Pdgڱ ),tnxB>ɦ*V`Fqe2hLPm6H\L#{ۦ>-Rm5idSHy13h&1& xes~\;>!\ ;x7 }~pU{룎ٕd.¹mNO8ML(aǚ}nI6ϔ8vo6u wvx=A0V݋cq?x':d.A8VATҧx\reoȘ64#G^n6aPD)ZLjpB iT蜜oߒ]f6mW^tB2+.+TA:/Z}z\bN8D^-IY~g}=O.In$ 6<0RF\}Q#Yu>ćf7䴏c%P1eg5hE -Ho:6>rlsn4GkSEAZo),L#qNJ!j*e4{te`&%^lv[-@FȿkCe;w""7ZpVLyQBFWS P65mC>On6'|0 5K.<;5."3ā.ubpVZPϬ"5 ݲ ꧤ\/@l8,)G+ˍY9?'.9)t>Z7m;S'\np8L  OPp'g};h~l[cَ7OL!1<%?r oL}-\I˘ oK3Ԓr=g%^ZB Ǔ3M:] f2\a12") T`m\(Amϵ 4Y \?wwpK U /0|PXj%{e°&B/2{F eOQ,a6 J*jYSV*g9lRFاR;+2b-Xf1<ۻԍ…\{K،7p]sAy' ]dG\J j!+ nag@G3W kZf>`gq衹۷M{)pr^g\|ѴDL2A>OJx^ yyR kHWtI_s>;ŠI[i$Z-ɥFG(S|".A$1.c{svڌ^?;斟&cC#h 7#eHB^{c;o ]̎tvVLлI)b|$Dol-Roj - x)Bs89gܶ3.6vGQ'' P|oPIK]#e"yUJnu">^C~F"¨ݘPY;'\:PAUq# MU5hh6vs9Ө4ثQ? 7d #Gr8Y޴ye,X' 4cMk("h팓u%&Jh <,!S C(\hU, ⢺Zgs)@(x!XR .[2C:L/.0E&ThF:DŽBB/ !r|fj^I[/3 V prrQ@[݃@,/r`Bثk=S(,\ *R E\ֲJ&w'MSIUZ1ǁӆ91Fj t- _21ҳz$ Miƣ|PeOsX)FTm6$}}s oww@Vi 4QvrbUTacn;?Ma|-g>tyÿTTx>>+b18x//$=CjJNI±Bit}~7Z[U*DέIĦwwXukDBJe4]W5Ϗ$8צI%OD|43W(8L!W : Xp}Eq>[mГѺVט_Vge//zx4W[*&+n6q3&9?Hw[Z- qt:Wm @ݰQ %(Mo.Շ"=aX  zMUTf.wJjL?UhfPdĉGNL*(µ,t˪I<6l|{>hC4 h],o@~TVP&K{d dM#1jm%v(7]&sY@L= H{.x+!t,o1Bv{Bei(9㼹&ɳ2n`r_Q5~9? #uC#u/`T,UH4ۀHAJ[rw? &uuxsVM@_%!z' `hnAwɒZ18(+14aHs|%m)J/Ff֏+BlYk' >?ʽd N}97s&w{F)OA* kyahet>#;`^-9C(v%yR_~ 쁓6 s<1v3d!Pi,~ .'#^^ W q}wΒO*# SĤIp/IlW1MGב J Ա[b!I) -9*q&DYl;K=5.Y_(kdy֝j V7 }Gy!bSTCv}z0U<,yˠcb/]l6^ƃتe'zMpdz=q~9%u0(݁KJ\'#XB߹MD<UٝS_nӉNJLހ8<6pPcuyuMpR;). dp0Q6ʼny;*i 0>lVa+8 \SQ-jxRJ;pWQ_;W0/mG :`5\՗Ae2YH|QĜؕjFTuN~f)Uv+\f罋5 rϧK_ Q;0N}c2@2BSFW 4R%\f,Wd+j.񤶝<i0Kĭӡ:X7` ﬣ ]jb̝m0A2Mxt}~Fl@ڴ^y TЗ('Y&J+|w!rWpk 8z l%箟P#.i:QY?o%PRܔġ‹4s p+DYTFn6=򵀳43߭ 3#~_f!@*1D?gho`޽%lؚ)7d7*B=#mA bφiD^xӁ tih@/y~N=NF4 DaQ ٕ-tgXpP +Qk4VٚR^FLQ1dCR $B*J&4U*~8Xm>3.ww$kjխ*&{d<ZވH$yW}'U/lDnS\\.G+|A:Ǩ׉<7bVhN Iu3 vl̶ R*RWaEm9eY(-|RtxvcR@f;߹2ZN7 LA쇭<%1$i;z$3ےg+ VEƺEa&Wb?W[PV4A?5nxI+S=[$T#~xEv@Ę -"t, 9IyĖ&&ֱ X1ɤD{FrO9:)u<+/>F}WX7+Z2 PTP5!_\})`33D#np"WHW讠J av9/$3́ἶhY)X}CU:B'vKXGſ/rw!x 8d_\}䍑Y|uB B516iٓG w03AA$axf/=S4 @B'jQBf,?)WV'q綻-9!`Bw@ ]H +t<]$Fm9萲G#-[$pD lvX!2n=dIN7x=@H\"+(H\ͳٴ^@4l1*s\l^tܿS1Қˎ G(ӻȖ+~Hub;3pRC})~-RcWQ^es ǭ*=)6[uZ"FBoG1Te1;7\RxO2r`#X%*D %M-4-3k\Sf8bԗ̒Ϲ2;H!D6Yc8e=5>Bc!9r\gηK?ͬz\ j0-wn 2F ^W>VиYۍ1j) VucI4F+[wdEP1 @juA ..[ցh+-'q#dex[FlfU{@{d֟\m ~RⷞSs$="ܹycW 1F*WY_ 8n25 j  6ֵ\XLc~3t:~%d?}.4D:%a@I/@oYrfT'C)VxIUAwie8c0w rԔ>j͏ljakpvO|iXgdV*5WJdffl1ٗHgDx(1seX_W[fV!U-PmD:tomO +Kߖe@)!I{|Ѷ$}LP-,,|\&ԠcH#漎37Q臝ina#+'h @f0p ~JBp۞͠~ ]lzS@.wrf^((zY;xb[+ļd*n<W+o%}w( !H#m(jk>@ $p L [䤱NXN)YT& l8 (IkO7f[ }2C{`ˮY=O9קD)rmPo;P*/h 呷BRzR_(.14*=Nrrfp!}Pqo."(ъi)L3Ǣ #lVqMim HSLh~qO㑵? R}ف[@y$@|3 XnRێ·k5Dm`R~CV얄/uB |[|,ҽd f}u(:ߔj[[k)U{~0k<%_ n-|@$oٱjC,0rn|֓@Xh3:V$)Dӓ{^͞먹)y׏aGQHR9\Db p&ô6OS>观 !42LEk&Jƒv/Pe$>!:jZgImܱID2Y{cN!]E5i˫-q9ߠ+~hO28=OHO-Pφj, *kh=纫\N]԰Rhƀc_|y XbREeTڗy@'T{-}tsm28fiVGejWؕ64=24p.@cΫ"xSK&Pv#ʦ2)l[ZުKߠHPJn>o;89x]8Q^/X\p{+X3N`~DDJb5|P.h:CaFPT=8uZs{I(Yg.oepo`jfPHxLy c] An K^BR_B׸u`nYeadla!EΌAKFar!BJԞ|ҿY Vr#<*dj [)~Rtn`9{UMGEaJ[mSs~%,>/N-m]EhU!bB`3OĮ={? fb!@݂ 5@yiLޅ&`AH4Q1'ȏ͇s/~ vCC=#Sq[=Btt:WȤoG'hD'Vx^S 0pv}DPJz ;WGZ HQnvً͋HGpt&`|AMUb'+;C 1'"F3])t׵Pϙ].<~ߕun{5 AWά1}p*>8D97?ڍK( Y6\")ҵ[D[z|Ap\ȩm%# X{B#-%-ǝ9&y SO㿺HG뎥6R.˺%I$N p6XQ{hҸ,!7,Rp] cS7 ?T u-oƥa;T)~RP1[r<wΘ`ŭZez_k\cm؏l!{rW7 I[6BwBݦ=DA dlcׯ0O/FӦ^w3vf˨S΃ ++]WyJ_;ֱjɒ;mm'BW^d >l*5 {⸅wMgS/[  M\*qa 1Wrk4Æ0[#uwUp]+UF d'nZ 2O$ftkKja2y"?u=aeJihK%+Ku:5zRQ2yG-XAsN3X?ZLHQbK+_2 /Q1d*Ӗp [.l²rГHg'=L" vnRPD;9&~o1etOxKy71wGTg[TU `:* CdE[* /Y捑Z@ ŁCWTLt)% L l[ miC,X6"YC]t 7@ӻ& PU(~)~'3햔=F>X=)YyGwTzN`-(>6W Je:#Hv&U\-sYD긆tUj}oR+MxB>7⮈ޟS|8{xp@Id`pձ|S¾ avMp5> br|̜:_XwSp_?dbir󟺒*CJC֒ĕoW'wx  Mce'ݦqIՌ5wU'RBmAI:p^ҵPhF]ZE;)?sɄG&! .'zS"u5;iF0Xl77$;5781 PpOWԂE_]3K[9`gab?&.>a6 6ف;>91 miX32̓4D=-yqm~ՅaG u h]͙%i6,ĞⰦs5Z)袜|D!jr x~ؐYȗX־d0ր @?1#= ߢ"rtZܔ`XCu;khŎݏ%8)K]ЕxA]-'5CIT߽~L$=2_ Be x&n{ pI0oKs9P?L%rCsLg7>XϏ! bqvw1F{U .^A瞾I~?94`M.o}ݽXhd9st&tۡoAkT] iґI/-*P"f]"iH/7k^D64<%.\~s`}<4RmRNKnfAS֭|U|Ni;EI@:uBS괙bnEvј0_i=xݕӊy{K(LˎI8M{ % ~5_ݧĢVqqč-I#`L$¼v%PٚRّmbBѡCq|6`زE潚ԍ%0 ݸ;A0nQ@sx*+8?³I^@T6_xX#L- $KTָkG< R6fHnujg`5ϞjoKgGF.~PЎ*}=7OVo`,35n [3}ۣ1)#Y)X~]i)d _ M'82>^QEs-' D2fn];o=j@ڊ"lE@^iy+V&O.T@ :krphqO9dw B3޽@m)+^:Hj&,.hj(z.dbog/Qs…2C#qԹ*4`?&"$2rdDZ s0].&*< \`W]}  D|#Uo,f zvfQRK2l ?HABxQ(>kUH4M){1}2M5G:.Eu%X'Sm|z#ΟQP̏QqOicEbÞЭOn_3鯁6RkJx2Εrܿ8~ 8k#&/C1]C@зtqaĿՃ:qSF̞Bz(L(IiLSE^

^ag;T)QB1ZK/O1 Y\Ѧn1Y1)?x>~A崘bx< +ll EUlo1_*ܻG <70I!”L Q9{Q?z#ɣ^fh715>3s}'Ԛf8}yP[g0naxuGa^hY4S2EQDTGXYG&#_(a7q&=`oyakI>M9_u|㘚;|͂#JјÝ˸d"9q  ?ZJ&M ?ڃR@HPH,_m{cL+R(ḨRL\&1I(,Tj(q׊G!k&sHƒ)yP,;ۡdL7=%(l3%rD%TǢ}\dzBZFIF|a*#줇EBx2{14J`!\SM2*A+MAeƙۭYy06}?CU`*`D/Z9T~ 4y-|](|HC3 6.=5obVߌ32ܰ te7ÊiSthm`(gleJ/^\I٬/꒸S>8W!@њ5K-I [! R1D& 5C d犿=.Ix=rmw}At %lIu**'D1h>t(>l{2]}ε_(-Fr,.SFtGz|i1|#8i uտ1Jbs8"Qug@GŮ@Ǝ#Prnw0j$ra!"/3V/$>IBX)WcQTE(a{]YR|Q;ۥdT?;D gLt'{甠J! {ݪ?8aZrQQۨ$ u溱ǝ;eY =]OtSIge@p^m.t83W(D~nfۦ;m($_7ya O9@.pw zG)ӑεE[$b P !JuP2:jҮ*ztϨ6!~U&ζV="+0<}cAG]ۍJ b3Hp+/WO_S~}Z݈=J@S^<6;¿"_YoW`_ ѕ,O&`VB6_rXpI8©Y7tj}d(caӺ*ʰ,W7Tˋ|O] ,g)h(>YD5(;7.T5:7ۖ= !#T;x–Y+j~;M s}aTfD)=dxcFn'3l π{eE:-G{ggy_[jztC Fur Ec :Q&^HBfqf/Ve M*T/HFd"FMc>sުQ(\֒Ι%N)Y)AXPvz7YTw]jԼ/UT*.u@*cf*Jg&*kS 1wĮBUPxs֬l*. ?u4>QN\aL)fPҤ$4۳{X:(^+-\P -:c'#iөJ-MU*f{{2V!sEq-Q SBݾJƄ Lw/s:|#5?FE.1C ҶB^oy핑Eۭ|^lȢe4·ćk+@;s+b>4^݆KYώس_:x_Q89^YͬŃFv^T;9t۟ѥ;5w4k,h+$@L*g z7G~.PU "|6PViPwt݂(KM6q" s%'|}\mmPmِ !hYQN*ҹꛦ0򓨠 C\vH8t>5P,!!8+dz=gk@Tss ikiU$1ˇz->P<;Ā;717 ۚ|*4mO{mC$T ))I(WJ,4 \<1!Z բ2$t&vbM?;"8 =iu|HPi#ɬ@;_,k"w\pb(BP /Ҁ+%>G/Q%@,&60.ہ.^cɓˈ8 ?LEMz6MDP!~$w?'bO+nP4'}So$|Q~Z"p`llJԯo{b2l[|!*(avlc#qH/8SiőDIB>Q ao>v] +:CWV(hgBhFЀ漏$O^m@? [f ұoŋ-ݒ悩3|]$a*-'ز%Zo\*P88">}A̎>q,1&6V+g'R:[=Ob'P踟$_US⚡N7b{%20 lgDDpN4W79?Csa0gɪ k̈;N9U_& FU'>.`+E֚S3RLR\5-k"uSmSY=Tۥ~wv7Už$cJ}tc>楏M#wzݎ[yV`9C;K aG㙃KX뮐*LoѧU%,7źÖ(tlP܎t?Y Iұ(3FRgk}icԢ3g i&'J<7 .@0z* /eEޔȌHamlykXvt _i]f|n-cN2>7w0T,n'a=G?ۤs}  *N8M}} 'P3&SVN"ri6N@/=/<ƵdF׳" 9)YkP{B1 ukD`7RӪp)4MiLa~.*ҔԫV??>(-ZbԲuRwC_],Ͼh 8f/]xMT**ODZ"En7BAm?lCEH" I/uKqe&t{`%t)93rRI8kj18Tc̄O[eOco'WVt?KWv6N<졭ޟ7ݢ1U^hײɀEcJ7cWT*Z ; . +x,[R]Tn4bz;*7i9C?ףTeeX#h<# T5bN[ˢ-L`k#ցizߏAa|UyxUqJskrG'4Hąlj{o4i9S9?8wcު{+' gg0LЪ\~7_ub2ڱVW7ioUNV *꿕.cKtl#?ڬBQrD< Ǘ=jJ/.n59z@R`Kajm1`zV7/^x`vF;|*G. >TyOF`a7'l]4 t*E" euPA0K^Q':j0\cN^ *}B1yN9qP;Bg:PؙĬ]w_5ݟE1f^.'lcdjUI|^'0цe !@[TPr^wmt>!ʤ+mI04DaS&r(fAֺN&u]JXvH;Gsq[3nr&,Fȏc^z` W$Ϳ}پģGmؘwp17oLxoh V7v+0 d" g7{s@"HT|%xg;uJc 9+~wIU F'1-$XP^aiƓen$ ]s02AX'&'M: #1s/1^<;ϓSE&/ X#L +h(К7EQ)" V/ {Pv5B d@d"F@yrRtҏ8M8G<;ÊaPLWȃ/ùW I7H4rZ9잍k:V=d70ӄfخ@k) rظy,gW#HYg1FSB}["|n`9}PV H.PˑN3/әi)Y_hT|5'껣olA#s.!߀+oWskh.m!wsQў!Ǻ*s+x1S|&XhR?} 9te)EQ5T.ڳWh}}ğ?;,bkF pTef mZ{'ID/_af؉K°ϯ$Ԕ=ڼ<ޱ,eYhB"7HՅjJ""E1e^S!)? /QE.H{6#7`,= 0$PfI)dc P㉞UÖTUcJ^C՚Yڕj\Te 'ewuu"g$⃺Ԟj񳈈}#\,~*[Rc1f76j=w~0,' ~kd5$]wK3ǓriD]eXB(:"A ZF p9-|L)wӷ7rn$fTA- T--0ƪQ؉GE?e }\1SM̈Gtԁ7]hMCQ\I}JJ\6'Џ*ݼs Gk Ɯ*ӽ965U`gr 3-Z2o.3b"Yǥ:|2p7hE[YrQϻ 5,7EC"۷0@>Xdb&[~MqB?YoMS m 7w!b}\`O'^"aA xo;R*,g5jLJ .Uu/7ڭ' 6҆01f$ LnS^"Ag$IiBOQ1 (ID%Lnȑ[h>RƑG,P?<K]j?S%}v]MA I/Cm\*@%)qd˴^bJAgɱ3eqUbl3:99핓DeiD[o]p"ӘcU"\,1blq`'oy2hĹO=Y:߁*d.b a!/q d,@rnTFa%ŚnjDRz'&㮰6Z#0ܝxD9ig\yc::hG-=B2}Nꡦ\+!5i:OΐovIt$|6qIЁ!nYgWE[53'v뮀 &mZDyӢ/.pMV>EJ=#쵵ƥ>m_!Ut'ly}!nx3:p ӥi87Aj4o(rwyAeF.EOf[R(RcjqH6B5߉" >q c  ޙ- poɫ,NS:5T/<,]k}pVb乧$޹˄+4~ 4,`|tpVPD83ĄxJNY˘TV^aq&S/Elg~MryF@#6PXg%w&`-Nf9?n*ķ;c D׏V*/vK><_HoF`b8`jp.C>Z{R"/?_kشhGywΒx pCrޕQGV%n2GT0|% ׷DDrt\ZKyiZhAjzR4*g|4)OEbv[I\6ާIW뫼.+I W4ѧ(:;:92d{SLoG:?x>V-DcۃT=ms?@~ʽxF͊.͡I{x38bZP~<[nUWbdT.Q-uay$E՗.1|ltk @wo7PE.T#~iZ2'9}<|3%~fsSoQt.搀BOJ0=FY+Io|wYoR:j^8YB,z-{9LQ\!QY͊A:{=!M|_,m(bs?e#GV؉@\ IbI>чt6v%7KN7n!$}16p;:LYQpQ٨rg)8=F-xP.2~jY$"D~b?w߂WrjN+z#ki#+uCZW4j"ym(\o *|!ۢJT-@:C}|^n_`W邶 <%g3=!JJfCp4H"-QX#45$E 5u WQ1@f  '@Sq*ئ#0كI>/I/C w}hX#`*5c]_NS#]H^Z^5zJrka v_ߴT +pKU{;H9"ՋŅ.J1dCZ-bu[!SfpcPze($qlgxU9HH&:FdTU j.VjmBE:;Ec@gfmvZ"~խ]1ma1UUzW>w{n!pЈ^RF紋7 j%ZJa"(ʕPuG_@ 6E15 mea9#|'y*O-DeT++ͨ>uJ WH\k5rhc"cS]9dԠ9WPX7?tM*d2azڴ/٨(s9vu *ʵȉUn`zf NNR%ԕ4um쎯a`xNm$=6ӱǺűG"2}3%q39~6,@δL]q8r)V\xNu_@hWv'#?|_޶ajR>ĘPJĩyr󈦥 zmفIF}ZTaMuY,،9 K (il4o*w5OF,9&'YБ&S Ý/1H2bZwtƀՠ/If jx L&8V(5153&5#t )|>sU0 nXen3 qq VMs@?r yjU |Q´z ]tdS5 L |%@ 5zy/5np2:H}.lҙ-Ȑݺl~! djTdzkӿxY^hpj#4d}=k⒕L{j2 ˜*:dH)%,,9cxG蚜1Ҿ,bR_]QlAJh ?edg$N75g%A/2KFRr4k>)6*?)s~]9^N!T:ib"I!(3griqגuG?RZ_f)&%80~3")fS)!zJ$*Nh/m=#;Qr"瑜 ]:yJϷ)t#G/6gO^XO<)rgļCFƂ˜w;^#0byWx<7?@9jygP$&!ʼbUdP d -D{PO;'#&n+pmomsM4VVe220E{yys?/פ*jdj~LbH2Q-=mP_,~Z ^J]p|jXKg)hrA!K\)Ri#ACSٝv)1DNJa@(#K_`uU݉7|ʓ8h]j$ 9[F5B"BӴ x.AZ/J~_!|mOZ4,3GWv](uY:5M\I %^"ͩy ec6bm #}i1Ê$Ŗ Wi~9jr$XD"B, 65q#=j:,Zhb+'$q jgY+/P#ؚٷ*5XwlGʤ"yjZlCzMvdUvcH&y̎'FQ]3p@!rڪmWI$e|BC"TƸ @,xE`Kpn5**~S@DѢ֝bBdGSj2PyS߄~QC[9~1϶|+8a:Yğu;uR R[p{#UaiuyjԢPLs2+9=$c̨H7ci]j PԨ|KThj_(IW&4g<QQr4]gDpmڰ4 urGFjD7Iw 7䫜"D;Y/3{;nLn\k+' 3F?y\jV3Ҋr 7fPC+\鮜+ ` v)/E+Z-v L` / 4ZjxG$:]- <)|֖RCz&= e암] G` EuLs$6yȲ&m:)p‡R' 9(~ 8*,2|j7gҋ^8]`Zwcw+TAkO Dx)#XA+Rpqcՙ wu0Z  i&iɮ`?oE}aze%;r䜆Ћ5MIF-#L=¹'GX\QbR6L}~_`[Aˑ}l9vݍ6k JJ?}/Ї zȫ<&qC\Yk5_~jO`npař˘ES9Xqs_^}]Eڤuu csPք Ê.)ʢmp@x"Svv)p]eI bblm!)z]l!3"8/38 hk Fލ~l]6cVaHw).ЃjfrV1a)jE2 !{&׈Q豘م܍^S]T}螼<' 22MƓԂ&cX`v( ωZ. cż?e 9lb^ISy%cZK +a个O HtԡF 5&؉nt#e88)”;#Lt+;KD]`05&hߘØ="PKbXbx > .Y%E4}%D" ؗ'J|;F|`Uq|)8r~{v/Od[.e+jl*6.E&$!5 bB q%"C(LI¨ԯ(Tq/ el ?H"2WkNyڌ!6]J_M8&P+DZεHR~=)"C+ }?r(AԐn!Y(-|3A3*20Ȱ~9:R_W@.Kv,@=u>ӿj{/ a8>Ӣ zesTCsP RJX{ _] ϑ%z<9KMHVhi{>O(u !UV1.iPu Й 'qYTf%QuzVK, ǽG&QT -!zK@:IZmo[7;=+ *SLo,2 hH{`powi>'wžq\4]ʜ2)WqQ gO*9${=ь\M D\Iօ)!S+/suMc81K`? Vfr޺lI94qP5Z|%斟rҥ)҇ J_XnGdyŕ N6~d9ܰw<0v7 ꠎH̥P\r,UI)؊+"͸ \t0ŸWQ=p&q]S,;dנ^½3$7R|z*~<nس_mt`v^+hz[5CCnي]l pRn!_3CPyݛ]?oaP 0tHU~d@QkBw&Pl԰ ̗"wxJCnNL|2+Tiu.n B|Qe#" kȻ%zѮ^Wnde=[`[k|4Mt=N};s45ܟI9rz0=1]kv˦⹘y'ўԱd)xm.Img gbO0qXdm:b1&Z"@W{LS/ Uysjb5E(=xo+ B;y[=<3e-8"-wVP&uw;2;V2 t18SkC+;N=m"$b2ܡc``( *2o>c2ifeNMثLpD }SBvЁr\*.,0xD:;ȩͲe<02LȦo'I!޹:3銡b8t!?j9컊ZU]nZN؃bQЭ B)\ q6@nՈ>m q|f^;ٙqfz;-4]4V. Y74:X :L,P@cJhX{..K&!T27$*O2eϚ$TŸi}YƇ#Qф).UtaMXn׏V$j(K8D_Bt&N6ߙn*톳+%$az\ 8e9W#y嬁EcK&zEA~U2u yТ!3݌%lȂmdQ02IGY}d"*8]=!ړ~K$$"v'')(-ki>>eUPM^bl/q:J­0*, t}l2N"ujrZ -Mnk=1e\2qX/14qϳ佧uy 3bD>[EcaKר%aN&n>UҪߙ C:dhf^U~ dUB.֍JvO]_= ˇeq/ζEzR7|`6ֹd*>?ѝ wф": 펅an^pkvP4;xZ$%^#Ћh~ ܴ'E"D^ESvtdz&H)m#6tSVt氉SR:MGzVѴA Pi[~B74Zu?#\EE-e"TjJȼ+k F7}&>[W1i^E⻭0i({D"xsܳ՘6*F|oh5.HEQtU'>g2$ op7&Becv>_p' t)bK!U!32Eļ:=H}&B:n9=7 8YflMƝ{}Ber]q4JBNM')I5ɽkc&:t43':(#|_MH0Glak:z )U@T>ij.lL/ ugSX|`eҤ l1JPؖf $noT6 bڜazA}DZ .e]p9Eܺ_13O崣&;S^q`hA v.HK@󔉒2@W3MwBL2|Єe~ωa+ic‰cXp˷H ڙo&XrfׇfDdtʲ+pBa" f\?y@JVre6+Һ0CA>PuIKG0qw@O!Bb0ގ@:U[Eѫ[,ŕX.ճ ȁm}h !NdN|E4cN=ኺ0'G˥6͟ݒ?J-^Y+>G^.Z%2f"?F1y,?f+ lhYijT^~jAEXɧǕC*16rlw8)dhIT_adF"VvHcP*X^mn—GYٻ ↔*5e 6:[$1=Zmh(w3mrO {H,2(sOW~6‹dk3?~jxi\QP9H[7*q x N`A cS/'@vq{p%ZO4f jfCɛF'GC3)%?W&B.'&^,d$~a_bk.DY>*`(D+`Hf,+3BĈ$#뻄JjTqg}98Lzϊt<`Fjpх4&g毄qnN\(j@G=F8zvLjQv>mXVVKQ WaLd0 KLiS!&.Z>FC+ٞd #E(ZwW*CUӊf9-90E{H~30xxTh(?{X O}~{p}sA%1E7,?0ѐ-;2\%Kq6#uYR5"h|&M?ܯJw/0.*_Ixv:AQ9XCP0v$RJ<)e&qjb nm4D)ȳٚΙ_4>r]IS|j_aޜn'~[_գ}xO'!ڒ7F,ۨq '<ܮG>͑Mv |lQP"JhцNb)[[ D+9)^^K֩en]R76{ډnNVg7e(&&-|_!?˵>Jdܜt7ؾfCl&Odޜ26eϿ"=OVI<}| o6AZCM&cJL5W T6?ƻ *ϼk%d~. s \Z‹}2#n%RYәt1d A!РAFwXL';/h,Ym[=b\~ dӝ 3eL^}R kM3}.)+Iwt@4ְ$.|5^jN2"{Ij $8t ^zxL:1Ρ#v> Kg8?kbE9 _F/D_mlntrd].Yƒ_ڛ/kV^ (yI h/>blD6(^ ?&s/>tRZ8V7"=siv:( |E}4SW?Y.WMU w MC١ؓ ̯}EnE$8:Dx7B] =^sDwtq<{r'+MuiG8/ hLNNhu6Fb.-U: DB5IIWot긯~<؜<Ҁ)%8CO,Ȱ0#Vg(Ok&ޅǍ `7v8f.v=i[w܆}GETtUz!? Cswpm*[ hNJmf{:KPq DCYR.sX &@X_R5O%o?a\|LOۻ q6k)UUs]>jUsyfYC(253EeTє/Wlt~q>v=vfDdžK#^7 /- YޥǕ]/~;rHraVMp"*{ ;=q_ G} -9DIсtC{5)t܍@h oEn˞WYH*0(&ge٤ҍЊ*7~nE8HX"02DOm[3# 4%ĆJh@D3DxAwe7hm Hc#.QxDqSDݫ=|>q9\1Z*kv\jǧA- n'MBrXWL8ucf5a?{T=2 dv:ImTtR >>tY5~dw}> E<1хjeg#*`|anuˉ8\t_godXT)C?eRƻjhBhٹw  +,ar:S$6 {e Av@"i,~uLX~Ei_53AQNc Dp[aۛyɦpD5\{$_`uНE cBM&l2$O-z" eصBi93:Ȇ~0%̃bx(FH'3̵.ZYOXKP0 f9:;GHߙYӭoc. n/1 v8J]u De޷}-` ;0:M=ː5 +G=)M(_$zԂ$~Yu+K%镤 ,*H`s T5Eo(FoVO/ Db!.BӞɿ\ %L?o57Hȇ>p 2ݷx)$ICkF#!RXDor[R3~IN99,ܗvoܵ>1?|%'Mbi0f qs~8z]E*%VT9H^9xjH/A h n0 !TE[ f7'$ΎI͙O\9Xm)O[ӧQӫxό}o+&M7N{̺˫R"{#2QjNp/a@קѳOy42 \lR:KgoWR*/E]",0r쾎Z\$2e"w/0mCcEZ#.Be 3Lem4mimȘAW鍓|ڨ㽣^hﮖ'ky *e&1i{Rh@<ga{%j'H^X\)1rpԩͲ E[_7\>CbhȡR\#jf ˭qYT8i8 PIE[pcx)6x@d"Ԟj .KvtOW7oyMjq&&MNn gOrMt&8$ ".A#g{a_gƖNtV{ ṧ# )$XF᯺vk*,<(×nї)2 ]S䉋'%jem4OŽ\jh(. ./6DbL$2ʳ^./jcWCsVoByO!붋JR&_i@Żęx2h\ImDИ(Jt-bHaӨ cX·mjpS-*K@îikl,Cz!밳@{%x`F. @[d1K-bV39XSOI6X5a -.8M A}`#i>"IG! jZy} 11{WGkݿͳGL*,EI`6p22I5F/jp涥cZCu<3jKcMPQ-E\$ul C ui";&jPl/O5K,D0wGEz9yFeJ#MrwCBlEa'ڠ.6T _1f!O }oPUй /ca%/J|]Y)olz6?/x"A.]9@,=M MZF&e0~kք-_աn>|B|Ǥ+^;G9eN' k9H}fFǻ6'aƹ8tV. /ƥnBHۆ҄P0'')ĩeX5ue#M$£/gE١3yRs*za抰縰[ZO;%I㱤\h\(TT` &̤׿T3D*H)B `쫾*ШBK(L|gm8Oec=핯񩯅|XۋE]KVn=!+ՆXUgɴ8lbiB*$~*w;U^YwU'sD_(NП-@7,:~^}HE[B謹$ʁP˥&ό)V<k^H_ʗX'2VW~ Y"!͕PYuSzur6=k7zM dj 2 /1CH _eX-8Lʪ 03 >uF$t-_&h^Zlj: @V|S01Fb  VJCz&=tOa&ҏA7U+dZ0B SzP-k` X0Dx;L9'Ci99#4AC:٫ :N!7}zv\!xWv{^BLtM ' Z&&W9Ct%+LeypL' \ !TEGH\"u)T, _t Z ڢBB#Pܛ}ٜ}W5 ZKPFmB9!^nQ{tE~3Č$EH]vVӊj א)I8`kaeNGU;B=zO<ݻ%T+6k=mw}TD6ROA 2NJ7C|/1Kn ?bP8RB=c ~q͟w +j.@#IJJy8݉V# xtb2DIgLNlHpB淹Ɛ)+NtSw[dA4):.rR~,A[Ja̅|V .U`Co!uSm _#vJˡ$zCA4!4+mT˔߰JIvksp!k |ylKvjhߎѻ|U1᡾Ւ o .fC z\I/~tһ)@dMVހ!? 9𥙷?=cB#߮W6¡>yZm|fHLf <2x ~Muy[lp*l_遁*/6iMC9s\n(d,H@i@rZkdM*-AUh1%&~oB@HF6z(J0*cbө ߰ށ$l'F?a;R=Y&AO?5#0R7@50JVNu`3 hQ%jRTD{tdgPl3a]c?dpgh4UUX6(e3[;+#ڹ*4't 6<3efM[biĖ-ڿtG̝~a-c| ľuA^L=1T$4G;"S LPknn|N8H=? |#!!W<8X$b'iʒ^~Z{j(yC]mTR!ʏDr_h8"撺W*;ҁc%Y&Y@ qTm5-.YLm VjbK6w1~ vI%`wBCS@hs.%[C[ۗ rq\|oc`ᢅ1:,}U5EP BVKxKP B@G R}#KJu{gzl.!.v(2JMt.u\T=(9x2FE\)GZHPtnvBj>=͋m6lU#ra5dvFQ q%YTS{pt (ma$@;N>>*.vʀ6‘a)"n Y?JwGA9ă2FW ޏD|,70)J!*΅ .{2xh 팀("Α_c!lSO0o{F`6K:bq2*HJYw:G-J8$^:'ͮv-):l뫖. eV6+&f8)1C{|~O8ٱS @\,eA,A ́&4-ѺF%$>pLiA/:w2=Ѥ*dd}>穹f_n~pL\M\z{4!.ww\ۚvfcSt4Ft:K/ST o,MtS%U]1(I INTD~m<# K;A!Kc8喖@T~|_jYdshy?8?Q5h+23 Ljf? zf J(1AD<>Fc\<,Baωa?Tמ dSE>`XyUNvR2m[lWm砪D3^}>U?ݔH=ҡM7oyIs)RxFy4g`Y焟LE[,d%a3jeg|讓d}oDzsċ^ !x_U7){ 9܅Dhp8ov | g熁 6_6hji"KoAS7 ӡAdhT@U]L@I+tG8 —A3X!I4EAP5)ن'SeY;ƻwc3>L:ovڏxoX;wBY&5I].4@-:2EIsxSq4mzEʧjm Bo(=ldtY1~A]W{9yK8bȸ45MB%5AIY4Lػ?)xibϦ670^q(:Oגw/_FϋFv6rP/&*;(C wF EެtXWجfí%@&?/~t([j1(U(#ST׮EIFJi (쒶}2~w efPnTd+lapx|"UhqrԖrV*Iv$̏X(7z8ԣTrȷ*cM|=&P)Fo(ɵ21>$.,C A>|N7EI ,/Z7|(4B?-[aw-95=vCHDG.0O?q9U﯒pk)u5v\b=;6sSWZΦFgzblIֽjdmtʘ=!$_SF:=̷ @IP!b`T_Bώ*VluLOhX!3 '?paf^ i'DR,smg϶MYrF w S%5lB{X8\ׄqtFω!푦'G]k2j5븾Tf/<Y`Z(T1GS0!{B=ڶ1^F,^?{/M^. 71< پݧa"HczM\7Lձ ^DIn ]YbjcReF4d˝&AIWN䎋&/r⤅PE$O#Lq"w3{T~-v,(-TlR<})eKd᷸ e{zxsPQFNsĻ2V"U׵I/!c7\09۴z˪kqMD '=Ŀ^.qMl%Y h u!)mٻo<6x{)̏⹆Ʒ1JkE£rdtnZ}Q7mh!oևR-oy\QMB:ղxy]HȏK)5xfQaE1A*tם(yr'y{М਷`1nf3zS-P=ϻB(=!{)C9i8u/M^ n"yLaYF9oX3x)ms|(հJcf/UWۻEj"'".NnPoK\uBL pJY`UݴD,wv2lU8M/t (CґosfDp4 d>J"#:)$y$AQ@RָxE )iZ*tsAlΆtTYn8 }bUfvV+PFP^[y5eFIϷx^)#!]E$F2ݸp2a5A]"l2iw:|GQ-|SXj!7Cr8κ.u9Alg]yEɝYH{;ef*Dn!Nr\04\Wdͬ/rgy8vhPT ߳oZcT"m:eNzM J۳y,b\YKCUmߓrq|).1#JH1* eS݄9փ;YZ'/>k пb#吩 aS'çYyo>ˬR75#tuEmv#+׭+>?*>Qvc?,Y@:$09f}Yט:*[0{L\]AE +PT.*eէg dvllĀw}ehO k~YO Y (כs6p?Z:\E[!\^^zsx({\҇1CWnDR{C3'e|idSJ ÙZ.κibPڬK l!>RfP%7@׈m*4K}K74,?Q|C~G0K7dqxA'n䏶D{km/9QUtOF TҤX dmb)(nppL _sBZjІMS] 5HYuw>'};gd?~E#9KlgU@fFP}V'|%RءiPpc[v3WƏ6U%L8Ԝ xb+Qw19I @tv쀹KjZ60:˰?LDċf%DGABP慘MQ+zJ{ֲ6t XqzHoyxSB zr9-=)WUZD֋ұIlm,aUh>U#b__,+| 33!$ CԎ&/-wvK#$P@9X)~ _}Z Uܾ':L4kFf?̔ 8caRVZ`R@Y>9 ;c]x .˶X[tuՀ؞'7+CQ{&>x NA g^2'+}˭\ 7Qc Y?P:djz DzPm+0H==6څYux1`&imŒ`^}Yv7v"xps44[ɀ4'rg'?4*<J+BecBW)ͧ S֔[n]ɖG{3yKpl:IWEΆrޗ*?eDoDU@=ٱ:7+Q׃%~~uþ "ꛟ3j,|"|?EͤU(#1(.)6|mo^XF'Yڎm=^-QK<~ #,WO@?mއ8X-qY[*Րb ]\⇸ ;-D)P7|烶S5tWn=q)B,>0#HT)#ޣ-2a7=TfN?>MGGfIYx3-* nikp{W?Bc^| ]iyC2\rYκ~Xc ΝrՔNwg3f8]X .תZqf9Q{:3_4a@08i\4Tك\r"JU8a-Ȓ 92*>f>5CT ,5l5o!ePL1[ l|~Un>ݍ{wAlJ͹&+9P0&d.ץ(-%:7U#"Xp̤e$@G {uT]Wo Eq5~ϩѱS3pcW,57+G̙ hG]F#KdW0Ud:qtimd?L3>䶟r^,(Qxm֢n; g}vhkir{6қX1)0=-ًEɻ ,Q} ҝV|v9iRF;fS|k9w'Ou0R'(S[ϝmTjKӏeD<^w]/HCc028i[t 18Z)(<#}J}%S$9 U&>X +EO_?fjǶq/gS6S@ 3%ZZlxLS.{W+P[TQWFQ|$rZy8 Ҫ$e"z9f%4KV' )T4YQ"CO&I> fU6}Y}!{%q4 9y8`#-YZ{1o4$Z,]/.g +js ~yZI#J H=C@R:U`$MW)'>61wg:ꪎ-ʫs1M_7mhBDK-ʟC>06a/L5&x$ 6q]5Hn|jeQԏ}x7Me* LXQD=.$KJ1? + C($;\zK] 'Kt_v [Bi@ `2C2:Vb}ڹ)i'L3IYP!_/|6a0속Toa`lNM>6HGKGD*ƙZ:"ySZ!݆Chc+zϠn>߉ r~n9=Mֶ`@z9cO@zz٤]B{t6q}NpYg` 7p2wqL{<+QƘalZ\+bԻ?v#0J =k,q@n0%cS0ltl0q}72ɠX4Q`c*C }wTi(mPӋbZ6؍x z` *"L" 3oF@REX}@% z7zp7*V @N'Ɠwd|>(+t"txKD,kXVE [ [5}]aAq+7,5]B %&A6]vf1,g^;n%9w!CbGw; W|o2&hll!S\qNMđz G0!J aG +0MH|b\SW0$O6 J" A!Cbx#iu}1X;|!|к F3FRąm;blh燢 rWw6*s ڀz;yaUxop4 fkKmk_r?Jp6KP-s֊>S-ׁyL *aïQQ."rdI!ɭ&^T+*+&'Vo cOB+UiCɳWOQnז5yRurOW ]Zfo)gOxo!umNp^ZvDO%gC(W_A]vy媕q^g~l ]P`~ۨbTKƗT Ԭ.ȺX' ^ft (8-GI[iF C+2`8iQyEIQϔ)o:2v>p3ѯXŨ?&*R&|Dk_,u:rYM=ng;,U*'ٹJR|ozcV.Dۭg@ﲋ@.c#pskG5g/+ `O{Ԇ t U*=ulLƓ)ft?nw\}o0R& #{wq@hE;t)yo!%| _: ^@ַC%ϗ%_v:Fᗄfl$K~7±2FWN!`1Wu_X\{y@ {xH-/ūx<3W>r-Wm0j4_F샲x?I} y !?{$ .J:U8Ւi*Q 2Z5.n nt=3^vm]dABƄ LR3GVhYʠOm˝؟Xߟ p(q+!lL:Otk-(%4\t_Ա*@ 4gk:j(/P PG~ 8zLz[FV%H,BNn l b3T췐<0Z{78-0X.-6@pLK-o(>waaRTAB4&5|`C Aa?#;>(F$\reNQGuWIqe/[]imLxvA4\B_zXxmrt% i\/tZ㢜Pk($A2)^:Kdk.Q}ΜuEE9I1ܜF<k> `+نMҭ3gdd-i0P pV:vsDU'=.STФZ6rs,К' Wd5!6V[lf8jQ5:&vtjD]:͸gގ$@u3w;<^y.<R}/4~NHpF;yғ͈((?֧-GxZ6J]`k[1\-B~J|5 ьYɣ;1g?z_rȆ3P2Gc" +ZGp)ϕBEH5Dkfp;5 yy}1Vm=60۶hMO'lqͣ:%QsUW6D (> 'j蹀ȡ\A c0fzn&eߚqrN7 _׃7=ox9zF*0_eH7⬝kx4)*m2ll1ͤצ'#doS={CB{O}|j|coRxcC#V$JŕJq pzf &n+D<`&|ZQOq t@ߕ2STS rS<<;+l: U*a_i>&?҂6{T]-]߼3lD-~ٹI@sjd,@VVV|ZACeD(Vxwmvg=צ5*vvCdeO9.Ȕ #&qO1WC}Bs2d^D]i2C a;-+\Vm%4b )2sihy̵<4gN=,;mvb{#B. NO_msQ$ro=+d \91g, tf7\\-aU2SSUBߝĞ% CH4/ Xa z3Iq}8jG[>ckWX7z/pNJkf-vrj>Az9'}r-X?ىri4\e瓕G[F1X1,Ư;[ܹZ`"E­諾4X(3{DDWI}x&:nXBx3J pE|Hu99jshfzCP5vpzԀ +7V JΥcH!S |ۜ崥%s^ Yqn v|5X.mTr%Qf}4Uyz{ OLK zck˺\˱b=P>J6{ H+fCޙ] e/!vl\F@=> ԘmE +ԏ P# mTgnoy2쿋!.-cWkfvƳ)W6 XIhMºq@BqeYW}B57Wg// 642/)Mp/Z0ad`,#3~OO=e?GqeBsu2_ϪNP8OOWYC3(<5`Qz\AwZ17=w;ETm@N67Yl~6u0Q$~ѽdaH̃ecHxl pزbO7YYzf.fCly̚pLGS6%;D#ǣ 4E[;֖)]戉$ry*m=5qv Wiyt5H]!a?L o7 #q?n1@iZu";$-Gv3Hi f=nnY8ga˚(DC2LD_rLPxhpA+~gG1+HuqR$bOkp;`VYɗؗ={cq黺{ck, x{p܀PA.ބkwaQVdݰp.לp^Q /;䏢u MqCƙ (@A'S=5Ă`0k;0Nؚɶ0GqR=k%iB&uޙUjmqXIo; *yu"B_Ѷ h~ZUcшN0* P񴫕އ'3V&4Bp4Q6$6?rd 3慣#[0 <ē: Z 4xE.5GwUm'/]9//e%L{9:i~sɬf:7iS h. t/Ԙ*n RwҍVCn %xa'-F%|#*G!cȐԅ}ـ%ntT}:lZNd'q?[cq#D4@a^}? [1s`.V[aƦ&DMמv2%-vkˍ3FqxRU nci9L|<5YnΪ׾ڄ'Ā32LS9(cReעr.[pg_)–ߐǤ82b!A1K5fB4 کF7L^hLH1mQZ8{a0]>>-&=y|UF ů{)||ՕN!jOEef 4twsEziZ2دeB^Ny9*O.OF⒱+&!wzL`MƹfB1+,nh9 ol ?~d}t݅M(Y6v v%2Q,ZGDȜőt'e"ܮwtC[i6j@-fR 5 A6w۴2Q[z69=brs;C=xʳV7)߬ō! R $S_Ճ y:f:)8'=?=6̺#^f/~D +eA҇(0.sJEYٱut :&p+e&"Ns{X:3سIgjO0AB]DBgW&6TwIlǘcׇwѐ[Ry3*[j7 qK@?åX7!%j& 7JJiޘ8F@k6zw0&bYor9.0_='#szhZ/p tKPr?#h%:6&r~`ҞX>`wIH% Kfk8:Hp]04,`xLY˫gNQ92s_aVySx⨂4e6LcTQA_kBI}sEnNOGI1Q_m)U`{}/+x!JҽCV #y”ZQ"Pڐxczl+xLC m 74p ήV}De,S}>N>"$ne;RSׂT> A61܄YЕ$v`ߤ&?"X5>\*7<oX6p7֭\|Fj岢8k\c{p=۪K,5 ։I|?+Rg<=NO|$S4lz>|!~ '֜̀>@$,F[d(/[ T͹GjwpRfa?GGgx\`ـ kVt|m9χΨW0Q+<;omЧHȴM|.B0F L<&"qVVK ]H%٩B@jJq] gwtEPjuLhw? W\d :Mp36l |9= 8z/2Sx2sͩ ztwmCtq\J 균+RZL$\G(b2Ar!gףb( Kf=M>r*Ňו,u'!%^Kޯmo*Dep]Lo|=SOY-E dH:d"t5[, %DVScJ^T1Ctdppa!V[W-O_,h%d "6ӓٿǾh:y(rtF0hb݁v}B9«cפ;JɱjT >#zơJ0of13%tkc \㘊H @p9~!J5ы9vv?L C :tWa]^j9>r];O2"]s7n||-HǸ|cI9,+ǻq'T?BTGyl3ƶN sDg=)F9+hy @9XYG<>{F7Zfʝ"WyE+K*8_.K2cՆ_բw.JJ+BZo2U1˧!>(z~X\屶7rI8RGQkڥ F8vժ+e˜oؿ0%WS4ڕ FDRRV->¼bn\͕TO"-@Iq$2cX" - 71{TW=d%^H3>>MԠd7fYF%3UۢT%(,d(U$/JP㵣ՔW:xоǭ3#' !|ohdpk".>$W Ss&r`b w `ÁtW\'-sSKKpYϹ\ܸrړR\J,' rnʮ; $r`TMw#e \t ƻ ߿uu!8@QV9=FyXRIEhs༧._ .F.} P5] |O7S8GLcyԕa&6vU)0T;Th DWZ]O E3zC)͗v/hD.F!dԳHw7+&Ei|&ozcJӼuV;EuF'If%k//:j?XMkIb:bK$~x]&uÜX]%-&] Ip% ]VqKp"(x=¼@J N {[B߸PK!DfqcqH?[lVi]' S_%vokn͑:{A"N%Z1b2> C.}vnޝ?kz7=ÉEuү;nƿ[,g&N#Lcl=5ňlG[ld}G)<9agW~$٠ZH.2V35̂*&Oݩ>n8XRwxm)v2L2 #XO D ;^}|7`mt9 -i8%hj\h+ [Ý}i)pQ^ 3~LT>`\{k޷Jpd: 2US$5>vJ#[52:gD!N]fQC[:3mcYꕸѰ~" =\mDh1I\7l8;uHzyDw X֙.{je145f#pt<,0&M:wmrD/?Cx#sXZ H9Xܑ&{rNG~*W*LAҼ󖍥\0"9/3b;6 z_?@C\wRqP@Qp wiF|9i)vҙ LΕY֋cYdoLJpzhi:d?P`Fn󝴴v+rG | ! XNw.9YL 8X IJdz2ϰuVWIڇwVn7#dcI7x]Cдh2.ٶF|C'eu:^?hȁP2bf]PqK(w~@\x [wVsB;xC{-V9&7H ^N6N , Mxbn(^!ZzDv0RLb97[^ċ5Wccփ+{ݨCP ^7F2\}ô@5<&VGOq"*xp@,Ʒ횥agvKRh6d^0vSN_PŹPWARaC:]{-]~==kxe@b?=.1DmKu-wt`n:[ P=r) c &QGRAW͝yA>B愣hoBX!0OS>26a74lbcFq?)$+G%| :y>q@ hY&IKY ɋ ?R2rx0 BT}‍䵀DY\vص:ߔ[xU)mgiLy؋T9ڭ7{HzcØ6܋)|@`l'Y= KGb,+ӗy;}RIw< /.S$E#]NjqkxUG(,Qٮ_I9ԈV`5eO˨Kvr Ij>fs$#&?KBsdj((ߺHw\lw썱c\«cl iDuXiE6spT×e/0H+gq%ˍulaFyq*6*mǞ'yFoM2x]7ޔhA!EoÔh#2^(*1$ܻ6)kBB@7!f(^ HIr:wFR@,/KRA-¡i_fdSO |Gj'O6IGQ*,DF?-1,50]wa|kaT$'q$+U=;(YԠ%Pvv*($tڛҔ+E__fy)(mBKxWrS1݁]JQxs9B ITl{ d ޙwäB4 erNdSՕ)OTpMf D#6B8F/ρaї $:_%?5P}&YJVDoSJj#a[ۢ3SgRvDP f e|A)އQjℵQ͆xCɘ8){57lyu zwGG(GY5r3Љ',@_>;WrW<>@㧪Iq8xd ,z&֗ gN,ɾr;2$<8I!!VH&ˀ%::|^/ UY.un| /db6_ҤP"}5Uٱw0MA tkV1\-cJX|FkH?$wG3Jgl0{nmpځ1Ҷ!BGFl* VCO,$\䣶W`RK0 ld,ۿ` <}B & mGutk,PGlov ptk Ǖ`99avY\MH< e`OeboūFC?L~lfiğ iĹg9z>aC wu $e'nHc w<=N' l_ݹ76ߧOT=[2$`A$<1K_Ү!) {.tM֕ΒVwTwSU*z%\H]j?@_՝] VX\7Ym79o]$ EIl^*`\qn= RMFճD5ΪNTPTMs 4jZt.Oue!C Kx FdaL6p@%X0 V^PnMP`!de$R4MK!22N[t˰bX#n@oz-tÐ"vÞEjƐL-HC|6_ys]KxUhexoMd\d?ŷ"2hKIsu&2T]sFWJ{CEՔ(}*{MvgAen[Ed+]i­q`Rd,s*ֳ 3i(e{E^A| f 0Ӗ[`j MvX2Zjd&xB2VSttȨOY;hR``WcD!,6dO'7緼E^ILi(C]ٲѥi< OFc :4ZT/lJُLņ*h!7{T{;QyW^d?ޗ'g20(st/IY5u(7[ͣu el|MDQ?Y2'J*dOtTO(w|sOvVpSǭ½Yv ^Lҳ3ZUJ,K >Y:5o V eټe+R*C1}G6i/稐=kxG*ߕ>6ŎokZ{}uY!̲aDo#X:>}y,A9GeG9LKDS„5w|!)2Qp8.wRy<ۓ֥98%]&wZ"+57=$! 5t1/㼼~1lԜ!c6&XUhT^ʾz1 _6k!nأh15d^ONv \v U|o[VPZSW`+gQ_>1J&d[ hU,+ai0/q?3#4f*)| sAf=D4hozi ݗn^)_Åƿv":yHղdC VTGۓ /d bΌ uq&i(<?R":Z,*) }ZNҊ285}f*Sj%XxdhNˁս`h *pzB 5p8/ֳ s2Y@ܔ yE7'8*V=Jj}๼rzS\*s|T>&r<芯@+oh". $$QO8r8_=H25 [|x;Ԫ,YV/A-˴Ue,B!5v"='] c4Lv$m{_ӱuNK 5Q{C\ՙiP/A@8vQvy/ y=Mmq}-o(WL`EIN;=D܀3S 4']@%TSt刕-ŀ"t8OZ =$qD@8=t4OŶ#]iE:N #1|Lt!pD0MMQ=6$Y>&dZ'U W5,eM xX;%{t*1NqEko0B֑#P'^a.է%j rUq PPQ9YL>0 YZAER/data/DJIA8012.rda0000644000176200001440000006024413616365113013317 0ustar liggesusers7zXZi"6!X`h])TW"nRʟKMd[_;zkj 0m&1@/c\@[i=yb$Jy l m=Yc!w=IC,?,E?F6*=C^m7ᡞ2o]]J*@EF'#d'6&T Nc--!Kwq), I:c<Y=/RyXKXͽXlpcF}rH(To`cΊpW%q-T[rBzm0x 0.f }Ԉ϶p@T$R!½Uk@$,Hq$̆RKuFt[>a5ewC/nF่&2x4_Ͷm]`+Xh!tXAEVwD0 cTM_'kn0$j 7%dYZH|u1\sSM ePFu?= G˜VZ0hTƞ1!p.̊n޿XEgBwt @3i"A\+ ȾrC9iw;}GɁ1j2r$xQtbp ڪ 5 S>:҅MVfY^,଄~8<2 dd_aÑ}rUi1CGSj 2J?eQhÆ owqߠxBS E@nl4h!ޕVD cNZW92g-wOjc Bq AL1G2TX _BtYtXWJ{|9{avʘo'UΡl {bzA#dn->*X I嬎˃0qY |^;!qܢh |;0r,ghy/Iw|FYxHfj"Pel]9D<k]VTm?CMZ%]H>}ĭs>:I#a$Y~uX=ң]&vٮϑYI5sۄnk{DS0,cL Kxg%J;Xlf?{>z͆iwԲs]V.$!Swm z8 o,,OWR[3^!*, 2K1ruVڐ~P;}D-X >Z8g'c̣ӎ,Q!%n~uX? `)moEXOʀF]ӘJU ȺEXWIBH< W+5qǛ,cd+m΀ E}K߰SDؤ\/(=-cgI $hiYN*:J_j IxLa̋},7)ho\(;}i /'N+t X籓ySHS-%H꒑41Km9S.BԤb&Ju_+ƿ} ݇>~2Lqf{EjfL~44ߔ4._ڮ &?zOS`zgxmΆ71@>r|ޠS,xg Iǁ:M"åX7+sK @.Il5?kj7a䢃JKjţ'&yj9ѩ()ao`Q+x,'=!|Dԥ0ɕ]u3߹C&'aHi9M[h^D'J %)v_dI̞{ :vD$-zκ1{J|?d.xM;ɉ?ox>*6q`S1JM 5E>v7LA:0L rHbijp(͊H*9R]Z Iϙ06tmſȁܻ?dؗ.9#}~vRʯ>OfUs{t.0)^Ndv̻Df'sR6 glC.BH橵nHac/+s4}B׋OKhlVfXGWԒ^|?v~Q%]B+(2+:Ηזʠ+?*J;a%$BrOo(j3Bޞ7ίOa*c0e]^tzu)BJ0^h^YɆv[_&j$IH *Cn{CSoU8#sR n/r`Nm1f|d*`*bV6Up+ MhO;d`z4i5=\AQ{beoSsl)Li-,ܗZoG(-d0]\Ez?dWѱ&y~ğuv ou3N&nk6UPJn0 KI)jǁV'+_ewvN=pkryV!NXMYiIUH$|߅GF> {aR}_$>B=B>@=}Vḛ8*yEVOMt3Oi=RSm.QId: /iJXj+q'>F6ĒtM tZ!d; 02;.-ci M9V2 i(&wA=r\DwU!-+j|} Vu //ɠHRubޖax3զU$0&dW t} R{k'=l1r/ԺAoXNXl=Y'~/pfep߉*mdFMb'˷M-㐳U2Wpȗħ[*can) Q f @4xBGQX~AzQƂ??=:rHw4o>͗ @q{ OG!H|ڵIfb/h%F@ط*d9ɑ]URo@`n",׊ :Ui:wɥc`&#fIm[Ҟ}fMG2[ݛ`lEEeV@@Om23@B~ })]m0:-wu1ʪt` X@xbO} mzIj{6XB-!9tJS*ݮpWˑ{+6t4V9.|zT"ek!2̜6gtB d]e"si%e#̸I8!ʞ^3V\X`_?'L@dP:.|>u=TU T݂=\9S4DĦ|Iv@n;jid 11.`k/6VA걢%S[@ԃyx՚@1Xprmj J̃X[cQ`&*P% ܞ;' hv G(;Saq'Ԁ>l->IitU2O{/ lssASoCdYONo^տDٮ]H A*cOVfX %l]GPuذn:7Da w'T5SqesׇIl-pyZmf_2˿'?ٳiNV48l'w۷~mv;{9J%/D|.XHvγs>'7RaPzWqr2 +R6P^m?\Z/ܦO?b]GA_c3S]wW}N}2Exŭ6 U&%W=w늇8 nNࡵs[U;ݲAeTn$Zn'"J! ;nK"FͅVl vPwa`DaThxP*  ؁8~ZDF3g`?Y!Fw㭕R;q&5S"miӽRjEK|G5Igپ vas!6V4d;6vY : Hye'7V:T,WH3]`G)5Z[˘{r'hM6c EC3yvcև!n}xb>;H)=xkJèyɓ}G)vѣggtF=ږsv߲EC,JpN4ɽ\ D2gl|ٴk&!)8yLH5"Bz,UW^CciB_rY]y̠ 3]~ 7#-:\wsr ߓblTRFambx:Gu]aqȹ`z@NMٳ1D,Z[9!TS'3hliS &J 2q4=pyyP:Z>kMs .ogN^~ e2֝ 宾l0McZNv[1|Y2ba[Z45Hf#ѨZ;piD׆\MvCuN@ lih H͊Zۆ3Dwx}Y{Ԧb 6q t~L d;̄,K!GNnQQf~Fs^MXmMu\z(wcL U U1, vontkq/?ÃPY}DG:ЉLٰὤ9j!L,о3$dӳ;Eܯ .iFGM<@l=t^67*UPGfA2=ƍm74 ݃9YF_Z{A ~=bØL`ףD],;9C~,ᱲ8hp`w_ҝU٩M+łyJ.g~{pIӐxzBWMi8K˓l@OVU6N<9F NJ0`# Di,(d :8 4BO2*p`iM^8HYLi0o5V%eeLJK]uW$W=Z6D^ AZ.Ln \$~f{ۊº@/9G60bq+Q lY::ڢk&X-Xw0dVm NE603"xPpؙQTKZ!.(` ʤ: hHuqurPٗ%lu#; ܗԢ=+ibp+HQ%;p\:32syBA3ԁy}x[Ko!3O]բpf2I[;'56/af|5|֩o?"$$A1Nwx)p\Ad&O:TtkuAe@)ۆA4[%?|7 1PÎأy;3Zdy#W4Z)Ez:4A(p xwe@Ljv[˗5ˆ )EGUpѸ~ͫ0}fEMzԆ҂ G a[נMt{W_'"ANl>70#-cȁGA279ڪW͔SXd_}$Bk06Gg\{*E?5mnEAهwUżo3< 0~ bGd5'!z M^QAA%kY}(+/rm7=oM 1v17^KUzi!B"X-nba CP<]IKzd_\s\y2WDF9V}&f*VV퀷Fˢ9[is(|SrE]>cCl ei60j=_>({sX<&5ȩ1Ȕ;ԅS1J$ _TD|HbvitUMft2zIkW )FD c71Ex:8Э$ >+wFEЯ0}FKj106ƑUj-'۬h7h)te?ʶ$W]^P]K+=PDkmiÓ(O?~Sc5t/:cku¶E(K;.|& j]ս:Ѡm /@-:ҝ? EWz~+;P_j1aOtI=w1 ")T\ʩTA, k]YfZ]5BQq*f,}C0[WC7Ð|1c Hlx9qr.3ZIg-5y 5Z`ٟ_JG,gvJ ƉC[-4`V[blBPiP- "]r=74  [o3GcD7w6*^ɞR~hNT"8 ͓#K=0sп&?|mtIcVAwtshhkrcZY,(Oxl5CK,N4hn rEǶM^k f)KMhmeըcdEcJB <˔H.wKt{V<T;N'BT+vMG= )3IhQ3r2dIk;^ΙL5qЍى92+.p 4, %DRw.}u*[YR҂^R*7[>إg#>"G*[pK' Bۉ-sup~I*ͬ U1C!f^TL+]Q*n[o !=]Q /hhՒq}pQ|jO,J90ؤi6y~qdeP}bT5Wv2>܂7e}lϗ~`2m d8q9uӝTc'3P6(:($1K\ZkP7K)~"r#aģ~\!5.4I@)lN׍A rOl? [13.) ºXd^~};.SVt6$xrnFFS8#kƵJ' m?3|u8COZ?Tq" g߽؇a lA#:r%UDbISYr-s3^v8Aylѯ-"a Wu笹ϿcUhxHI/%gz]!F#4lG?~Tw`٪WR[ o]qe0^"jvexaO!Og:9#7͢}>fx@,j!?RPYgyr=XSUk:fXS('}U?:]WXO͸-7&NUaq؎{65X`Fuh#Yf>xWWĬ^"MeAu*y ]X?7ggUjra< 0())}Rꋞ|5sG3Љ`'q:4\vjyA=%i1JnIk8;A\@ TgSXзvU$$|DV NO$iv4Eȗn' Kt)LibK+ "^mֹɖ~ʞfjيV+@SA_DU{%k7q̀>+a0!tw}}\a%Lb"s mj])r:W$JT6l.P{{I,ubswN"B.:h l${48saȥb'Dv9Ĉ &ׁr EGC5aߋDUJK^Z@ [جV*{GAm3]OAp G`;!orQzn[%rbc+ZN:f餴DM8SE'Dƙ?&ub@ĩbcU `2; sĿSb:)iu_J3KBJ Jܪpo+T=PFTUw'WGQOzٕ5 ʃbaxc3wzxI %).{yxjf͆Ȕ~I\HsӁx ׊^>}@)kܼ0;( .zkQ)o9?44y(cLV V@ALJxB[dDy2Kuu<˗f.[t{sXRmm,|۴O;kBHϡglnנ||Q~5W*$%Eom 4eL4r3>q}o!(z_:O9TMCXbuu$1 @d G-c. GqK8ǠBF˚s [銊Yp#cg<,\ h-?wf^烧"r t eYp}Vk|4l$C1xvq|{yz^̝ +$fCxSyFhSͷ' h6O|5WWΟ!4"׬aΊ! 뺟'g}HTz%%^b%yv܊:7ek&txLBh㏢F& яGAx4y ק2{KdNo>"+4wA }TLZ`#yE'H,5b9܍ Nk3Šl-iBTV-4T#I!?$#"=uǣʬ"ːqepx( v㕠,N"xکN.toGbX_bExOv4$(&ADWM"FB*YHl_ úvLx%,p%odW? "1|WuU/6|QTi5K-HU/u_BzB@WO]qwk>ߒ-BůS&XGhJč'eaL~߿i)SB<D*OdKͥ-a6/>=b"輧QQqS3gf)E䰁M8@0~}Ѡ8fο-Chtr QwR `ݣj5\;naqV86ǢZ|f猁/Sl|= k~v|sQ652X: B:LZ{tfHqIgڑjQ ż5nr4\Ӗ~yG5•PcF<܃^05N% Էثs>I*M2hn>1R ߆gbXqos6:N?j g:S ##NՍCe^^4.㌯~az@}t)MA'ΜxmIZL-O6o؊Т-dɥN˙(Q=֙,8`COzx9kZMf@? 㙺osJ\$wH[ƴi$ dy^q#d/\<>%w}q9KK30Zc}6AJO0dv޲(YR{RLYl{/!ܐrj w#']|>Jlſ Im-?9d1 ܅@Dh8ԏ_}Q8|w&d!23=*Sa%';Є1W][iYs鄷sT 1MLNB@ $h&2)șKB+f_gzkiHDdl CU-uB./yUkQJbML`Ka[SG|~89H-.G,I): i7;[R6L-"?z-fO"n0LSW5}sJp|AWѻڎAtzrˆ>h*wz n4HK.h#ڮOi!%F' &ě{Lb ó8>- K!`j竝<ɳ9Ź]sQ!+V< {"׉Xۦ Sm#NBRPy:8Z"pN:-bIfʰ*yڂ\ 8b:A)T~ڧupcnhO( F:I밨~#|',H^.jN)0jgvSBv#qa#a2r92(XdCv)};H 3t2Dˌܝ@$]%vUЫMq @靓yҎe@+2gڏ_W'| s Z;.T7X~#[В=(p`pq[ޟ:4x5|R&O}Ym ]],TX~[ f%@vyϕP6OtjŜ5f9T$zM\߀Fy/A(EL˧?-&`hcvjNHU LQaHd>(Pn OJqKr!Zbb 8ؑK{@F9[@=2] ms XQF=o7IQy$;sar d/\nHp MR}e(;^g]qZ|H&w[QӴۼ)vLիAyZ3`~b]ş^w hm&#o37 =^N2.Q2+%PhRF6Ѵ9yFpk7Hf7}EBzC[_*㮙Oq8SP( :f ?cVs%>6j×GOc\݇~Sn댑CNY0u |(2re#u|Y-xn#uԵzRwíT@pcJ?x%a(8f C@i&7JH>ojG,eo"Ix#g0EzHXJSAU+QاW[j,:F^b%P g~c@Qz`HF/)HLGW|:.9ظCBl͢xe Vk54a(v31-{Gа"^Z9o&ժRف"_߮M$? 13C%@}A$RL"U?K3 &?F a ÌũqTgSj ̫3F,X{&iJ$ژe]PTh{9c _a< j$低 y7p2=4ܝ ч*/9/|qArpEh}{fpy;ohC ?0sc !l-̓phYxe6O%I* ~)a$.z)֡jD$>~RlL<&텳>[_Ŕ5y"?ZBAB{?yLp8.RN-*u-U0Zyۣ9(l`M:J,TپT!e:mLRt<Q/wɩsYDB~L( g9{g6(BSߎ=@ݙff5t zi f?و8MΫG=Obpl#N;|t8iZ4!uY{1l_)v<$"q Ca<2D 1nE7հhbdk>|5lq fCEFtr/7{8C6s9y1KD2h¦Xe/k1%!֧.=(s++&DTp R}g'Pe~\JD2PPIvAa7 `PWc ZS4 Q.U .݃eAS|Om%SAt$u4B{D0[|g*{o?ih:jT,0f=S;cW{LN̾,vXR F3ۣTiş',1KD")׏ƗVL |0GpJ I]Xe7'\A=̮X ZJYwQ~Ǵ孷ܠ e*߯rjvȱfRxuh4YbmG4^f3@FN%x:{Kjnsf3*1&6 p]wps.5RŘ+oL:e ʋ۪6)7d0K8~G/kwt3 28팊t:m,2 ?6xp_1ʈ0<P9(YE*E_e,Ÿ&sUXP/gGr'1JL `Uw|mPȡbDc4~4hҵ _ ÎJC95[]*7,1ws4#Zs0nN(0g 몺7X'8݃ aƏ77vB'77_N(*0y 6fJmK-G-ܐ?dUnS3.b-x+ҒaJ_{`q𻔔*|ƣ*} %;pTڞ،DeW 9 GEpK|dsgM&%b$9C NpXGnS$I"ʃnws ՝TL,*{r]/>nr9dIcٿ2؂K(ڽrhVm^q>0~Y.ik)KŸtg_?O]&6ǐY/V.zR}CZ4T8JXn="9ꃬ=f%x?0L U92y6o3+v0>pWiX-Uj-K쟹K&aw QWv{\wј/(m ]aU'fX;LSͯXw$upP5;LBQ?LuR0St7 uL)uTe&8+E߷lBth7BŻ*G[UsJ%u¿HHiM{@;SF7I4{V4<pA[ͩ58_+ȳ(ϙ[pw}g߯)c\kGc_Q/rMb(FQ<9ԫk93?=E+*҆Yf5vgqI^Z Sr@]CM`{ILHvm?pb801ٖsfL=q/1*WG h$*.VnUh;N"Da@76/i넞h!xlEdm,eն 3qg!ĪX5 A23"@S!}ez2%S9d&8 T:sGx{f`yWfc&frG<._X$T2N)r 8\P/Wz[puzD#mwYMwԌ~9o:3A+\!F[*/t?MjC~&Z4,/͙m[&yey&E%Zg\ށD*M@kTb]|xy #3;)ˎni!S/ya1ܞka)#E5C` 4;_@o+2 %EFWm H0W1$#yd5 SlǑ2B7ծ",SJ=~.NDɦI#'R?@xq0Nx0Q jJi)AЪJ`8V.Z=KqK(`򟓓 b͌ǧoAU{H79Y<xuYBm*Ʈmn*W!ٻ?SMkp}RYR[DK<+rCw8vRC>vTntṚ7?TA:h+5jd,^ONs7,eN Hٰ=h;-/M34^܉/|?g5o͎{[ʹe:8@MwV[ʩ'Y[._y]"FKIbY-UK m$7R[Z{>t%RH# DRsz@lrd>-;sybtV uJoY@t}2^n2"K)A.r.#n-d-ƒތS^͞: [̞y ?a N/Z"ЕJPBGѴanVKuS+26ݫ sͬ&NO#ba^f{S>ͨsggO ],{V j& J{l: ~Tb _Lj$B9Ԕ\Kix3S Eju RoP,RA' )胘ӟ7 =gSK:i.۾U沁ҭ)3 M89z/~EοD ZY흼DzlODw?w+t3 rMsёЍQg{n@ Fz>\Ț!jj^J[ EŪ%ʳsxsX5at:"^k'~: 󭾿%,q_5Rol]Puwࠌ۶)=Ʉd4YtRA4@02R|& ɪgȿ*ɕ?bnԕJք@ggE0Kf,FqTXCZĶ8Ewg"435$JNx4@*$:_ ZblZrMwf2n>)V;χDABIU(R@]~ۦ2# w| 8d7iOajy^Β57{ tu ?}yQrT7 I;|hJ()~%k`*">0 YZAER/data/UKInflation.rda0000644000176200001440000000105013616365126014452 0ustar liggesusers]S=H[Q'/>MD3ڂt{(VG $1 :gvnv][psv,`w Ɂs|v0-M3-R [kt^s;p!wˍ׫qvo2;~_VN'®T o)T:~#9mؤcƵzb\%{O7Z)/Pv4>zKFH^#B}Gp*FPgN2 HNaD}g, 걀SO}/®8q}yo^`g s`dO@?ˉ'hd+bqL%T:^uz#z:qyȢym6$Ywܢ/<_c-'جvci>=pAER/data/CPSSW8.rda0000644000176200001440000061042013616365112013260 0ustar liggesusers7zXZi"6!X2H])TW"nRʟKMd[_;zk-m ИAk?ȐV8Kw& uAe gSdj; "AWlT-25)=Fst՞s|6Cͫ*IDoG.;ASҭ^20@ߥNZ3ִiv3Z|b׷Ҩ5L?8$61bJ]3IFP, #muY~\ @1ZaKVE묒blojW0`x8Un,M) [Oj*[<~mG:!~? }MX6*:Be>e͍:|`}hOD 7+>lU;iqO 6rh+xJln3\o$͚vβ9G!_Q!I.WPa"(?Ig8F\EE/{9jJV227wMy`An>Il~2d)s.P̡̞aו_n>Q'Sp^۬}D!1G :ѯz&>]&o;~N9X??l->',v񈱩 7^_aۀEⰄNq ɌD|vu [qUOj #Hg29 ;Fq8|qՈ7y 7DW\/TUΝ҄d@t=[#k[-@5pZd:N#n ތ E2gs#ՇmCaus9"+M[sS$_it|jAv n ʪ4 Df '&Hb3;]cR4D| M.{ #3<17RMFw"e nWE(.н`}d(fG ITYQF3xmD3X@#oD7 {nᛵE$3 PkxZsZcsυgQt {w{^9y-# 3lnwlYz[[ȅ8Fڅf[D`,컽-mryE QkWZ}כ`V+(G!Цnl=Kϒ8|QO5 hf=y(z?@?&, 2=IFӡ-ޅnq$7`k+=X74G0.=};mbva74q 8aU^]$=a~&D5tfl UKP b86̆v^z~.v+3[j{*g;7*B;Em9v(x 3$͐PNNjr vf ke٭5Ic0A)T3CYM6YG)um#dp_Cmۢ -zs߁weNG)aT zs@: MB"ml%=ۣ FiyL+A+' ;Tor悗!eVR|UԷL%li u;z@rr9 L]Ϟ9 i*Ӊɑ; /ʞI+rBs Zn \'d&M2 8sJ[Jfgڍ>[[ .GE~qv,}vV#!!NUi`kbsLPYi©RwR"q(JߜLk{;UV[2ċ ,'Su3{%rY}#e ˠ+M)OG0{:9mCb/sQ{\D^3|cƢ4 i,Җw!@k^OP%j_z{z']8݉an+7jNst! =Edקi Vb} ]|)|Ly02hiM948>Gr~ #0B֮bg(vB6T K^DptͯO䵕p*+]7guswj:77K+2"${Ӳ,9Orb%L7 NV-2h,9(E1GGEuZڴӹpg u2Yv@M.C~!yy>࣓`oQGֹ*rֱv"KcI&4Y8|bA& hEi|W.R+Ul Ƅ 1Gb%Xg"kbniyKMPQ+]S2dg` ŌGLQQT{5v,Eצlh)ٽaNa;ʼYԱ݌Q0h;2^ڬ&<%?bZ|#!\ngj.$YG@~+ϓ[zjSxRJ}Qa] Bb/hP|j X|D?zIwO|tt'<^kٴj8r[7 }Gl&5Eb-PPbwQ.8YMzT% M9DpѰjђ8˜u$ '? -E{I\g$ɄI72+Ix|+e.; "yT+q)¬R}! ?\FZEnfV"㏽۝YG6'uW9"8ubWE("ijSSf/KLNґ`N0IZi NTGCۆU'(2?v+K.,Xv_#6%|/@KƗаyr7"GCZ0tyȧY~v]xln @F (B6 8odfA-k4>ɰTwrMȋ=Dkl<4iK'"Bs7U U wR>ADK(`!,HVzrZ)zgζ_| M&Qiנ^Ê\ٺn"`@V?kUM9}9P(2CARtpB],6ヵDt|RaG/AX'C*t0d4' wbPǪeffR>HyRաv3gHԤl p q!E6A .$co#@/BI1>:ņ^y4η/ X!Z 3c:d :XUN?n2GRۘE\OL'A$.mM4K44jIxo}q/T *nz+Tۿo[BB qWv (pHكGs:#*?8%Ƥ5#v&7jGMZ[JS3WPjKΡPwL!ɤwIXs\sc7B%R+!IYiڊJ88+>g*Ł6j!mv `#h~|]/DK(#8:4xQ5|[SFKBZE:R\ l]q).%atZtG$cB?Ϥٶ ipm8a]^#"Gk2gv*W8WBNQˣpWOjNKO K(:ȯ-MM,w^wض4΃C:9k ?X(Q3tIȪ4 ҮwEV#vs䍵P?1z3mHF!Ḽ;;zA!Ȕ,tT>Ϋ,_N&zeQX07\Q93٬ZXY_U V@#ae`񓥚ߪg\}y:4)S;UA7)gWI 5>(SdʹS +D)5$kQ԰r5GhzrrY,69F(> a"<dW_I%(N͂]#H\."D/sPV_iO"w+=Nri UbW#z!VVFFg,pԹ`Y9!悆j.’e4W`5dUt34 iߟyp@ڕ{Rٻ`f/ו׺9c!RcrQA=oq.y&>;fP sLM4J%ѣ~ájkzGMJ͵&Sr1aeсqKŐ-: Eg\8vX15&D03Ԫyluq}e#~Ԍ/?FrdSޥ12;EHttk,ח㧐š2@I (؍[yK_8W&w.Pڱ6=ZPv9l6 ^TC *^Dѱ:Aۍ \*f;j}ȡ c1vTAIQco["xHq`ѴQUM[%X*CFW6C=ID #^_ Zn2qF4$0)Mj֣X>>Df/uz`Y1\0{@@UZV%!I?*ub Uac :4XƄOɤ 6-q͝cڃ@/1`l8JX'kk8BΑuPNİB{vI{&f5uL*⑸$:w9'Rr0-NLȿCΏG='N/ѣ K&#DF#T=]Z,f3l>Y jPaP|@gc9>Qm[|5/9rRYr蝔` LX| ηr;jVqUnK;(c\ŢuЬ㲰Nٸ}#Dt2o`;rWDj!'E>} k>ѡI,0 <笑!v^s ΀Uc+uفly2E_NMH:[oG;W^JBëPԟU4W.5xo1wݑΦ$[7u%=rN7XET*mx?aCríx7N= \س8[R;w?i.T͂қ33)erg(?xٽ5g:i;]<++s#~&>#h yf6KA/|i먺c[.Ӹ5d fd<>q;*;X!9Ѱ ZS7,,i~2״ 7G 5 k(bp/Dá֟Q=<򁼃7rh/"y ͡VqK!p1[o9KAz~UcdwUZN,lx7}dX/͊OZV N!^*Waԉ~}f"Wh6B0gl~,F H()_ej=JJVv"Kf$ˀIPJγI"g"=~G;TDQ'޹ j9mWSRtJ59jEw$ !){UrML-u)FAbB: |Ӎ% 1uGҍT1=7aCurG;k6c(CǹVJI 7u; @ 1UNGml>SjP#H A(kt{o4(K$-rD'YK͙h`Xf2Su73jT9q}0S-gnz}MG7=T+ LkEe보6@3ba \GO,nE:V [o#-uPѵs›J]F4,la(ŷcE nhY(=>ylX!cDnQ`[9aE;gF_!{;f.r (lRbpim<Ib"U%=grM<-pxLspؚ<Z*gn#s(@oks.<s|CtG]rB;FM!sd+ g+ȅBljwH0yg+׶#yVI{ҋ #4҂=FyF#'3~rge.hG~4't e6XtkTm/1.EHRݶ~7|0qVpgW PzihleGϥg@ eFXӯtu-ߨo5_(I-4 usL^ U O6d'D`sO @-ʡZԛ-;6:WӉb0eF jh5Fat{i'wxG@a 3K/|krp bU`$:c?aCTvWSekǹڬH-,AOlw{BaKBq|l7zUA2 aU-YRܟQ:D({4-:D7Wbm,h~Py(؏U+5мHFj,m[7\#8uP)iXOI8w]3p#:~ SFe$r+-VlF""UMv$f̑jJg¹k xYV>P4*܂*Pmuԙ 2Ve~(pR =4E+y% BXm'~%dL}Ηx(8үrxW_WBˠO\p7WptçX#7+u4vFNUs*EbTDzFg2")$Ǐ>giۦ–WSc\Rk!7z3=+n;`,rOӆY~"75}L%&7HQͦam|ML'&3} |HeP)7siM]xtg-σ@}*DsYp )_0n2Oܢ ŻWvs w$6ch%JL+1i=#{?m+LaAI#1+['0ښcyԍf (˵3ޕƬu* 0!ۂ/\2D~E|!{ܵw Ȫѧʇg.tkՍ13i%멇]9u ݺ5nOai q\Ⱦz0 BupNudy}v&c+Cqp֮䩽hbb?=F#҅Ohc2 <΄wHg2-P J Չ yBCJDYgAɞ %4`&ͮU4M?T~j('V;h®uc1o#l2:UzDLb) )M۰+̪tc ڞ^M%5A`1#*&NN9ԶW:AX̕ȬL{sPO$M9ۦ/6>fl\QZz&zVW 0yڦP[֞d*e"g7'n蹹icȀu,w !` B2J Gx:\Z08/_rFB"izP5,5LBU c,G-1kyKo#KNX4AP.rR{)=Ly>~,K_ ! ~gFZNÖ097t|;(e ӑGXfBdJ 7+$JEgϡr)H:PSb_%iڛ1RT2J t%StY]jc=ܐN,>sX4mu&g\UNkƄUEX&7 1+K X&OlL3~A1 7^8| %0b{,D$l+ >pEg:[`qd<VԶ|9! Śx3VQALJ Ukf89 .:{Xg¹Goױq$?\딖e Ka52!~-{mrS8uaNvy3P S7B羯5yAo.`vN(tu^^+GTX<,#N$P  s9zp庁'\}X4xnQ$,>aef[[P13;j2@Cx*d3e+ Vӹ Q>6 $B&eb5jFыtǫ4gB_%kOo>6;U͒10W G^D>XHRGd$LW7$*J]C<h)VJ@qT*.u:GA);372 ;M"NBRj+ rm&Fs{'SC-DwBEi< J*ΎuYf -~k>AܖDm]V;\+\ QuN<]H(i;4Nwga۰ =gdt ;3.\ƍ/q'W /+)r(pޙgԿ0Ssa$WQ/4β18Ab?Yi&**Sp,4{' \cZA`ZUQU`|#YQĜz F@;DA=آnafXmxk=-Ȟ!drr?z@s'kS%Y+,>jtG]?PujCF?6MZi14}2zD S`-k!\qƉ_RN߼a_ p8^6 *dzDŽcp>$F/Q[L5mp(Օ\!ccaj>)壸áp}8/f,Z\Cm XF/Oɞ|&o6q?RDt-cC|%Poxg4 #_Vϗ^N1lbrmdt|77!~xr^+~[qf/?9hUŠ"{DjG&Hw?AxXnfnj}|#%Jh**vKqI9G A&atÎ8WD14*FBê .6K pNq"Q1G{A3LMr.~fPͳ49RTux_?~ \0VK^)]P+2&+nyh] ۻ]Fxfg$t@4uN/s/é=l&Mx)@vd֨$_)6|b/$Pbr}Bs!>` m}zgfb?:jYJy)$|Pk{|{ߺ}4w"!hb;jMLwy'ay.M‹%xdEJ m#'w!|UP:Z.>ڢ_)/vlܯM[Kwg5w2}#+S7oGSUR4TGXqRdD\P"ޱ)-U]@T,\y!J(Uz'~w HhRz!ё3nLl3mc()@]%tFZg.@Ce6A !#I AjUJ#\bg*`w*A,hd)o_K{VI?7eR.g3Z42}jЋgVs6[Kjl^!΂W?W=Gk׹A :Q|,{c[ Ul1s6̞1T f=zLoelT$!Ypɡr1&W⺲80;`x [@>{˺㙲@ò4܋/n-N~uDG&("(ff;eƑ\Z4bW WW'l]\49 o&p&͉cmzCL` Ͷ?=s+F(Y&nX)0qf<`-D^ \Kx>;ZKuK5m?ǧ= 3DXJxȉ|b:w4A!X-X.QGʖoEI>/\kt҆{2qKdsyTW lf%;:Io].1Y\JJBa~`,IK/⩀,3wje0\eqUVSl{{Q/\djn;]ؓ)'bR$ dw#=zMb5nsJV?g3JpD'w5Ńx+Ǒ,`bjY#Y ֙ jE(.jSÕM+cQU_2E7;v {K( ύBoMM?Zw8~sp-r#8,. [^p-?y!ЄzaLW XQ 5WN4V9ƃsKla2s"OȑUTp*"4rv4Ӝr^3$x8-;p5 gyR/]w<5vs_0q=*༘z:] 7& jop /jI)sXH^0q UHg M[wC"N5[F¬Y@PrPZYxlbIm\x5d=ظŸv=O+O͒%zCHvZ(*H)V3FD\-+~U}R~k[:(|SU ږ@pJ!BȤfT| r7c3*n(YscK[!|]$A2抸v'V_o\_xĥ?+iѷ]<I~]q{=p3.']5FB0OVqڎe񅊰fn|{ې뤴¾$,:V.Yj~g :*7on?WZ(upBz[4R;:kG_U&XY5' gq ɔڒTC|e+׵ +mL7c"QJti!Dr9MMB긺h@&>wlQ#oA?an[qYXI,C?Ӛ' x ̱Qߥ˽(V^ЎAVa^4.lүQ͢O,!{ CMp4xo3P] hF_3(zYrw# Ѭa~^ []Ӑ0%ReJ3wgҭKBκoq(+=ɹjKS)rjϋIЮS.󼌕[eQtv!jcKWddrǃG`߆ m8m%dy dG)6<ˮ@rLSS&Vǚs3HNql`mQ(x|O*كv>¼ho;^-LjƝYf&ڀ=/pXwBϋU6םq1I 'eb|ipV^I!ͺAe)g9>L9w 2 mI@\%P)TlDڊe4i~7f_H:'M챽 0qS.SaK0_CO_^`}vK?p%8SOUn_ґtjU1DV^-VDrY/힡>jB* ݜyB$PmA;12Twwͦ  jKvx"뒎ϺiwFWD^XaYLׂGC1Ka;њP]wa ɼ_Ll"&Nb*f,V2FnS{o;-P1Fzj_3ʷ;aȠS6#.B+3mlYN?ʱZ|4ϸ gKuK)vs^HwN 2rNyߝ(V1CgcX(@_LAKp<{n2`J} }i}Fx^XT^8cA1WFCE]oMz5F!js{-$ӽW%H$S#@MMG-K$/~TBB3ƹz>wbwIyoWף6gƟaEk'޺(zHw58cc~fxy{̨՘hCbV@ >|I-#,A:Q+[p* "gU1]u#1-'-$%uA ]ژMw/ɔUP[,/Nb^`E MGYEz"$N6l#3;_xjLq(^'$ ]q@/`SAgNH_ᏦS/qJq߼'w$7J6Z.z2 q9`2XKѧkȚw!;䴖c$4N4,tపbK=aZ=H4Le|ީ|FU[/GPo ?A,\zE =:@CAnyɂʙ+5]eaPS1W?j#;U^z6 :hL9O-(dieDGmY[TY%ٯbY;]ӃH:S3 .6dK-db6:HOvqxQ}o,nI ;?ϝ5OB>l?fxe<n!@=uK ?IP"h>mL5 /fG;i܇J$OKroBZet&i~jL=sXO۝ e1>$t眻pk<Ԍ1( RPm?/bwš%cv0OT7}Q_zrޟ(߲5쁘MŬC #t @sSo!OKowWa]|p%wE#Q#*-&k$]CRE[X_$tJOsDdnI !,:\/k^’(IעCT6;F6RTG_2Eʰ/V 25ֱYƶ^&Q7$g-BSt"G[Chn +s $gzJ0*f Tk/NagݚD+0g˾A9~;F&[9ozrc*ȗ^B.uKU%x8_X7RpYlDǝOoEoŐ#z>qf a` -CҤYRA =6JĮ*CUg{Ns[pH|uAeJ}<^0Xe LrYT J$LZ (2rp`r?Jnխ 3{$A<,(>k, tS*,&bEmao%Og(rᢦIy,'LVu4d u᚞YSlG# }jyȑYƻ\lᭋ`+= K9'`' ϰMG(dy?m5)" V}2`|hz u TeЁObL8ԔڍcVb2p!TS[$jRj6dua- (qF /[+\$t8HL,.<*!4d]yw\ OI>>?.gg1]S5+Q!4^¯2%ތDB r!vD##g -^N9,l|r1/b΃M)칺+ba=@M("#w+^`bJB4!4*(iB~E (MfAHPhBi^< E|+٭fOfR^>) |=l~Ѷd]CZnɟ+zFz([6ry|:W?:?6lଧ/:Wmd+]tB303jH%%kpT+F$Ut|#rvA>9"J3+Y LP|4qh?s[Xr"7Qؚ05 r"caL G' pD-e̽Jp;jc"ϑ%䈘NJw.92MMo@$ǛpiR$j'CATvVf+Eb@bE3L g2ʈC4Ў{Ib_l?O8kXAX+͟2`BcyM '4 mw=T 2$`K/,'Aۚ} Yf}8TjqR1Y^ = @u`TZBtgW~Jk3k΁!)8ê!vƂNHzgcBj?a(?ql'[DlUeވ˯pjU, 2x\OI=r#rDCHf̺fךgJ*ߓ p~E:\#$2Jn1K*= VL6K6Zj_Ǘ!~ޕ>oUāaڿ3[~i hٵ3hq\Yq8eP +"Q-yE?/}= z(TJp\vA h, Q}6-`K* : O%j" Qw|r"!0%?o0}{sភ#ΜWn(Mhkbn@}<,9x[Vp7m_Ae,9H:8)gGk,E:"C^pPqٚծ@OaK6gtR51fK0[/s) #;0"<_du2hf@>o0RhxHo`leB6OFv8%iǂTCfվca9QJEvng,v\Ǽ Xɸ(eYm8ݩU?V=KlD,jw/ u7usfǐ?XO9Gv䄳c]{ 3 {=sniGwQ,6tPL1\:3ů 9XX>νKQk4 u]pGz&kiZf.{&!GM:ڊEՊ~Úmh=sᅔnOۧ[cc&_"9EyKo.ɽjXe9&Qu##fKQRer^[ZIi>Xkpw3. eꫝ -loҕ_<2㷂(oӸHJ%Xofl&uC-1/(6غ¹,_ULqM rD;[CMۏlP`\bV44ā3'YA/J Cdݿ1VjWXWU ӻk3 Z| `Z$6ib*)GY3]X*tk>.Lmdc/{c/cMs˘h]%ťB.0%G]ذ!cfB^50%Օ.*+Ƚ>(;(vY$I~ׁ4܄Q_:?k9W ('9#Oi]~<^W&JKQCs;/c6-XcR4H/5{ H<B-OL砍R61Ho,.KKÊ?77' ƖGkl(#!Pp.KaKm 0O@h ]rX:[]Wj6Hm 7|T3q+1=UKĄkR"ZUYdSݐq Y l!`qgGwBaIhSx*ihSBuZJO{!Rj|  vθVuR&n~eL`k( `#HAce}. |`ۨI%)X;&c;WeI;1wb;[d[УrS~5.qr0޹l[U"DMr_+KgԶ1KnDRcOU{/i+1/ƞnOnc)h_YC=rѕEQ0Y46*0]^WjD%{MR_lշg4=tV+xPˣ+O%|c~,MU-s!tZ~P,am+B u+[;kR<@}1ֆ?/EצsaQ>:^ͱy dE ]"A6@fy{t f$>y>У3;":}+,_=;dk-#-ZTK;! FB|OCǟ).Ϟ: 0~gŇ+;Cy ms(N[JRrQr7ZE<aڝ Cع# UƓ7nP xS螐uݟkd/w R\Q?kzlJxG$:%=V΂eUazd,饲.4_Zhz:v:~e=K7x)1 XW+BֹnN|*){Fh>. LUw\/B9s~Z4\blG9rω8礭 9 b-VɎ+c%YЇ$QKvkґ'{楘[Ჷ/ T=ŷ.Q<|RBDP^+q O3p<_c mc̩6Hs$Mnz.=jpCG85)o;YzsdݢA` MHٯOdZJq'$;a)=eK> Z<,[$S۞3`;d|b!5E5-NnQY jiMFW`<^6^m5$R:?Qf>Vߕpߥb s %me!Sxs{\uݲb9=/\Nq{]sa[JyϤk62>^ZVzC-JЅu{˕G[{MtrjBrӪLGr2:֛/Hf:a:^+MS t~Mq t6."in.bU98Iδ;Po (^j0fPM_RB8B SʋoWxXg :?oא">ω=RΉG͂ToqKE#y%| ӝ7w {TVmIr|W!犥Z3LZy'亰}Azx$-~h1,o۲@7R9[M}FJA mswi|&>M$ZeO(UKx!mo6jD.m$zwH'~L.+#^e}]6qڼ<$ q|WGɧi=s7'r-Y G^eK?$ud%/˩&%ڃΙ߰X@\Ȱ8$lxhpd *쒱Rìޫ-H1t=Vei{"5KP}n2X6_\+pcΈxN>u-" Mp%}z~[::=m#ЗXu;[EGC5\t^bo7e _Xs9CrI 3?b8ϿYw \HZ 5V c rJHs {d^ݏ;.x$jv1!J`w3dTӰ A<% U#7U(ܵN'MƂ S|W<_DʹH+3"=/lYؗVmK?ac*ևo]߾g:ځ Kc!?  cfJ9,*C+e{<(c?q@ ų^\y2TmtT Fa- |Nm#cxvճ9F /*;45-u[`i7 3\/4Y64?ѕ828*B;\,O17Io‡;K{^:KmE2S! Mޞ"#H*I*O=Г8)X1:'ɋQ7g`x:J>7AHfߊO[ҷ&I5 }BeJbJz<;oM%OW.F&kpO' ~76k(zW[Ytz<)  3D Jq%e?,ןy22]_#?0 Nm*V+5N ؖgaxeH@eY)R[- A%E#eO҃0FL_;]Oe c'pBC8$"PaIC$f5]w(/u]deFlu&*1Rt̚t.;$8Pu!oj J{3+V.$ 4&~p `CkASn?`U!S D/ J;rU" Q 0ק82lTBGHӴ:zmSnm: `dt%(iP|^<]q (Cp(QUHR|"vkhڰ ɊdJ0:q` p\o=Da: Cxyv,7^c{o+jXC9" /|iCE~?ձ[YS_;&0 6Ke8 W tm(yGڻ}Ɗ(m3^r?~Abuh5[MZ;NE[D~u5ԯg.:I(+qDQn7ă&y I@SIu= e -wqHrihի )]ʓ*b<_s/.BE @N3Niʂop/TvJ}UUdW+V2,H9-ol6Qt*2$\U\- MBHZ?H?>do9v䯌"$GXO G&>wQMߊDw{yn:e-f$`xn+|ݽ.px5-\&ky$x]XY-JLZ$R ;SM9Y+G@M6uF wČy^4t V1jv zעW5=nmr!HV2Y^u"N4(nT\[@3A =zUθ rTyc *S`]+zny:Z8c/&ovBvw/9F#J*x2^yF$I.(DLgzxW6iMG2ɄABvd0q_؍3u4&dr|/@;NM Pc3d#wBt `̉p-x~1앲B4h243Tg迄j`ok)ْWk#gYVѡB D0?Tسj濂gX=C C3:-tIlXV{fȟ^W1HP\x'A\ac *;4/Ń^1FnٴqƝƵ Ϫ-C+gdjoR>u{ˋ=E?7I?OLyJa*ǰ!#eMy_4oPmo6O,dcUAOެ+03R¸OF GEp Qco̾+AUT.9+*0 yHD@VAѥ]89T-8M:Vf5Z )". !=Txƛ&#_m"-k9=8&lSm Ú~Ļ,Q!Vn?A U <&[i~ދ<QvD3l2 H gZQCZ:pPVQ:[:'tZKUP ?YM(aύV@G%@+7R7*'Qqw@! jsn5(o Ljw1u:ĵн*}Ynx""…+EE^7z"A\%`[NB r4I{QFňn928[F ij wTa#>ty{~<|lg!+:`[D΄TN}S-c$5??{.qr:)^DNE'ُ+F A%zC;hb^`r(s%J$rIQ*ZHYtp8YM$jEߓ5Gw~ `! p_ K5tAͼR?bU. qH?}x0n9O(>R̠ri * 5N[D}|P./qBS)\^*8ĤC $+l[Ĉ'`n&98maĊ@ד[{|`~'QHZ!qj -"ޔnsN@=U+b>C>2BkG&轃_]FUZK}]);a'yK:7!:|)#Ӌ%yAs%Zi]3&zd!^w۲F}()!7ʒ~-ۣ2vxP=s9(3sq=a Z^rhO56 > n`1uI93AAq{AҦtvvΖ'ׁU:C$A[\j?g$O`L,$޶B(ֹ+ţh X >ĸFâωQ¤VZ#H9ԫΕ(PA'd):i-( r_A#Z ʼVX+26W"gɴOhA+s?4C(5ЫJ&(x-`1aU}حF ,1p5%[pblV<[]/E; BXeIcMkz5Z,idiV"Q$TFiځ^P#ϗ-.b 2۸vG c/ L;dD8"/8fs X@EP̢6<bQ7VspE+1Z)2[d (?'#pKj.:kd#z)XzyĖJUA&/kc^0ϊ@ 3eZ}w}`A>Dyxt/|=ނ|SIVE&3 sK#¢8R[3kz M7 }\-{$[4As~q;LozMjiG(-SlFSy6ۺr*&--w\$7oA•ʀk6d 1ңy$7>*_sMtMh*p#I&lVb*|WAdc֣Rx mmEcgIje3]渇F݂'®;Fmgֳ+xIۑpя;tFhey^ fou0x\>ݷRq86ܔ X3IOk(m7 IH'mi9rb;h-.w=CLm__RXqSz n!gQ`qU*HxAh#͚,cu齶ő.,oǽ9755#~!>WBQCl2/Ub[-nG55$wаF$= jIEi엁s'[zwVgek5?=,b,b]pp) żɥdp@E/Xqrs&-䪊$C _ރ^lu;mėVw,źQ|5&wW HgU؛iIlrY _+;!e&K8w gyl]z|Pr?Υn7/OV:V7DhO0[vF:&ǑR| fnyr+d]٧uL4o{)`}6}BIαI9j.rdٔ=w{;naGfLFy<# @\!*FgdQ4W*֩F]4ņ19loHmɍؘzXV)("Hõ%4%)zp+M7KPi=9gD$lrJαnKtiDrv+?䁍\>a5ze=X~,]њaÙ|悪ptkr0ɡ!j>[߲4 | ӴoRL!z.){d:g+/xBX{@z.8)Qaɒ^Y`Q).SJB%͡*y ` yG](NyЉppuR#$"Qx%KiÌ$ 6vcTuF/{og 4(=|!am056o|0^"Z6=[j݁8^-%3)(nUmecl as O.-{F~0BHUZ=J;2x_fiůU=r7D)– 0XΥj5KC4s`!h@&Dw0q&511opaT| B*/ۜ8{D$OQ _&xJސ!Ŭ,ܻRcWӓ'cesDgy."76Yid֚Ln4+) F%ޟV)1 CQOp<&!Re$OV bo*֯K ^# iLVsD=8hK!n [fZ@0q!)A5kw02*bk}v3 zH #uT^rr[1Wջ(/r剚 e̦TT9H?VKkJ;' Bxc ⨸ya?1?NS4w+c.قK=vMs샰poZsO76x )N.Jp#M4-eBQ_p~2WlGDU͂PlU8W"d8ۼ/hܮ@fk4STLgP4un%]e`j[he̥e]ZԲE=ƫ(_!Qa>4DhLn7T819NnuekrVD2:^# *Uv^+LUvLG@4WȔJ%;$=fˬWԄf|^B뾽ea: 5r_$7K@lzk;Z}OYg [b,̯mnF ZMUw<$u_Bog$z 8:-Ϧ%޵Fh84-/!jh54'+Oװ WbⓐϚ(-v}=h} LKMaTpRqa8.sԏL+ Eo"dotoDٍCJNf4޶Ul|8X < V68;*\b"P^ȟE^OT.%*8>8+豼*Z1jN1{ Y&,0Ll=+PZ=,yн }r)n\Ⱦg70&U[XHiF.HJTA4QKPht`%&uUET+Jp ,;~k OqIЎog?~khLW k 1Z|]4Cik&Yr(CDS=[S^Mmg!K-q.W\Ҍ^ ӉEiB)7ݣ8wKXRmi&V|(<ųr:bp,o M?T2,{Uoَr{ߍw4H(\ldR[.cy+d\~0|d'WJ ?Ru8;uxnX9M=mm^ƗV M?HӤ ? T-te1U5^̺Ccf2Z/aCYQ[V|G+[ye@^7:3֫IFcGu;T^,_? $20+\[^xvTH< IFFe& 2MMA V`r 'c䞈Cv*K]j~@H8C#lլ>y*Oq: nƾ ׎"ټ?i5+> a=u'7R'`2^] xjYgخz|گó;!Fԅ'oe)"&YBl\lW[L%WPQR~{ S!p,<[es9y?Z4ge;rz؛˧eyKEDCoF `h"-AW4v9&($*|ƏώI"5C a?N{Mx s˥qc5ւJj/ut1.KKc;Re^D, {DŽEc-pݠǙen;;v9AkGFtأh{nc<+t7cnݏf?72cq ܠBZ,V6ZٓE¹~>l Z+h{K*mR_YhO* ' yX(L9:PCuP!3P}njK^¸޴MH 3. I FHυEͰb*҉/ *Wnt.LK/ehmA%@-2VJBŁX%l^#Ts%vKId<$$ &Eqq 3A|6½ \g1=ޘA@W g .~dtQGހ]X>4Q}f*ds"[Otϟjplz-s͡p,Kj#*{x`B5x@Rs0T9>I]!r+W^iX^y _LBt>Z2Uu'JMZO -B]c+ J-B/.q+ە%~Ms;sT~B{$Ҡe ktWx6." N caDkJDbO(9<7c@|E|RGvlQhZ߇GLnV?zaJyj)f J0NPq6OkEhQqKr vB#?a.srB5dӹnw^BQ޷炱lyA;Ζhwئ !sCeW7!KQ] L(E{+v.xN,h91o$x"H{H!-(8RΘ' Ҡ6Kђ%K*&T$|E7'8vH;sz=ܗF FrWb[ߴurF-24%; ;".W/,+nB& {wxjclF'[BoZw*ۚNlS B˽Pՙ(ˠtC)NK3z |(S[^!/1g}&+KYJu ԢYBSܻcC';42\QnTR{2rJ4qæ+i> c͕~I#6wKziĦ&S8+ɊIXF>&mxr T67a)EOPQj |Q8eR#;lEmy|7ư;F͢N+cǨΠ17BQ *{> KThzo[kܓƖxֆܼDãK7T͋` ȋRUμnҶ*]ds H2<NM@.kV;rqr \G:C H- ذI$*$#N]\iG2?#&=ڧaB1*xK㻈& ʦ(\4lUa,=]>_8m67ى8@+IYiM3'6tCh9k&"2 *1˙\qY5@HwO<ߍoܺF~[IRKz"u)kNLliս ] EUC, P d(&I#~b!JЃI, $;tX| Chua5/ƥXE]7bL tL4 ~o74'!rG u1mɇLA|6$cھ{n\K-g38KNCrj$ڌ:=iE먕j1~[Ć1t[F(G 7}CP2@]FvSIJ][ѥM2nN^] 9[' $>n kD #ZH&ۦjZYDY&>+W#ǜu'aXt-$+vsܲszAp c8O2P݉j̟͵b/ɤ/-rfaE}cMo]Q ! u{Opv6yn0(Z+9PGT!jaP-Pb }RzǺ QG1d)fY}k&ɮA&%+OQu!GaN1qF?靓$` vcEg)4VGeb̼LOZ1)'>O>ļxzyyE"< [TV(J, ir MsvB+ se=k \ԢrNvT9c;g/@);?䈭yb7Ale׮j!ɠ6t`Úk@UoU_l@߿b%% QQQR89C:hah{R 'g|uwBS7*Ǣm`s~Lx+M^+seidVkK\,Aq05 ^7kHܿ m Z/6,Mb='g̹n>$kb۱ФЊSsx g_Vlxίr>}LlwA;/~HoQ7x%tKb$CUOiLr` ֈPL `rlrP8vO衵U=Ԧ3dz ·5vceM13@KGxś Z**ﰞr0(s \Hl;mVEM(ZثG3IytF^ǁi0T@*ٞ#y3JRM>Ќ-+@K=!-^Ŷ/M(&gmj֝r<.~ޱ(}1jNB gQΕ0}uFz,bj $EUa>^X=\l6o 9] 8d_Mrʤ+44 Q~B:b(Co7qFU( [ NTȨ0kǨ֮~uŴFeh50>>@ѐdlV"SdI 2Sݔϓ5g'FO>l}P _"`F>HJak ӭ[nwZ W$ޖ$  νy\s}D;@=+W^+H؁jFg !LAᮯyȑzG<*AH([TɅǯk@}WoU[;Ə 5А]~aPD F>j~ӔTf |ڋ=*yoR\D0% g_+߉q7> UˊG#u;E^/EG[sR:LͰ &PGq~a=w"hu/de{7{ajS6TlbiD~~L0jp͊ŷBZPY|*hRPiw|[ \^;3jR`7YUfAoɲXcɘ6_2E!C!:<  ء74wܹ8/O5Ŭ7kz!XPq%1 uY֧7 3ۤAL")JfQijhQbtR!PMNع$+okᏤƋ,3IDuO>;[fX2rE_60贆!$!Tb\6tjb;'6Mߺ^ FSIj#pAE-<7Sr&$s:ޚlI|JZbȢp'Z3 l56_pS y_N|lakԐÔ@ D@yܒ hx;Y0S'uW/}!P+2jF܉AEOEyPGSbf;}ܺ}I91%ay)׎&PxL 6JBJZ|%\8Q;ݴf8Q X޳|K%JL/E%*z0c@Hm#WcH#fa#W_ڷ2!kbwƱŤrm髷!)]p7kK u[hj*M=|} 3 hvBo}GfdR_`&/>/j4S4,R@ b]nRW͜.bü`mLnܩAFTCw,[ypee)QOw6 o|ϒ˰ 6ֳO"ᡏOՖ#͜vKHFΧs܌UçXm}L3$z^"0F5u{,bTvk?9ͺ^h*i/Rq] yo=$@6)m@ gB!5bDScU kmpa u;2FcjUhjӼ-g(:04z~k|>"MNCד̠sx+02Y0`^';ڿib(iidL`b$`\ 'Kw 4<ˮ v?F5uSx9slCq/gUcXRD 0ғQx;v;kkUD b_.Wc'萒[M2(qP%s6SLhϨ339WPeE$ċA0|5`?u0 )P$!JjݘS&p$+`CU@0O\{\pa"Fn#Qykz?FvI(Κ:]J̴Uuy-Ǎ}=|j;$ z 5.\7ngzĬ(d TQu!$5J!-̙kZeTk5;|+]|[ p-iV%[wu}uGe߀9?->[9VAdSŵ(\(FV}C[-9[Rt'GRCh~,-1.5ZFyaÏ3D,nF1h& (9WYUR@$}[y.JmLN3{l=@˵S,Iw&# 3WS<ӷ`Y1 z"ok&?E;_=hI) ֦:ȁ?z߯GGò?J Gf$Dihw0ft% ~Џ;nuh"ުyY8FF鎺rʑP='HU, R Oo]SWh: 3O|GW3gCX۝*$inD܁p2|pzBaJx9O&HğCc1B.)# q߭ktIlqf}vi9tShExIXwN d[ҿ?*0.$-[+ #!ƜMDR><%ɧ 7Qüd-+ׂHiub ۤ(@;_(xݿ@2:bF#' Ma`hJH;mɺi ,2#uxMVg8kYJ]EqY3vK ;Ytz nFJ<0 >^sTÊS7uO$a}/S}xՄ㿲ӊȈ~10Vf 0v!ҞH#14#X Z"3r%AI-ڷPlpMKɉa$ bHYlŔ>)KgGhAƺ;wa 9qqU_"]Ćy%qMڂbSh#Y|}/! JC#;{RvS$6QJB8-N=Xaym "y`% ]yaڇI {Fu .1wZRQ<\.ZIIq^E( k{ryxbS^]JZMZҤ.%=ks%U(ډO=d49i˻9TЂ2X|c乣iEz|Z;ru~)H#D!} Dkhe2Z-0Č.aGFsLjO*D/k01xX)e{z7gv7!qD>>6΂\ ˦Gė4PRa~ۓ̨:"[2D7(o) XVwؼL>uluݬbY{A-73 ~ Ud,E5% %'=C|_>> hi}4>>Qɿ^OJR >HAsŢy+_VCF%Tj{H"q~~KPDK DBu-tCӾ +*pBv髪ܜ~:*F`Dj YHe}Yn[#迻q4W=Gq=d<}(i=a q_NT2P2}> jWV'0m[D'(ѥ=ioma62%$[zUq/oRMlě/lmj.4VjS w 3".t$(ӆb0jT{=1R2> FՈsL"|4;MiyIyMr+8vkcPr{%29޽Sb4:`,[Ay_ҩD{^Z -a?G+,1ڢAٙoQ"s]vbu "TYsuCG#GSP+&@D'fNdՔNo[*b̳nEy O} gj'LWssL|kǔvю fPI)^=p1ץ\i\u5-/-v]@J Dh@+Oz4v#`ư42MW|8{-~aԨ _X?YW _/lOg`RV2'4aC\Էlqȁix"3WЧOj凢(ﴻ`Lcp 䄊z"y]bP!TnF*+,FUJB$f\w<aaDK O&G(bξVxN37!'. 6sO9Xw1+(jDbI93ko)y#LoТ\̼$/ t\.|_ HeY2Qv`IYfa3b)O/M,&3 89nZ0m?TS8OE"^HRsO_ȡKICZD LD[13shEHLv?W]G ,]y0AseϜFa `PbA,Sz ʙ#>Tq= 運#[N '3ςQ.@)9nZaDB\I$PzN&W08d*^d.tױ%zqo@l $o̱X;.vЮJP^]^;m$n}foBݲƱj42}⧵=~NS %9~E{EWV^W7V-JTȀt)_1SͣkhV̤r_TQwGg)5ݹSV u^^ @oV-&{LSb*s6B;Uo gP}~9W گVt2Xfkgw̛3Rfi]"Lˑ(4J4zmZI+_ ԝi!' `S = N z9Χ;,JW-XS>Ȇ -#+/&t~$R'!GP5 "hwNe*mfv8y"oEg՜zGAd +r0$' AL|~z.T3#B^!oAqmK}x[umM#w~cv}5~N7w~̠1.cՒ(]ZX(%N5oqXig9K!_ aP΂0O(f&-6VB qܒ(zڃ^1iuS>1VK7( |MϕA2(GTL.~^C=# 1-0I@#pƽp: qXrC_$c]vڏ53E`-q9Ň QETJ*b ƬX?;uIrHkoa+sn7;gy Q0jrBFD#hodžTUR_MͭؾiWݯ~(sm4M)!cX`Q[m)ɝ"u8zuŃJE/+耐>)0a^o{Ofd:Ek#2}>K^>$HthO`,h5aC2,6^f߂*S*? b,yZ^A331}]d#JrliD;wm&B/.YNghxM#P܏4! Dw5?{#h5ΘF +:'^YL#$b=Y%1!t('=3S)Zܧ;%":S*@yjITQP粻Pwp*=# i1o5ΰ3ЙԷ3mڥjV(74N&ѶʷYknJ11=fPLݟq_S/ =!_pѫCsT$1%GPY.Tu&֩ s:rl_YHؤh ZX3y7Fu ԑ+}ڠx{ʄZtWgx}GЈ_ouMgBY>$KqvUOb4gE)pӱ<(b$a ߭N\հ\;XKB^Ͽ/EگR@ZY1[{Dz'xjZprfC NRkz F)m:G:U#N޷O; |p>+/!n5N]t;w=uit9:m׷g} վ?aТᙞ+NneKsjT&ҽ|q3 J^ ZoEk@w' c"(MAhPNAJMlBCʪمup;_ 7iě§/$J=1|\r㯁~L#7Usؔk=s<~/ᆍ֠6 ʁaR82nZ3E q>( UV eR[ϕd02' q#B3b^2 @PQFǜ ĴD^~G=@Z AM="!AG1q٧ރ\F('x<׷#aC:Xql)yd^D牬>Uw`*Q#84'g;G~8p<`kerJjӏ\ !tᇕ J$pO cQILThQMC\`:a,j@@WW8aFP Ix.}ݣlG M:[.s|Ñw@[X4yn9/{WńxyxN`C&e:Ge:s'H T=G&8^=eX*輑i3P[ַW2KBPev2z7eAA@ =}8Iݎve719hb|Es &=71/ׯ&<ޜStv/WPk=QrA0FOP (` PbFr~"oZIlj ákUkفp#1dʉ 9(`ra@Cb1xH_yCen}E%3֨{{%5)a'z{ĸaG\W&5eMeZ.!8@wUtt.bp&_ٲaDP#cҪq] |x:Ss-膙3x?ґK}@ĎgJs>qҎ?w\ }k?̨BAMtM[rx)$ EC7j' *Б (K~cғZ$m#H^id2xΘn{mBV96W|D7k$"O#ɡq809MD?H}\Mifb 8TqX񏄌4 FHI]7[cd:$Cc1=D8^N"Z33;^] >N܋Э:X.L0#pΙ> `. F/wu2,|`G[pvi~RS6H$P C,Us}+^)asԷdϭb5!907GYdUBg$jruK8+ܾN b͚}][6+dMH[@$._; ^/1V$`NR9ܩn*H$}!*_Eˠ[(Y|ުl :͍~3.b|"?k/öc~Sǘ m~\G!ǕdHA D|̄+Uu_GUX//)j ^:3ߝUMl}*Vr:hQC6q?,nƇz_+r 'mRI;א71#¥)Қ(Ld,Z\Ve;bݪGUWHcP|1}vP^b. p;a"@~ĝ^3m4LL>vab36 ͿC2YT M:f@p9"󯱘1:]*\W;[ B3]=CXM.OdI#_MO9XQ$%H4m1I2E3՘v8dvC5 vzk NF4-9gC%Qr{ȅtz=,S_rYGche'?7Vc0?!wTB1aW@WQ}F rG*f\h06s*m'7fGb!(JݴJf Wh[3ǯfc^3O\pN+[l. 1_Wa]f4@D]D'\xoZA(")}-݁ԥ'OOYO|Zv}g¥E[Lɍ[0l ϻ:ބa蚡d@`NI޿)r\OP:{YOpSh4@ Ų0<[Ӽ?"֣GiZpu1if`:0W# 2b<-1qeċmwG!Q2=8eeD^" f7N_9'MwlO|fAlx+lK@xۦfo!4Ylă[Hh;pН}2̰BϚ60>@xtc|0,J@>ūEnySduSthVi\Q'E&ulC'm9[mX@C Ÿ@ܫϦ;%6=E r_c`,oߋ)7Ft˕@9](qɍ<_D4G 3v\ uyذfQ'cxy!ޣEL\˶bgX?mX[޸G0ґ{IOf8$7Vf -;sW ,'1-߂;x'ʹS9^wi+&猢c% z[P exˉlG¡^NTh_@hA VTfvkb|rM񪥯Y/G׉×rba%^hJS]6Q/V>5Ukt Bښ䱆I|D1/mT+!O-ρO/_wN ߞ 5'?~/Sp?P-TKfrȽpk\W#1)$qSd>z&/zb-L5)~ۉ% DU6\Lbm3[!;$ɠCvL`?sH{Mɩ^+^AY4pVpFjS HI9e[.xwn\)6( ^RB_3ӛChRׅaPotp(~JE%z2u) ~!L+ Xvʫ~EX4 EQ;Da-AwTۚD5ɡY,嗽@ՓD9.]R8SL.H'F uM9z oK/4te]Y㆞ P霹+y+Ycs7MZ.F9$>w>^2 S(_qO[^̖X7仳خgd5<^0`n 3;O%u jGYņk ֹaС/H7A3sl=½+h.͙0 1 | ׉OKf@ J!Îcڌp OkTnE"X5 sЍftWcJoCBy$sN]o b4.m2J̈t$g- W,LlрC!`e~~37u6pý$jA'1*Sq?RA+ٞQ#(bvE@78GKHq-@?V4gjl'ޱr/Yj,r)# gԻYMsEgFϦPuC }n-egZDFXG}>)]?8[Ex$Isb2La|=EEgF/Cn|90)k<$ R/\]w:n5^s1f.Y~ryQP*jXz<0a||HGK4%mĭ>KJ:ĸM%M*b^rT ;AA")N&'VWpfȸ΢6DRU:̯GRvW815gnOLNզ+.C* Y+s6KX&HT6¼^n[ M7d wQS X%ƪ!4as[ePtԾ1Frμxr D!W&Ɛ($7.CVP$ y'P( Az:@~OK~ۘ@$L4S~j']5GR XWH]IX1yQQQ탉Fkπ~dzHaeAut[ L{1<_;LK<*W}6gc(CL֢VdܪcRgAxnfû-+pk<2Gx?ې: \a-Gˏ04v SIxc&V׸ʡzH|Z8(rEir'\ߚvX 9n=RR-TVTśD> jOU4^#BܖtF,sIUԶ{ @[`s'5Z C{8@gsIE8P!7_(mxz::;|*=s)ҞDg҉㢥?:8`@O^"йٳl<7G*K{y5p7WBE,u:S*B#*ms*T5+H5"jfg`:kB u#!evRjբ#,Ӽ*dT&Dc3'M8ȱ`b'Y@),,vKZ$EjIY}Ԉ\/cv2[S'M8rBQ5hP+MqM)430`T c%G(569{i虼%~ {OC/MIHDD나FhmJ|T[?Z`$Ւ%N(etjXֽ2wNlk9Vxc%yUgmv T"9[3_ЧWK&eÎ g0{0nAOyLX[(9Ѣ8kP)>uoq-K-glu}65]oUGUU-zB%d.sNeV3ae,A)` G*IyȅB$!SP~Z5&13d3/?_v,E=9F--ɺه}Gےd:1T^RF1u='JE6!JQ[B3Y1:ۗ޶fB3 vqY:)c 6-`@Kv<f,fXmiY`*3h[,),ljeFBQtM)=[*r7P_ ΘЈݷBOKh]]zpl )J69,~Z,{α!N%ޟHoW05׺,zU o!x]fGILazZ(!T(LWjvˆ" ^{tO kzDExlW 8QsAK>ߺC0~o4$){ERoCQxX|D?#A\ŃsNzcLT$`/.=+ r4[,47tnZ#`(.b:*Є\؃AnXlgpn̋Ǹ Dw#oJtarx) }1.=<252%0lf ~?e'Fa,U i5sx=Yc_v8+@y7բyQ!kzJX[; DJuRK%dž.CcLv+&dbM"SXbpaA$={dy'fD1 Q'zBQ|ӐgWYجJDy_6&Ǜ`9f CCޮiMgC* 5|2鮈pз`l/\_lztn:M}*Ln9 뙘r'Bhq#)6ʛwѾfwiZO S \at .w׃?L Beh[_ 4Hip}>!k^Xu]#sAۘϲbpї^#Ċ B4kK\Y pٝ#' "՛Hۛl;f?4%9H6@TG̗KW2i獞FX8;O)(t7Tqu1(xb9OhKeE)Cex{1/I|W'4[ɠ&.~T=qqIpIG`w$m,]i MF$d ?kmjn ]I:baam.߶9:_>w(!5؋*w\)+~ˡ̀wgqcf۟HL'X}Q MDX19FU- DKg‰<5Qm뵯%|rU7U JΕ*=:8uv%?fH֖jHmch%1 fT!SU[{;$ /nŝ/9D[wJ(沓 ^r&nBy&#lw_*\r1ȸKx 2`+ؖhA 9"5 (-Q~8lh{WtB.U(IXdm߽ aVbޱ&O.a@)=̧K>l:sYLu9v ~xKw&-8iB`Xh&)hx:N~Xw=A?A=ֲj! aY=Z TNS9Db3nN{7x:I DPvf;;_Qhwr?WK0'=gIO @4JyY9G$Z-տ&x儍UB]F^r ^Dg,j+;}$ YdV)?r@tȬIEHMY<ɞ}e^+f*SIpPgYlέEEli-+~WǾcn-|ӝ-ȴq:E^١Pu)7R{Wszx6G#24-V{9~!VoCOC}+Xg8=_hT< o50 4OPm 圙V+pYkSwFfcq.-ރ+ق*SsmD:ǛOZsWe 䳄z3CpQ]LώGT:Jq?tJG,gH:.\l%A}b`@44iT2eÔqp5@5P}ʢtEp>{SIǣu+;P,y2Jb`t lIy[]G"dzծAP ()2b{E %`$?|C঳b~`Nvjo(= Q)ts ~ZM3,GX{?oڂ4ϨWr<0SÐbxWi t[=,IA+&p~^VEͫ*or` <)ygyXA;Azv-";q!:Dzo=E 0ȁw)jEKxfYCn7W4(C90|aHZ}=e7Fٷ%ըX,L8ƋdHrf@B"DX"690"4g4N4Ez"pC 3~Y{#'z8b@oj%AqoW@2%a*IuفoР^q,%"|TF+)0A u,poPSabq"fS oI +ƭk.m1Ou2D!IBliI 5]xBr iܢ a=!;޻#a`|l~%F1+]O{wXoG >Lvؿ%i@4.ڛT? L^ڢXI .[ixp< u! c@onu@ƝUl@k @qAY!r )9 FJq,OPEv>_~>|H_L`B(.[{vƺYNŪd6JMG7I~C2]ƥ\)׹Vb2QF$8yfl_K: l`ln `cRiOz>#uHR@Y6BNx Q߼YHq//:jbi?4Щ. D¿e[ f  i7|SxG _77a🼬6Ȑ?Z^jC鈴R&djD_0F+Af⳱:x9S⍽H)p"I6y T`nH%"c+jLSsh\Q쟌g!-t\^Yg$!^JSoJf( ,btWuS:. O vscVk?:4f@4a<:J} b z#?YU#QpaB.VCSi1`|YChġ*8Om ӻi*A]GBS8νVrfߒ7EBQ>7j\L&eh/ gM}!j^>_BB+ofl HيnM {_6ɰ;P=&$;_1/>ތt˚` |TB\?FZ?@=GGF cn޾~22-e@YK$xy kۤέ(IosaC V( 3F.* (kVAQW'p=]wA_#pc}"lBSH)?A;J̫Ҕ99c_fQz! V0M)1nکQ$7p]+J:5 6q~6$!7BnRHtHY6! {וfP/5~k[ְ6Jo/!䡌OD0`3W*/ q6O.UT6F`*}l7~VN|vQx 4?10CA]BAt>~<)b HN9UI`-1" k/!Zd[=%_ЊhUariE9՗p@VI|~gxd>酱2'S:5f}=GCcw:LLWqZt^lbWn]~!}mcH0XG$嶼/u*K0Q Hpeq`q@o "!%׋Z|#ٽ$+'_Vm~nWv@*1+&UJHf/.?ugg5&>k7(#c ~0q]s Z9]5 k*oʘ1&dJ ir@z+Β9 ['DVR^xߟiv-v@o_-?b5^Y6RZvq*]LAFZe `+*E6QT|~?ULw3gSQIU's3^4r3Orً,/Fm 1o_ G]ҍ-*7Ry?$[WHB6"YB`#U묫32,'in}H3ۄ@HY*o[-x>5fǻJplFI2NpvkQȣQl)&mTHIR I\XBSQ |bF\0$CM3TM}) I٩<+pb 3]{Db$ץj|~.{zyЋ9%I"z#/Ho~^ ϴc^Ҕ 8=^aA\$% +<m S.0+k#^Ö\zYt@;Ywb?l3 c+VW/f4M5QZj,c4ǩ`7xpi:Mv]Nm%(M?ωpW\ q℘hbGq Bɩ_vGmd/+,!}:q ꦕBK67]Nq_e yfk(28wb%ͽ\&U}>'g%d9]eH3"\N :QW_Vr<%&1J+dM8I)ŧ@my'רmyC j HwN&" d}MP'tm{$ֈhgXRO!aKC]'x( Y >kZ <eWSS@I+ 3컀5kH)$|z3!w#x1!R0Bn(Ći38iMK{P<[d'^;buj Dz v~Kƨ7z}5ޢ$1o#(;B~yVjNefC6~z?zk[1r6˂nIX_UspBpWPsX*\`3`Ec#^Z@ѯJ,І/t(94x45ux<A+h1΍!<"A%`MlJCO*X-M|ɥ\SR,~ "Ö. Q{q: ㎂u.)1D;!9;ƒ[(G_Ns{Zd?>YL>.r3-hA4}dј2ߒ{\ھ3ƙak{0jSXa.asI( HIA~pqk}oFgJAfXO6#z3l^E )2C1IP\O(P6[s Lz{lSR!2e [gć0"|>?Ec4>E=yRЩlNPUhCG U쀵ҁ^aqGS{&r =إ@L1BCc[3IvmJ-=m ㋌ySڴ_4FΡ'Gύ?9Tliӥtmy.'K$^%_OkߋľgĴFڏ]}_$~KQ&mەN ؕ0H()C'L ~{ŎW@ JMoyn3&"*dyP+&撪d*e>|Ct&b)R/īE %qD&R:9>Ϯ8~š~\]0mύE=CaoV35+'ExH[]5{"<=D= qG~|NVЀ̘rt+la\D,UmM!x,oAz|tW .KVQ>Yl, I0Ums" 6,J!~(Q g&(Ė7lCLKAf7r*[ De"\CAuDwT@ZlW>2T; l}wĨ#DNVv; f)} VZ!&ǰiv:-X8|4:T0NW!FIgc 6/ty |Or54";Ht;[@Dl/0ksO ppyʇޣUVe/JSfjxU x%8b Y1ŋP8ҩCjj ȵFIC626 0|on{F=-kx:g16?.Q4 R *RSg c @W5pE_xYvQB.d0>GͿC>*dXo$>a;"N̩۔ Mwg E,WxW  0X@D|D8 .4H;Z.6p ɮ l+5qa0?h:DW1Ì't0\%VK5 *e6:"4/ E6*ݘ$zp~ЖzCO"Җ ckz»dkMP1%A`äl/I'VRLh+3|c1cϠ bT2)HE9zs0v&!TN~=Dk j95LJHpiVJތh Ċ!ԿjS&~nFKSQb˶wO=u:v@3Ȳu}VPQeW'}tFb~4L⫞yRw8TK|+:-PdXa\Sx%:yJu>)x5L$zS ~ ;4<o$<4,sҫ̳{Mz!cu >bCPRi^Yro&Ț5D0،dAqE 9Fsy:_NEmnd;854{z}Y>+k&շ#ԅ0hLvw}ۭS7*1VP;k((5a*uKs2vIz hJ9>5UPCY%0bJE^0 Sƴ[i7߻/8b]z2j췻Zd2$jΘȝZZNU0/fY0@w(?w{__ mDBo@(+wc_wVt̮#k@r&N܇C fEY4"d7V &Dt_Bˈu[e*SEJ!|V&hݜڈo y`qqF=M(iy kE,VxhnÕQJdEJP~L!$߾)+Z/.8OowSmG5l|8(MuuN?MGhG96@Mek=z"stnG&E15(O@S5xu'`B!ZYl)eXH) 2< +8J$)-p!9n{ϥv i= XƉs'`|q T$$7ڮ֙H=*wD$KoD< -;e;y (_½Ps*V[Ĩ]Mrnf+8D>B."kJȗpShJ/~e.6i(%łyF2iqJDJ.#?rRsՂS@DLcΣ\YF'^p$Iazύd~<SZ}&yQ*IGf)\)qA:7UO }/ C! *vf6)ϴi瑽HuF:GMgŎ8A'VN^(D"\pMwZs8w@wS%tP;weۦ FRŽ6a<̉aߓ9&*:,J;v-WlH V7!:v @#UVYj[x4XZ8תyw~]H;\iB{/.#dٶ(hR&JκBcJ s¹#_ލ!d͞ FKYbKpSx 7'޸"D>s-F1z|; ,(cIكXWU݌AyBuFOM$CpH .pW%3ܨ+_@L? 28HL/O=oۆaS?QG(Y3S <GQ+8Z OKGHPW:3zb휗!֮s[|1kTV^n=5Q*?d2N95[B'hbǏ=TxdXhzw:gℤr-/?,&`ؑ`t)́S+NԞbEP35(%ozv'g%Q/}Re&`\ Cdڕ\ D-H6X '_3mT $AI_N*nFȟxm`鳨YbieyD*aPc]>ّ\ x%C Qc{H]a**츤}b0 ƸBĐ/s:ghe<ͽAL^9pGf4}l'jBPExhcKAQ}ls1F.rFYIP瓽m6 cr \Zt큘/䞙Z3F18: TtUvb$RbR1Yq j{7k/!^+ 0, rw9 q{ r Қ{wN~,)10jpRSbu<^Hh-')>VHO"=ybd xXA"KT 2 O@RO2AQld[&{-h?TY{KkkE307m,=nEl$GfJ2a H{ E6I'ԩt m"#OE`x_uţM|׵&d5}52NcYLX9p&uqأnwcm@ԒhtpW;ȵyGn}ыfVy`q@GH@j4NxwyX)j([]ٲH<k1dVWsZB] Q i 5o4A؅JMk‚ IFgfRIu,?]H^ƴ@KLBN.!%QI7  BԹ +Wqb L\QX0`5{, ]WS׼[lE7Y!멝w}mwj`VE5@:]'Gf.DI664T2b &DN~ r\&t@ϫ!aٮxr#&Ҷ]Չ< 4 ~abɈȍ -k8G*].}{d4>}6=p 9QQuḩ=?rK4Zl:*ly=VǪE>0*ZXl,^:Jqqs2n0]4^5 V'ڳmDasg^O]ʳhx R0ͶUԱB@NXrfu% JBʺQ]_wf´lj=̶MPo%o;nd5n5cR2Y9%jr VPy4O"TY*,S'ƌ #:h儠i+ǙJ*s8"ڌ_90Br*2jJ?ntSMoz]6w+Ds>짮W sƆD!Ӎĝ2EƄjFkǎ#BEKn´l/+췬*&RrMz:|k6JR#q2zZa4@hQğQ(\(Op<Q$]؊0VnJbpD'$"JL6Yw_ʢ"jr-F6g˖5P4 ,_+t%|#!)=sJ|Vʡys@1^J{Y5 (8 fҧK3* b;} :m !v֐9d%?LLωa06i_P_khĚ^RqrIgn11ZcvBp)"I/瀼2“5tHY>AkA>ݗ~h?3C{^rbU`VWڧpQYsˈ6^`9mam>g;&yWW"0f) xLn^UqM]1xqE6 >pPņ`–#C (v@9Q;lҴ͔ݚ%D.w_VV/UKlq7A 1k}?<9=${?\6CM3,V:S%Ҏ4CqԏD n0.G }sc=61 idr9jݍf}P.ƴ9]lhY?pZA#eܔ+YC,MkHtR9ulWM\>DpB ϳrRsS)Q؃V ^7 8FwDljW26tHڌd[J68@nӡ9S@~3mOL+TcՉ#)SGY/:Vbs|NVވ\J\hUR(Uª/~NoN(As>-R\wNFh3I{rDx3=^*Gڷ,_YsL0N £ILۀJ"\jQϑ, Y0P3(.8֨*&fFuY߶R;B'܎xA9n7P9aѝKoMCRBb&4`Q@4(iVh9-w3 3!gOGF|DBHflbr+BLyc)N=S@:B|f?H_4*Csd{G#/ )>KfSXQٖӕE ­x~"-kޢxۊF3[356sm8ovE4.lZ*be.ϕe:G;JvGg!.B5`F7_5LQި9[c!\7WO:Yɀ;yYk4a;!aЃFh^ >~r?)op)rחaLjj:ޘ\y\iFKKXq$G?]'3@>$ט($|ɺVB1e52PLվ?l3/;i3P= BVDtXAb'#bsO#wrvu݊np!Juɨ&,ܽ {ༀ}m֐r4+^ngQ+#_T=G ik9-Y+]%SF[ nmh#Ϛ/3093JRp&8OܡXyȕ3! '5=!dNQyh".&Nl׌}/dêȮKHS=QlYL{4%2s [ճ9J iþb)aM'ՎefWp_([#BM#ygv:|Emo g D#fu\,$[Հwb4GEdVXJ1q'(EvXp9֙Z1[{ݝzZK 7댙#qvVع;QTʝ[zw;dI 5@W~.VHȌ\ȮŘeKd\ 0&~I- bϋPl.RVvtqMHitWaneIrd9t+tiou5yE>=΃0DOܚ7Aq119[W'NN>2C4c{v<ꮨ*w z􅘬s! ХI[2vh9ߠ{LCLtQI%adtT+Y2,6|W׫WK#/3nd7 _e\utwtu;GSC_/#jO8ѫ'{*Ln>BZ9a81<'^ -z` g8e[lyL (2fL^ ^&Q&҉&ggMBz=8n!džk:sBiWVAe5OưI_ϥwW kK]$*ZWb&k}J~UR^P_G\4z~5rՕW-zTl>Ǎ/ ʲ 9/Ȃn]f؆/> ts>w+ɥ8ʆog-'@Hu/Cᢻ+=š *hZ8p?C`{h,̢G*fMd}fջcPqwdЫ\;? IJK,#2qϣˢZ^X]“K_$X3]ܕfs$ _`uZLu BN3YnF/F^Ř(;u\H #f׉ }FlZ}d(3BwxĈ X"D '8^X5p3f`{#_q>^W74 ۝GwCi*sy$Kck`P:.pl7ܤ5+SM@^e{ Hݩ@ zoeqAvFK_&)^An5ߓ)Q,iVۃNLH`Б'(,L_m̝K02b)dۧ`2\VP;f'z)4Rvd#/R |Sn)fޘ}~H[nUYsxӰ/]MϮz,1ICgE3"7׻Tvfa]Kj[]Cb@18qY?(H\X7̝dõ$x K\hfNI=᯦RL/ll N# ot͂$F&"*oT̓қ |<(_q6jT.X~tnm WJ뻠K<) hƇ= L `݅9%Pe}wj"+G[F@ X9̯7=26Y tad+4-/}QJ-oT8!z)D/ a"TJ*jT&)@f]40Y|Q/Bx!tʞ::|~(_݌+ΝQ 5^}w;`3[c#*d=WЎHkvټ76,ho'*ߊvn_Q~("nHs"ƻ= I 5<#ԃ]A|(QL&όSLfRIٲNZ,1 ug[$(k[H82"Lŷq-m]DLF%߭9vDT{6[[©w]n=wc\ |R*;86 8j^}xmL$I˵e]!A'цa~E} n-]ih/Sƹ}gWwwtuB*s% k҂,X SW[gr;A/DDP{_k 2t6Z=L;unlȕ %\5)w_bLπ,uQ*Ɍ9t!rdF(oʦd@hnEFM 0ɡZLHLh=œ]lnqgl˚*sLNvRpNAL@M|SrMTڥUB>- ×顜)J& bQ~ O[ Ux_l□fVH6Tƶ\ ԑT%0/۠ABG-׸\(Jܞn2 х8|]f̵'#Dd ŧ~ o)-p@\l/#4L_ɰA=4+~Ǝ0rfj֪c*H›5G 5*lђk^4(m# 疻k3V9{o\*x/5  ~uiƕxЂN*47:22_14]]–8,-oPʝ$֊Lg\j` Q3F/GW6Q:Z΢j'֏RJ IS`(Uޗ ޷uoY[?ʫdҨzb?iFʿͨ"aI(5(V2>F.jfy+Ar4,Y4l(>} %}OBA0 ~3"<>ISUvWgBJK&H۽cZ26a2X[NT҂!4حzAf[~j1g}q}@d1戔J$d!irZZht) \k"h(2'_wqgTm56tiTzFt}2 }q! X9 ͻ{C.A W\}0h*K#םd(\RPz_v7xh_\9y!_O%3׺$B(D/2vr M|{e"*š ߟ.,tbM#%exnhȎl[y6 M9)ŮuTHC:w+}-/NZƻ//s云ź\hށ ÙhUPzF ]7*yu.#Cb# 85:mMeL{x%qYH낈B'n+M.SFt4]w29ׯ]WUN]XζS+5 Y)啫%]2uH4`~sEW8:nb%Ff)\4'E wq2™!2_*Q)=>@1p b"UxMNN ,`ޘtud۪Ϻjig?5 Ζl%$ "`%̋%F^?ߘ_{t2!f6h)^PFɻWTI:f]zV; (Ň_sZYp7:w= UHt_Q2%y#Dp8r7&Iέ!`CCwv}*S-fœ<@#G͇^9©}%?퇺8s=]tR!d;TSźM.?eG9=AI-RQ?0 It "K r .؛'QJ!u/Bjk@&#2Op !HX9|#HLt&\MAA`ԱVZRij`Dkl`+< Z6\T`T~_0'.1,m'}>MTiM5>=BVKpM6\]gz4y[WL fQd@q{FZ!7'[;Y@ew!))Ҹd9؏E4Iu[f>*SBT*>R 1zy[$&_~|pV)#̈́F̺SvsUV)ըܨyHn(+l䋳r'CȮ DQh^|Tk*19FϋV_\x9F^bֺضd"2u YU)XdQ'!J)"/}Ik>=ftQl7֌H2\>:sDaRcGk?``Ȳ~/SO1^ngT% &A*;ױ1gGy;M'a=áf̬j3[wUp nt(8!),l Dl+ihةU?6S/eW"SdүhSW\ve/zM>R)3mZbΨF pD6_HIo!z3;mARvpo'#hR,T3yYC $CG핍4[=7bujekzѐ$(js }U5!㐬#1czMܔ[f x\!l2QfF&6tcZ3e=;ވuݡM1F@ZʹZU%I:0dZ9әjrRH 3A14J &9` njA/y{U4Ы#C]U{il9K`SWֲRBF\QXo0~y#9<4n@J{7l:ݟw)1\*x)b-%S\Dq>5#<nڪO6 >`xhPX:4(`@i^-(I\YcaC#V%[/钸FJ#5P`UʂH}>5dZxCC<;[M-#km7yG f%h7O,LOp6i<-:q0fggqP@Xi"Au<*c&=[ӥjvS*[{2.Nc.v{އR:10.όa_ scF-uqg)RoԶO`*D (~wr7%th+Tx=v0.7k# }dBVqQ D3G>59=.{/?r|TҖaK(S|ߣeK] 9FTxLK[ud()reǐLi!}#iI%93+Zl(cUf f6zT\ @ Ⴗ1PnWl#|w\N;7Wu`CLNc0fZ:y#~n*^CzF"Q6HrQ/_\H}}Q&#EP&5\hsTjT/E āـ .R{'u8+\"}BSN Mz-?w{%sPj*N s1>:kQ+f(2,l` nvk/haY?a#B.!WBߪO:wpS6V,@`}6 DžJQiSCD7W96h^`mEfz|ire^`C*u8Kh37'?di2- {#3%az 1= ] -32 t+Μ]L%թ6O˲ m=9:+H`/Me'IJ|/% bwNO+0b-qx{"r"70Ep-tۘw٠wSj;]bԐ*;c36b87[)%yG/0 hl•odqgo4:ҀEk 紱*~@ϥWQkQyR UͫbR8}&rؽW9$d/-(DEǙBjf:_@d^ s$#rSSL\U}a!\(0wKʂ]ːgyҩ)GOߟx {atn(< άUd%78ռ@fe/{p5lKNd ƽoWJ%q\˃A|JݶrI[UE(Bzj'S\[lYEsbAv|ޅqΰL\(8RFt)(rED%|gw!Wx^&5Z/$':'a|yOPB |w&ː*˚q,^,g27bss> ( A')C]qvo_~!Iyr\O S߂EU+6 h&JFY5TZJQ XL7*$=e ސgl[_CO~"a y8^xѹuJ7 LMB(cߩmIWwqFqagRJd.& ӏ uR}tvr6:Xr3 vl_Ӝ"@_5=&5I4 "SҠ"7[>Ctg:@HrEz}dmg,į'<֨ܛ{asco׊ :.b^<ڭ\T ;t>KdF6G9%S #SG Rp#C㻝8I,l3ucv)Ja[*0!˳%>kB|Di7l^ԓ ^Um>AMw(k,N SIqmW~hb J#D91q\(Q\`9qY2t5YvaQ0r*]}u6'#Þ/h[km큑$(ق%{.`6(9}:'oݩ4, k9YQ!kwH;kJ2½q(IŲ1UOrm-~=Z>HnԔTbq K6Js.)6 >4lbj\JL"9?acx3&XS!䄞Atwbwzs=Àw)A(Tm%ZvWa}!rQ1OuyDHh旎n󠕄įag593o̤A@pa]řDtœ#S?"w-_:2#Sݍѐqs/$~.^{*dR"E#ʨJ fwCL2,2HiW)@9o>(wHT4BduLX6? <`L,:MIZcX:9k;x8$1t$7gcdC$CSbꈗJ]Ig+,_2%`~vUH?lju.H]ٶ 2OrVO}">]M*TY4o=qӻT)5˭i0|Ax0wJ9{%`y因V.$PKfWJD1kߘuw}Q6"-?>[*~i&֮A.aɤcᐛudʋxfk |)2;,9gFQZ'd|}P 7Cdge^Pzo3Pw|6h(\r+|#m&M=mþ|aԬ FU>p끲Ы7sυ*?M6m7䗔0z# [J }+R 7w*/eJCaYeGzS\v/[&9VzT0\f`ֺ@>#_ Gqɩjo:W>ټAQFx}meŔ Aq6 hƥ5HDvhsf.*Caiy(.8+o ߄aZQ2Slp6yk>r|KЋ$Y.Nd`"9jѡ1XCXX_B? O?Gc'\ӢMqȫ"KYµ`vB `\jߤ'ޖm($SFk֏g_I~6w&N"O62˥9ފs[u]̻}&>@mVK U\p>r^!)1V1yKRGN_3qx>'&8foxUF7u8+vz 4,$A̗7Z0NI!2tІp33F3n!O@}M@ND%/:-LP0*w12ڑ6ӛwnjT3܃yqbiz!K@r;yi`CFg4+Fahq.lf,\LyLa^[}5!ɭa?]Dέ_bG( {& Qj6 i| ȅOL^#,X(fD4J{T/g|%({ "6E-'D ! !ԅ*7 t/ևTjRCqU)26"#nqu(G$ ^aI_x }6uQ#*OI*BL4mi""E\+@k{Ar,ڜ][\!ml%$C@ {Ѽˆcp)2M4F>;iwoAU\TG݈]]l-y߭䤱E&L S|B; XgibQ7hqͦQoT&XO!ߓ̜ AFĭ `E2}<-Y̹|>n^ުZcBI4IQ;Kg,^s]򹈡_رqHV`ǝ"Vj}t @FN Mh%]9پ~ dTw<VjC_#JՙA;\&_MfܨQcuKpH" l[ +iФ:,bŽ^6{Nk0r lū)ů}p+DazP]YѾ16&aA6R3^/rw0a+*! /2b}=e; H Y s$ص\Ebs}h͠[i .xT?h^2۳bХԒZ(GN#*lܜ,Qz{+eaR/GѮ=Wt; ;f3x) &'dJ7=ݴHfƄEbSf92Fxd9(Edl2c Ċd{%Dȶbjϸc]ڪX=y.t9Ȍc;gb>HڦQ+9 _9o75*|Uk.p&Ky!G5ov+&"\ ԓU'Z ӧg?oX ;Tft@6~Ick*t?r]I}e1 x'Ϲ||_8~dbI0` $a#:A!r2`|RBϮ?(agN4Ʃj!Xk iBD\ݮ 0E%(Q!,ʣaL1@(zV NWC׭Gdn6L=Re8jOt}RD>' b~.{[@y]Š%ozp}qjJKYq\` 5$P-gꍭkYJZ}SZM܊)OK,Vez7ko"],G}v:n{!8:E>D]k 8hu PRց$Ln1<̦l9Uo1 G@G-K9u(6!"c_y [ibLXx0qpi!C-qD13٪kYmnA3hERL?rU4,6Ue3/yY3g]>t>{.ilٵ觡F5qd@`b#1 iT5K+]cg F8kU$mAjMH3' ~,H1C7AM骄߱/c@b0a|oGF6<" D|]XuL nh %e[;ijq=v۬Ăz88>'@(0fo.}+ADkS^xsO?!""ݫ4R"M($NuId-؇_&_&c h9B"?oSlKdh7 0<^l\Jq>çLV7m_~]6T4#ʢG)=ĹicE>diЫ(-λ—0ʋѡ jB*>[{TR(ٜ$ b=ex=prFی+a=ex?HMeTW ;#ynz3D=g<9~a}f-zWTbUqHM>(ݢjԕ]:eJd{L/J c JSF1bDutC,+ 7'Nr& LE}sCB=ZRQa~mBmt1%o̯sEޛiQ\ vj"&?J/yw:Aֱ1Q/]zNҐK*7cGMvNVi 1|l+UE*SPbsuY\DN(EpF5"3^ &jSo@GqFh}َJQi/b֣rm(C8hvQ3 k TCjAVʮҾe;q(zdk}?xl E.n;/Xljž_f5V|9.O7 _8:4oU%_qΈ2V+\ˡ|KᝰS]JDTs Bu7X[qIZ-@Mɐdاڐ"4}A+A2ag{U6KQ!, 08U6L3Uozʱcf66J_ʑQ=@<7^X"]d(뜞RP^b/opKU+dv|M,T[wi%lG6D@GϐCe8jNQC޲tTS8q~as҇*!QC` 0-9V~O.[A_aN}j8&-kvfn:moNx%ŔLa&ж9p^1IpZ_R vc:~U]yt)8.=:5*v. 0t {Wx6y8`'p4iIl7g¾ej"u0&iI5>' 7&꩘P B05w"EF"x?Ҷ"ҵƠhaZrrxS(g}tԟi! l@GCۆv'KZWbCAsuq,tٟ %taCX+8ŋ4 <N#Z7{EhVS6ӷ~Vz;?qpxy~<6^'˳v2'[:CQhw4UI g]QюgzH$ 1(#WD8v˻`b-EADgOf/g%L/ 2oZ{.)aW n6$Tƃ3f (rj쭁Ic[m/ ΫsSהa$-j.8I!SW.% ܼi5.ڊo YR|8 -!wvD{>mw&M\u(a? @>Q $|SQϼa8=1acJ]y]VɄ_%]#~6yrN%k@r2zlv;|zq1$i :oWOMb_1g'87CSLWs1v5Pr"r(km@%+_Cܔ/Ӟ|>0c Q7IBLy^'ò,5Y!̢a/XJ_Y$i&nYP縚:s|)%-CtSDPdB}Bז#XF9`4Qink1r IHck$6O=eN,\Q@PʏzN70gg>sPBYZ|PaRU,ad<0 uV$#9:*퇯[yon#@b3ʍ䂄Y?=XL p_!!a3\Xl+g5N!_-Ô2 pǽ:S*vuuA\K')%G31GOBkQwNw[=9yƁaeC})$8E&[ KF&{2l4j%tb+ 8;ku1iN[[)zM gTu۩҉'rq>jހѡR]Ks~ČxA0-SU?'r#깢"/5ф[NE "w[2\`sM{8 v2)l Yۑ YD%T_%=-G8tUIK.k?B(;4=vr>JO ٲHEWq6\ne70*GC+Z!dG]Oj.݂G_ͨH&h*(G,m^3i |[[6U:cA ggU`K8L1_v ̽NcYFqq[I,P fUWoj* (/ yx4*bX]49<`ٌT5ԕaŏOiZ"* jFQ O0B+,\ar4;))e"H6ZSx˷ʒu[ɶk+ua6>A#$F%E0tW/?%Ge͜ʇ | uP`P6\~ӯ 1c&}ŕ UjT/F51чV s00ΪHC|倲()rQc'n2> ۇ41'{Q D@rzS)ϒ8 Q^z:΄4 x;)k}#HFM=xƽ.i:]7Fzs'n xS 4KTDu]P{%f|(26LP𣘑mR. ökkJ~+UOQm,⛟vs((uBbGЄ+1EqIЈ3O{]zgHDƔ&y d10Ɍ+_1Wu~_x'v"Vzw":"&ܼbfp ّa0[.qMV=H)){zEXuB ;O/&dOrV|1 #e=l:H'u(taV9̀?#)RI"qu?WnWU)7u9mŵ:n='{Pm=yVK< s4cl{U{@9 dB@5e꺲LzUL3`WH1%UR# C"))1FƺvOnk 㿯s>} 0m!;R qV@ UE8 4Z6 BBT Q[2]a36* >SP2nS|н'i(5.aXzS@]6aQ7/^'V5&@MWFPʄ)(Q&5-2 #a~=x6ԾH;pvgJ{ĿrB+ :k",d)-vX~rQɳ*8WdU\'%8ς1`@o?3"w}&]BYDwa8;쐸4\}yHS?/95ߪK!29]S[.Q@4_Ka:|6;c6jߏz_2LJ5f[* `>#6A292RMI`T^*ח̓mwW|N`Dg]\bd8̢,u#VyTf',85\܁5b%i']egxyMgĭ@&eFإ{i~IQ0O1͈t6"_G;(/Po>vZȑb cD&d z 1KM P+o Y¶KԼ(oRY %RPAǟ) pPOVBhaqn3k]S:u4)k63^ PLuFyoB:ᗚX'qkuN1[!cAۊfx7xA'x:#:Wf/vTQά]ð# GSCCTJ|5XQ\*#Ѐ}3‗4P=,@,9H}:NɣK'FN I.ڢZNᲹ`ˠlVojCJq5<; c0ʭ0oy+auzO `kd/tbgp?] 28\oj.N.3=[BO?dT4ޅN5$2[.yY՚a.x{nl;ʅhΥR?ɮEj zj㊫uSY#?88``WੑreЭ}e2KDU{e7>-eXrjL|C6 Wxg) G(@4T 7 r'mzޣ+6Ү-.~p$Dɢ hy{+ڨܒI9V}4ݴQ3\< ED#L =aHJw>X9듊)JpO5q(g&5zXc9H/!g=8Tf+sf:m6zɍTk,}}GէVlCm,jL"G_z|{  b0=3wxvJ=;){w$@HdU[C.eerS#nH?Aô"bd^`D{dM#nz^TН%u`1@uC bI>j1l>-5`D#p)3ʞ>&ާ)סfc!c5d2% E[jЊ{+tu}<8DA \}b)*"ڠ_TdžgEpKhmr^,9/5CkƳ\O~8Dtrjֿ _@# vU6MN]˛@\#bjW5jǗdKg@GOfD_`/]ȿ'T*g&+*!+*u|?7Um{My?]S1]ϽT6e9g}F!8D}E̘ClF{f]&E:L,Q$v0W)/TbZm'fcǼT͔Rp\OECBuU zz2-:W3cջϊ$WN~U07 g cQr/(Wa BsFi@ԭX>K@ebo^h6o댳BBMk <|Y3l|+x饋̟?S%jS 9lTɗꝓôE3M+74!V"%&hba&0^c no#:O/p+SDKkhg!Ky 4HPI~N. YYi}6 bN;ai~#8Af⁕q[;XG])ꊖe)}qm4^8?\9EXR#O||+r592Qi`[|wVr' *SDAkx`s|DkDyԉ/l@6RX3ܼ;3n\` $8VF!]e .i> 1)4Z/qa#')~VvLʜn"ep-p@89S!Sne+Bxh[En,ju]L#$>k_~'M<5(Gn_t5(@eqp$`+D~8ZT@,~Fu|]ޔ4-TCEDBv߶g*/DC͌dAarQD1xYtVנL"az ^fSjF*ĈJ{>Ig_]V͔:`L 6#WeA(r^gǴ6:W0o wIԿgcr(@lo6oP?R'd |vEp FiXJZ -I%eghj" B%䈯[q: v A h:A2b&ϦwJCJМg)zPVasm] ?QWo*fj^%ÔWM8dB7XR/JEB:{Q[ѣGԋz16•+itj=RI_|BW.905<.4&Q5K H So !UцqώRC Ͳ6X;D-vlMYgzSؗwK5ڠPrSNg'Xޯ Bش}p])uu8pl '!S7C촥t_ FhrT[i7)0@.fVHG06YOϖU2 4cvP軤 A~i*XʣUoKDz9k;y辁X1L44ͅ{UBy9k(UϬҚk*5"Mq*yjYqfD(2|C p N2FnuK]^ ]Hq˚t6ɫC1NJ浟'om9WdhdKT,TYa{ͱ Un0SFn8>T:: /ŕõ@6/9)vpCzMS(O3~v5Z;DZZ}&ut 3#ט3^_;yŚd&mubSSD1ʯcC!(ݓLLwGd#BdU9G%}*3U:I0;D8So8 9-|6_& ;a;e槒yD<3:̇oغ/n_ՙh$yU47oO*44HR;@O|+[n +`*A3ZwFOCUT0l2V pB#{5lT<ೇ+}gl+0; uTaek\kC1HGUנ@Ih^d?0-8Es$'ٍҽ9}mt LJG]~Ow8)ujc>?v=T|;qN aƐ:U1P |2:;m[O춲R1>@_&J[ҡNBH7akGms5qf>M@&-c_*iZ8+=ԀAB9B)GLrFD3u&$/7vˮ#J'ja"%9I. ww%^q&';4(hfm3Zbu, md#d;؝og͠P ED ƧZթr "4yǮމm *T +UFNi I&;Mskj;end;|h<, #prnapv. NE Ulf*geZNG /s?YiKL_l4ثK嘱D.+/d̘y&uI:bC1 }3(%Nn_:+Z\6[*Zḥ?l=\M[r҇e@%cEK;s!=p"",­cK%'~2{ W/F-(-"EKFqY\OUD uaW輋Cu$J-UyU2hMq s(݃M՘ZQ'V^z$ĊcHp=څIOG.ퟡBkvG\Ԑ[ Ճ] ؼ;#{29:OvWQW(z5,A4m5BO&## tQ g) d}-*aC$(wnFED&[}0 ,&zk.# |$|:"J-(rOKmE H_? ұ=L-ޢ(_ȉ(y`yVd ۥmM]Sq ҉cΊLE;(W:N6,VArMWURf`a*B'RPq7$BNU=26jcI~P !k4lflƋ`w"0YtԠw(~![Rv5Y(K (07SH&[_0/D]Ky(IY1/ޟvYHV~4[a"fR~(Qd ib3q60j^FL^A(KXBF~6ȵ%(ZhֶW\"ŎW[=2vw> vNj,sF}yȱzDAUS+ru7Ϯ8Prgl$q!!"va1߰I=&vLHec6R3#r |PYAzE"J~%XOD cmS'.syV͠H{(nVz U]95#]+"JsoP)>T:|58̽vT.9Vd҈9K\N*fyUQ*/?M eҙ mN5pY3 cv $~'eA4Hbu_tbސ4<%zH]d.oOMذZ$Qt0fe@NU$$L9 ЄXk0)!+3F{'٠P ~K_8iAo64|id#'v X]LJpr qA2y6L|%r_9Ui8s}fJ\aEIɯP_"O\3]+ȎJ]RQU]wȓW.e+Osi+*. GH7焤 Yz eTu⟱^z+l:_\T g~*2'0kaW;8#1TAIDiLŻlgYNJ)lG$bZG7p+06q]5R;>҂dqD1jcJ^4kF=X8 #(˦l`~m 3 0Ϡw gnqx7o.U90l!tޯ8$KG Kd$k ׽&LiՍ`J5 `ܐSN b7?(r/dD4reoxH*QDOSE,Syʻk!Mb0 /A_j*6͏$C!4Dv7mfq%&#8PokˆM8KQZZuqeIw;a ^+]ǁPoKy.ּ-PbB-ύTN21+ n]`tW%2p*tr~I^,YPa$YLWҙ];j/0U)!ț > U/T*tƃi3vQp@II>5=Wi@{Mr ~z;4xB&R8ީA/ǞNc歵'"+88/3lV#}أ(HRr'),@. F6`̿d)A՜W%Prm* =Vq4T-S${6x oV>8 (q˃ˑd=ˡhȈ+r.:t` zL >}#Lxg"=,.1AjtWJ솪Qe5-o9 t^v?94)*_ŰI C`  <)5 GReOOlYaHQGfRƍ'@'ϗ Ń!"j~yaGc8θ5_%F}v+cˑmlf#'t0p$P?AooVϱZٙVi=E$ԍ^]GyRN^/3*dcr9$OH&|x۵ u{qlc$s]+Dg;{썠%wƄ [VUBe[yo*euJ]d-`8ϴVp0[@ w!lU w*ZdkuK2nƈ,{s!\GcOn8DڐeXt@}#7rR E:A#Crɡ>,0 Ψ#p*U$`>{Z˒椐SҲ=.?wx4BW3_;7TwbaE0a2E5|w1F{^{ ŭ=g&'+zD$A9p#R:A D8wD=kAvdPSiGq[~p_ú}RŮoMSϦeI8E}?u+kOxBn [IDl϶M' (qvab%Z6@HV˥0be_mf Q8[)2ǒV#.wwpΎhG:wl~< Ik%sGYqh5EA@ "Wpc((뺦94ͱS6f`{;=Ps; 2e3K8zIvs~e3l>cQN '؂CC7<1JʣZKf=f֤:B%W.&5xϼ d_MXe`tQ;a 0=冠Zܣgt&fUsLH cۢF}o3/ RV;6`(&@1BtZԡÈ_v$^\yџ x.}7cJx D Xr@ ^Sq. O:ER4G5%#JfNRE'D`H}xI\W8H䎸Yf9 sGO B\vYˤj )[t>-{}T(.ta{ԗϯޡ(Ks{ee#ʏ{;[Wv81b f<}M[&sI7 4PXrH +~wSkMGD>"ۿəa9w ~\!jev.Y鎣ƭ,%TGO!ŏ;M h+Q`aRbO:U3l\(lLXQw&[k_rfb`\gN\>) zkjK}oB9.ʞ? oC`̞Uma9++DmM;(q!ھ}b}? @[tl[1fF߳'"x;ko9 ]rk<;/rn>n@! > Deo^+Q(7Gd^WX}|<@UyeY N ѣ-antOR'%hvuK$}B%Ǒw: -aMNDJ8JJVm+ NF5$xcJ浉9dj@߃Z|tyDsFDYߦ xU6 | '%rGWbgo'7%uT<þSTD:au"r$&u~Cӫ:*(0H ./>cp"-&:u6WP(\9 @ko>:z? j0Y2@sVl{O&J"LS 4eL5E7sR B_=!ΔFtcTБ9OV22_3BkaK8Fk[7E!CqI63Lj$,ii˽+ ʪP^@Q?0@~Hy y/\fڧpb0h71A7г6nMX6эCz#>6n?õY]+#b=jL6og"T>{}4I+MR *SSd]vp5jV.3/ش\s"Eh~F#糙et=*o?`2(d#L~ S=á aKso=#?BqWK[Wolz.n^GSx.URQ@X'; dCULTYlcߒh,Bq S BO$ ۈѶ$ +%SKJԾ, D;Lۻ48Lir՛EbNPk^e>)\D2;Mt4hF_n nL'Y\1ʉaYMS)1},0Vu^B/ҡwTF !#%nhcr2PHf:ۛoE b Rǘ0 $r32DP5h5Եv\A/( -&ru`+Ӎ/.7Ʋ)U#a^1$b&Y : >o*m Ftx ѝy$Z)JV`Xa^̭/dyߔXmAQ{_>&yܩcN31='S [9,5|S0pp9I~M| #lDޝ؊ڶ},LSaq,Jyɶ_k^ehOQ%Sw4rŇt5r{)/rYf]x6jrNT߳|ٴjF YU3iY؝ñ# ̗R5 8SyY¿3rHHrOD ,bg] []MTYM&O!:]LW} >l.46X[?k4MӐEeqe,ݙ٠`~L{qlxI =ldvA2f4{Y$3ш*HA%hxgIWc$XzO6w(W$9T$('z0oV?H_pM,Lz7b*y%- LP FZw}hL 8m}~سB##-g0/Nэѻ!AV4+:1D"GRp 6L)X=HRaaaͯw+J*f5}2#7R^3.ri?ug#rNokQZ0<'pI y?n QA fRby;%flI[heUrqf,_hK(~@~5Mz0QX\YA.-t&ڌsMؕc'jTYK&_rH>mSc=uDt=I@|pЙ˵ S3UUT`|U=0sߊɶcYā]! Rdh2n%]I# *:o_$Vf5RsF&@0,]DYB2B`` Ǵ|WQuG*HA<;>S2)zUђbqL!gkи/EMլ/S\.o܌dEOrOIdr,7הQϮSWL3ݲ9ޟ@2ԭv-*VQHؗ#;6k̓;jsεɏ?ZeLy\r i^oTib 8!?r!pǾID2I)O=gVBdY**ol71y\Hz݇F0[+Eb$;V 4- p0#Loç鉸ƉCX"ݤt{=tx`jtiODl>x,1Li,P>'^']+e5?; wѿ0GU2?6 W:?@5ʗ'6|>ע2Ը3TXdosw6@?ρ痄+{Z*M8"Idˀ8edDo8w[z}#Fhm/|1陃 AG{WA1vVijMAmT lQ"l&!_^0zb!Z2+VvI};V&N\G}25;;{Rw.~aߡu_B6(|n C{SH>BRt NgW,`s[JOdU֚3">ߗF@ \" ? "d oGE . Q0.ANϳ~(&64gW~򯚶Ȅjmb.Jkh6nEKonܠ;8jXi9\ `#. =_IO8#V+j s@JdK@߱cV@s w,.uqyL.xޣZcoߨ{simz))~ 5ZM.7l?p~0_Phݴi\]ۙؾY<'Əj䏐SxȽeH)PڻC>ly軯& Rk‘W\$jhPɽ*'g%W_o^`l*1#\JWq_ Ha 6~a߀qIo\M0aV]j.UՌFWkK3T? T!>:}^SYտNF,0gu^,s+HkP,< y6@A(ΕΈDv# C-yLS\~3%w&PA>V{ZY K`3c=]ai\0Xv$z/Y廈VIGmKGͼΦ8)ڬ 9Ҫ*̷{ mؖ0gXwe(7u sfg}>L $˿w6CΙ6JT5./IZ :wyhy< FDSeSSݏ!<c2:"MPwB ${`Kf™H3"Cqi{<Úu =;۪qHlAx‰_j$^qIU1`6) CǘȦuP#4ʴc=*Z3%p .}_gRg"DmV/m"|7UI`LNCΑѰWjR)q5Q 7 էāhW!0.] I @01- r7?9tձo/&,llB_TBE9TsP qeF[fpOϟغGP{<[k!>Hy;! 5=_= 9ӍZ֫@rNƅeY74J[۝#~)96͈RQ@ n%V /Cp>M#}d%|H=gs Ln UkW?]2YҮc'rYw-f_8 '$lvQN2rJc|1~3M<]QD~[k5 dxZ!EnXmy@j ei |`VPfF<}heFl/%RI]OOtPhL\|T)z-##񆀢~$Tz 8ÉdG\ݎTC-lN+ - F1C>á3M!|88mt G[=l0SbVAl9z')8` ;>_E}5ɘ Tvq ƖNrO񌆑­Upo7G)ό QmUgqP5Zk\#Um%IE#HhL;(2ٛհ+ѣ>:DPҬ;,D*җ:)u-.Y89X'֭z2~~- @ǹ_hއpi]-zAzŃWzFq]%֖8O(},҂:4:{;yN?J]᭲K̞(@cD@kzjsC޼I)_!K)쟚_ǡ:v$*R@4r,KЅN}:]f6Ϛ5٢(^੏|=˰xzcEUI_ ,m ,.:-$_g~x̦ܪsg[U2tGlS vڭhzq1.lX$lqزZT`9O 3p9Ȥ`}WmFO&WB#C )Õ1dDZ]v3ebm`OZE AV#]]hPP?F<p1*Y+ifQzFgoԦX tuqz<նK0XTۘD=T^|bdZWr!tJ0j|b=jӂGarؐ;.ޚ¨:g7~$&MZ0Mk]x.icc$)ۂ3+~7\m7XX^^?C?6 i7\9ci8dվF^zx&lUndW5q>JE'r>0)drhZ!~b>@k,%yZmkza_7ěvU[U+~yfau8Hw|4=? > ̀mÛg!9wf(AFe2,ʴ iITlnD[![P+]fAMBΐϋ>d6ƫU49շ!&2§ L"(lޑfKB˛e?+vW/ 5-##k5g asv<wwZTcܦsm G,}sj;o9 m,I[do+ Jjjvs0ܹSqBbUl/T+~\\WʸZS?ugvF5조3)YsBY1Z)4&9tu-RH2& |<OxlNYHQ&2^~$eD Ր/,)"ĺQem*N?F1㇌Z͝ BU%ڦ <%H5R@F@G p%y[DpEʑ|(i "xvP[ Ab~_h/KhGf]~NO F_k|Xd5@VuR:<< zW/ '[7玭ND9UFyF@?!spb-~ w'eC\)t1~JW21-U[OQ8v8S::Q O!G {h,s3BJgﬧY住B\J;+O;0 ٬ -}VJH {Nz\Ն/:Ú8\Afy֮}Jk# N_t #2=7tUayլKc#zݡWY/c;BF>Faۥ,ίǫסiiB6]D5/ `z>X={~!gH\`Ts]ciQ.K47I[VWg.ɦ"&a$ 2)'&Xy"1/'ai \tro>EH _zCϰ~`;Hh P-.o<a H3WWGw'ncArq,b1;]97Dَx!g-O*FT$Spޮ,#VrKY0maڌy.Mizz-qChSysv\)&vm r1 ~wG5|HĶn=,>Kʓ0%Jmvv6Rz GV՛VEhM ]K5T&227*Er2$_w)kt>TÜ$~c)2Y:cQI@O DKS]jujl<)`M1D АE߰M]EIal.aZ * ۗ;b#^s#=s j=*ߋ ICd cLJ@X$7N~U`t+AS{K|0QճGj(s !1n;K21 00 /=TAۏ0D jÏ+܏go<\~b^pMѭŵXќP\V:Rm蟆#iHҐֽ *9̷ /xp' =%E۾QʰQsGlGJ5mp-XIž 9Ώ}_{yGr Z1-|m2`tر,oBqfY~4^ÐevMsU0SŭwU36\~.4#dG֪$CSIɅǽFCl&K7mX锛2I F8,"X4$ =ߍKM Ffy}z?N,,.UϡD~j,!dXt,\\jci Rt,?֧)5өuH:NHmN0wEPDrjaH3X0M$?T Bc^',}[ 9$7UI7_m{3tx8Ҁ"zR0f?8B2Üg7v"[vk B1aHB⣮Ѵ'$ҟ7"+R.U$͐mDk3pEOvW$+x,#]D>nq%3+40~S# "s?jh#ߏ\vW|4Y!I_>hq)cD5ۡ__LE__<&U˵e "*pC۞h>)O{0 `PsfWס+ɬŕɸ\ּb*r#,yŹ7 38 ?;rQ47^B뭵\!Smzx͡~??~||oCyP˥q%yU1u IqUqxˮy[Fxg¶ҩPgēw4hb=β5D[HTe}Dfq{YO!ϦDž^&][8/z؂`JpU5gśE̽Q1S6:3Z)u0%: oǐTor+lud:Z|𖔈OU6&B1d\Dctu9x:z6-m7㎖$a\8#'`fO1֟Tl.F s'A݀\-ew3wng`0$^TЙ~ф^y7U%JҢ|piX1F*(*IRT$+A[U̗p.bdFem9^яՑC=ءoH=KE+3F夀Z5ݿF@/t6^U81E$Fv *LU԰@W4(^0!{o36'#r[w)K~A$mkh$|gٵ~NUY8TT|l哶l^lHvf(w_(Ҍwf[D%)Z=G89 _6tgS$S'*z۰~kԦr.XKӈH PkƦrK!$폎ց!)9F`"H{i椺nM,(sfpI ǝ Awd?>du剮^E?MƶŅz.y+T͛6VXL1Y,6[}pt]ew9?QQ׋x"^cgY wܡ o1z,o:*vχgKC֗ ~y WW\) d oΛ x9n PAT@u;)EQ3lbZyG5lקIށQm"9/A+(qӌ5`2 UE'm9&afUJhǿ&w;!&smC<[VG[='We-, 1r|誔MP$Di f 2SkyS78(31{[6vT}pRY³Rq7|~( |/,׊{^N2 ־q6ԟ5+F.zCS31n5#.j?Z*~' 5I% RNme~m2L5PO_~qʕKU=Rw72ˣ]4{Mp+f0f`r޹-PѴ B-;ոR= AiwN/Ō}Y<RE #˳#Ӌ;H9A֌_&13g9i"Bjk3S,Jܒ"YLV]Cx-H_}yּϟU?}{}u=<ԻWi:$5`@:&|~5/S`ɸgֈnS8[:oh[ T&'|LpK$A'3Ah::HkfƲEzzئT:>iXZ1"J%?x*=R2*igmq"ו-bpq?"+U3(BuHE~*d*!3ߪW>û"Niݦ0_ߊ ?`WG\2@p@ZȦaY,(>a$yNcݨG&W0xOAP}7cKfv0TF\pbRڄCsm}DKoәlv\b@nE[\wYMcZQza ZB1ruI~*e'b#];鄡0?mV༢ ,n֙Xŗ 1j,n'ӂ^}Sjk)/h.6d-)p&`ZkeUo4kOhs?('/w).q"R͆B.Hx̩Nٞ2;{Q9g" ѶA<[ģe>HʰX fr.pyXZl3#EC݈@e#'GSI\y==O\{~ iS͢S X~jUWw߾Xsx%|(us{kp3]trQ !\R7B؅:qۣU`ık/%v: -5bl2X߿FErx%MܹLբO|YjFVo&a&LJjm$SoPUOA?ln`eYDۯȀ\֫m= =o[JpDDK#bWǿo>-*>Z-%jh&tIW*i-QM/åV @㩻mФ9=`B,ĪDVZU;,}Xm![4V(.aDgQ?F SOke&GZ,4(HVfZȬD(3ĈCKC‹3HЗQ~_t ;*62pF0 Ϡ4I#ي_&e!+L0 v? oȇ(gcunmy7N1ϰoRF@B򊃓1OvfYM;h]K婜ThYt'=YtG"u\ j50^ŷ1TS2Kgvb_C̓h[kI@YU T55X{zsg{t'n: /']|$×=lZM;vz{.5އ{bz}3%tXF HD3sf A v5dU n5we^:Ƙf+D%D1M;72%-8V.I*}C#hHYQ6?:\Wkv d0|v᷷ESjbJ2|x\(YN9%e|5Rq-p0+])ߠNi:̓eݝQ z3I|O02%]P8ᄃ&WF$R$ٛp6BuŖ ) P\+|Gg.:&ڇ~[@}%L z  y-@F! t%:ۼ6\J.]<4(z8H'h.Ig1t="X+o3HO_Zx픦r/V}Ԧp ~igRI2EHx@.H9Dv[!.VQAő&ͽJ8֑-MF !x1=@~צQ̄q߼.ٔQo?-L$IUp2ܟk(a[6]Oov旟نnhFT%`2DZ`YsU ZUU8\Z o$OHvV=r#?c` Y,Fג"y)wH~xW)Z+2En=QT~nzgӣ{݈7;lv|2N;`$ñ_>!yTHo?VM־*,OJbf N65ҒTc*g!_̦ 6/\mFH)1 ׁ~<+o[*c]FJu[LvF^3WYHک,,O s|ZV N]MOf;BgbP.SYj12& &јm.멽TGH4<\M/\5{U P̤ݏ+^x9 ,ە8&Exl@&Ht4ImN79!K(߼+,)|Aa)ʵ G "f'hlibO1jWdXGT\ ]e jAaNM0rf=d%&4+ Q> &>(Ӧo*%|ϫN !|MPK^]Bqy0/W謬nC ;50EM~u$+1#QrRn.VId]&!zv2$\ywh%^~xk@v)kwc1NQgg8cւ7Ww(Q@4A9lYҢx  ͉ .UR:7u&-7ڋ(WALd'<LʰM3,#}*̤[j`Bp[i=j0[4SL*[2n6zO=@dXjŗi,h!$ Ɛ=}ݕ9(l68N# KژvCm9O&H *[ K׼ZNo} Q덺>7<';OH0 V.1 eVm(dnO)@5w-!oRWBl爯RSwo]HARR=A0cqZXMe{@zX3,D8DV"yX8"fMp鸑5 L'xt09ngcBuBvtwv aYizk+ƀ@r$ x8"b)ߊ=qKCf J*R~:ߗk64"_*+FǿH5!%SYw\mgcg !\C=vzvX>أӾVV-1z%zj1U2hx cw"~Fͽ_ԥ%qZcS fѯ{F0\B2 ;VvtAtn{rW FX+LxN6h7?]kXīI!иuQ-hkH PW*F"]f,8"}- 4 r]p%eNH=Meu ؖT d _$/M82,5G>t"R<( [Hu }l`}ߡZQe2gJTD"gjH3V(x a*_ :֪? >LAs0*p,+h7pLN=޴%1*}"} NZ߻٤yOV**2B+* 8s5Rxnw6>B'S" #* vdh8⇫@-ſe7zAPwY~Z3PС\e.eLRƟM_x}M 7Qi|y:`c_]jKq3"r]ˁ!5A80иI P_-0~K_~S(s:XRa&0J0tb9{kݯ|ǮxYвe˭hcjk`ד| `"]rde"d ҴS1 ;&1o0=7pV2"S>:lb*%|O/lEH"H8§h̻L SSmBȅ2}sĺgǮ`P z te hN8ΜniFUH~Wr)udPZ͞Y#y0JF*b}>['{zyg;qApS 3[%. /7+QάBro_Sd߶uѽ`#\sBmoq$)p`v}n|C`~ɠz|1$+iw~C"Q1qϜi/f2hKfYz)=F_ডx{)Y.B$y IѕQ.ZKϰYҥMsR\ӄ@LnT|8 MuI!Y ΅$9<r'@=sX_үPsda%vFejE^eljjk2I]N9c-):4!i`Yx$'"N'+:>M P!'%‡jj 3`m?Ӳ<ÚX Kg뇟 .Vg2B3f mA0Je L_r5 N|U=RL8m7ŐdbB{ WN3qܷ8P:X%XP^ξRsQ@/‘.C5% )EBA2 2Дu Ό2UrZEZSY3H YShXw&+KլcJL9J,wwHPka@Ak*i$-H uX .]K^ьPLSEh$x~s9IB $^.ߑ[DЮ2dv]#=dW r>S#H[H:_Uef2Eb]g'{ȇ'GlNsN]~.yOnVSń `ӊ=Mk]iMeTI{jN<Ǩ>1\aύ/9H ŝ& x\͸ [ eVN UϮX xddXFy0&}mUΦmXe6 >LSrH.m[4CBT@Ā1Äl ,7&ˡ隇D_2Il5CG&sT$~+<͘|HzIoJ=db䁫 zpwR(=S]T.-*1>L"no8"9V`r[h95Oōm` mߊhVht\Gea6)B[`<~U~ʪ.8YKrF:"ptN7*nYR+s|!LK%R+,4j~"&<(H{Y@ \껟u=KT0G372EN601$f)oQ}&+8KjtF[׿E ^.q|"iT3 Fخ 5[;͓5ƫouDO`7\}`4Tq*zv&/,zk/A^Jɴ3EѦm&+Ck(\2E^'!ҔM&8ti'V _v PYڤJ_+kfz*2&:!jRI9X+z{D̓@WX Vd̒fRq&_yE{tz<+K\Zh>ŀ('/Fc >"%S_ux&'']ƆRϱύc1"%˕PcVòpkoMr `5YO,bjz(W+ Gb=cP}6XM_{jOE|rUucҩo ҈(  Ni=\1_2̛rGu%`;lt;lρ3*7zDtc|z )N9G׍mK0l I J#ZK@KE:2HkH"5\!>m/:dՎ5Xw 2's]>5FD6rJ+ߵʆ lxES&J,,n棠\-5XJSy}f#ߛ{Kj7s߁/iw'PgHu7eŇꈗBh,{mNYBGkN3cKL&#*`vk#eihn=w1 ƪ$7Yx2wBēHR8*B*{e$Dյ'9u ,9 @$Tb*o?" V/?d#Sa9xgT,i%* ´QxdA7w\p!t+3&tȑڀxJNdCI)UaS-Ȥds&l g`0K\|g9\W0 ;)5# DŬ%w|A_ Ϋ7im##ju?EN\""`(ox/ʯo=*fmKK=?D7yg4bajT-h.5'׃Dl3a}L>rzZ]E8a5?܂=p,0_цZrWUgjS\fjܽdmMZHD2_1hCD,7G'q r RNCT~4&o'M[Ddkv/51D?!^e)N; ~@%<_ƪ%잍6Xݧ͂$tޜ6;F:a:mث$.W6Wo մ]W'_,_MhX/X.a=;vۗ^[b>^TfvGSM;VT%8 Yʈ.ُi^8@ϒ/R܃&DF>, ?Iu01:1~jۄg*؟־c s#PBWaLɡi]VIx hĜ9I{竎G>JL<;'O|lTwU_˛&W>^-|^z֣!TcV)~䂒V14` rR{ԙ,z}>݌on;tiެbkɥRdjQXp)A#q硐ttDu 9Az1|9ӦX fG gB*^ߞ2mFNA\q@^i# zt/o.8l(0xYHPYwP#jڰfۉ[!SXW-`CIqX f4kjFɘ&x:RK/mL_=l_ŗ $ػ:zÉ ȝ_KNsx@I"Ìe":8V;{:s;ۼ"!ZOZh:mv,Bt㉧ _wG oΚ+sk;> bi}[z y2pgÕX`gr>vx:Hn@i3br7ke6 % :m=B"-[CR<_0J ad8o܍ 25u';F>=I02OkVw_7 xL.bOaϷ@ "#+R|{ 1$cb b5g](.h)"Tϐ^r=4L&t+y "v( f_K#"I!6eOZL67l' Ӡpz!GSqǚ4[ ;#٪p[?ݺk"OҀN5f9ؒ*3ݽ N翍:0#`o:2pS=,x inq2-Aep+)kEۇiwc`aUFXy;bA{mT;t_f0޺_fdAvL\M]t-H4w1;J-CVa!gY W9tXqTfù?6F϶_*D*<;70g䬜jQUYrBGɖ_SnM_/LJ.p]vi%ԤhCČ'dǃ܌E@|SUJ^>eM׹KO#Y9[k@B4eḡj{*j =U " g%D%^@UfO;ČWm%^] ] X6pUq.c86*m:#@~e$D`ܰBͮ@u_kPab k[ίhh ) (Z}"E(6=bhAʜwò\']*/^VIu3`^}Ca{56N(~)bb&e 1\e\1_"GHr=ՆDz%}1SK^}6")c8I(TxѾJ/ .>[uQyV!ZDTv  0FL4">IPKR6DKeϽA;afy}s!c4z 8#+tW6D;H%wOve5 ^#; LmnY/9}ЉT֎!Ps}=_r_? TS}1,+5FM81Ե8n}7|XI'D̽$3T&GW$R;h@]@mB!|+%՚b3HXϝǟuʽI'N|Axmp,mOdR085E :&o2c>h<%?%1`#[vkl|fg8"mJ.78p`{x_.N0}̏Y3E)U{b 7!,R!/BYKM`,k^v1DK {70>ƱIrs4DUZ^/%פWȻ9a?=k*}4õ땝#mJ~P\S1d˦G9 iXF^-'ʀuBV=}xbc0*wI-L]\P^al?:ekKݝ0v~Jw5]oeı<𢻣 USEU4eHb0b5qU$߿>'ȯl*@Q,[R YF1WISc$!UV*2Ic~F'TH=o{Fcs~LZ G+e/ cq^^hY!tUN CaOE$-EIDyvOZ;bV^She TkY郱ҥxp,=i̱%1AH[L>ZhBA-30YnlliVDl|,)&eEKʤZAIT>##hՅ@x(7ŗd1]cť{0"'pQ ; e|+ԾvyK,"QCL vIl1(GI\R<,HN ~G+D40Prm6sf׬ p^\.Ƅb5hns*f:PzOwz½Jx9[IM߶ 3]@b+ 5: 4C FUkRՏ+ @a6!9\@L!FNK|T !UAx5jξȈ1nMnЂu};?F>uz\f RņTɠV 9 jcT`MsXIUlC\w>憸pP ԻM^8ڇ"-9&\C0Wb; !PE^<6`4Yˤ@mnٳi? N"^/B f>BG/vy(=}rH`)6O 5o ` 2;c 1̈́|Hh~ݫ%k6*R .hgC$x{[Zcu\xkPDEQYl gȬ&,75ABw̪G6ξ7 2o4Ť}xTn8Q#1 YLPX` c}JCxHv,w.),=c9 ,YW lQ 9]fo+zz if PEfXLYµ~{15N8_hIop7FllBVTe ņh6m ~ˤxj^ 'ثwBB$;E}Ceg { 8-cnAXLTt>eO;7Xs;FrBWxiqrϧ/!M԰>p#s\z9o ]T?ǃ=^ ŏs;᳐Nwc"g-)Zn>UV7tKAZu/!oْfk7 8:q&@d'AYg;*x!@kr\f 0E/6EmMp ,f `e ?I!j}v:UuO<}Wϵ.g )EfßoM_TdďQ!JSu㻸U1.,oE旓hϑgd,uYIF o_d[Ǯ 7CHcSRoBEB&"b{u]_;4)FK?rQ,j_GYϪNB9"KجnmJI*}ŨO 'nm۩XY*;Eqk+}'!7LaHbQ q*C^ rdx2L΁ k äP}fGOMmiXZbÂҹIfy0xmwUح;VBRD& X%n!BZ.991\iO.P$! P@3 v}7i^#~/`\ ݿl~طP &X=/BgT7A|x}Kw'%o:AV㟂^u~.H (ET r:_&-τR&{9#'y'$_Ib@<}VD+=b:dg-SPx\Qo]B."^hϽ0%uG=)imx sKBP^xS0K|1:UTd^V:KJ N":- &lf-fw:pWN,,0I.n*ٯA%CLJf:\ef=`{D[fbLfuzvx5 H<֋ώa+ܤl݃Z3ŪR yф7QHoV ^ q*U ur&1<'텕gIy*|é=0t$\`-O1ŎDm JZ࣓>e[!4zQ}d%-Hgs:홫yLSǔ=PJ- ă6HMA[+j2ތ]1Cs5@>}?=JK0FF2\觤տoa"&;'2h3 ei\Y^ &%HQ=x5?~0sFTWWyx""+`{FuS"(!?>.X07n1lA9P <QV_%L6rע@h "/?ae˳۴Da^g,Ӧ1ykwCJ&K '6L/F;1% %ꂆNhsǢ\BzwZ -ՈȳG"Պ ;ʨa h @wa'g;Gu,'Q?;)(mwk 9!#5A2#w)d8mB[^n\< >V|0шleD]f9b)ޚ`tZ:L~chAZ 3GyǮ^ nWhO5z7}? WeJ P C$Ё]}N)~xqpe/PJD-C2%gA9+/J8-叀{7ujWW?>w4;ܫP=R3u-DI*Z L9xa87|@/!~[ϝhpD{ Ad7zN35BFo}\nvs}*x]-d=<5?eQɭx,w}^b?/_beƋx]b&u9uY B, Va30Dd 9bort/2 fXq,[7#:eOO܏&cy47n~JP>.o8fVtZD\`^Q:FvDpD(WC$yȪ!\v;;ȢTdݑۤ~ΛUWzDׅ-s?%Z))}yr/(^-O ΜHZ17ݐK`75N^ρ9Dl}wZװn`Gy` 8% -j͝&f2Tq5}K"v-Kk %  &}jEtW ɾ!aSÈɑ_~[?YւfuNJ0hƪ#+^x5Q8)%%8De0_A#!/rئ,:¹U,/D5>&[fb˘;3Dlm']=Q &z>ݒ#[bp;'j=pYl7vC<o*vIaP[9| ObQԓc&.ri&aKM8p$FmO#<:;F]4 38YKy2^l<ۺ\ϕ}]!Tvlޭ߯ 1Xb{ #ԛټxLJG~md38Yȡ2Yn Ȍ.Wqwa-1 Aj6M1H'(I6@haFCՌ%RP +w*8c%,g(ݪT%[:%>gcBʑ5ƨ'U~tMhm e?/ i?qܠ*tʛ=xM3rxYlShB뀶}#0`_AI 7j F2Tgߡ)  ooT\h=LCh/|qxnp+!( ]>A|ΝI<;|(RL9_wucH(k`. t^VF4h#gMs)8"\v#*bg4uT6hfx`ش|+Ø)0M]IZBV4Hޗ`\tYD<_9rm8# EaU31hzT4ЛUx{:wggnV~8d@*)>(>ad,XV?~|'EsysK`xr$(ER}uƆfQACL\ުjx+]c2I }(SW=[w{Co<wj&RBJLp |{:@N |vQwC^tT06$YB~ 5UkW+m5-itvj, Um]]&:򓑾Ժ fb6OE3\%%ӤX2oHR"#ST C;}k(n=[Z@}SWGhtjN wKpL6?UJAtl+[ h՟ѳھ+0nrZ2S[On9WտÙwAKP]@:Ph]+Q\"![Z1Pk=+犐*&BCf5. QOl$"dAq C׽SVU%7˦>I"ThA0~K.s"0.0- Iv⛎{۞("1ƴv )9=9& "nuA)m(/ btdž7s XB=0|au.¼LO^VS\ 3.9jAZj(߾%a=Lv6ĺWbo4 ba٥ /0v1)96ۗ8ӠZFBIB{䵹jFPߞs<.-sex;hy4IJED'+Jʈ#O׭+ոCU9!zSx8MsV*ʇ~c g3x=uG  )*0Xb!Z 6hF;.l7I:7pkz겄=u{p?OSK໭ز}MRY^| Gb:01+So Qv lr@ˉaIhN(7 f1N:9|~sZ(IiN0RV܏<0?R)wp~:Bs3';55 _{:~]zKOcUgy."h6K< |}s] zݦƺfձW.,&Q.񳬝eFl]O["w !/5V?^z,,[(}lu-i )| Cw'aAL(67@>!&i,#Q{E~^OR,th6PIVvi4S/n >Ʒps7pZ,xU1YI [UÁ9tXn [pZL9&/ K^ .C=|.<g(<(ӝna|>eO6BD.%SV*<oM6dE;cnbזG#W~% GP5IlF\+jK_]mT5ev>?N k'G #aFh]pxBe^6b4˻p:ם`,#^Rs:ZX)HA53+PNIM+ݸ~"C>F7*ڙA N]]|L*.u“3wE/N{ȥn`RD;b7?F+J~2 \ޒ%0' DX"QϓaHi]~ 5Ŝ4m &Q6Cdð"_̽ES2ζ&r~F݅'p̧F}iP?$1TqikެG^$u=_AKJv4Z1gbn0=>^Y"$ =m8[v6Zmł̌b6ڍMٟ^V>_ӨX\]GqI$hgN!N/~\WPc<^E6/pW cQ듫:0 R6piʹw~[WОx่iI!]ڱ$lߌd]]֐/ ޠէkI>$!z86{ mk16aJ c$+6L Njn;)jvᲤRD]Kdx8d߿aD d $~jm*MxgqA`iҗg_/SDu;t(*1FNMyˌ'*+S*_JM7Xn=C,׍Kr33Aöa^J%PQ4,j6ܮ`YnGGS ۢLr'k腊3i.p/K!Pݠ݄/nWOTi-e乢tѻ ^&*k1޾hb̀@myD?f-M>we mkP`\> Op‰]%rpLɹid C ?xf @ %}*[=* cH$@\@(qٻB W*-mԦ3PWm^y(֖ m)ϰ*gJo -z 8 pDaF +% ZQR0OQ< g4SE(`le瞬`-eW1}#pZɇV/*1TcmW]eʵhu"&XCDG `bm5|{xW'1_x^,yfX 9t'rBn>|w믣Ke`ph!bZ{oiEx @? 'dąu oHz>Y(+o&RiB` Dg`k79j5 0l<;+-2yDR/Z= V3;`vD'8e\2Nᯅ8:^t ݎwecmݚ457WlgQɆ9%S!zzB# =jjﭶ6!#kp-l9uR{ڈql;",O"P3rT!\%Q'fŔ^5ȐO3{$p^ ʔH^.ϭ*M~-Q̮AX2hKP}| ijhHrL gp)UVY>Sb5-5~>g3#Ԭ}UDY_~>J(GY;j|#! !b6fRLF6Y'eb5^%19SI^0TWf^[ @-z H /WYm L/z4 ^`Υ <>2sdqvB.qeDY0-[~d.U1c/ZWE䲄x4ΉJe/g}'iejbH{Ż<(NtG=(0ʱn. 0|A`HL(b/>/MwQ8""Efsf"㏬6G7 75/HD,ZDPX~~s8kXIW2AWj$ڒ|=ϯG;7)@Nsc`eqeȻ\2bTm/eZ4ge0KN (w^dc9zkڙ y@ ^ÜȪvNs:?R5bHyiyACt 4P˅\5e-ǒ4Xi%7ބ:AeFN*H!Ymbլ+[$UB-.*y:Q|X΢T8>`)]wQ2N/y?QgѢ}:2߃S!YjX R9 A!gd8:i2QŴ1>ĭw06).rmɣ΋J, bbmȂ:>c OU|' pQ[2>V_d pꬾظHQTwɑDžO^]̫ mT%} ŀUty'פ/y v_/MpU{o-q?CI1}4HI >fEa}2++vS[>:Yƶ V'b#u6ksc\0e>$5:"AM.Ds; ?pRص|C7af^fD lT s]7G֯Rz+c;j'c_ Ch-@ln/_[﵄6d1'Bz2HȔK:qfȗs(.Tz9kOX^*.'"My,ܮ4FdfnB%2\7o*~:Ô#NUN5VR*Ap\дgu0 ht`'.@+Ɍ?u]cj7輄Bd/[{T3 wV`%UlVGzW;cIz;j[H `04ML{CZ$Sgn=Y9߃ҭZYbv={X} RӜG)2vWXD*В5dQSB##,ƣѺ(jREqSfVɓ:CV#GP=_;/?㷽tMnS U0Ei;.*>t](Ѹ0Feayko_cEU@c&}WQ/S,.u/`>cM#TۖCRW^'ݯ0gjzVUNyU$9yéPy%@T;7un_h 'nl伏ay}56Q3'v|{Xߖt3G\)/ J$4֯yPSrMt JEŻ&`ha]kQ0:h@ ꣡m/OrY.F`9,̢Y9YiM \?Xq]!y? ]x۸=S5 }oJw$VdDXtvJ9 m%_X-54ɋ;Oӄ?/|nqë>B¾̾ӄ-$O)Viu(]c42{fpDefFcR e;"bΕp%di2 h~wmޑ3ݨr"~Z"v"Bi :1H\F-)F&^*a@BT(ˢlLDq,͡ l3us_M-\ɤ_|Hm?I҈S$ M`(t VKTm8%M=d~|s1Dٌ/C/22p@y\ؿg)ڵ˓LJ^Th;rMtb+,.CX!;6e*h8Eq\=Ѱmp$qVwO4CϯuS/`Ȋ_*qdY6J &梆"?|2_P Z <.A݌n]2vHO(N2 Õ^EXԛ=½RN&ۓ~X\A>1Cڬ|X Lͣ,Jkɢ)+ld Clc *EY4BE r}] P`0wKA52Zi򿘆=1iIJJ[q E?_fB  *ųG D2MQVvcx'+ɛd`p(/Jxmk g#xoO~Vd|GҸE*&^hޓf5V%4JSYewOt p0E!l^LKX.4{T$)`.'RI%&@k&c(.xy9M=2’Lˆ_CCI}q8na'4 k " b.g?@͈X.M;uqgmUTdOŤ~z֫ vPW%:$)/~BŘc(ɕr9L.<PˆSEj]8] Aߦ +ȗ MomDSm-L2 ⪇<$Op K*p[%!f;|ȫW&01:;CM%+=%iv'蝱v1([\k[q5\֫ɬAc*7:?A Tk⽁/&?p\ǖӭ`Ug2 <^Yk6c#9N "e&>i$ս^+\^ڳ-G%> !u\E*J%yPq\WVYOﱁaۆCua'1,yڏ *@=@ǯ_Ǣ4xkzWLޗu,(0?Fb;7z:jIN)5Uu=(d .TgvU]s5-Wg-n{z+td+8j\(ѷd}ͥ-IU3H[obw;.w.]}pbJ'L7F.ǼOXϭN -2G{&G(ϺObK'#!&>.1 zI0N\${]vuB,, m0Vmsףct,)XBu65+\Չ~QJ^jK%A_wZ70=T$3Y숲=QxA޾G!s,fE#d5;k4=׆5*(m9!x8O6Cy^oЎ/&:p?ob!;3- )a7!<8<=p9@L)d  ̫كSI_l%/q:ke HTU?I YTF;J#WH^ʫ;w1~ dB#')5~"i̒M Q_vIme8׻Pe剤b;gCr9=w e 8^PR[[27V`Mm_ lc>l\-*A BTPF>4O0vYɿ'\׺sdz yTef~Ik)bﴜͶz;v{ O[BQF(]wUA4-w WqHkH$E*]2+& P C0D < (++oQְ戒57+V (Q6-w.?qw6gN/('c~Q IS? a4OYK>%&emq@׼+hG |? XeMŇbLJAឬA:B EoӃMORʮ#?ћB.Pc,DƷ-Aoc{I*(E%Di0(r' ~u<1^x-a$yJet!ނ= yG:b~8~J~9Ȝ;x*bFV3)є$<@kO6_rЭj}6ڒ11,Tf:^}N^:?l{RO8y !)[NWo2=Ngά~0]<]Xi)W}NhX4{Ih+4YBrQr5MMgr.M w~ -\ h[G弘gT:\1^ fJjZψNJյǯxޝfg"vvhyhWיlqsQAVAɨ‰>Jql nfpW@U}velw`u^BG py36!_bm\lڦ' ;x᭱},p>xQ E6]F7^`x&׳8mRl o0qG &ö|`uB&K/Px͓BO[~bzgǖ.!A|gyZ2 dd Tı=q02NIM!Hý]1ZV5۝gf#+ʭ@:;Z+^3ḾW)W'kX(؊ro#b'ewە158؜!xK~6 uS d3׵nd:1ynf3?O1"8 mbɘ^ԁm7^H /I/-TY? T_[?\6WKMݾq9ݞwlq kv!]df}ayқX=GI8@1C nei&g>cU,v/M^槜΍P^\ S+11dxщ;}R>o@eAW,r#udUhv~GxVqgX8U{i*πVc@Bg月P_ LR{ ҨĎ9~.[SYwK?0;z2r{&Asӡ??5h)ӥQ IE־^+f/8jBՌ-{Y?ACJ !Jru 6BZkO1_C9^Uřm ZV$r1"KmCEA" 3ܾ.ff8#Q QGh:7R$"qoY L 8 jbn ٥*R<@dYjp!u1x=:-]86ň"YfpO}Z@ kTgLzza$}OХphU"D]UPp9wQ`ozȫSd~cf~5,_ǰ&y S,K!\s,]eU]랩qw U]o{{t1sh8\=O$mRVn636F&gg\X)|B}ުx>_r ENBux0oWHM0'y-6gK5rSS>``*%^l 튊g(yyE`9*z v#ئ\0 #Hxd?ton0'켵}SrAM(@I| 5kf:ӸZ˖)Հs`K%e$Qэa4Z$&!t__e퓛^4MX$~@*%ի]˗ b8CfDAsTqLq>+;_le4Rm"ڊR_(M;@+G=n ި@YIPlej|Ҡ1x:-V 2쫳$0w;Iw[?͌2M֌g2:1g(/#4ibm}=w:b].'dzmd!pzh^ʁSz6vNep \G*Ka}O/z< )AWX+q\evkL|)2S]>bA( @gǚɪo(--旭\d=$#bHbz^~~üT/A&Tcs!ήi5Z܃{VKϖ@oռ4(vք~ve 0Q.;QDDcd}7Wߖk ^oDNi){Xvivvi(7F'?6&4`VeϚ6 f`ZU щ% ?$e:87ǰ~臚(I &f6B|k\3}k.mp&:3_*[@8_ϕ$flL1Y=t,u8u]A4^͔l2qxo ],1Ky ږ;͠/G\㴃eLjs Nl"<3%Bġrɗ|f]xaD=O ؼep^,uz6z<^N*άuuP ƃ5ՠeCu*+?IVL0g0#/ ڹ F3]۬끱Pwc.!@ xZ#`6'_VɆ*k[1z+ īAL2F{Ёu|Z\u?rñJJO2k tlO瑹mA (})$¹4D0iX8yڹ骘2_ǣ! "6IqJFC{w!vh !eN1h P ~7Q2Lg88JoVD :a7}'v:&":̅ҥV&L'qFYY &w<{xږI~)E73}~Y:N `tqf\99E|[njSOP2El& Z=[z|=U9loU} <ͽm^yTUGK*x;鱀x+P wW`\{o(hx.t?Q}0!(rugk:?[l޸H6 ѩfk}.o 4Ή0mOBs|`HgQ׷FbM~k\@g{iV;7ǜ* +4_؂J /viQV-0SjKًǵkə`rU__uWT2iMy!N@!np"\Z[v<3۸rcd}nZ>KĂ =Rqd"yT"zªWn (wm>[U j5tMmT޾gK1K ې j#´ ʥ6k6r^o+F Xnw8FJ= k}bQIʭFυvbɝ:k:CC1EնWLXjABem[V-ie:ō˴ÆZ-Gt?;%q0BV>J(ȥt]uqoaU/c)Wu@/޾\ Պw>+_:qJa^m ajmOM,j#\ߏv@]L7n_yJQT9-ђ^@ <{9ذ9UDOh0z))җBSP4ttM7 8D^;iऱfx:Mo7ƒ}}#rO/U+$j$Sw L DSBwu^_,(X݃sBh 95^sk"-6x2轧5ҳ%(3/s>(K=ywdc"#CD5w+a,Z0 @4c;z <ߵ.ҁ:rq ϕN%?ߒs#a4bc 9(z2BbO3i2 .G3l06sn|4SGm đ˹>ec$mWx%xDGNZI"1u=Ab=rt쬙 i ) _#rwl0̙ɑm]L{l`dJyHs?$Tq-n4 GŒlRYck{H -[~M;{EN}8\K!tu8I^S6RĚ`Hoi:HrGznU%GYOgwoGZ檟p2cͳ.7&BkG1chSż26fRU.Mz5<]uɀ|lQ~OFYulP88ZK4g$ zf}U 7s" QS +t_*X׾[5TnI ^9͞X豏Ȯ1u@cgUyLrU-#/qOUB*ω/Ҟ9\҄$hsQE&TY_ ,JRث%U cj:þ刚-pKvfBvuշjMֈ\kr4E3!| i')ړZ&8J}n9L%yCWo1ypnnC\jrX̜5'X]U^HygS4ГLTi f[%1Bs_+UDhzKxx)+ʵ"+ekG3/HWنzQ?|gJ4gpm G3Q `#[4雁bQs01F.UGec˅KRVǂzcFaC$K6k+pv9#ZWsᮋx.71;+ᒫ 'M6&'YQa!Zt B=8CaM+% v S.['xxʭ e~ 0KΉQhi] YGv!C0 ѐɃ/cS'Hg``Ir ރĿ"yI1nb$wtj](f?'=O H QHeNNQ<֑Dfr1c57ݿF*E&(``N3qܽɎ s p3ƹjEOk!:ēzU9u0M8yBPky6M 3M4.HXn@F0!BEu;:+TfŚV iu4S2L`WZUr(K 8Ki]'`6kT\=ǃ#LcNE1aA-yvy+կTP`Y;5Da:Fμ6Os6lcd5`=z T{B9|V+o`n+A2Kֺ@[1W30tE-!EbZ~RN2Y![T'R5"p %- =0RU_1##0ϨoxΡmխ7׮ Qie}}VbmwXo39F4}r6> uP]T2bZG5-}ƾ1^y)(ELwƍu;.% G`-)0j(B]Y4;"$Up~zuxj(Y6#k|1$QSEa Ǻ9Зx !uHf{~d09ki.B8d)tIʞ8nS; ӎqN@WK`>,\ܙ'tf7{r`f(T͵`͓l|L76yt6P66A{Y 4az]j7 &ir7[K>W]&B\PYf?bFd `"5}PfMBTDS]6owYkh/4`4dA$UiIUrmuBы!ǚuoT}YZO7yFcT٭kaQ3^tx7X~S(ګD@q8MS+͍__#c$?Q gvk+ 0D{ Xdn47L7I)xHJ}.nD2ǑReۼJn^wqQcs AUfK яøͨ#6Tvs&b.ux} #J;^ %^" ,Fg˟ަ\Q#55R W+JH͔u墂o4ٚi :-徑bO366ug;'~$a" EKqd`5Żȉnۨ:Nr:ZC|§r"ڲoc:Fq is&[J)R5$; 0n,]K$NE5tiTrL}Y aoJd ^s,tB<_i;_p(`7yy, g/]繋QA|PWJl+!XlU\_ "; ?彼ijK!1"HLiK"g A.*ȧdDP/6(ҁ1!4U_d4 Teh%4Up0 f2Dp'8@*9{uy~zl"YX(Y^tE[}^cAWdq:D\#F^ƽےBXn.6uV0r$I9WQ_?ߘНO$~=7\s(캥s ܅A3έW_ƙolf$<қ2׌nr}.gams1<3IOc0iFiVpwd9XۓCnzәШBm-lʳ!NB|H ]ߛTM̶% W$()}\N'.Ȭ-'0MM) cܙawb\Ftqݲ"%P{_>ȕ+L J4{gڰuH7; eN(r1&h]vpy45)I Uy f#:bp)=9+֝i ϰadu@%i"| 2G# ]w9})R[.+c\2ax9ŋi.S^@saТوCӁY[A1yZќaشJ2 KgY.lٿƒ t5|H }`տ,DłR9x@z%=*LkQ}"{gʫR~|CM|"S|4ĶVF\T}.k $SH)U/:*IgZC{0OdLrX:ߟ|C;9`^K|&Prd"Sl坬 /A5p ]Ef~L>S ʅ<ӽUnE`͒gߔHO-UFXNL668f3s2R tQ@'6lRiXb$#h_ +nVdB#D 6sY5^lPeS]KnX&3CAjH5ȊJaq8%䭆 hѕ`fOJJ抽5:aC|+-mZu | )F[nPEB3*Ow1 ;no&k%V|~MB!V8@:Z}%^~z,_ybx=a'M|g*l7ރQ>|L4RՍ\ښ͕Nů*\؞֢l3fRZH算f`Ƛ-9n= lf}H:J#8S=cL^2\0\u#LD *JC 'C v1d0qES=>5rx< H.Mޫ,d\d2ZVZf<ђ_׸',6i-~bdsT׏ c/ HaO@kۘ+gfYF "Yw t%"mI OQU\w/PM!$^NJ-͕CJM~ng{++-v"G}5t~;M8<ۀxP=6ۯiOwS[ *kk/ad"p갯4+dl֛.`Wy ~#[0Q6n?Ar&<Ƚel$#Ļ@m? 9Jٸ1#ym98O3; \o ś@4"rǵ? N>X#348'm Q'p8#g8*1X5? ;O1%y岟|Nx/ !]bI<n/jhPTB+ 'NE:w'z K+-c"%Qb=6=yK5[a@1uDC'; &@5xgdNfA_^yXx:Õ`ݒǟӤy;qÒ'uOF3{0BWjfrN8Lkё,Z~P9XGtPSp*[ lƨynMw+KRUe2Ee,zӀ;pv|iI;~?"B#K=&Eb׃aHW10Lb"';|f]k:AƁmt_|u%m_@~xF'1t8&=w9G۸{JyEDoh&ߖrD>11D7pOM5d$f-:nzk)6c2#9h1OlĈ~^KMO5p!i8ȶJ9L1. cQ#_Nm`[`75D{L϶O)x͗eƳS($l9=ҏo,("RH.v3Lu-vPLWpX? Rosj빓5]ALk)%]h3OxKBf MXyJ3FAl,i|*sOm$ Sm/$T `05/PGM'C$nK&i#bTdÓO«iq;̍T=^XD|3;CB@4ǭY䋼@TE!)}LfjEl.<ڢSD>i6 $( ^ Mܠ/'24'N9;V%G1 $;5 ".hXhRdp(==ִX/B5x"HV&)LUGmD#̳|847sIU}f|}K4=DF 46Qӆr`#Nap3(V׺'=%&%>ݨ:םn67IHx -RXn$`hǃ5 | Pm\-ZU%x g$hBVnjR\_33Kڋ$NjJTNOk7D#IiP:*gËPئumHeWM]@uxXcZ=3Hr-SK w>hD#9Mn+_Ɇ 'o\C`?Q=s,52.b0ޮnT8ѵ[##d$<0O-'M4wD#ݎj @[ =`\M:7#߳QoF[C.$U\`yƵeaХwERQd^J^V>kJ:Zd89טm8P` mBj8`|̋IpץQ3^T?n>[ O3~7cl4ABk*%רq nL=e1ͭn yMpx6<vL8!1y-|K`M3 ^MR5> FAE~_-[R _qX/H{xuj#4+-QtzDl6hNե ! 1 \?ge!%/z &>䬏Xڍo.&#FFag $+fni>yWܱL *Ѫ$J$k@"?%l%H%WL9U;yL-ȥhfXs 5̼6hB}r?Rs,`6, 9?vew`Y[W-.άZ ͓^QOчh/Zj]VF]?l(~ Ԉ t?}Aw M$&6ڦہ˴L;}HfFND'F^\`n2e\KGyMYa8@!T)XKh\V0;H-Bs/"bbsYD׹,*g<qFBG {Y%'5&g7n -p3-,8(3U"Iv%`,fY2ćL}dJv]+I' &}PN Q%fQ7kB $O#mvV)Zg>(y& !Y""Շf4V#/Ί1`pLXJ]oasj]녀[ƮbEFXmozg3.UBAz =&s(b%VrN#}Ys mr ! or,+rٷYoh."D!rtߞ , %LbfzxI滃dXe/en2Xp34xy??x#sNTXԂ5ٚn'jGam$Wb}onmCy]GW\8;{ZvW ,ꆘR>ݰZք''V?k260]@W}yMpaiW1Ck(_&A2W6e$XR*- ;_%\L1lse7_842|=HXg [!W7EF2l%SOU=tz~MI^#=c.`㕏c7p%7u(ްurUKƑ-/ήo! !e|tVT39ǤFZCg<$wT#x鴝E=<З'*EuBq.W[vZ:BÀ<5(}l^l| MX"_9ɭnZq~5m:Lrhlߺ U^,Xߦ]Oԇ5<_ɓ);Deu#&TK5w+7)ciP΃9oHkBmM8_!u'WMcnj8mEȈX8kh~W%L(ע 'A'gcG2AH@h 8&bni:m=%_,Z31JƂѫd2YV<%Y&_?]rƱc+۱vAKM餑vVRLjQة:#/xP59N/a SkM)1Y _ZEqE?tyDm` O:I1{ˈF`z>(s6g:^}W\cvuwЌ k ;LP\+znJ'2t:I`lWn}f?|ꮹ9{ q((!34Kj/!;º: tr_C),d-~O|srzx'Vb>k1JEOm-.{jᥩ`ƹQ#mۭ!ś5LEdO=q/NP{քrYumUZXW DKx|GLۮ6bPQt' ,bHȀ 7q0$k7H䅏؎s!W[@̈́BעpoQ Z\w#6nX ࿍[Ώ/?% S;tR{I9/3[K" Tu(KXQ#/{ZC<9^Yo; Ֆ*t#_)RD,QPp"lf9GPgBN3SHcMNY$;[E$n^9G`TMlFSo2D*:s p!Js B3GEI#!Mvvv^396Qa;kvPkSQ?r6\t]WÇV 69ъ\WD&ոy=e )*QS&sȑDŽ)Ǖ \ZIVJ[v@o"O;9S >2w$bVz=$a}kk9 d1`޳ #w(}Ayw9W?T83^ap͂2v]bj8G&uF$qԤ 5`S(~U:JBxGv,vV)vU/P>K |`.) ށ'rG_*h+UN4)yOV,C'"N;0!ocw1N:2yGAa~r63}(0Z֤`N&rņL4 ف`1 L*9Fn4`,ZLQ϶ ΦfqF #ޓ~V |no5bNhe4*y|/lfdqLJVt y.NM5Wxki$A!Cv6C⨈(Mi T [briĢP̵ڕ azJG8e>jP7uD e{6Ntd%#Rl;ʠ, CL@Ҡ m?{:4`|ud>ugd-74O+ 9>U{ԟ ,=7&q6T͆V.go6^x?UЀNQJu($c!Uِ6!G5nC`{8UhN݄ė8!3)G:1 6v(CҖ(i./ wʊ$;u0&m;޴EFRP씝-{K7BG  Lۉ>'cPPKH} GB x"&򹀣A8xZ)d5F?Be:dC=NlEW(ف,LHW忌FUhqL*&{+Za!pp 4-$-JrW`yx7.BL*&UEV816Ǟ88[56_!u}>t.a$x IVйf8%i׾;$"B(:B0G6(Y6Ln^x>e>XTSI4 kW=Xg]ZZ~ʪ <0ӼhmJ޴ ]{[MQ$GP=уy%h9f/7WjyCl귅. ZgǡkJK)Jp_biG+"JAFq(w^"<S6Yo-b 8a!8~r袶&V|0&o?9%s5fZsoB@x;/gѬĎmnK=9N,oMU5z2Y_聓gQ(GTF@pc*rHY"Ǟrg.6W8Xd 'V ;Ǟ-Nkqcj_+8)hGM0Xx\c%1 D x>^ nk:n@jGi!pGaCj| D>Ζb AbQ|ayjeCS^nzw"m|5 g [HZWX ^B5nDw?II((>XHӯ}*SA)1x_}RFu_6}l)wEL v )G7\sq2,dB1i4AJoovc'TX%%GAbQv]co˒Dfq l'M]=K9Ψ~ LH0 3_ڭ6+{E-:GeJxϊ`(PI[& YS-CNaup+o,9ғB|RE_z2T6-Tqd;``.*ӫԀp. dpQaNQG.wX#7wzυ}94*)Apܞ Ǿ f'#G+RT|viYҢ4ɷVzi `)`)˗^[Pܤs韬ICѿ!VK:1 ffx$H:>RgVx^xN!3V ;ȳUO QBͳ$\4)Y*5!zBvbjvsVc$ )PB\Ps~Kܵ?Ǎ\Hy2ik߅)ܼ swKƝ˩#;(i?-De!.UEu|ZH*eBt?ػ3&Ũ U(ndÛ\ߵBHm7~D#cq'ap.'53- !Zڥt,NgCe0V}5=i!tOAbvxS!4˙7<]9\d|]OK{dwʇq&X}e@6Vr.ӿ% N e v^؈HpTY)~z7>kji}[(uP x/R:I-p (z:.7%9ː9٧2YX%|Byj$^ZWK zTie( Ȱ: vJ:Fc9A#ܼ|eX ^ΐU4Tň*G,ʡKഞd0^ڋw.J~IѴPq 7qcg#7x4- !@[5K!(vtc:!7gs<Y# t9Xb߱o-J6`J#Ԍ&9U& hs AN\M[b\ڏJH|vU'vɶj2` <(>hp|ME kԃLQ_ 8ey-*Z7Ϟb/M&zP +mKvi~툙GsK8/ (8_\m_mdv{Ԕ)՜w21DLP~]BzlGjph6i=AI$qz.eh FĦ~BнLpm,@<̞JJPi]`{+q)7N]*R R''W9UCڄ#+.e/Þl^}>Lo j ? 54&.~UtN6 RbNnI?J\v$Ut4+4t 5 E=_'W1:H4nO7]r񿠾ҏP6+1ic':a}?t (;| ̑4IOu\bm@HXKT%QŦi 2W۟ۓAWIIφ*23"Xu \IZw3EaP J7)l 2m[O>O0 @\n59[C)~VtW  z.qo9'Λ]% .wn $<Y:Y=121Euwg>_QWQ'YI-~;%Q Ͽҕ)lDOՍ؎R@+]{m\_7#K伷sJw8z#!:4nxgHeh0O^I߸. 7>4bj*@H`l{K¥P9~%竦.<n"Y:&Æ=~ gO6~`1 eݙ@((E2Vr]p0ω~xUv3_O>Lϓ)6f́FM, nV;~)=߅hL0YmA I Q@]hsce6Fx︍! ?b|U"3Vz\N:%vaO.@<;jkoHe>z@pI/>}޾PQk(I&]jc&bw]НĚ.|3u*N]fv`5X-KӪ]d ƂMM H~ZMR߳Zs#/YE ,>2"‚>BI?JŇ*q͝RKQhʉ4zkYXu$66nnsc1{% %ՈAcAVr H9=O2Ȏ5񅿵Y# \UoЪYaYAJ$٤Wl6SɁԋp~%? ;_ضY[ ĐF~ )F-K/祫yo֢wf@AӨY[Tmp^W6ܝK4pG*ZlW:V]2 Miw3ɷa ޟy;eo?7E#'1 e4mX|k/Ԕu7eE:y7yշpY^adžt-\4452 [㲵߳YϠVǐSgO` vQ9坪Nrp& ϧ m BTĕBA`qD|[S pSUeU Us0*Q?ndMVBKKNkrp?Y|p6:ð0E 5Q̴]XIș&g*5aE$b\wUkP/-2 C=ukH :ܪt6j, Q1J5?l6,4 @i- vQxٔi&IqtNX՟EH߸Zqʓk'!?R?Φ,^g\e)z|.32E]׼L4:˳'}Rg <~ʚr KHц($ AIɑaK><گ|l;v HX^䒑{5?7=CA^8F)6MF^峻ˬ3.Hu#qL?≰0sF"4aqSm DWbKBzKl Yֵ@E{ɻ5绞feKqَq&Te¿AWx&S&=ʘPx yՁLɺ Fr M_:Ss ֬ŀ`ohx Jt8[j8 73a On4`F(`P]pt_=ZoH91m71O++4-kI0QNOŲM9 ( 3Q:ê>g'X`'xsc+rӖw2FЌyoJ&4٥Ki^|&1~ OmF[{3*d&_H#6p"d)ɥ)h`fD"Ds4c-/u@jl@l2u.ctY0&ճƎE2i c0*[V,Bm!1F3+fb+-%͉-YV(ȞAb xuŊ/}.ZOBy3| ;ZS(>Vw ;S ~߃Ե\12S5F y6>8 CF-:#^ٸ !b#+ ˯ 5Q(M~y9ǒVq='ƶΰ#BP̙Lܡgk4$7_ƓuS|UStR˷!NMl-"&:5#7F`ig``瑚$ZQ* :̓!~%G}39Y]y#ҵRG-knH?BQ-\ F.}f.?_$.H1Iy@XS/G3 [[#.C_gN[$ddJ j]Re~@rf@O$ZHWH^#;ͥGܭ,c,Ш.$ךåQvLO_NVOʃ/MC՗c2E_EZc0a6P# o7`j(IQ>W6;А|F(!{Mܦ$Rz\6Z:>`6[eD15){ N6ҙ;N&=I"GP@9^24+A gakNm$ D@5ܳ`hJu[;heI2 XG9v$0kʿ6ZgH)>],:~\05mݹ?jÑ$Tvd>l@b-񗱍騧z%j:7ȕ,?NYmNwP]Zk@ȳUciӹ8;.QGI.Hڱ4WHZ󷖓]Ϝ-mCÃ'3 GpvkGN N_a`UcЯ9I~G^T࿢D,ɱュdZ`'uKt i)JZR2K$؉Fk6` Bk* k p,@~#(EcMRrh@Lw9ڷ *[uEsOiuTH~ HX(I3J G @9}z]9a *Ƙ4YR|;< <׮JpMpz:uFp߈#%!ؔGUHH+G|杁^{~ |F"K6!" Ҧ7\jƆ]pv%b)F~O{9e}_8;CM*|%qF|N/\-55da>"Rd?pXbhysYsK{[cǬdiޡ Dz>TOe ?JSeu:i_ # BqOjM5[۠ur13D6,2phZ/DYI\V"޵4&i էq{, bWVO%Q\*]vkbc@ ʼn,6#3hэ(J̧ fcdÍ+3W⢸=qagO#1n-4\RE e}6_Ѣw8  !Nvr‚Lmn kD>t[_l= X,aN(ެp#ضS}xcup^ 00zp^B8JKf_;D*W!C@[YD]La0]( ٣rpx.Oh/W8WNs?em 'zM .mAu'*$tGT,n&,p׳mޅC{6U3n9_-Tºڄ<oF ^開0o2adM庅MzW$|j:).d2^RFЙ{zz j\E47{pwX?|;F_cBlrRN#[A1诜W Sng§Ya9H uu<@ 2:(jNIV US_햛FP*9 =*S'= dB+{i&n[<3߉`-p}hMn-g5*Dsh&jnȢ A+q~_K1:9"{VLϵS.mry!"6 )꛹E4w U4s|ϵXL-%DrS.<\lFi& I u@XW)\Y0hI 5_9 ?Ln̤ NF8A:P4>;["Ge^X.QGV*0;uxцj//K:ZTmIQ vZMH`7F @%^B~$RCT֚\)ٻH KKjkxVL\+A?GS,z -`vG*'Hltiw3[_5/ՙbʣ>մh*h`ry랈,bZ 3m 0ׄü c@FNq1E 155kUw9AELQz fi:\ W*Ҧ r &NM &XR uBkE߻#9E syj{v/^茨AaFYs)SGUl JbnJc{hdXrAb_T;IXt+S]t?{f~+{k}Wʜ+&_gc1>jPA~Oɣh*]KIz`F ZI~>I<)P7~I!Emµ!M@-*@*H㦔FoSNwF;߾ww&o,&8&[U(7/:r fpUI]4Ƶ-{d]u\{b bbJO/%EZ펼+vh)-`c μZZ@u+&fX˳aI]@Q<ƶi&AKKQ|p]505-S&& VxbZɁiDZ;zgAd^dV)2L;=}Ru-gKo x.ܼ.ÕSOWy9 Z@ ?ቷRl^HY“8w?}IkwR_kR89+{]@^cQ+Wb,Kƨ m1N~~Beir}X5g=3p-93FpFL61\8$"@syiU ($G~L 7+gn){#-|%oAe[Svͪn'0D, ]s``L!6|`f|yl&0IN[c6{H*ϳ/Z5gQӦf %-Iq8jaHG!)a2wJ`bX8?'%04hZLIbbC壕Nf^q5Zަ )=ǜe[Fgb]p6Rͥo:סVi1c&Ue9 i_aTu%[niW6Gkh+ oۙ0jG XD+qBsCDHUE05mA|63lx~# CȖBnAqV!۴"BO0l4^)?.]]=~hLPJbyV z3Җѱݤ3N$L9AܤKopA>BXF'+&S>C waV#8lҥ9Bt t0jZlDv 9{w@` ӹOut^}YL~ro,6ҒEªzj`^.+:Ioؤ֬CV\t1ro|4vL"̜׼ vZEڒ;2. $Ak-W5&;Xr$;q~67;><񈆛pؗYcYcTA46s[)50EAuGo>Dl;5i*[.C,%V@"dEJO/ۻ1FgDd]c˟ϺdIiPw frͱiƄoW.dIWIE{Dlj)i;*M1MB)z6IR& ?gi(jν7& d {}R2䧝eW((~OS-l춺mk#6Á/_.]AykL̀p TT_`M*nqj385a2 (&eNk:7pd t̖7PWIIve\%ý*Rװ)ED ciqZ>6,-RI\Ur= %U8V^$^@QO@jֿ[ٳͽχrG֜ӣuU|z8p>1Ҳi0yF 6}~4oSXn;(ՈkẀtpkDvʌm)Dft\n<596X})OP]hRӉϖlP/[τ/d޸ϸ"rM ]4)i*bdZG\;EW<k.C3M$Uc:e2 T{L;;2i`=dRA715@8՝54Y=6l,dCHV{£`#0h8?%%-u|lIS:iW t>@BH쑄׃gUj^u@858;v;Q)@ (wEnCƉf,16s4*g *:5fa#z6'džxފT—\E 1+u=I+U)^TbUCRRǽaZ!" A\'Y>WytF{v΃ YGR'cW-ԙ4Qy _v$6Z:(FOs.4wbĎ E4t .&VY,aܱt[iv)J)hn*s1kG5&l 1:%%mʂrPPAY^X_UHdf]kU(Gڹ<V3EϏfJZ92akIS>ػd*X,+_iS}u&7b6pĶPSC&]}ZcFs}7sXǘ+?7άۻF`63>MN]x'b%V~Ko&J9. r4~86< [)G@HCZ1}]uku WW(K<[Y-$??tuں 4Z>;n<Ľ62pѧ2e{ԤiZ4w'v؂9Nc弧\ yvǙL-ӰӇa"|TC/2s刿A,'gyZҮPiѕ*CXF_C(Or3Cڅ5K1F^.Qep0"G%9b.SN,}9izXo۵^P-Ε՜Fx!63-[1uSt[zυeN`R/̔\n+N6&oG*nܫ={&I >q<(g*Ec'sE5$+٘UP L'QW6{j>ҳ^*#aIOHs۲#)!v9RVCL#BR"wFU] Joy$uE$2Pp=| r 0ڂ'L1xIaP0sh穗X /:Fz5*Q.6tS ٝE%_xHRۅmV6csc4fϘ"s;޿²DTqخOr+97Z%35SBl[)r(V+Q?:,65 h2v+olow׭t4#́.9C;?,d)s܋ ^xIm/NO1QknM=sC9AmWȍA.Bѹm45AhyTȵݟEm3:ak\j,6Xrw xll2"lʏO1ǫ 3"]A'^k*mN*ECN靖`: k...ߩ9EN%/?ɐ~#Lia<Bp{pea ?KKh%Ո5 :h_ib̫$E-&YQ44|*0 -)KNu"47'}ס"$ طfUxnM>6?qHE$fC%RZ8QVB|g0+;;}cdv -\SAƜ񬬐cЄٸ!2P$ZU'p(]zaj[߈Ve X<}zu[a&ܚ G!3͉Ke@SGhC|n.9 DU7M3Tc.ϖ(7\f=WdQKz"B a4y5m!xql,/ٶ4f&Had$A{3@B~PyR,Yēx( ,EojrgIUl+t4-bdIȥh'S>kzWDq+D.{~ )́I86ܰg/Mrv$mc?}9Ç.8J:@īdh6Ώaao>]ݻr8昊m**Kl PL,pyȖSA5-TSKathѤ`A)&?%$P~43 HnzDJK%?zM@0cl1b融j)laF[,!5xp!E!t$@+LPpr=E?E9(q#C.Iـ X!h K*E=I{1 R6OpcmW.kcOx+ȫD$e4GMLi!q1aެb_${%#-듳f34,,22T[%+{L)|ntn֨0 #F[y&>q !TSZʿ}hMDd3 *\z! ,[į#Tۄa3 6snVYXPzH E2oo#kdm,yˌ@VAi@?J LT\Gg :xܩaTr$0yUbųPwY/΋)1ۂ%^lX`6ʇ:ԧ7s2{ #ᙈo{by3 ҥDɚKg䴷M3FޘL;Q ̽n\>BGXR$#CaZop.mĞ[YM'CjA4>oك+Ҫ[3904 ""JA0ok;%@BJ*Lx KS;4(ZijX||&3hbe6$ۡDEoap`Z5no|u|N(:_Ejq:XGWsR>EtQK(KG(VمVdVI$rG I $AN @&0/ hctw)=xi2bIg2XFxr9=ʅ岕@z3be;AxaZȠ OoOD & 4M(g_:3u{q A?Y OknIkAifU=Lz4ӄ7v*{{"$|IJo{::gB}k ژ%UTw٤ޕ VJpdQ ,ЂXMDNߗPۂ}O71cW(2{`.3'۰`Lelh+oo9Ca]&Mi^Yar]iϊtT=L|*fO֊ΌB}aQ&#xvXkND)8w Ǵ }q,Kd`V[3u^nFs"|N,SӽVϩO;907&'ӣY*60&[NF%/9 A6p;󪑺- ʘ1 .޴^ꌶiqI.(S32 nxgxLfSg~=!20:QR1m˞&͈8EȘVS&c(XӿJo{::gȡv)X5t-xΐ/lz^}Ml~EsJ6Z)6rO/q_em h{7 7HF"ubD(ЍDG<{1'G-8Z3pTOޕQbn,ґo FéS :) :㭞F6ONTgM^5V{d:g~i2 n!dΰf_ NМ/ 6qKjwJ&f=۪YU'O>hw/& 6FA =rlJ|jԓDa YB.oEMX"8AXt\n/F ѵ$:63nOhK4[{ޔΟ䎜If7Cݚ\xzt5Q~L7BV-[_19KR1Ov;CEȿ6AM-8Rt^ң`l evT$s 7TT;>>%sOD.Ya6\*f;Ж,)ؾk1^]~vܣ#!LQ"^Fo{CF>QO *b@j&La1(@)Q_?A y\ՒgG9DPH-zrX[ҹK۔\tm9Y*E3s iF-lK1®L.쿌.>Vۿ{PurQ;i oKNlT2"=VMV-%,%ѾBBq~p<`a7~OCXXDO]EgLo˶ť-A0Q{!~V?Tӌ! R~gN(q#k]PV@.إbUOQர@kP^(jJpnx07dr0M|;~cIw4?N9q,EAe7|$#xAʇ9tMvR'r>t=;Y]U@kU䶿g\t6 _lt{!(P/0uck]4;vSC$MW,agv&& [KA$ˀn_ |"ha)WLM6]"qy3qˌ­ NenG_Ϯ&R(`-, nղE[0TL k89R0R#v o=J.t#Z-4KXk9ف""?o'ؽwҨ3vPKŏAfÓO 0!aZ3#yD 9?4A5U ݝqeUA„r0qJB(YdT&oZ|+>LϚBw3߂'8F߫%~WwNm&UT/$rX\`_5# #X3.D"v ,`-#Zn?E&;i&Dd=Hq ׀, 9` YR櫄O/:فs珑"[FB'{`H3e08 ..1 j ,zu?2(ErV3NNkMsT+wV. <\W7잔>^U zf[P7W:`pNh!N%$}u !/k܁S1̴5|K xH0 R.hܻ:2ǹ}L#0V2}@)fU '%ݘ5Vn oc4Rj~bns%S\Тxt *>v_p2r>J}Z=ٗ- 8oMFNd~$v"quLxrGݮ^Cރ_R}#esZMbUD-InNW>uM.Hi= Vw^ 8re Hx AlXf zXɬAD-noAd#d-5KA4bN',k\Fù6f/ dغDcw:g$5b`v:GAQ*0'GH)+|5BO'Qaؙ``>']4 ġܼK&8u9lK3"|.LOPsnHH4޷B%i?s~B5 B 9,~ K7j^+Ij}Zɨ]tnd#k RV}ѪGT}LXڃ֭I{ umj<߀Z.w)Lll̩]yu<'NעDZL,T$E]^JW٨]Gfh*cKnTWn LGDbZ%vIto ֜r}0\4s!x xsH2u4opVgkfȸŢ ښ2?TT 1r6 |usYۮ⡡9W0!䊝\}dTI7F@v=tFO 5*^Dl=udF׃PS2-fJQF8C;F#;_({jL諞Sr2"OC[snr$?X:4d<ci3XY͢[{\5޼k5 a<`x)PAM[chC:y@wK0#L}ֱ!2G3Ҙ)/3ż践SwkXw r9"!N2tu $ UR-CST*U _05NDzyGz骥}:D/*lb+ǡ.fB-/O.bרj 'LYu?C| G=qS}sx?EW6T#9-YKyGYz| s:1@(0XgiҘ V#=ˢEcq;|1{hpE6k֚D}y#l潋׊A!4{ƲS⽉uyԮ2F2(ו Қb|IBTudjve=WMz16Mwtٝ™)Ԋp8yv9]$Eh摮iK{l h>DeIe  7}9ME^sUӨ?(ˣeJ!uxJru&VFY7,av2w嫛3*"0 )3ĚD[6v9\݆Ck:[w J@H<&(18j;1Ɋ$1%=H#s/" *PxV^| X ^TSÐwGNg #[cn0 wWΫO-M:ma2bnn;#BWbd?UP+^J(`d ?ePN)^\W h@@ɨA*uO͞2?)s_ dHa'H7ِJ+S\(~.- v lA)/>i#d C`??늉fvx:e}To( ~9QDmmTB^΃?bNi6be+1X i9?Tƃị$igQ+F3z0C>o3ܪp-[oJn Hƞǧ% D(}6Jw>ݦ4iyDb銼NO}o.DoL>#1g\v`Uo'`tB!/'<ڟw 6KULQ?.FjDc<=\XIPZK KXH)=%yRWqTUm$;#:j,rv*ugxF*ܸW܄-s.t{3LMTw;(Z6\.:^T<.u׬{8~>:,dBy;u$ ?;Dgl#˻4k] ,gp,ri"\7ʛiK,fW~t@'Lvn+Zs qWxC,jRG0*X5t_dJUBK!!/k)TȟO@g F'Qџ@8I,V`m yKm+/g5GJ0|VO:=߿ 7jDo} 3\#'3#L#HH;Fm.V7S}K5Jmj(IG0ٍseOG,\5(bo0rY.ajboQ@JwD,ZK<嚁ow 0)B7B!tBKVoФ#J`o DcP+GC[{:6o偩)^8r21q1Ň qvdҦ4Z2O=rӏV YC's'-͵7U Y{C_0 Zӂf2O¦5B;L%D?m'ǖ0[{tയ6[2Eh8 6|eiGm+ōGYg:I["U9ʜkuPNf+}eBE2\15P%uڽeX~ K/fw#8kOPuSԌ&]Yloq,k+f/8mFU寁?c=C,|+9<ûw/ٌϭ%rY0:w+Â$8:b[т4V)ծMn,Ygg|&h(ܔ20yEWPm!xxoD/]YRu~d (#E[[H1킿]Q-3bB q5,o9F6b7Y!gfULg[vV;Mu/ci#3vSpaJ#(=JBz1I^5ǐ!oGg_O%1}qI+E~qz!>ʂ@K4Xrwq6ih!{ ;'QVF'$8(Kr`tgXߎ~QVVg(zWJKG`PjNnjy/Θ 0q-Z4qP#@(#LAB]dp S|b*bڍѬ 3hެ ݨe6sżĮy\js |T3%Oa6obo<\i#)5{ /#ͫf\EΊ*W/8,Hká9|L`"|*Gaj:Cg0+x56b[:HK"M,|۾Oh/q.8XʪQoM$`lW}$`bdôE5@~7W#JßqY QK[H=O_^ rYƾr|vΰ$^d| #̯=o{oT!: ޟ}` E rјR/Ty-q8+ڞ 3F6@lPCK1y}o>Bƍr(уs$~g]5eYHu'4 ja^/֑N`{{#JgʊȏY$yF3 jWo \6 rJ_G:eDd)N I<߯`!VHq7V:j3faY1k~"sn4-C{ #I3$]%ө=vx1A?XE"19ƕ~Knk+4<RܶHύ7߈l+=Ajp;_ڱy]tw3+rH6Ü5*tq>0i}rMڇr$2'vY&)\#bC[ڂ$2ϙ"s'{{ƶExghoSv|iS$f##R6%FlL-rcK`BMqM\OsiUaNͰnIcZm!?Akr eLQdNVOZ4A䣉t-Ƌg;a$nvntu3@rM<BQC J'N'_+Y$f^}::tp ;5B'XpQȼ3qUdόGP<(h/qғ KȍVG Hh-K{ u'>No!Ӹ_B -` 6xQ@YZAER/data/Fertility.rda0000644000176200001440000142606713616365121014261 0ustar liggesusersBZh91AY&SYtQo}r@$ a=FM4z4h2BF@h$Td42TF hd4h2N.*)#%2$1$#& K"d0dDJ) i&(؄P))I4IlhhHT $0&BQ$ADD %4"&AX1R(%,A(RfA͖D6J"ƌQAcc d@lb!BLC B "A$f31FJB#2 lh)1Q`& hJ!6Mj ŤشDHѣdB i$ QHY5qL) FHh҅ RQ+!L(eƆl0DBKI4RjJMMH HLMT,cLLQ"H Qb fX$ (0@EQK$"`"Ƒ# b$ AILa6M%H̙F #,a &ؤh(M,1$JIFh̙"TXE)R&L2%H*Hē &HBJ "d c@ F,#%() !$ PH !i X)6DQbQ&2hbI HɴJJf I% a2 )#`-+$dX)(,PE 0$1Dc$ HRid4DĈɋ0beESHJ@h1IbLbL$fE (MDRb&h!I0fMI$B J2TȲA1QEc`+ 1R&0ffA6P&&3(Ic&4 a2dM$KMF+&@ٔȈFcb44Q$$) Baa4 bBe`iL,ȡ,!&M(BDS-т,h(bQ0# cFR*e2$1`!JɤeJ LK Dal$he bJiC0Q&D4D$ђ HDE4"@b(FRdTXD0 d1J$"bcE$BE!`dR30bE#! LR- C$ (Č#JI"HbD$$"I$ #(Ƀ! "1QHђLT, a&,Q 1*LIaF`&HA"1`5d)1FLM Qa62L#Kb$IdLAf`Q BF6 D(Ć d$a ,C*(3a bX4F"aQb2f%h$FbdсJRId!6L 02R )LaL@J"4# 0LD3"&Af b QBfXѤ Q!IFHdAS)C00)DDF)XRI!c!%#$ĐA!FI̖Xf4$i Q&%4Ih#&cA2lA($AREdXBX" $QMQ&fŀI`(d "AIdQ!e 0 FA*! (VBBaH1R LD%ɐ1D4b R $b@,)IlH&ihbdD` PiKI36#$XH DX3L(E2 DVD !Dk TČ`f((L&$dRE2,lBE3I BfV&2`fDbb2EL1 DŌi,JVR &46 T%4mI C22d$I6*dbMDDIhA`B"(1C3`iM#2IA I cQ 1L(&FQ20f%I5$b1+I$1H&@f CD@J")4Ԋ $D Ze`Q%6&00E سJ 4(&e ĢHB&CY H" #!Z4DbE53!2 AAD$""֒4LD H&( d!&4bc хH&h@0L 2AD BLiID&,YQLiJ%A( ȥ1Qf H,(#$LKM),(aP&BTQ"(BФL$fdJF 4m42f$T&$f@S )!,̒e$JJJMa1HdFDYL(LF(2HBcJJ2&$ hRJLDRFR1&1bBCJSF I&#DRĠɠ$QhhcQ)X I("1F65 l&&ȥ"1#,HQ4cLPCRd CFlM61$DI&I12"& I,*#"QA1j,QD"CF  EQe"`AD@(1 &Y!)#,l"J) IcQ !I124"BQ3B"E )4&("% $c"3AIdPD,DH2dbBRX"HH3J$ Pa $A1MFd h̓ $A)h0fBS LȂ""14* (&R`#!$2,ƒI3QY""$bM  0DE11Ba11i#bJI`h@&l*1$ āHEIl!&HhZLDi$l!,R MP2b,B HhQFM(fh MAADJ)-#AJK*fHYM(̈́"̆ PddQ3`cHb( mjDb,d$P""$!M`QƂLfL(4i$ ,0I1()I h XE(`BB5AB2I ddHa2HFR36)%56 &QF"1L`("M!Ba$efH2dd L"H$AXFHPl͒•3A`%Uiٔ%D! XFJ$H͂Fa"!HhJX$"* I0B,)D@E$E$ SfRC-C&RJR$#,DرF!@1%Lрd$X&FH 2EdP#!cdHH& ؃QDQ dRXP3f&("LDLlF"cJQ)!"%&!I$Pb)$̓IhTiDXeHQ!e@bF 2Rc#J2T h2PI0d(iAň(0l"F#EIdH D ,@(B%)cY&4 B&)061Ăh0"hI$2Di"HD34&1"i&fQ&@$c6e!Rh24D4҄Bb11Aa 1Bc$ĐQ Ѣ Rl"DX2QJIf$QC $Ab)R@b$0 @AI b %H҉1QcP&&%@A)Dc`Ѥ̤a%(i&J4 #`$XXLH$E0$ Dd6 "$BA$`XY40Ia&6( D$ņE,$R!c6)CCQ ,i 0Hl`fJ)#0TTa$ɌFƐ2P3$ADm0Bl$("H$))b"BFSad2be%B&d)$Fa 3dl2RA$BTJd$4ZCQQfLF(4`̄ATh$ 2A E31&͑"”EI&4$RI$QM X& e0I(J1$QF%"AIi1h65&a i4H 4PĈIF6R"D Q`LiBQ4X e,a1$4A$$1Аc2,Q@hLѓ$X4 2dF bRlC(Q&i(a$ddD!$ c4I,&`IX2Lb̍2̢$Ȓ1D!2EF0L"$@J3 d&…-Jc M&B32S›$4L*LbiR -&S&d@S X@͛"C4@Ѧ$DE$IBiBHƌ&1FME1H$ ،i3-RI @I0H4*&dL`( ALMɁA1 T4(I0C$f"1J3 Fd`$d#! R)" Ih0@S4@( QDFADjHƆdH4H ET Bh2ɒTBc1d+#4hC $X`60(LHA`# )FK$4,ŒH6R(FAZBD$F DTAJ2`ITȨh2#A$d ( MCdRAFL2i!C6&2HF4&cQ("-i 4Tj4PAT2 1̑4$&" 1"LL`I $@DIKA"!ZЖDSHJhP&(!,&#`!JɌEL&,X@M C L#@%A2,TYT2 3dF6dQhEC4BQ3Fh2C 51Q& SJB)M,d1TؤM1(1$La1CI4H`4d31%Di$Jb & 2)4dQA`FHMQDfD 34Q#H@f04&cPbYMAM(A@$I"ff16@(6J4a) !A(ё1 AR@DMcR@B#2X d!aB 4I(DL4FS "D̡ C&))$,#DL1)( ## DIC-ѳLPj*D"R&fIIʂ2$D) &D%&`L4 MdD3F B6-%&HA2"""Td`& #&&2F4,TL,`DS $eBDhiE3!6L̍I@Ll2ѲDRX(!$e&PIDH&&F K$c0"# #6ƌ$2 H4,2IFɈFFDP%25 &h4,a10)M€ƅ Hm($$A!Q(3IF&6($%"X`(J &id%!E,4H) ,hSE)42M$Rb2QCP4 4I(Mb 22dL0 DTFfD1e(C,L*F& MR%2hca" PŒ"LM(i APdTJF  A B#$2JH$F(BLcLl0 BRR% dI$%%&lLAfA 1ADX"F1)2`$L6!ę)d44i$(FH(a4BRQEL$&QA64RFIH،$&bf04A%C"I#@bRZCHie(f)HZ(A%&B*BL*0LRbP0P̔"Q0&!$"I$2PhX"I,Pld&bDX2&D!d*B0 F&,e)`lIH$,h&B)4b&@& L2Q&$aM $ƉH̆#ɑ#@QAM ))&"4Dddea1EdIF PID 50FJL&HE3d,Elca-"Bi 32 !Y0lQA&"$b432bF"HHc!$Ѣ)#ɤI"!%IX Q, FH4!bD LII$3 &P@"HdLe $"#1j"H% d 3 LRK !&%%*L6c!A*$͙4FLBI& cF,`f$e 2e0" h&D2bidLE0cX h&1"DQL f1$dh fME6bhMI2aK%Ld2B`1&#H"JEJ RQɲ!DRlɍh&Ɍ#S F4dE$Fɣ&,RDQcL&BL &QBab hHADED)PXF &! K,A(@6$œDT&2X@I E$$Db0  MFTcLk#J*2S(2L3"J EdD A(HPaHb$%2*e 1S,$c1A(0c ``54DɊ4M%2&S*,FiHRDbЉ4Lh*Aٚ,*HFSFI0i)&Y,bi$%"aŀ 4Q4DI"dɢ1CcL1LLL(L%%)(БE&J&@ ԌRLlc F$ɄF2YLMH!DƠ2FJ 6 f4!,$,ɒ(ɳ#(cM 4X!I,XE0B" D hBPE($iLQa%H EIE Le 130IZ25$E0hdh`m% $AHfA$dA0h&$!)FKFb1Y, )&HB2B1AX,J$D21%AE,IFK2#J(H2dd cHL̩LeA3`(U AF4ld(HI 3,ȈFMH؈$1LA Pb ś1@,LB  h2$DQ&"HJ6@K#i (d  &dRd#Q4i& !dhELDH (LDEEMJ1&,DILb1 %02P$dH`$6H @P"H&f%"PTD&JH&hjD FB )a2!DjTS0$D612XCac32,H# $&PF #PQF$a"HLi$є`!)( 4R"d"1fHZ(Ą0 (,&ѨɔȣdPXfl(2PDM& Z"HI$4d$1"K`Xd 1وci$I()(A"%I(IRJ0,&JcM01E"HQ@`1 E!IPbCQ$DaCaA`ń I hfbLR i4Q)  ( M&fHI26!L%I( 0I*c df0R&1Ԣd0d0K$ eƔP  $@J@#*4R24IBY,D#!h@, ,XC "JT(ԤZ F"2c%4c1LĢe$2dH16Yh6H6fAI0I lY,d!DhY$0 I! J b+0I"3AR1&#!IEM&34iac!@`c`6D`Ḷ4Hl@H́(00M0beE*H(M( 1!DFM42l D34d4lbcK22&D ! c&A[$PXRLQQ64c0S (JHhhe "XQ ))JҘJb B(FCc%FĢDPR!d CAcJPb A&$2%%0HP cDDi#2`ѤLF! $Ѧ ƍ bCbe$hK1hhDEL42&MFD̊"&`1@K1HBHȤ$fl iI!$ưdRS aI@ C`-bd0bE!,  TSɢiY1 0d&1Dj )hHA2`M5*(S2 ""cbэ,BdJ(fLPFL`ȅ (Rɉ#Q1&YQBXĆR$dLb#2H02"B!$PP(cd"@D%1I2E%2JJf#a "HbdQ$` ,I" H  $J1ԙ0S (XM,3D$̕24liKX0)XLD!%D&Pb#()L $Q#E2Ib3 43aA(ƢBbMDca36flTi&QdD0)B)(*(%62@E5,R`) 2f1RS4$b0#Qf&@ HchBa&F"Q&hb0e &d2Hѱ *Li1Q@M$Ad̢*D3BS`%,H!5dJDBlDcI3$h$!J $l`i)MMII(1F(CdЂA"ah)Ih@S)#1RHY$A&) "Li0S1H&Ab2ba2DA F"ɄؤCM&D 2Y$DDFLb$!)D )ɰH`4AQF#*R"BX) (6H@d`b 2FLd$IQ!IIcdBH̆iDbJ0 DjD3* 1$!&ě0R0lIFJ&m()(b!DDI1&$ !4`P2MBLƂ 2AX F@C b1d! DͱhȲ12($Hj Rfd DLaȂei242 )#"$Q$2"D`2`FDAb$Lf%X%`1HA( 4BF ,EJ ERiBQL &S I`C)d0(`T$I6D(4Ʉ$jTĂP͉!E f!L#AR$Y ̘K"1Qb!(B Dj,`H4`2Ĉf!,RIjI(30,bdX1Be@Ih$D(Ɛ# i4 2h" 4b,DFS",Q%L4i B&DR)Lh Ac LhI"%&$`C(QF3$Je f #F(c#I*$aCFRDF4  A,0XԘK"BFeHL"4M3B S!1f2bL`LTbB!$dњlX!5)F !dPE$D &JYF1 12HL2`ja #h2j4̆Ɗ)b1$ƤAA4#ce #ILBE%4I`Ec&eL3@lf&I"Hc ɐL JIRэf)A)"BL"$K3$VJ2`#E",)&1dيXB2QB$DɊ(#l c3HDjK&c") 62QF2RiAII&(A4(RP`D&1 ihI$Fa#4J DI2TEc0l*4#D$bhL$C ١)PI42AFb1 ($ЊhD$dFMCa D!!h Ё1T23CD$$Q2I@DI!bYD&PM%HBl#!# R Q *B1(FDF" `JRVMI &0fFD"MP0#D,LA2i #B CL0dEd$DiLٚF"5aH$Ĥe!c14d Dhbh2&D   B"ňfR`̈M0!L!!"MBREJQȌ"3")$H&H ,"DE `”HF hTRLS,c #Ah$FR,b22C34Š)  d2@hЅ PH`KFlab ͱ" )@Lы &-DIE2(#)( RTE"E0M, "aF4Qb2b(FMIb3 4a1224@шK%LƃQIID SDQRe J4#!&J&% ! H $#dBFLJ́bK #6 `h Q"3"S 2BX0),AHdAƆ$ɲf!%dDflI2Qe(H4l&0`1,Jh%i3!b"eIA%3dтb&hHɊ 4m $EbE,FBd HhH$ĀI%i"Ac#&f&E6*1I&S1%&,iD(Ѣ  PF!JBP`!#bMEaEcADQF2B`abd&M&L1*db#b#M L!#i!" DlPD̄I"ɓ$L1#Q$Lf6FŐQ2,A&4H& F4fF" L2X&fc`4ёed$I3(((&3&RIc AMQ (13 @A0)1IcF)&B()RdM4$M0e"4Ylc02DMD&) $ A!$B4& CE0ѢL)$R%!Aa)BlIY$ZL4 $$h2ɌFI BFbMFb %$FFذ"DdA DHHQ@L"*#&DQ El$h Iقh$4$HЈD2ы F3$(BI E0HD2F 6Hj1H&, $ 0h%FDSM#4IDD3H"i j!I$A  Ɛ J S"# B2B&Q0QB"M D%3 !%ِ%bRLI,$$$JIEE)2EIIQCR`l,Pрԡ 0@bad(i1E!RPBI"`-De1LALhd!Ȇd2((#1a P %#1Md11aBRRĖB@ j*e(Bi@ň2 bdBLRfhF& %A210A@!"H̒`1M$$R 12j Q)$BDFcAFa(15 dX2`ȚD See$S2LE a3$I h(ԉ! JMb؉[DHLa"F3(T,$ɔD"LhH`R!d DFS #$II H) Pa&J ZM& Dh2`$$F 40B2P#6&""0`P )IF̉(KL@ &ƢiA(HLbс5Lb) & &,baC#cE@C#1!$Ȁ4F"&E )lB#h`h"4&KCM4 14J2%!HhC%II&XR$Ad*HȂPR& J"2AEE66&J@ŃLEDɑbR222%Je0d ddEaŌ6 2"cPeMd@E&&DbR LbD%ELIe0I!F (J`A6$"$DQFĀcb%6H 4% LiPFJ- )hȐD†HD%ƐL@DJ["D2RIRXD,X%RR* iD̛FhF 4$Ả&#Bb,QQ)D1Bb#!)(FM2#DBdfJ$Hi2IbIa-)2M0a"$؈,`ia(! %&Hbc3`&&ōL*A1&ŊKde&#("hH#Hb,Fƃb(F(a4FB3 ,CBhB  3 jD0"I2"M%SibP(I4F6&0$2&HFdJdlI Fő#J%&fD% !2TF("D"LHD d!Lȡa4(,fP!e&BI 6 " Dh"3(c##*id0 "R4d"D (EP0fD%"Ide0ı 2S)D(ɠED RD&4I1 )#)F XE! I d!eLhhEc RddPQF H̄!Y51L "i2BdLb16L1HdĚ3 `ذL$2!bc$bJiC&FQ"LhA"$("XГP$efز&H1J2ILdb ad"h!0D4Q&2AcFFSe"HшE2MXA‘F ld2Di)IhL2FJ ABfELLB5!M 4Qi$1$DL2l(,"R 0 "$lIbB04 d%4Đ d&2&DPD`,S)M14@L14EF̰(ٙ4`1h ldBA,fYBcY b66A2E ",h& BBXc3h FYBF)!Sh6!F$-2  4#2LA4A"$Te2eJHd "2S Q` &MF2F2&)`К#bdB2RB#I K,mL&Qc1, f1TLfbI&LA&$H3,fi0IF!4P `(i$i"HhD$#`( LBI$DPf22ARJX)AR$3Ji2lE 0`eT224E&"H c"%"2aF!P()#Dah)`MD!I c!Af,"6BA EH S!"1& I`%)$%H!HT`ٚ FH`X63 0TT)$Pi$L$Ra%(5$2@2 ŒFla0b61BSI#Fb(MI0ѓBLCA 61FE1&afDd$&e! 4cM4d#!lBXəHĐA 1$I! &a4 iBLXb +M#J2($([3LQbf !E6,I!1$E&1 e 5)Y1 DIH0$JHQ&LIM C1D2&JM 42M&f@1IH6@IdD̘b3H6H%""$؈4LbEbIC2`Eȉ&ę 36$ 1)P$̛%h1f&BI%Jb 43D$2&PBh3@H$Q"QDa SE5@EPd4`0!FX4422S4c%"2X4%! # 0)dIh1QE!Fc6,%"FHFB6BF (J %%$X 0JI0`EiDь1"4"AL,D $b0T%&ILjibJDH2h(E" DFdbfRIiE 6#L 1dH20` ")&lB& %$1ԔQC2SD1@d &LR"EF S(FHŒ2$Q 44  FTReEDDƔ2&36 4EAHd%(im 1$$ؙ$I"ƍH02YcEhifDP%3AIRXBĉIdC&JlB&FcELEHhLRH4lS*&"h Ff3!# (Hi)$F$2S224`%FbQfH!L)I"2L!Q)3(i$1&254(,EhIQD$B dɤRI-Lb4R1RC"DEHibQh0&,K!AiĥdȐbaQ6$ b4cJPf(AR"J(&i(P ,li0%30bAFL1A!!2YDf0 "BZIK(Hm"lf0L2C" bQ TQ)06C&Qc@aLQ!RRf@D`FJ 5 b`J$X2h)Z4Q1($D$M!d0ƁQ`% F11!,EHJE"(#)*2QA2cHʒ@HI1&@dFI,E)LLJ fFM2dS4HhDDC&(DE 0Ƅd BDJK#(ѠhT@hE "f2BRj2I6"3 ILBXb0R!,hʅ %,(ɍ)$$LD"("Rd,Y$& $"b E)$!4LDh%cDI `*!f)2E 1"%$&J 3fIL& fId$3I(M,aBdQ3BiA 4( f6S#&"BāPh F &Ѥэ"ăI6$`KIEFH(2QhX&f1I)4a ,bIJP#F$,3A  2PEF3Mb 4F!% eI! 6$1J"dA 1(DAHXfĐF-&X2jd`K `I",I$LF"CHͤeAbMFL 02aMHd%lȲQDfD$Q1PR0M$`01R Xh"k(DD1ddDcE)2FS(L`L fS&h2Lf6BBdb)`P " @͆d0F&Z daBhDhc$a#ēI4i34i4cHC"BSILؙRShdB#&C3* 4(ca(2FH̕12&IlE&LJIEB"0J1bh@&HF&$d ,"A&h 6MF ,E(M10f,%E QRQȉƚIIS30M1BXTȣBCF"LيhP11!cF3DQd4$fdJHL4I€`L$$Q(!$"KhY#IQ 12Fb2(1$ĥ*$IA# %@MD& 2bJ LQD3h4$iBRcF0&BؑJ$&2(BTIhL1ca0cS ,$1S#AE0 32 dș XтJBD"ĉ#@hfiɤ,DFe#f0$d4FD $d2 2 AKB"JbC)%$#B%Rd Y#M1EMJc KJM"B$d" J&F(!2f6f@2Б42A"Y#hI)1!"(% ! $3"D4m$b!TR2"&™RQ QѐL1*@K)D3 0( H( (X%*#HIRDfd$(1EBQL$A2HHE""J )F&-1IXHH& lAI4LДjY ),IfE"""F#F$1$bIf,&ER(l1!0dI$`Y,ƒb +("2H$h#RB%"Xh1h͂ɍQDcd B2B )1X1I" `I@TL) F&Bdfd$A"BѠ1 P)!$)1"A%`$& IfD& %c4h@SJH (l1aF&QDF0&fD Bh0a@2c1HF1@lL)*Dl& $ 52eIXQ"&a$b#H bdIDbk4hfI"R 6,&( (eԔ&fI%B4 A&3H%(ɋ$$H0  4&H f#&d Б1F a @f! ЌƂHH4ȑae !DL#Ib4!LIIR($Ye2DLR2"YIJ, Ii0$Y6$DIJYJZ$0H)(L# ȘcE6DaDD ȉ,QE56#FQ,J%+Iأd3#LIdD$&(J $iE2BB3fHD(lbM h0F`&$1@cff`A( MI  I0DHc))! Q3 6DHLb)(l2MALf(H B% 111&eEC,d`b$M  !ifЁi6h "ɅS6I A"hĒ3)$DQEH&f4BlDP2b`1lR L4ZB1IfB5IJD#$RdhJBQID  !&  L"Mb(DDLA) &RRP01"dRHdl I1XB(@̙dbm4!@ &2LQ X"R2)S!BYbPّE4"- `fDJ`Z1&1dI2JCI4Bdh1X bD#4ʼn$DdY$HI!fdI@, A)E$,Dcbm% `62"Q dҊLBI!4D1AH31 32A J)$XR"AdPI30*0@ 0JMRc 3$% F ADbHjFJ%2H"f4dP J  )$K0)0FHMd2 10lR!*,E%ET a1d"$( 1cF$1&AJfTb&(6( F2"L`B1&A ll$L)(P1$M2mbfRhF4Q"a@D(%I!HXc2#IHBFdBĒ,BQ4"&#HI0QFdR0$$̍,I!$ H1db3"20L3Dɣ&,FfeI)FIAFXHF%#13@D"0(*CL"X̡ fL()X"4C3A6HLDHlMٌ"HLF,a`#`Bfaf#%!&2I#fjBfQB F(4l S ţfAAQE$dQJE3h@()"beј̀"Z"&ZK@ EDELFE!ch2*P)L(("`bFf -&R4H, $P$ `Ё!@L"Lm0P%f lb)(5%f6LԘ"H0!iIK&2BJc@ILA!RB2)a4̙*Q0KhX)MDd+$QiFر" cP%%db4h062F64b `QE)(B * 1FҖ"cd$I1D4ȐLD4*K`$"ň$1"&Mhѱ4 H"IQAFP ,0ԥ0ѨEJFfAP@D  R4"Ě("2Ij$e#J 3H d b6fȆ)dMd Y$h I@e)P&QA !ĖXBC&F3Fh"&()1J# @D`1V0XYB4ȓ2!!cd 40 RiC- hHY600Ȗf, %M3()"3D`HE%(fFm !`&5(L$dAPd&P0#@B,f, $Ȅ$"2I4Hh"1`0̰ &̤IIDRe)1"(iIH,LL%J14BIJA22Dh)CQH2I,!H2" ! `)L% JfDƢ12P`L2S1# iJbdA@b"2Y$H!RLDFTRJ&A1#) 02@DlTAFcIh4 i4fFa&Le"6 "H$Be1d BdLA Z0edc(!PAe5!2R#JiF(E%16 FDj D HI$*hSR $fPfe!$3"LH3" 1DEJdJcLYDZ(F61@0Pd3ȡ 4QS$f#̋(Ɠ&c,F؃E1YIQ @h,IDC4DPHbkLVP2Z1"i"IB4#f( LbLQLD4XCHXI* DMb@FD&LbɱbC2Qe,HfHJR1Q2D1bH L HE 2FM2@f3L4 4#2ʒLje0dfHRf5 ` 11a1$`4 Lh(c 1F0` FA&$DFŒ$dA$e,c ̳,iJ0h0fĦ3#XfD6 I&lRD$hf B4R`ġ3$ add#3FJK PLD"ɤ4m&RM2 S13(T& Ȉ 1(&,fAYDJ EbRDj1BlH2 $ @$IHd $Hd4,03RI$))`cQ%!- &4@F#HhP! i) @4X-)E&IH3&hdʼn"lJdC&0!1  H `dd!hbd#HI H$hIPI &Y#4R&$Le4(&fb!LR$13"l$ 02FHJfқ!d4ȲFC&bJ3#DR1LbQC2QF41cEIE"aA!)&*M "ɂ43 RbDQhH2!%% )Di Cb Bd (fdQ 2KFdXadHC22fF RBD@@f̒" E& J1I%4"&A 4,b@ɉ-0A)2Ll -,b 1L F&h4d2I b hRF`11RI ,eH* hL10, A6(H&3,` &a )0 $ɢM$&LblD#hFkb2! X iMƙ"i`E2*H`MFM0Rh" 1D2F!IYF B4A3ahȐdIDЁF1Q&f0bD`(hĆ D&H#(H"2R(I(2E 2CI%@RFCFI Ii $dȄ&h2cj$%ɢ2L4M"A0IAd0Q(14$4IhSfIc!fJjFM% fBeEBdca4 C&4ddB cF061c2!h$l,i &" I` b@& ɒIB R%(i#!f1 "eJ P$P$̌FLlʄ(c&)Ba&Rb5&aA4"IHH1L1Di$AĆHFX0(I#Y`XLD1* h(#RXB(0 &DbF&#@H%FĂI"MJQ  1b $,0A@`A$& " j,‰%#(F1F"LF(L$" #(L,Z(0PJI фDEfB)BdM"a( F5`H@l4bX0%22IIH4* (fC$fLċ) PLD1"cLfK!#%QDE2 10I2Q`BC"24(4 $HI2DI JH̛L Df4 Q2)XI04, @LDёRHiHDb,R))#,XcaHV5 QFI"2S6)0h"1#Q$ҙ1ILRP4@I#2Q!10Q3b#D! ,K& 5iHDdX ),hɊ"(HƉ( cPPc` &a2cb"#i51" )0`" R $bAD#&BdL#JdLID$YD1Ye4RE6FFh3!@Qd"$̄LADb&`I!J$L aHFIRII(!i,Y0F2hA0Lb0L(ɠ1D 62R"&)fFf$d(( ) 42c"c4X$4m1P))()@4Fc%D3)(h"62I d2c$iTLƍ(,f4`"R#I d2X"4Dl@dD&d A)&PX#E!J)$ISf(Ș3 `d Є)L Ld$L&T6U$2Qa2iRF, b"(!Kh@f0LL,&"a$RHY2bHlŘ$DLM%%2PJdLѱ$Ԛ"HH̤ I(@(ARbE% H"Q0&"cFEB`L35(BI( +$MDRFE`B!dLLe2RdldHc Faa)16I$iL D2i(LddQB&)(C`PH`M A1@̘"J! HC(HjME6(Hb2f&HYf DIɂJ`hBF(,,h4lX,E M)@ɚlSLfX$̀HEE1IdMLBQ&LXCH@LLl#"64RiIQ"ّ4RFI"MEL(D,F122h aL#4d F@BTHc%$LjDЙE5 Bdd "̂d  L$# PE"$#f,I&Q!4fa)6Lh))C&&"`6 ęHD`0cLMf%&$YD 3Ld"lb2(C#dfj1$EĄ%Ć2di%b2,#BBb !a4I0#@ɢH ,(&b  dB &dE3d(I6BC0##Lf`e%!Y2hd4bƱQI&YHPY%d1`,@Jf%j+$f IPh C"@D2LA&f$IdPa(Šь" X$"Љ 203HE ( l!$FHbF1HRm0H,A0IDb0!& #2242cQK2ȁ@)6b!0 B(dc4 ,d#cdI DE$I (Z"D""Jb!6Q5RIB R"LI$Li("a(eDTC$,YHH3 JH IIFHXChdZL#cFLFDԂX(ifa1hF4 IHFi FM   ai4Hb ɦT"Y4L cI!IDF) ḥ%[cE%2a(@ȉ$52B&bh c!Iԑ$%@"Li-FѢJ$D6AIAf1D B0H‘Z-D,II($ cE"C 2Ħ̑(0f!F"R3FQB0#LZM2bɦEQA( RCH22"""$I4l"caDшc2 `411c`@$Z$a"fDl"1X@Q1h`$02@RQQ% C$d*&jRm$TAƂ$E`H$6 ف@,V J@ $1%&*F412)#IDA L, QiPL *4aL` $EXPc(4iQQ!E- R)J14$"")`0l&FP0Ƀ&b$,$fHRRE@)0S,HXd  X DQl0IF IHdIP`JlDh3$$4X"L40YLh#$h 1%mI2$,&61$b1 Y!,hHPJ#3hh TD d$K4 %F! Y2JFb$c(B Y1A&$" "i0C#d$)IŒ1F%1QD 1,ɂ0R$bE ɱ0X1 M"TE%$̓BmF 2a1 %)"0RF&41Bc#D)1&J$ 2 0$D,h$20FL`d##& Lɣ%CE)) I4662h(M$ $R%0$$"b1$&Q(LHKDb%1@(SF%"`(M Hĕ(dMI A a 6L$̤6dRf`Di"lJF(ɂ( &34m)&Z"YE2 A)2f$d̢BQflI`&XY(% 1ABY#( 1b"&YE0DA2 ! C$44b Ah1i61E3$5FLC$$b$QLD$&ŒL&CS6Y `e&K 4d4FҘ1H$$` B00h1F4ƤĚJB#,(!AFR)FBJ$&XɈ$B3J`"Dȃ#A( ͘% 3L64&H14lA",2 A(ffaDMdѦIcI C6%$@ А ID̕ IbC%$Qc&E,F*R 1H"#"" ! JfEL 5"6*,bE -&&V bl `Q(Ih0dX4QF)F3$Ff%& AdJ(HIY Fɦl4 4l e(LFE& LXd6h2  БH2 6L( LY0 ƈ &QEf LD$)"4F41Cd2L%0(H ٖD&̍$IfJF2A1$HJIII4$Q LdIK&JJ HTPI4cEc4EeFPfI( h&3%E1 $&$R0XI a1E@A6%6 L,22P122J*B0$LhĤH0I0L$h0% "S ,QCbdŌl#6-[3JP`*X!,"HMDi"4064- )0F&aPdA% $i%B"(4LH` QK)d)`I1d"B,F3, d!Lb$)`Ab(RL c,$bD2`dhPh$BbHjadF4c Fb XJM&KE̋ J)4 Fы!f#KdQS*&L&2$$i (BLf@ccI J4,4LId%%BbTdقddI `l2a1DZ(Le% l0B(E m0A1E$V &FPƒD $BDi3 BbL$d*M1Q h1I"Ș$E)" F14(҃C)`ƍA")&$&@S-3L"$Hd4d,hIJĈ`)  M1 !1̓0 f&3",$$h$Fi"PLDbؒMe#YI#$@$eI4$ʉ)H@bXS ̥*4 L 6-ؙD AbPA@ bhBĊѓFD"Li,Q$Q1LYhC)PD3a&L@ȓF@bd%@)LL PfDX!C*4`Q J"I1"QB$6i!1HQ%Ie6(IJ$D&c$d$Jhb)`A 5%! IPi(ą1L@e RP 6 )4Rȋ"@d$I!%H"0) C%&)LbL*fPFa#M%aafAD,$Q٤if h ʙ`XEF DIfDDQHʀH2$c$$ ɈE#)4d,P1,L22b2l3Bc4Qc"Rd@d6hCM1I% !0HBe3%1JdiH&Ha &12B!(ĂD (bi FA$PlXR1PHJ2cIL4f1fQ"iU3$P2ə(hƍ#(HPhJd3 J,c#"ƐMABDѣ!Lf"dli QJC"04L,"(b 2PS4%2C(Q&b dҚMIZ#FH@ʌ(0hdBFIHH"( EL&hf"4D 0i1LɱF12ƆB)%2$XB# Ebf) XQ&Jc!3 2EBEI,l DM"J"ID4R FIEh5 &%( "F1 X2a1HIEF$,Q)52dc&(Af$6Ma)M dF1Dh@`4BXS$H Afd$A$JDLf)&))baA$P"RI$(RfDDXa(c&ɉ0)"h% Q$P̑0$Q A6D6"ĢPHHK&ch&EF)"acFh` BIADbFB1F1R#(LQfI(CDT%44AY)Mf0LT i0ddlM MBMCbЍI$-d,4F hLAM#& LF M( df L@ic` IR IF2$F&I %BD&4J5 B6LI 1% &36(HJI4i !SJLF $&DDhXL1)DH%A4d cI!@`$ & ҔlRQL(*,c!dB#"FH3MJ4  I4XdJllɱ!(¡6"(!2&)e1 4Mh1 HA bIFdi,V f$ 3A!LIb@b aBQ)LRB)Q&1ZDd3؄)2f2 B5MII31#H £FK"d21 $ɑ 4Y4J,jĴi 2iP"d1JQ5)L!E2 6 1(fQD0%4bRIfb@214dZ!RЖRYDi2 1&L" @ ȅ"L14 C 1FQ&M1`Q0Ƀ,H (IHFI ALH$""Ȗ ((IA2h!)AHcBf3Da# @ @M(I cIHhQ$H(fd$(#4MS#DF04F(fb3 3"C$$ e IILALJ"2lRDh1")dblA"C DR&fF!i0bDК"M@Ri0TP3"@`hآ2L) 1L@f3fȄFX0(dX؄hbQ#C"!" bK"fŒ&03&E ̒R)&*D%!$T$1dM&#Bf (d4Re M$dR$6- IQ#1@Ib1EI2"fQ"AD&HBAId& P&EHfd fllL(`&MQ@h,fPR-F &b)234F4h(1HDFTc !d4d&Q3P% ̑dFBf5$!DFLX!5LA!H I L,DdZ(4&1$ EQLQD "( 2IM0`BLƚd4,1 )fPDͣ45"4Rb`ā %&lh)"%a)4f4DP)2JF1(D, h)&*YL)XM"1m!1 E )60Y60l0Dh"#Fa)3H14!cd0),2*H 6LQfS20-4J!CeHFi!"f(1+ jH 1%%F aS"RLɂP@@a01JE @ !63"2`dل&F "F&D&$D,41)F`ƒ1@Q( dRd(!e4 Dm DDI!%"HI,DQIF`4$d4Eb " c` `eAJBbi(Ihƙhd1$QF Q 2Y "4D@&dhʁIcF IF&l&D1(JD XF*HPDjE(i$JL1hQM L$I$$e0DIA&ȂM #2M H1$d`0&i22T PH!I,fJf#EBlflf ReILBQ@3d"@S)d)HDJHF,3("HJ`цY$Rc"P" IiH#YQe1C"ĘF,f"" h@46 J&L 34X$*4cHhh6e"! ) bHQH`1)3&F*("HQ 0c)QDh I2ifQ$i Jla(f# 1$  )2Y P@AB2dilI(HAId̤ 2 AIHI)3 $4LY 2(R%DL @"i@d$(P hI&ifFDQ$I !T!%& ` dI&bb$у $`2j )c@D€Liad!$$d`(2MI`2@0$BIA HȨEM$I"F2H "ɍ`ffDEĆ32&"C ,ЂBaKcIhbi6PF`шL4l& "-F`PP1((QH &&–KX lQPI) 1B4ĐQ&!AI0JE$(#)("hƒIba`#&2X)1Ldh(E2b(cB) Dőb$A ERђ 6Ƣ1c)̓I (bLC &"bA&5 dƌ2&)HDE 3a4 &%$,d`0ģ!RH&LIdLELTQPECJ&&BQFTHŊ1&I5Hc LE$j)2 B0bQM%I2F2D$M6A M)&A(FLcDS0F0DXęIA&M,F$ c``b(A ŃY)@"&QFZCCHԙ!#XRDIL La 3)  1FE$ XT eF2Q &Di-̅&i()1H2RDiL),dcI %LF`i$JA ( M #@ȠDRE2FH҂H4̒1*iAL4 A1&HBQE(L& `H !Ȥ4,2&e`ȘLb hBB%e&43i( 4ZDDԐQhИY4DH$Q22&d6$He24$  Dbb@PQb1ٔa"2!bA 1$Hb 0$(&)@XF!&YhIhԤ1EFd 5$[3%DCH)iMD1I DDdD)!RJfHIH"2lF ̖PiL)B15IF  CM"&HLD3QDHQ4J2h"$$0(F)$Fd͛bh"! )2K)+%eYE D4T%"f$04)f( dFBMђH@2(aAL(I`2̉2hIHѰd2`6Bf3(&lL2La2h((3*e0RBdIRH&$*1c3fY04FLb HHCIFh4(LRVLQ FI Q!2idPJY4cS6DQD E%`DĢdC0!4lٔ"2Rc114C&[AIEDFX"DlId 405$2,XHid2 i)%bR$,0,&)-"L &ě &R2&J"20ЍBő&Tj$eRPbH(B`a# "hHK24$I4+K&X($ 4Jci6%1ASa#$5RLiHL#F4fca4Hem(ɒbQFA0dd(C1%&F`,D2J)X ELDDQA3D$CLdb &LF4dAc!&Q&"0DK42eHcBI1)4hE@- F%&B%D0!%I`5II(!dDHX DARPII(bFI!0BbL)0-1E3%fYh@"&D2a SIB BD)R$CE D0(HB L j$I4&@#H&cDe1$b, Ed"2`L 2La 2BXM)%(L͉"$1 HJa$EQ1B(1!% C2LfIH ""$hdILbTAFM4DQđA2))(0"DI4IXŒ1 0`(f#d Ri22RaQI)#bS6Fc@F(2i"6F64&J$D`-14hP&!*((dƂS$hdQ"PeBR@2#F#ȦQF"I1)FRI"BIhi!@#&HLP2*RD##4#FRIȘ&E@Q1`S,LE$41LhcHhƈIdY 1ABd M&L,(i"22XiA0bLԑ I,B&#I`)Hb, &13 3XFLA$h@X#!$ ld S 23A`i)0LD #1AJI "Idb6,hB#"E "HIIER(`A&&iIɰ!d@JBQ2"Ad mC(2H Q DE B@"Z"D0hIe2Hb@$R(CFS`$(F( $I%DRC%4A fdfIlIA0IB2RX3I@$$MBEm!$ؒH Kf& LABJl% 31Lfa4)Lb$RB$"&YML&)5IALD2#H0"dhD$ɉ1RA"D+3R$1 %(S"E$2Ic2J@F#"L41@ɂe   BL$i#LbPBc$%%)h2" 22&a&bD X4(щ1P"Q0,&H$@(0IF)I Ɋ14E0 D"eIBMb S % M0I) JC(3 EL $&D5C ,ɑHF&bA 6FQai cD $I$l"aDIHd4Y#B&0"BLecb̐SBfIPPD6" D1BRQ!DBL2ɈQ0 LE1$0)" 2&M%$HI-$6A2X@d E&  @F E$d 3F$f%B1IcQ`2)%!$a&Jf4ȍ#(K-"!3LM1FHD JSXLF3#FH R"3M4 KII&a3XF̔̄eIBR$)"& 4 IFYJ!(HɱI1B4M4#H5jAAF DH)(f@&&`(1@̐Q&H b0bDf$Q&QȀB#3("#1XT2RRS#cE0X3Bh) X 4Z"Q4FLḌBQ 1 L5&C$͓LdQH&S621$),HPMcFi&&a*#AH2č1@f3#2(Q2 HDa52M ȐXTH"6Ɓ 3#d$(E%KI(LL"KHT"T@ JD1C(e 2a )B2Ca2RDYL! 2(21L,&f$$RPdRHĊlȣfHH0F 3$"  K%DF@&R6"4i A1I 4aFH&b& &H,b1&B41(e ʼn"(fI&HD&QF"A"&E262RC*D0a4F  (E" 0 MFFAB1D$ 1fF1"bR!H)0Ѡ")cBHAL"FE(dDM,IPBbL Lؘd()1$h&`Ab hJ#Ji4@(&Q%fbBѱbM0ŌfC4"M"0h"P (1Ѵ$Ҍd)!#$ (HC$XBR0HlCi $ C HC3L&JP,JhD*AFHFD3DD@ 14"PF4` IEA0$BDQf R""-hA0FM$DD `IId2 B I@LBPF$FL$L"4)IFҐjHCHF,,R"&ʘRL$ Ƙfd-bY J$JDH@I0Y&bS#B%&$1 #Bhl422DEA0F%1!FK&LF3 R!IDł`PTM&YJ4d$& 3" 130F(0@ifL!V)$#D[!$#LQL%&D"$i% )* &HRDD S)5hb4&b$dJ#RHa"ƆD)2&L$ID$CE0F)$Ll$RB h dȉB4̌R%,L!I!"cbHhF )0&@L,č*)501Ő 2̤FD (%% Cb@2BaT` cLT4 )0H 5e#L2B2L3f)BII! "RIY(R@ !1hb)$)((RR BHbAIDB 22i A 0DfJ`&TbFBFIɰHC(E Ф1!(& E$D !L1@X$ I&2bAI @!a I dM2!Cd0 &dɄHP,MJRd 1$&%)LS$@g/Kk[[iFw(mbR 0HIU'"/,(+$k'\$Ž\1?H`",9 SFF@ɐP4 OU*@*M 0Phs1m1!fc% hHc2HJccIJeH0$ј$`Q"`B& MMiLLҙ6`Ԋi1aHLFi$Ē3I#$ $IAfF%RLJ(̈ 2!J !R1e" bYBF@A4Li!J&a!dS$2D)!dā IęR5ҙL͌ -` 1! )LbBFRD$a1&,HH HiAF cHQLIb#Bd" I D1HL2IIRHRHiFIQ(M2d0Jc4EJD,LHɂ`#" Fi42Di(22BbdDfBHXK22B2fC1c3@S&l&f1&FP!F HbR$b $ d$d$K")dH%J%DD$&i "ALd)He 0E$cM&0LI S$&2 A1 D1LFB#f!&JdFXbD6$4d#($D *I)A H(1AI$d$dC(4LQ 3$ 1̔Ј$#0i2I HC2(DB(dPf$X)JL"E0I !$K3A e*4`dHH4H(%$IB,2IQ0R @2R II!DʘC04 dfBM $,% M0A "F!̊S1 2&"d4)d)LM &I%,HF"E 1 IHiL(HH"3̓D%0I(,IK2L AY6aJ Q ̈6J ĢHD%!F dLFˆ)0ј$ RJ )d1 &HX1 2fHXd)$E,LlYd"Y36li4bI$ %4"a  !!1 "dfYaI43Lf)f)L$ICH @iC"ALaC&DEI@A4AK`dA(F05"A&4@d&S"CIA$LɄ"H$0Pɱ(JMC)$&RHH`FM1 1d#a3Q4Jd4 2QFa2Bș!(R24$Xْ2$12PBa&&fbHd@Ra$dM"2Rc LR DI))1&$ 4&ԩ%!L&јAA&,QFH1D @F(1I&QŤ&RJI&@a1A"2h4(A#(h2!JR%,"")( X,L$Ȓ R H d$ƔI!)2d4Ade$ %$HƐfB L0iBh ̑BLDL(1LBbYHd`P(Ś#) D(`)BňL,@dI#(dĚ)$M#EDH%,b1)Y#dB12bf2̔,3"E1Bbhd! 2Pc A(#H$6#db F&d1 4LɅaDҘ3)b@0Hd4("FL$BFd&h(D%fdJ)&%%0,fL$f 0ȤBIJBd#2$&Y( L I0cLFe6 , !2b%2&e$dDI@ ) )d ,I  $`dIS 1H(̈iI$MBM$L(P$(Ƞh DM H1L$HY,A2 &a$)%" 40f% %()BE!0 &LYE2iDc!Af!),K 1 ’3"I1&i!KIHM2` "%&dc!LJ2"B d3#"0H(Q$&faHi%401)E&!lĒ @A !HL&RDH1$L2 R@)Ɋd$E% H͖ CfLi0@Hh,$dYh332d3Dcd" i e&Yc!!I&!dIQ4QLYJLcHa2B%H34&hƈIF SILDHJ JHșLJLe $Fȍ@Fe!C*(1K"F(ؓh$FX!&0 D &J&`fFJ(Q3`( Q6LDb,a 0FP̐ $A(4$H$ DM$ؚ &Hٰ -QL͐1I2M ,Q&KJ 0(DB#0"iD$ 2 Vi&$,&bM2&c&Ii)FfD1 "&cf$Mha4@RH12I6d&$*A&#(LL4$Y2PdBE"H!Fd iDM!)Ih ,bM1B1D&HY4B%&h `"32F4(M)#S"hCJ,a,$I cd )1fbd"iDd1  $ĒBȘSdC@"$L"JP$d`$J2d0&$,@MM0C)@SDe)%#&ML"I4$l,bRJPI DD!I Ғ$a)QFR R$c`JbRhF6D2Pґ3&b6((d1D( i"hHiiB&&HXɂ4"4d$`"%2 $b3,&RJh(#I!4M H&RJ"b1FMB % 1!4͌D@$TL#MDBBR aH D1,$"`M2L*@"ʼn$i"$ɐhJaDє ib!!fbH1, c$))#)D)C42"ɡ  &h&HJ !D&eFSJ3,"E a"dI(fLF F#%4, $0$%(I)LJ#$BD&AHi& LPJ%H2hi$"L0@I1"M)cL$i`4fi3C$cFfbA`!P"C1$ @DBdRa$*,LLD# D$X@fLL@`HDi$Bd&2XI$e!h($ 3"#"dBb)-”!2K!C54d@&D!$2bS)2Y Y J 3% R"$J f!I$ذ,JdFS!ALI %$bHR1dM!`ZPe02"aA A@Dɲ0L11M%12DP4PB$lJ4$@̡ @P4 @M&ac2BQ0I"L dPfFS Ē"I 4B` I))B SLe(̊ cFXE3&,R`ƀĆdHP14RP̉fi"™#d@c2B(Ĉ*@R2I3&Pц"ILXd$RɈ͉ 0b)%#$fdH1HII3CD2)J DL4I2PLL #%0Ј@"53(DE(b(i T@Q@ !I$bDbL4!"JbP,4 4I0HdD#,@lbi!2L)13%34 0bSDɔ,В3 4E 4$d0$dB$E4c$ibL$$$$*L!la)da &P10FA"R$%C0I$ ")C IH!b @Fd&I)2iC1JFXL 0(EBF$ 4 i2L0I2dbƌfQL LЉFL2S !0$)BED҆)5шa#` D6T$0Ѳ3 DLI&ĦdPL)IF 52R,(QAТa I 0b#$I &1)BLBL)#dČ,de (4"SB*F((DfSb2!RE $&X JL2@1PّA @f2Yf! R 3&LJ1I"h@Li#B"fa J2Ca@FRF I$cMB)0!PhD12(h$B, JM10$ 4PJ&EiF!HHP($23!2bI3&HJ&1%( ę @Б1ZRH(I L@L3JaC4A(Ȕ"T&I&DQI!&,BS`@bH0c@e,%%$Fh"$#C2Y"$R2bh! L 3@4 1Kh!" 4RfC"I)FD#f,H͍&Li Fh)"J$bIHQc&YiHcP1&  L!ǎ%JLBDJP1"FADE *(ȉ̦fQ`S DD0Ĉ&`İC aSDQJ#1HHC#3$J&F !dE!")(I&(f& !ALĘH(&HI0d&E1I2ddi1bZ!H"L@AFC 1LHɔɕ,6l&X@$DК$& BE T‘2Hd(i B$dhHj,MBSHb1J2f$$h1C$Le!Dъ"1"I L1$b$2LI#aJ"JChC ę3"i !3$bC0E)(QY3!&I 2bb jc)fP@I2hb"0& LI I)&HF6&(1$3($A&1 !hEHE22Ԗ20""F&,2DYHC0@ҙD$d@(Ri1(@I!&d$($)1(I)l4%%,e %L1L #(1(͘cJ(&Y ɛ10d$ RPdbCBR(0bBH 4L21Cc3dQ3L D`"%(̌1(iL $PDIa)0*%A3L`Dh36$#6@d&01!2i&i "ef%&0() SBH& 2RQ$!@&JD#$$ $E (P)(!  1"b A$ha f$AI2!)!L F"hP$@1bc`4!LS0BSK,HIb6IH%"P(ȨalDLI1 ) C$hF@dbLȍ1 Rld@F$ d M4f, ML &@cH Ф l*%ؔX 3", L@Lb(J 20&"(I LDFa4`jBR" RH2bb i&lb &e0dcHИRH) iDe)$e( iIM4EI ICFae(ha M!0`Pa$J4(E`%!$dDXL#Bd)d0F,0&*b&0HE  &3)45"Xf!$Б514  &&SdBB"&"JPI$#() $P* E3! b!!M2$$F) D,̒$3"2,1RfB)1͈ʀ@ 3FƓfhaRidLJ0I&E(Lđ J3 ` " !a0e$dI`CB) M4 !ED" 1(0LDK"Y%&3$bl 4c&)*`(1FD`2)AB&Y0PT"CDa)3)4JQdH3-33Xa0(`H"̠H4#L"iC2i!S&h Ѱ) Ȥ˜DA 0MJE cR@f0$ D&F#bB( )1i03!I 3edI d32‹e0CD&PX$aBbID 3*fffM )$% QHM&`D)RdBI ,3(fAS!"H `E %0IY İb&aa$RB 2Pm  !2JĒI)d& H̓()4 `L&S"S!H%"0P(j" PXRe&DfI&B@aIن̑iJBfD&FBEJ1MM&JF 0&*"D) d)BL0$i L14`@`&$ 21MRPJIb"  aXQbH&D F(dd%%"P$2J4LR&@2D 2DD0c*Yb$J 4#$DLP` LiL4XɌSEIK0QH"% $b!!PPH$Q($efIe H"Y#&a2LZ6P( Bf)DPei0 RLF4Q!ē4 $%a$0B(R0RfBR(0HDB$Ą%M!(ј F 1)$d I(2RIfFFe2"0dj" F (`e&L1$P3HBfb(4&( L A20DI"iK"4 dȌ$$ &ńH2bI#JK4LƂ@`"HI!,LaH)HLA(lfH&1M$Q$! $M2!!a i"&LǍaEHi@f"EFDY fEc4ffP` Li ,Y*@h$0&H)2&ŃQ͘$FS (h&D&BJ&((A0b1)1HL͚B@`J$c1%%3 44&(,4Qe%3c&LRb PLJ$B C2h0BH P(  4D 124!2bD4RJCX2$FȔ`fc"jdDB 40@HdLl"BM$H6 BY CEHX@Ā@hc1 E&C$BY1& *4mJi6 S2bM2# "lHE#( ( Q!0 T@"mI0RF ٦T")JR1QI Dh)F0$MF1H"A#"R ,`edL@X) !(f6d dFR X D( Ba&M$a2H`˜cLPdd&`Qdc!ALa)D$iI4,&1Mhi 1%(dRCBŒD12hMa"d4 H L D($2"a$`FDѠ)$!&$%#FB$d $Q!E eIS %,)(R) ,I% 2HPS !@P̣(b1 لcQH0!YI$&Db&C3(h ‘2i4f&iDHAa$J2& a4 6LdA@F) !H L%$I2١Id@2 +&)I)b4 l 13# I% Q $6a H@Q DBJHB&Q(dQ#I ,@A&T(4 RF2 L!M a2"@ (@$&"aA0$ A2"DI1"e& A!(d!3I"dČId e223E(FI`32%Pd&FM 2FHaDd1dIDH1((PB@@Ą"h fb̦b!&3$ P24a&@$ȓd$cEd",aRH )A**H"d)Hf0fJIFBa !I"D$R0d,Ŕ1) &ƘI3L (#I &C3 DL,J1Ȍ2 Y2$QD!"d@d` FI1LC)L̒ɐDRMLDc22e4!RYIb!H FD)M00 %(X4JI 1 #&)(BH JFdÌHBfQ$I2A$dBb#d3E)BR!JLS%b,H$P2!d "1S $ ai&()0LfD$,*iBL$PIFD4Ѥ)4S#4"RJL-"m1(B2@,0 a%%b h 4LXI"I"L!Hɍca,P1 В A  Ji4FIh Hd#0DLQ2` P FS1cL4 `H)4$`L$""!)D%MLD%!XJ1& Ha! H $L"  LHD$0ȃDcbP0`&QHHK&0"H#Mh))fF@P i1E"LD!4&%H DD$1,IPLDD#)2D(@J`de2R1 LRPLcJ#&D) %+C0$ģ2!B0X)3"( 0DѢf$ɀb6d4̑I(Ɖ%IDf HE$@1&D,d2 MHPIbF6 d0#2HI!cA6)$HjBDDe2  3%A"EILFIdHb!HLF1!Df$&f*K 3"% D3@JAI &E,ȔLAFL4iILffS HC(fLJ A@b&J1a)!M$JX1DCL$@Ț$CKJ $&%11(L0! @H(hb1d$HBa&$DJaAQH`,(3#%L)0 f QD(a1J2$ bHERA3! F 1bDІ$4Q44  &bdP@&`A$BDR A,HI flh"`FHHhh46b2JX)DhbBL2(SFHƒcI,b&14! H43J4FIF` %&AJF 1#L& €%Bf6%  J$ QE"I@IPɓL 4f11 H4b`h1H IL`D#$$Rb(B$c4e 4 bI2#H00%5$%Q4iLfiE$D&R̙ $d)2X%$)2bb Р3"32bTɔєcH40a MI4DMEe!aK hC#%"C%"M$ЉF$d!%3$$ЩbĚ#`D& F!&ɲ"HFL(HiL&@% г f"JLLf  2!E` c)a3F1$ ddBA"fP"a 4II FID 1`! 3K03dHMTBC$0bd%$`SC3Q 0c2 32I$M3d4"2JeRi%bd0$B2(̦E!2bH(!fD4%HM$fAfbF1i L L2ɡ!0e%Fb)"$ %lX I01d6!&LFIRk)@ $XHFd`R#R4A)FL# $E DFc"1 50)E 2hS@L S2cFY466BRH(I@D%2H 2"I%3ac10͙d4a Hc bBD‚&&%1)dA)F I,DLF( &R"2hh#4A333!J$`%H2D@4!11$@I"L(A L"1)F""($PQIJ%2B X DcLbd!0C21F (BA̦ɌJJQ)d#1%@i2RHH&JM$AEY$ 24#LJ2B B4%2h4"P6I$DCILBah̘ 12MF("D&0 b!0ai4e#@ aƘFlB`d3$ (HCM@h "(3d1&DJ ʒ̓ I!! &"2d3$(@h $"&aFC 3$Y ,#4@R1Ib%($"ـ,ʍ" D&1L LD h@I!3&d FE2aLdc##4hHɑ,e T $fh,ɔ& DM D4A$h2`d" ɠ,h4&#dJdJaf&4Ȧd"PPlLD `SFa K% De1)KёAEY$d dd !0h2 `b0J$bD&(ш22"a@f3&cA$"1a)4,JX3 D3(DD&"B"0RI&@bH21 !% )@@T4$2JM&(YiM" XĘ@H BLBe(hIH$P&&R"LFM"A,L2(54XAH3 ,Db͚JaF$ dJ2Ҁ !3&IMbM%ؓ#0!E @)L2JP)JIbL0SFbi"D2(HA(@bIQ$"`be4fh)#4i(d4F#LL#eQI$DB̑fZ@i2iie14ҙB@M46M3")i$114 j D4M& fi"`PdXLBK)Ȑ H%P!%Ff$1(M Đ(HS66 $2#ILQMBƒ@XFB!MD)@# 4Rb„%E!c3@24@Q4iJY"( LD)0$2c "l,J`PњQ0e YFJ 3FddD a(!L &H!f`E І1F)!%̉D" $LDY !Bh (4,1HEA!D̋ d Mh(!hFXH(dIC&hf$Id&i )#3MCd(`b,M@Li "$fHR "hDPP`ddd2L$ 4Q)Q) Y04 !2(͉HXęA)bH$قDBȖ $$E$AIX&,A% &$B,beHl4H$4$bLfaH"P%)1Q0HFLM4PAJH2 $LH2)%i 0%h!AA3Q"2!4PɠQ 2HHBC&h`a#0&if!(0iŀ1LFL@H$ 4L!J4fb#&HLɆ0)ILJddcC )@B!$E!D&A4f aHH#j 2dBd*SH#D"RP,͚R4!K Lfe5RCIbрC0"%&HdC @!1 5ih$# D!Lc@ĚcR!"H D fABbf$"D&A2 I($dB H 3I"F J)bbM b1HB$$lHĤIL&H2I"33A `%H$M("L3031SFJe @H#b!$Lĥ 1AA$1bQde&"e0162! e1"bS%`c4H #,2dBc"!ĒM"EI30 !(IiiD 4fB`FR"a bHa(Lɉ)I)$$S0 4DF&ȈDPd 1d#J"*0IBbFDQ JHSI! L(X!&B 1 bPHL (X!FB3XJ!21&J3I) 1 4ț$,ƙ, bR4$R)hFB1B2L,BT҄%fPf)ifDRm34 C0IJ#LQDZf`Pj 3@I$dR3FFFI%&$b!2J@DFlMe% "Ta4IQh1Pd3(BB`ʂ AFa,L3$ņ 1&R$I1B&$&BLQŐ R@(&%LXR#$6)0hcH"S RY)#(EK0DEIԃ3edB2#3 cIXƐF )Fi"RcJ$@@bCIY14 LɈD $H1QhdRe!,$H$QbeQ!",hQF!34 %)%0IBHH!`f2A$c"J@e1,`LhHY,Rd0H2̙)K)Bd"0 J$LS&J$(eҔ(FLPA b2c5 P "lMAf,DfL$c&DM"4S!I I[%)1I#!b(a0b&C !2 " "!IS3JSHI&$1"L!BA"$(fl(L)P’a((@# VfiI)FHblP%3#2QF,$HII$e DhTD"@d (F,fC$BEL1 &#2I@FPF&$(PQH&$0,dD&iiB) #"  4##31Bdā H4hH#2bAId!F d1)Id$ čdb D&(i& 4҆&R @H"M(JBFiD"2$4%3E4 LJPa" hd(I24Y$$1 l1(D"dhbĒP 213&0IL6Bh"HJ!3e2PH2XH2 J1ADȢ$,!fh! &4P$bQ 3$&F(MHa2DfH ha2dHJ6d#(сM$6#) lDD1eȔ&D(%LSJIŐ@R!P33lb!iaT!1$$l)&Ȁ3%&H$*f*PB1S3LPdc( bRdM"C$F0l $ 2!"6 RH)dM!cI0RHiB@HEBH`#)E(I &DFdQdML"!ID &DP2M0f JĒ`0bRRf)$6H1 @2`"&$LL3(L!% YBF!!!M HSAFcA&H "L$@B2fDQBRB`D DA1,d" bFM`(1$ lKII(2I%(B 4D4E"1$1(I X6!dDI"R"LЉ$%P&bLA ىAB"d 1B%& IR&b$d D30 13` I&()%3BS L  ")QI22J1Eɂ2̊I2% )Fh!0EZH"JE) )$0F FM,fYBK1(PEM(f4L$ K hPRFPH͆D FDHEh$f4Q(DM) ,dIIfdFLb&a1ȁAd42))! DDXJ4@@A$ ,PS$dH̉`R0)ČS4dI*RLD@F`0Eb% 2"!@Hf$lh Hbc "  E,a&$4 BH$2bEi1 4e1#"Qb!a@HJ0L$bPL0 șdCAh" C!ĄlDC$D,$IAġIBfR&S2F!"$ e %FRR@Y d iPIA*M&@$FR ЉH$D% 3D$e)&P 1 `I&dI6ˆˆa42CIL4$BL)4aRJH CC@L1`H ((K(d IML$*IA$)LDLj"DDA!dY&bF)D $4$a $Td)Le Ld`I$Hl4ŔDЀ,E!d*" I,)3A"Q RY%D2 $lE#4A%$IIMb) "JL6d)hHdФIi$hBJi31@FȄf"FYD@MHe$HRьA!2)#B"1LQ2dHi @1!"RF)b AHD@@D04C@f`6hF4(YD13ѱ&C$DcE,XB) af HdE0hdؠS$2E1DQJ02Y4 IL0`$ 1 C2&eIR$$0HA@4c!M%A"P@EA1004 &A%(2%L D,)3&)0 F(h!(PPfĈDȆFD2XPC&P`0AHLYF(E&DDE2hQ #Y i#(Ihcf2EP1D$Ɔ2D)aI30H 4ca QBR$C1D )L%B2de %*bƓD%H K$2hRf$2"3 1JL$HE1,f`Bf3I !"BHd3)0I I&I ē4"`M H! h2%@4LfI6ɑ$4S LP62Y I )!d!Q#2#DM%&&bIl%LDiMA)4I0L4H 4HBA"4   ѤQXSR(aS%dC1"F!"RJH4D Jɰl"3`L""$HCA&a*fQ*H D1IDɂ$!LE2cȄ"R!HdK "d M )$@0bšR$H,P$J4`)@$$RH2"d2l"*) 3Y@FR% `&̌P HQfɑdLH)"ƀIQF2L$&B ME4Lj"1IhH!L 02ab`Q&FD2RʒM PDM3a3DI"(Sb(!Aa2$)D`0c"II #Kbe  S %LR&!1$2))$,̄&de43)C 1XLƦha (1#e1% `!K2D&%&4C $ ҄ĔA%@IdPH" 3)!@̂)&(S PJ Hh0MM)!) D$h"& #Je!0D )$2I43$aL4%"ȉ&2i#DRHS TaLF1H1S$F 0!ȌIH d@ىHPdfD4MКLDɑ*IJIb1IdBIM &ABcB22I1@226 LPd!$(`0ц !PhL" E2Y %I#&4iČH!2ZfQDLa)LDQ$))dЙȈ" BĦJ"F%I@L(i4Di(L(Bd# 10R,B"!H1AbT@!!(L( D $&$(#34(BhI&bЈ1"&H$PM$H!D!&"(E12f$ņM LD,MDI"Ɂ0 `BdQHєI2YC0 D"`d #!"!12bd2 LLFba@3D́*I$PK M&BJD%L "&DH 0D2ˆ)RJ"2%)M42F)"Ql)$RdRMLQ d(h"B1E!#"L4E2@ I4Db ML!C6BP"1M lB% 2"RJe 2a(0L b%Id 45&Y"H*I&$4HR@%2iHcAI2%50LȈ1L Ɣ0"ae"Ē@1DFB Li 2J!H)CI!@!C!b 2&Ё"f0D*`,F"I$A3A & DC΍aebB,J(I%MB(Kd̉ILɄ(P&`ȉE&Q&3HѐIdLJ! ) 3#0L) &F4JD3R%R (E1I@KRHbALH4 SS J$CI0S LJRA"l$K@$E(HHa(`bM0E$TI&1Ih)%0 b3"D LMHHH̦L&Cl F 0đe)F,D1 H bRa$HRI A4Y M 3$1)da (Lf 6D$LiDFA#RZHF$Ȧ2f"1 "3DɆ Rȓ30 (fSIȆLD`&Sb؉3, c"HFdȄ2d@A0`#Mc"(d&"BBQ6cK2aFAL"ɲ(&fcb,PdhI24$ E#"BPdИ&"!2PPj1)2D6a DF` &RIS)LJB2&& &$ f0S!0lD$(H0220L$Аdi2bL,Q4#PdJRF&&I`$HB4@FX"6I % J)D" 3b͒ 1Re11iLhdfRѲfS#(&R$3@ҔY0i 0)1`$d̉De22i"D4bJ(%!2da`c%bHe HE!L0Ґ!E$E`0B!dIJbBS@fID0LR,dcIh,hII2&!1bdL! Yb BB &ffl6 Ca`40,l00&AL0BffF4"$Bba PiD$Y FdCJ%B#B$Hie `ʚBe(@eh2ȄT"LD@$ 1#Y!DABLX QH Bi* `A)H43"X,Hِ$" %&# L&4#Q2f %Q& (@ 4ƤDFƘ2DQ0S2I1K$)K")LTD1R %0FBA2$iEL$&&62D)& A") IHa20!L&"f$1D3, , Dʊȡb f124 XI0PaMQA$B M"!$ (X$0dDi 4 4"iL" &)Ȧ3SB#$dd$A&Q dLfE &d,6!fQI L3 !A&RXF` H#B0I`2fdfHC0Q4L,$!$DѣP$LdK)CC0D2iK& I3`e `L" "L$R4ffF !3M# HIXف()d,fIL & XH`Q HƖLhPIH lћd#JB A$QJ#"3$"BdhBM4)FJ $6T"$ɦ 54faDaSb1&&L(32RJ&fLbShA$dAEL"2J6()Ri5,d  fLIc,@2E0H!$%X$iY0aȉC"&"J!KAD,EI3,H RLD LD (T RRF01H& ȆaJ3)%4"#&4 `$̅DҘ2 I6( DDbaDa F2E$1d b$$ a`2AI$"͌2BHd$1L1"bI,6$""&4$R%4I(%a,3C0&hEDi40bLBLBPe5!6hX&cf" 2ĘAI"EH"&4%&AJ2"&I b"&f$X ,$0"f`,II&2)$(3 ,QbI)!$(Xى$A"I4@Jb"#)щ#fDQR@3"dQM,@1HLٓ"MFTL)LY$LK )&XPI$Q"(4B a$4$aL 2c &JbA1iDdPLDR42L@RhH2a(RAH!%4@f!F`f LFR("3If0X0,ȤML!4I1S#D2fd%F!!! D %ha3$)%ĈI2RFhQe&,-$a(M #cɑ41220) )0S&LI%iI $P3 FQ#$3HIҘ$ $ bLI @0f dPMC$bHbPba &TIBMLRc!@ Ae2(L#B&!XQ B`JR4HJb4DAI+4,BP# %0D! LF(! 2 4020$4!B`Q% 3ə@` ђ1F 3Ll%1(QM0!* ) )Fm2lRJA$!%1 $ldRbb2I4 F B2#dRd``bDR4Dc"ДHfJDF* DP`H2DB 2P@& RH0M1!4̒RE3c!@HaL"  b4L,hDc$[($i$Jd)J`B3DI"$ %)4L%$*2&DiIIMȉF&3J4FRR K!,Q 6BBCJL@C$$D2#(d1"IhȔ%A@ HRLYd4QdH1 #IDQ)0% $dȆSK4lb$dPbAF4!0)I %iD 2ȄBcIFM1Q@K#@d b#2&`QHI Rɳ! @BF2b1BY(IM1$e$I0d1D(4Df!2&D& HPƀe($fD́L$2XDSC(i) d`3 #c #1"&hRIb)$ĦhA$DI @a4L! !d($H)Dɤ$ &0c*B Hf$B0`I(&E hJXM ,3!$IIT!f $,HcI1`#i(HDLbHDLd)&,Ij1HQ2`V$ѥF3H$CA)d! ,c"H(Jh4B`4(a((aI"SJd$" $@(ebbeF4!HR%4L11PŘS$f$!)$aFdP%6F&Qb((L (Q4A6hI!&  %EF&0Bc12CdMɔRARM"S k3K$bRadQBd0`L` &e" !P$$BH1!AlA"FL"ff2LĒFIh $J$A$#L@FH&C)*#14&#J4B)D@0d 0a!I1 4"Q!0$DC$)  %2&*i@)ZI1*d%32BDR(L0&3! "C21$ƑQDe)CbjH32LCL Ha%ʍI) 1SHf`L$"1fi$AM&1J1& d!4!$iH10 JdD0&LDc"F#I4Lf idA(*"Ј$fF@ĐF4Dȉa4$AID*b$fH#dPFS`!4I J2(SdLbI d&h#"&`HhF4$R C0eS4i2 F2 CHfTCJDFafFL f4BBiBXB$1bFd 4*#$0H)FH 3!%E@LLDH $` †PTQi $)ILDQ&DZc a $ EBIP C L1IaML"&Ed0LA ȓaLM)@!dR$($ HȒ &,2e!2 eDFH$fahDŔce"B#0$&0A2$3@"M2h̄`0 (&d%2!Ja&#!HBdSlfE 2KDHL(™AIAfC͉fF@؄ &"`"0H d1D`a  E!DfDi&!$fCa4Bh)6hɒb fA6 E Đi$IQDRR)"m16c)ddD)"R,BhPRL L̚D" %"$5)&3$JI360 E)D3F $e6l"LT&$K $2̙C %B1&(F1L"16%fQ30 X$1`1bȈئLhK f23M!0"i&LM,B3bA ! #FlD"4# )DQE% C Hd&(&3K&a$ɂ%f$4F4 Ha&dYB0eaC hI fPPd) Ii3 #)H$HHi!&H Ll3*ibQBP"IRF0 `IRPA"0bD2$&%&d@ؑ 3Cm2b )IE6@Rd 0RZQ3 )XeFFfHHH L$S 1K1Bd̚``fa &"H4 LPIJK(Č2I @bF`"#LE BBe11EDBFEX B4H)IhL"DP%X$H ̢FL"D%H4&F A(JLDI04H )fED((#!ҒQ 2(fCI3E!I d,LCdbb&( D1 IHbF`Rbd !H&ALld PK "!%D "IDhH%  $)$3AJbĄ&S2,%"iS@$ e)&$,l$A1 C!#f QaI2% E(1 $Bc"$ H1ihf`AK$JSMHȔj (I 4)M)$H!4hʂ"FDL @E4 "`Q3B ̒aM4`R H!LL hR1i$dƙ(H"FZ$($L@&aH,$PBM,LC&#$BYXؔɉM%F$0D!D$LYa2d`H,1&4Je&B!2H026!c"2̆DL3!,DYcIhDC3 Id#" e"RlBLfR   "FA$a R))#ID(1"-JBƐ(6!#Y !`M0BDPDCLJ"DddȌLR#1"!" 2D4B В"&``M4DF RəLLdF(,$fHIHSLZLȴ" 62J4!d IB( CIPȈBD$$1F!36%%Q4ALfh !LM%C ddR3"%0H@AE ā$I&Q1!41&d*hDHdE4eH%B1`$h6AD2bPJ2HS2PB& JE AE dh(MX"fHCH% aa"L6I2BRFHD3M)&R 30PdD`` (3& $2A A ddf%BȞ)#F QFH I$!eIL`MHR̓@IF Y l"FME 2 $P *2cDR"̲L!#HBLB6"QQ2,"(ɢL232H B2DL"0a!FdHLLdQ%2d`X"1 4LJD@"fLȈ,I  iIPQbbBb B(c0$bHb"2 6Dbi b14A1aHc`,4I f & M,#( ѥ2,% P!A`iJ4(Z"h& $I$P% $$d"F$I")I(i!IP14$ D MS(1&b ȉFl20I4!La"ͅTM2HR`B,B3BL34$B 0$dd2l12& d L1# 2QCH &FJDD̥!CLe2"L"LH#B !0X  b4Hf L$2P1DM J6d303% 3" 4D4!0Y!0fɑJD(RbI&$PBl `(B F@ b((d1$Lj hf(~sd "DbR@()4#!FHLhd(L)d(#`ɡ!"J!(L1&#!"HS&F1BiSF )$00dȚ4L,P(RD$ !T&$ $Q&bacJI)Ih PDRdDM)FdPLc@R)(L2h@1 La(ReT&ȖH"$`1&LbcF!DiЄL"L̑hJR1LI0MLMSJ &@MJT e,FdH" 1DI$&IQ )ȂiQ0)31M( $ALRh(I 3$"EBDF& P(4IHHK cIRdRl̡JD&"S!bA2 QPIJ2Pb HLc1# E&E!%(%D،B0ٱF2d&DHaI Y!#LfY&IfJLR"RE A4YA$LT!"ĄFY FLD2AhSE1 e0 d!&AQf@1M EK bdD؃3c% L$!C!#$f(Dd$ȠL) LfM$ 2D$ 0Ʉ`$a̓ 2@3B%&)B&RR&Xd3F@Ic1DAM3!%!6ĥ"LAE"2j!03F@1!() A(4"AI("d@HH2LbBB) &,Ɗ $ R HM!#"QbYFiL$&!4B6 "IM#BeD#4 H)5)E)IT bIJcHY ْIɄ!)A!!LR(PLdI!5lE& D) IR(0Ji1F! IID0 a D(ba!lH ``i @`JBɄ)C1P$R02bi LX&`D ",ba5&3 IDC! RDDE iL#$@1 e2RP f"""i!(c! @"MA($$Y$LБ$)3 d!BшBfbd&SJhƉ"J )L B`"A%&HBd JMRh)L R L`6fH#L D4BLƑ!%2$Jj1%!0e""Ș4!#0$ 1d1Q&c#$bMId@LLbJ"F!S H@I2@Y%hX$R&%d#BdJD&a(deeP%$, dlfbJ)cfL!J) 4!,0& DHFEd22bYȤPLBXD"AF̃@$̐FlM2AdY4a$dFJ%L E BI0̊RB#A2Ce#AR&H@32A1"JD Y*#B@ I"I&$&B$E,P$HH"" d&dHLcf`%&( iCdHB,E"B!$h )$F L13#BD (%3LѠILe2LL$J)d$1HB0#bCEbY)HDH #P&f`* 3 EBJ JDML2 %Iā#!"L,h4&F2)Db  Z%2bJ"0BJC ffR$a!1$C)P DE1& H4FR$a)1H&Bd2H 2D0I$)&E2f2̱(1I4R2@JDf4$YLh2 PC6%f!LJ Q4bLd`Y H#$2ф0QF!21K2 CA0J)D dĄ1$H," 2@d"I3E&QEB1$M,2̦,FdAM$DDAiB$Bd))ђ4A44 E&C2L f($HJJ$LR&5 12bLDA&0I"FeP4C@%2b4҄i! Bf3(2B@1 SLLdHiFThad0a "FAM"YbbB"Z@5FC FAdjlL$DdfADM2 D0fbDA$`1ELf0Ab$ JH"d)Ie01&i!DTR@0HME$(@!1RPL 2C DF40f)ҙL@H0`1&f2P#@dM1"2 )2 R H2L220C"I!RJl&6FR"dC41$AE) ĄLLL$ĒhS)$He( Y$&Ff)DPY(IE&d MC$P 3%3 2ABAdFih( !  !3ei)̡$@0A!F2ILQI4Ʉ"hh F&0ah"RlH0ɒ3 IEBP$&@)4d@"e$bfeL`"i0J3!Y@dҔ(B)$h"RI!L4X #(X2JH2IH2C`Ɓ1@MX( Q  `HH!cd d Q3c4Li$dM4AFDfʄ0(iE!10̌Fh1cd$H 3)IĖfIb(A"M10 *F)$JI S`IFJ"2HDDIHPDRdFB4 Rd` R(l` I&"01#DH22 23 @D$aH"Hf$e"F@RcHXJ"%,fS2c#ISC J3I)&2)XLI%)!2h$RPba( ҅4)FbjLIh2$AEbXHLU`36J&2FF(fd%2i1FF$a,6,JR*1Q@i IHf 04`Ƀ I2F%Ȧ)H@$I2M"%*` 1 dFJhXd J(Bd&dD"a@PI`@&I4D&2!B@A)D%R$ f#A SCI%,(ł1dɘHL@2)H% 2A1 b1*X&H!&`̤&fѓ$HIJe"Ɗ6BAf0 H5)$b0D $ID)ATH `$`I$i$ 4&2IA F1!$L$Jd4&FCHELFI + $eI4 2DH&IfĄ`0"DDc`fe0ƂAaahDLbMD¦Jl$J`cC"@2 4I!ĤE$m lc&Y $ȢH6Y11"Ē$ H@ DFH& $DD*J"R("L4`K # 00ȅ)4#JD3LX!H$@"I$"RHe$ S !) $I1 RH`)DF0QcD$b"ɘi1LaI&BR Ē`$J!"`“A  "Q%0LI(dQ!`iF$AfIIрL2 IFS@ ,fDM2IAF`&&EHLl$ )"BɈe`iF )$H 3D2&D`IBHI11&C4 ))dfL( M2&!D1E#)`$ R3bP$&IBJ1 I"("c$E4"LFd4YRb@$dllBLYBLi4$HaD f $H(Є`DBl$ 3 Fi%,E"b!F"0!R36bLM32X &(H" f""D S0$0R3 T I,dġ0ɘٌ014&c,A,MH( ̐H FɌ)$&1`,`)$QLS" 14$fbhd$QA60̴I J@B"1B IHɍ13% b, FQ@@JfiFCM)dX d1""S$FI ’ĉ$B4#30ĤҒ3 j)aIBhYR ěiL11(Y($ R"2 X0dP$ 1$lLd$%0 (di2&" # "h ",()F1d0ș R I($K%2 $Јe %BH3$4bA1&aE D #F@&Dab!P2!%f #J# I(ƒ dҙCLPDH1(&YI(aLI$IF͒`)41 `6$2fhDQ30@PL(DHL&`L"DP)AEb "aCF2 F210f),! ɂ00FI!!4#$$(DԄcб P` ئ1Sɘ$&Jhb0 H4#44fHd`D 3BfH!bC(D "((!!2"2$A0ēI1!2 RA1H@,a(36C)+LBL2 Cd!4$2Iȡ4ؠɚH$ę40A!dF!R$d,@$&"I3RD$"JJDfFD Rf a3 L!bBcM I% !LЅ  hL0́1I$3)$" 0ILb#,d0  QF$2QIC FHJbFɃ#$fFEII("R4Ha`I((В!Hِ311#HAHC 6aB1%H! "@HI, Lff14&dL)BQL$ Ȅ2R%&D0i3"c$(40&HbJ3`bdDH1)-& bH`Sd1$Q#JL$I!F,ce@IJ ɒĀ0*% iH&J #,!h,12f$!L1 fYIiDTԠ2D(DiH A,ˆ dd2 "cFJE!J4(M!AE#$)B# BQ ) "dI#c"M ˜D$+(̍1R4bbLF $Al$F I D" Cf$RB2)ZIDH)$)Q4))HQY$2302&H jDdF2P& @b3%4dA&S i4Cc#$2e#1)d)&2$PLIddPL23eK#DLѡHɊeD($he&B  1@#Q$(2a"!QH$ H!JiH3F dd dJFAP&$P&a4d!&$$K!3,(H"I!I4f#Ff ` e6`dɓA  $BH a&4bJH24ID00dLBRAɠ13$&cBI""33(FD1 @f`l%AIM2̀hdH)dɉIl$eaJ,l&D$Fb1Bl2 )eI $ȡfMC, &IR@HH`c$4 K42mR(YBf$`"d0# Pd) Bc& 0 2a)0(B h(HM"bII! Ii1Sc$ijD!&(%4hc bHH&R$f`PD&M0$ML1 $dɈ,LTJDfL Qc`FƊL)BR 1I"dHJdI1 bfM""M,BYhLDdJ (dP@BB3`FK d!2$ĉ Ȕm4HR@IF’ H(iIC$lIBi3@IP&Khh01&a @2 X$IQQ1d& $Y BQ&e(@d)2ba&"0``eFd bFQ" 1 BRD"dbLe0S4 #23&HČ%$R0J A fQ҃ )HȂ &fI) m%1#4He"#iR$ P,DP"& %$dI&6$Ԇ1,`2&I ԔP%LH@J0$X$b!Hb0$2faS&̚4ęLf$bЊ$PCRQ0$34bR"Ă L3 $& b$%)@$@PR$A S0a!BP$)A"&#FR2@ fH&"RҐ1 3!`T‚dIH433$L) $ѱ2CEĐD$0Fb͚$DQHCH$ cdF&K$cąJ(̊#I14(!b$F2($M4dD&F l44JRd`2bDidLa2M" Y$0&L"fbDK6LID4PH$H d1LL(#faLSDXIb C1hMXPJAFLe&3&$DRL%! 2̩ HP`1Lb0 4A S13),$% C!%K bD &PQ"(fe" i@Fس &! & 0RRR2Y4@330"LbHD44&l$"FLDe2h3( P2iɁ),eCAIJBj̥&M cX E$D@&4% @I$2($he!!`[4"#$`I04A3@)"#6, B%,16HJ  R̘E(I1!(ѓ2$, $2a"̐J ͔ b"d#ѥMS$Fi4fdH2&l12@fB0"A&Lhb)  2"F HM2e4ɚ &E`LLRFd@ TBeI 2M 1 X)K"-C) (&I&d"̔&Ra, If4dIXd 3dJL@̀1`fXRhLd$F 4 H-2Lb0`!4&f6 HilC@$ h2 1#$2L&b0@1!3$ Bf@RL)1"BɘlFcb&A"IcDDQJ1 a& bH 2&iLSI bQF"AL3"aFclR,Ha"(A(I2P%d%,(D((`3,A!a$3@Q I@i) F 4L4&cR)HH)TRbL`H&LYLMƅ 03@B`"(dESD"daB$HLhXb6(@CFJf"Dȕ`))̦4a3)ID!S(2!A 6E3Jf@dD!(S hC*h `$ALb3`QL Xʙ 1d BB Df"I)XB,4@%,P$0 CbBR3$M42YX P(Lb&ThLAJ2$2 $Hb1̘&FI $ B&)(&`&bC & Ġ"S!& "`dȟ#0i1@1&&BS 2Ģ$0$12LDR(HRfBcF"fE"&F#C$E bb!&2i M1"`DaJ *bLdьJ,d$,1Ib4$D@b`H5(%#S 4Y&LElC2 $1)ba(#$A0LE6 IFRfP$)h4BdQf""RDH1 )0 F$IJ) $QLL!$RDd5"&"P$BF`1b3X$J0QL1!2f&Aa0"DS),LdD #6Ȓɔc&JA D% cL&P 2!4 &2P$`AdȁC4!baB"FIȣM,,3IHB#H d`H$aB$$#*J `c"M3$ HYd)SCP!$L!0$eC) a#I(DPi3%$ (`LH D)FP$ALLhKHh CI02c1, )HY i* DXLP HBb,a )%$I(i(@BIHJ&$1$,LF&B$#b(cI 04 D)3,1"4L2ReJDfJP!$Xc) ,S0HR&,da&CH PQLiChF0lE")С1@0)"hS &2fFd&hLҋ% % TE " iFfBHIH ȘD@HPA )T%!1L30I(I%&`L*Fi(d6HbAXH%IcBH R$Iс0ԉDf&)ER)2$`2dE0h̄TH412H0$Id 3&!cMf1(D4F"2LL1D) )"b&AfL3,QJ& 0 H2Lb$R&-1$b2RDP2Dbe 1 d*I3DYbPT0J&l %0 ,IIDD $L 3DA(DJM d!2($1 H"MRP bH1"H$`l)0DBh1(l&LLȡDP3aF0!!0H JBBX1"4IH@&aPBI4A"I( RRF dAeKA M$hE!CC &H% #D) P$S )LDQ f)!EDD 2LB"dĚ(,%$"Y2)"I$̆H И@0d30ah0IHAL$C1L0bM0RR"d $,4`$PbEe1d%2 ͈J"Bf H`&a(DFbDTdbJcFX$XA& 4FH`R AQ!HP( c@ddlP&$21iLJ& I%f2S& 2Rhɳ" h)$IȐhaE$% $ɔًD Jb(B") &f & 01Db$LF2`1Ldc(4#41LlfPX1)Y&II%(fLL` !%()ɀ1 J`&!"&dI#DS$L@"I EdP#d !$LQHĄ &DLI$QB`B$c&",bC&fQd)H"XbhfL($HL 2H FI"a1F,@Ȅ,B2I%1e Dc #($) )!&F )$l%)F#2SCD )&$4(&S&3C % Y,1eBY H&iHRHPD`(II B!JM!I&M!&!D!bfdխut f* *(u@QTQ<&"*A*zۊvt:&[1AY&SY;Rt"@+˜︈4 2h4УF@ѦFOU*h4Fd4&9\%3c#"DS3Y3 f$I"B03M2Jdi`)i K22I!(!E&J2-$% $Y& H@Aə@$!4H$a)#̙d #haS JA2%L&IL&)`J4A0 !eLc 0R` e$2`ё)L31E &PLP LI,hLL($(De3$b 2 (Jd Hh , )DBd (0b`I!2 LƊH &L`3&$I JE$"HF("ɑɳFJF0ђFC$S2D(aIC6&(22IYTIĒHI$i)@Y0X$X&3MfQ"%a$MhS `a)H,3))$,)$LlȒ`bHDMA11 X ddI3bM4b DB`@%QPAD2&3$ȥ$Đ`b"a%C)f1M4&(QJbI3&FH͐F"b`H$ДX 0%afB! BD3 J!@T1"cD ER1HL),Q`" "0$ $ X0dL&C0@4& %&Fdd%,&i$@S&RfĔ4LD f(ba$Dbd1QbHSL4DRDfh$(Ƞb143b% dLL@Q26i1LDє,B P(EaLZ#)3 L4H1M%@L0X, L@#&)"I"LFI$X&a"aa24dʌdI ia00l@I36X 2eaRIBDfȔdFI&!4HFM &e`L dLJ$$%$1LD)%)AE%!0$&Hb$H %,)HJf,a&6X(cS$H1"e0D $f3%$&43$iH $`)0DD 2)DBIi4$dBH&1)R! di$aA4J&$M44lP`H( YBL1 hA!LB AL2 "&M(3Ld("21(ĦĒA2c JJ1LD0cdɒ d4!$dLT RL2)De1H26E@@0HhQ,1FI#hLS#3 @!F %)J2 fI(e) c"$$iZC D#AB F2XȃJP &Aɑ)FF1LL!Lf21 I" HI231*id$ `H43 BC"PQIf#I"4!)%F$$)ID٠!$"i` I$A c%Fa!#a"`H̔LL) $؄BeJb"HL@FLDBP$fI&H$ILщ"R14%!DFa"&̑$AƁ(`%`a$1)L(FYBFI(#lHf0JP06D)D!LL2! 4P fhdȈDb1F F`1"@c124Jbl0 i,d(C!)FA%E JPbQ MHƌb"aɋ&f$d$`a1!I2c R iBȁARc"$QFdĉe1ĥ&C(ƒI 1bf0S("$S,*h !HId`a6Q*f Bb J21 &`IARQF$PɆc)dd`ILHF1 JJa a"hHM!),A1&Idd4,I,(2d@,d @FE2RFf$MBHd#IC1LhL#FHȲ10$ED)2C ADC 4BE (E(E"DRbdD22E I4)$S&)($B1CDhQ$J 0d4b0ER-$$$f4BbĤ)"# Db#MM`P$PI 3HƑLA"eA)FR R$))*I @SDXhD)%2$@lDD#"0d404SRBȉC@`aFȣL# BLf (0$&F$4 $$P4HB!F(104e0P  ̉0Dd2`i Y e0 IDb$I@FP1Hhh$DDƉBFA0&&2 a&2BaD  H"$H5"Pj22$"0P4a2"F$bD(fQ C`Ȅ6(R!L  ifH3̊L&͢f1 bL""J#S"DA 0f "2A(H$"D)I& $2d)4 LXIH!h`B"Bf(FH,Eb 4RiCE1 a)ٔ2bHL#LBAhffKE6L !QbHdI##)$%IDPT 4MKe1eda Li*b&Rb "f1 M,H!A$KLd̚HR0iY&(IJDa0H!2`L3 D4XE434S!&,& h`DD0 FHTbe H6 LI4"%,X " "2XH@ɒd)DXD d3 LA @ 3$&I"4DI112Dё)LB%0j  #FPMD&bfA1ddA26 ! FIbiI@0R̄JL)C ER!Md(cE4M&B&fHhL3) &H&E0`%,1e !1L0c D1E1$$QIDSLS03 )h"hHdSh4 JL!! H HfaLRiRh &%`(HD11)4 &QL R`$"Si&B$ff&D"dC̈1(20 F 3 ǎd"d,be ѓK& ل"$ a %K&(` R@Q2R&LbHJE$$4Id4!IV!1LPbhA BIɉ0BIM hŔ3i̒(d1BF!fLBfɑ$aHE!BbE ɄDJ4$d ADiH$ %DIJQB#$F)0Q d$a((&dIbC(F2$ JSȁȌh0(  ѤQ4#%LBHQ)bbjLi b0HdQA!) ĠjY$)$"Jf&i ID%)d"3cH&!B3 I1$)"H̙dd42H X"",(Ib!d@L1bje@FI`$,1$DH# D#b%) !20DdE!cDF "ERR(р3`$@aEIadI,HI0L! d` 2&fIDQ0 P DAa)dc)0bdd F &PB3K$2RaM0%4)4R&" P&I2"I("&PTL41) hdŒ̉D)24AJ! Q0ȢF1ĆRHɒA1C!`R F@) `"6ɐ#Hd̠Љ2aIQHB$(0I D LI$@eRIbA0 H DEE%$4"PA"&fB@(XQMSB&e!10M 2!F)RE40RFFQѦd€$ljB$ѡ !(b2))(lAˆ$)%0$S! HLL4d&, k @h@@&LDD))D %"HB2hHRJILdFaFA$́&(b$E!Ld) ) E$Z&!M d3 mD$ @I26$1da@%$4 !!$$2fhK$E2% 3)(JK2Ȉi&&$$f)0P!$$  (iL`e6PdC$ɋ!&b&`4!bd&22$R L) %4R2M(hJIPdFaA2jJ$(cDf3F0Cf0I iD$2QAa& dbHFY)0Jb1"̓,I%&hdH6aB@M ɐ4A1%$S#0JRXF (b3"0YD@EB)dd e4b!M f#iB3 P5 BHD4IQRCLB#&BEjH$L@DL"fd3BIJA$ D3LadP0 44# "ɡ)fR)14IC)DF! A0BALX *1A6@(IHfC (aLllld2P1(P LX2Fd jfBFCE(LHP2IQ1&b"&DILF(bM4 4hIƔF" !de1)0%1!HC""D $$$A f)b "cJY`@& bB@3$1D"@̀Ɖ "B$ F e$PLTL# H$Ba@XLIM3I ,!2D Q D0HLLBcIcd@Ѝ"&1`!C22MdB3dE2E`A (0͘d&FF f&X3e Ll21!44E"iL XDH@""PHd RHCjiI",$(A$d2d@Yi2$Ɂ  H(if2) MM22RR4TIb )"31bњ%X!&)K% H`0)"JAe1)H$bY@D1(f) dH& b I4 44I JL 0cAE$Y&!1!2 DPd &A#$IIhfD Lec$I(aFQ2a 2ɂR( ()LM"S$#%2 11$ `)$"L Jd#@bQ0hQ TRK&I 0dL F64)4C a(6"#"L&!H)A#A&df()&P#)&&MADA FB+4đ4$6SA 2$0H&S0 bYedM `6P@IRfQ&2!!DbJ "32"`Ra H-(RaCb B@lđ&1$QD$"ɢDbIHљ4T" 2KL$12RdFf22!$FH H "D@(!`Li!$&De1a4#$E,C#AC$04b2B$č(i3Jf PC 6K$%"H CLЙ  d FL"IFF i D0 4PL2` )hS0A&$HD1@F4)a L RaB%#&dI&&D2%d aB % B(H&QD$AHD"M!HPE"LJ$c,&2PM()DLDC$0hd&&b)22i!2@@ )R033I4L@&R3 4d$C3H&P BA4a %L LA!2H"K)"HŐd2bHe Q L4RS4(I4#2f BDJ3D"Qdi3$,21$ ,!$e%e4C$I(B2!"2dI6(&2$&"Q$S!@ 0R($! I H#a,Qę FQ$&")2`"S `e! )HȌH LD! L D0 #0h $A"&LM(Ĥ*,X! "Ih(̢)JfFiI, bA"PƊF()M3H)!@`ЙF(LA Ē,$L I&& L$3" "a(LaRR1FL0I!4 b3"L$$& X&1(`3DJh &c0@ Fҙ-2H! Da2%$iJfPY2ifm1 (Ii&LHiJP$i,,1D" fB&If , $RH(L0 a&E!F4 (R`6lLL4$h d2d!HRM*dF0hDdB!($1bPf@j LIS& H A@ #(aIlc$McLB"BD&L b̙)H B@QH&64fF Xld2" C4cA fflRfHK(Y`"bP%2I!`K$C@XQ,IJBP(2e($ E1b Q,0Ph 0d!`)fP`4RFDȩ&4aa )ٙģaJ0IM2)(ACB d! &",!j#  $Afc D!!&!!442SB1DL+3E ȐPL"#!fM) bM0If`e$aIHB) haEBf$M% %F&2@i) eKBf) 2) d&KdIFJFA$ 2c Le1Fi0B1&ĔS1&I$cFBH4!4$Y(D$b1& E&RdfBbQ )4 hBB(iBHPR 44lXdɔ(̲(I  BPM&0,`,1 "4&Qb)!SH) "" BbdE!#$"™c!(4##)#RiBK$™4E( #B#(AdȓD1 (̠%aLLɌ ̀a&ҒDF(Idc($2E10ĤPbLe!4 dь4 ̙ 4RY 6FI(3@2%!&T@D&1҅ )#0!eD!3&Sc S ffJbJQ"fb,BBiĒ)2KLI4"TSE6A i(F Le&d(# l`13,DcB í` $(LPd"F,&e LL%(`e f4dl "&HFĂbeBf0ш bd,Bm 2&a(jR@$ )a)hIH 2BLLaRdˆ13DD(6Q2H )F@S"#̄L2&13%DH$I0L Ra6`I)a)Hc!3)4de @ D!B4`! !&MDS2D &HDʌ4ʂHd")Ab#4Lb 4)M4Ɋ,a4F 3 AJQD)@4ƙfb IE LbF&DY0b( ț)HhfHA!2KL Fa4 DD*aHi fli`)0I$f("4A2 MHM"HIJA3) "F0HDAXơF6ldQ$$$A&L%"L1IHѣd$QȍC) PI(1)L0 C ,2D F(, )f6(̒CBdE ! E ȈDDBQ hI3$S (IDQ,e% e) $I&2dLT`)%Ba4C0 ,"fSXP"10LPaC4̡"dFɘ!AFBe%2$`$1"0BMcILa$e"H(dDa(@iCbH`Ћ$!PLH6C Rd MF @AJHiDH2iaS)aML6$Y1  3$h2$lM*!"1HA0hdf@&$31IFM"DRh& 4PH!0JdB"D dQ,`b &c H!IR)LJ@# !$#!EA )4(fhl,4 1HD&)D#21# 1 !fdLF2 (a4)0# Q @%Yd)`%DI@e l١$&`  *4F &)fHl2"bP3Da&4D̂d0,  R@%b4Ă*df@LH"BI`Bh D% 3ɳ)&`6B0d31Laȓ&d ̌dJ BdA&I(F"$dL AfRQ C&!bPB@%C2I1 J#J 10R!(E`"2I4""FA$F4 )he03a )6i (1X &PHFY) dI @hh(dSD24MD cRfIf2`JQ, 2"aYYL, ARhP ™dJc0Q)!J)0aP ,$%@bdJ3%#3B3(EI bd4I Lj  !B (f4L, A 2f32dIHd)3&""&4 ( a!00 3D0"Q"B`$ !Љ"(JL&!&hDFL"aJRLf2% `0 2Q"&S@%I&RM&h$Ii)b" Dh%Qb`SDJLHdC,""`RdRHɚd$bieD 4$HLc "Ę""fhA &H2F%(& ! FH`2FRa "3,$21$b)2&FHc0%IbK)$2AB!% IBdH c,IF0$D$&2fJRC)J"2)@h3a@2J` LE)L` QAR@4(bdB)&c4LFY4Rl̡"fdf$Q )@@ha! RL$iL0!&h)4AF $))11b42ADHŀ,cIC$I)D$` 0QFd1$Ȓ"$`#F@ЌP3fR(0d fLh"&H02)&IѠDDcBQ$ؖ%! L!H5a,%dC #F)(IMPb$ $($P E &FIb4D"QDHD@ 2RD0c"i" #DH HBPi#!1DA2$ ,i$ J2 &`HI,$f40B@0Bai$2 %&(IL`$&B4%21j$@) ARD2)LS 01 $Q& $"IF&$C((Y3)HbL)dJLf$MFb" L$FDFLقA$6d2J %! Qb@d&I0J`HH`hI1aMư((@e$Ra2(0FH(AHF"4d&22Q c(L$ŒL)!"I32dDL%L`̑)(%QdlaR4$a2F2#@SdfK4aB"4 C&M(R&C Ad" D$4!i&@$$̢##HMBl ,(% H@1(&L&Qd$R$DDS&4P 1bHB1A()4$hH`)HL")M2$b02aZQ AQ L!1"DHlD`M a2Q$@Y&Ā"E4FH"b4REd%%(bBƒc HYALIfi4iLHbD 3*@Ĉh0IH aT!IIABC,&$)BB J0Q 2!02ID1&!!Hc4c$3"S(ɦ)L!&$11dDd$Da1Xf$HaM"HBaSAL" $CR3!DD4 !! 2d1)@$e`)4"2JJ PJI&4HA@%#a!"43 24D C&"A2BRd1M$LcE("hAcQ2H D#(@F"@0Jdd# @CL fd$&d D,"!dM,(CB(ĉ Q$ȠP@D`"h$d6C#@RP10E#$$&`$3$0&&!dhцLdL BJDlc&" JfHY%%H4̙!@LLB& "JR#a" 6#$C$X&@)BRE&#@YLR %2H$afIdȠ4"1L)SHDFBH h02i I4I` DSDȡ1HfIh„S1, FC) &hH$Ƃ! C"*Qe*)ie`4B!dH  %Hd$K 0I F`heLf*a@h  i B`L )!,DH ̀2RɆ1e0ؐ fS A&"44H4TaS!ȆęSE"$$54"BBBRD h&e1H# LB22YHDi$(ALe1 FH*%&@lP LDfDS2SI)$ "4 BHQȓ&$Fe231f(K2,I6JD f AA34a3(C!dL$3HR#0I 2$f22LfC`J(,%fiDfdi2JI(1J@bP3203ADB @d"0!#*R!4 $a&ICH"HDH&I&("R(T&M$d!Ȑ$HP%LI2JȦbD &C %$Q %abibC(B3& h $L 13HA #F ! 3)(62)!,PƢHd!F%da@B%(D1,PH  H4cH2F`h%e(C(D&I (d̀&c,AHI FAab1"a0DF%5D6bFEaBi0db @!4hJHXh4 `DP" BD)&HA5&Yd@dI$AA$EIRD&i1 fFL4AZɊH c3RSffc "2(2HA&EdȢR(D%$J2j H@f!4IRDH)I#! 2 (aE $K&S&BhcEB)HQ)4&f)2 R@̢RL) (d424d ( 1 d4@B2$T$!)$HCB0#Ai$%Le0$@њIb( Q"0ld̆64Hh$!Pde)A6LPFK̉" Fc&% "A3!&c ,BH,11%)P11@b)$h$SC "4E @e$"2A&`CH`I#"Rd1)4f #1"P2 `!@H( ,#f0 DF"`! Y1) &ę Ȕ$dE BJ"d$ e#$L,LHd#0R%$@f1"HQٛ(a S&2Z! `!$d"#I Ji,BH,P!LP2ddaL)LJRP6`fA dl&(̆J#1BFf113aF )LPX,( C,%$ĒB"@Y @l"$$!D`L‰4"@*"B4FH JB IFd 2`@F L D$IH ɠI0L&&aba32DE&l024"3(A&JdC`ahb`bdH201iJ&(J@) hM@"!D ٙi IbILI ͒P&MA%3JH1 hlARaf&D$FB M5dffBfdJ$bY %)I C& 2PM)$ FPH&( ))$I4,I$ "4IDL 3)&c@(AI4 VQB4Q0S,)DhFL$hbhL4RE&I$j$ @͑ %1dRAREh͐$M` fLJ(6HP$0D&T II2JHȈ`$LR6DJH 2Q2RIM%4"Rdf$3 C$DD YMEBl b13d1Y %YCIhY $)E$ș@Me0lI )),%iJfcSC"H YI&4 e R 46e11$ɁIF FhR h"fY$!CLchDD&b… &CQIDCH1Lf&R@T$AY3# fbP$Қe2H iI&f`MB10JIf4ib3&Y40L2FhAM04"e,&R)bI$ 14H)aff$Ddd ) E2(Hi1łA MI0 $h12A& 3# K ` &a"A4h̉PC%,b,b&a4R`L#Lc$L) $#3@IDE3d!$4i"€Ya2ƑRd1"E D 2@#LZ)SL!HffD $C & D4d21H$ѕ*MI @a%`@*!fMD!"@3$E"FaIfH͘B1 fȖL4(̘Y &"HY,J#% HDY1"J i Ȋ Ff(e 0& $D$ 2("i hM&6h # !J$ Q ,FHeH$#!eLcH̢CDI&BLD"")2 1@š`1%"!Q%faA(R$ea1 !D6$AIb!%SQ"aÏ`HL2LJda0&F4H44Q)3"Q&a 22E$ 2dF6!I$CE0dJA%Y&ɉ2Hƌ&EK1e" $ЈI#%B1%$hfe#&Xb#@1" @Xa1 "RMF&H0)$@"i!fH5B42R&MLhD`FfelBS& $diH,IFI d`$DI L!&l,#ILR2`L``i "HH"$LA )(ƥ2 (Q 2% )6#I2c4ide103FdDd)#FB$F2%2dX$d)DiE 3Kif2L&A$RTєaBLEJF#DH 4M$J"ia%2" ! fJfHlRQ3HD$)$e00B0 Jc J3 c,( J#0)`D "(2e0 B0FDؤ4&$% ))(`DM$&FZ2D!1$Ѕȩ0 HY2Y)0$ !J"4#4 "a(JM 6M,ad d @J!"A" ȢDFXLII b2fDRH# !a)1T J D@I)P`K2"&aJi2I3DiA)6ɉa RID("(HD$0L3!# X"4)0@411(e c4FI,Ĕɳ0@4ȟb`fAA&R4H4 &!dD&& F#3 Dc$ dLaJAF& ,DMD%&f Ai&%4,R#J#@H0HDdؒȓFL h؋FI$"I2Q0 KD$JQ4#f,S3 2$DBRJf(hLL aSIȠRa2H($612 $$dF$&fRD2 "b3 efR JŒ 2HP 1R1 &! Fd&e#$)be$ K)H4K4 BȂI HcLȄAH1Bb"I2H$ ġThdfFHJD 31#"HQi HH%ASBB&&,L$"2!2 ‹! 1Ld(ɓ`AR1I%$13 H&L&D,ddQBLЁ"R d4VRc"HH&&bb#D$E daI#0 L6$Y,hPF@3&2DBQ1h2C" 0I#f(JI&$4Q"I  1@H&0BBB(PRED`L0L0"EYK !f3L3@E,Im&FBS)DHXDHĔE&R&I6HB@F(544L2#b,Q0BA! L, cAI1LLbE"P@D20ѐd͔R$0P"0a3`IIAH DR21A)02&!H4Ě& ̆# 4f̙I&1$4(&1 (`#I(,FL!HBbf0̓"D`B(0LH$bd &b&ĉ0 &FFbDIE@DR$RBIhLdPA"BB @  4P$ DbbDIHID)P&$F$C(aFђ10`LĐ 0L`&D"bHI23$ #,)(!) H)ILdLPLɐ"i) MIKDJP&#JC 3J H#a&,IBB`Be 2"d2 e!DD!2H cS#$FȘHId$X&FdM2$%2!0f2QIS$2I$RX13!$#0($ID#%3R$M ả$S"l&DJe( A`DFQ a2$H$"YDHMlIPe 0(0 & E b4MlZ@  !2,4@aLbdEaB3b 1C) e4#hLRAYLcJ4@fF"I"i4LPC#&I(5lI(&2HQPhL1McI,lT1F" H"M ̚""II#3b)A(d)$DEa"&e&"J2Y (ILH$ID%HfR e"b"ID$ƀ$ $c2ŒQ02 I1dID Rl@3J @)F C0 (HIfQ%!AcY3))!2&"SLe2B&LhA0 4Hd"4d0AHdd$"f0IF(!b7͙2`32c` %1!! X$0 1 JdA#M I$IJD!fRBLLS$A2&BhX"d h HYA5M&bіQ&Lb Ѝ#M, 1LRB LRDe*i34`@h02C4C$ȈD$&%22$Hf0&&$# D$S#`A L0h D2HfibH%$adIE dHfb ABe$dC,) DIHL4Pb$hdbi1 fhA0&J2QFI3Fd CLI $%!%A(`@(ƚB6D&bF QL"TP D!C)d 2R ƉJLFP1#Kefd@I&DL IL) L0K(0I$E& `# 4%$131D(1AHdS  (i&I "XH6",JdCL$f2F()JA6i6d12AJ6,2 4T)ґ*i&"a0a% @HDJ (HF)!Qd 0(4LHБJ""1Cb4IBA0XB)$ћ I&hfSHDJ@&LC TLCI"LM TؙAPJ*b@ DAJ!!DQ$02đI H̅2HE FB2YD) 0C!Sb$2B2@0%$dfH&DB41EDh@K2Q2DRQ10!!Bd& @4ę1(,F"„Dda$ !" 2h1 2PDF2dC1*JX2e i$1&cI&RSL1!P)*$đ RE!J $FI)3 $b1)DF2J& (d!B %H BE%hBI$"Idl"SȌ",0DHD&h`MlJFcHS2Pɓ)HPbBC"&#"@”Ŋ dAS! FeCJl!ebM 0IA&XĘM$"J 0Pb) @LĄY $Be1JR$ P"YM2 BI ғ IXi( &@IX& 0H@JYE12)5 LAHE$2@2LRc$@(Ȍȡ 10`bXC4ScR` M$2& 0R&AH2L$ $I"26(& B0 AE CQmB dF`L&I0Ɛ%"4 D4`1FLIbS 6J!LDDL4LДM(@lB2(J`d3F,D̢ $Bc$E$Y2ILB$&E!@2̒fFa"@!&d$S$f0)hSf3$(d&&)B(BK0(3 f"4LҨ(!4@IDQRЈBLbfIDXaJL&l IXK1 RHE1(dd0f(&4$ 1HM i"D’f3"*Z@i$"iSQ!4",I2&$R JDaFd"K(PJB, abc3 H#J !2DDaaLRX (A$b$f1"i%# FD$FB(3)  B (3LbAL"1"i)$aD F*24АF4)c S2d@0 Іed!M$D1`LM26(Db"BHh#,&`IEPc K"DЉ !!)C40L%4a)f&A1FD0SDR"2$2L1@Ȁ"`K"`JDL2J E!%&& " Y4f2AQdRb"B !M10Qf2 S2Q&d&a*L"D1Rœ,̔PA ͘dRAf4P0MHS0i%hB1LE&H I2`1J4)&LB I$ٰIYXY&R`4d00V2f0A&eL2$& QL1 e0 "( Y2b2DD@ @hlHY@f bXE4P &P4LH2DP‘a ȑ%hf) &b%Yf"L4I2I)ŊPDFI3Če0IAS-&3eL32DBDQ#!DL4hLd "Rd 2dL Lhf1(! B "0&1Ff)l %1K F B0 D`&K#D Ɖ e`ŁC($A)Pd&32l P`c Ib0)% I0e# lf)2%1%$bI!,H̤IeHE`E %LD04 HhS &3Ie&ffT2@dLcLM2Q L@idaPb`# %,Q2d"!MD% 1$Ј04B)S)(0FI2 a&d#d$1IIddDdH3!4b)(h12"HIM)bH!2ERdC"$I&$XI0hѤRI&M0)!2C(IBRAeHLBDѦ&@LH4D"H&HHfA "HLa2$lB $2K0Ie&f@jI$2D$34 2)(!3 X$EI)4fD4HB)2I#,d)Đ ƙLHb#b!`LddH(@j&`K2LA dȌIdje H0 I1!H"F@  dd 5$&L R51E$&da " )4e)D10dLɰ(Q$3`$l04(H i L&iI eI2 %dK#De"Qd, D"&$ E2,RDIJF0XS(M34J )(2Č`@3)E3$i$C!Yf3LMH2iBɤLѤ4$&IdbAS#J Ґ$ S40I3 D!(b`LPF bDB@i0Ɠ#f`D AbdFdLd"4D 23 C(Id 0*LLHM)12b12h &bh4hiLiIdii YAM hJRf4 A%)H3#)B&ĤȐ@1M%2P(dR $1H"ȄAa"aC("I)BXئRB 2F,BB C243I#""L4(QŌI,MlĢ$$C2Ja$%E 2%&#,hhRl0I1TFI "ID21FD$ Ff$KĒ E$ 4*i!Hɘd$ $ H%Li$Й` @b"FSDP3)AR)B3 PEb%LɒJaQ30 RRa f1C$(!c # BA2P)A)"4""$M&d!IH1ID$dJ40c(2%(F1"Ɉc$a @cRb2#CDЀ̌I%c ͒JQC4؈Y2(FXK&&`FH`f2,dLI6b !M(™l F$CLbdB6HS fblf)DFR!L$IșI$0"C0J#$ 1Ca ̚ @&%)2HHb",$a !J He$I 1LFE2eFLHh2I42R1B&ʓJH!)  "PJ A$Xaj( R $(cBeE "%љDR @HД&I&Fh"$ )cBlR2  0$ d4 Bff`$"HSD1 D*I@" ™R0HfH&1I K2FDb͒`dd%$0f e `bPbd3$"LA"Q$HC&ҁRH%4D)B&ĥ2!)dJD4)B)Hfh  Ɍ 4 4@"Lf&P,"  "b4&%$(D5,2% "ɔ(f(%2 Tš1@HYJe "`Ph#L Ba#&D$ȐDh& IHI#(4H)C4D%$ $dFbD & $LDF$ 0 d-Y$D%$ @)ɂ& $$H(%! )HAF J6$e4S% 0L"D2IHR2 &0&"$&IE$A2F&""@E C,i! S&&LCDjIMa 2  @Y&dQ fTHR@bdF4"H#RR#LY21YLIBa) &1&"Bc@RDɩ30M,Ią30eHdP&P$SBIBHRD%I6ad4AFSC3$Y1(34ADFSLHA Li @Ę P&4Ԡ32aF̆J$H& ČL1H%L,1H b(QD4&,@bXP #EJ1@3 @C"C)a0bĖ2 DHaK"FlH$A3&fHaHA(Q1@bA)h"%"b($DF! $F$F$Fb"M !Q@$i0ВLȈ$$)I0I S1"Bьd i(TDfEi "h#HB* TYeIHE"! 41BDAB@M$ Y `LE%2d(1h(b E PMID)QR 11(""Q Ra))F,3#Hcf*0 @)$ePMB@S DMR 6(!1R#H&hI4daBI Y La0K$&!fd$&Rb )$@d!2LS$*41 4@He IFH %4$M1&hœ PbDJi2 H1)%3I (IDHc $H!ME1(iIH!DL$ ĔF"6`ԡ,e4B#4H4@CJ(٦)2I2PBD0C! )`R)IdRHCI$"b&iP44i$f K311RA0(FIDRd"RJFERF 3I &1$Ib0FbL 1LDD$B@F E a#)dL`@a(@$I$&L22"IM33 M2L$H"LE1,Rb@)D$(Ģ3#1"2 R` )Q$ؤl(DT( S0)@& BDČfAS0"!I (@L0b0C!6,SA30LHQL##2X)0RB%DBBI20d2RhbdC0K ( 3F ILhICC`"2%3l BR%!2a$bC )S4(&14L4hɘ,ILT$)E$(L1)C&ɒP% 3(R LP34$1&4Bd!d3%$("FX14("R34I&,&Sc 2H JPL HF2h IbEdHdĉLbh4!!2HLғ2%&RA (0L!1R1E!$0*4D &( cI$ i3Llhf$SH Q$DP3f5"cLQ$%%`JL%4aAB C%$!04`($(`f R& I)0H$`301d&$Ȕ1&d0P ,*)FAA)! D 1$1%D,6`d  F""$dF2RPDj !@d")0@L PH "fM!$d4B``ń(FJ4C"%$3I Di52TbM0@JJ RDĦYb% i11 "$e,`0IDi @RS"i$  J i$fL B@ i)L4SFI$đ# Da JR,X Rdi`ID4hLĚ2HCE5!!,A R$$BL@FHC @B)A`(4LaCL(!XY#1D @Db6*Q $@I6Si&2d$Œfi B2E$ d(4)2LFF)d6AIa%#)F̔$3 L1L@Pf(b&fQD,JLĒ!l̒J biiI R ZY3 JR3(E"QbcL1($6i&H  dJLd`lRS#J&I M%2`(1&2DiIL$0L 1fc2dcJ$ 1 L$Fb0X Ġd4JB0`aHR%$ fBf2b%(f@LH1 MLH Qb FPS(L"E2&da%$ 3$ ""20d@FH2BJYLa"f0)),H1&B1fA`ٙ1)f0đ AF3,HDd`Y0i4@&f&L $bDH)H(#$ ##LA!"H !1(BDLɁfRKB($6ɚ13IHa4I FD$H@Bd$  1L4L$B&E!Ll4R I,QSd3bA& 0ƚL"JAFI adIf )Ƞ4 "!R&!4 1Q"DQ(E(D 3$10EdEAb0l4ABI11!! YP"D(H`a% !2 2fbI!E4K1J4 0L(ȀRd@fE l$D"L35)$3cL2̲@bJ)0!H!iAS*"S$ Jh!2dBd0RI"h)D@d1bI%&3B!&DII$L%fIAab10 Hd `4, iM,2PaȢ1e)JdEh)dLe#IdS D!`(le ABbF#HIFe,`Ō )4L"bLd$fY(b)$`$HFDb! "4(M0H 2 P)#`̡(RDř@D3DIdBe` 3$!fd"`0"",ȥ&MQ3 CLXR&(B)4, f$ҘI1B "AHQ36Y@1!#`#  (JM"(2I1&I2"42Ibb"%"iI $1H#@,a"#(!d# %Fcd2I2)(1aHI*3&2` )!Bɦ,T-hD2&1A(31SDB1D%%1)̔` DL&)@JR$Mȥ"JRFDd2I(M(QJ bQ&4M$$SC ,fFR db %3LD))#eE&#JFD ZhL $D2AI!B&HL2YIB$L0LCF)2D(Fi Hē%$fE 3D BB0T L1LAl2$R3 2F2# DPM4#ID!3C) `1IA$J M0eJfP$@XLM͔S)h!4(` 1 C@DI$i$DDE!& Be(1BJd$24"ha)h" d (Fe) 1D`FIR3LbԤ40042%&4!L&M)Ab)c 4J ,bDH$% &K#)!RHDLb&J$Dɰh` "3C $bl H22#4a&D11 $`H!32I0ɔRHHȒf4I(i@FĒbFJB``M40#&0" M4)d F` fIIDf3DCd2I1@!0M#RLILFQL&`&Ѣ e&JJ& HJPH E#3P10  $"3(a@HA&2i &`C)(1h`CQJ4dC@C2f)d`hbfXI,F"2& !3-`@LC4IAB!D(BP$$ (H3*i2ce)(dj4d)Mb  (HѳDRfH2 I`&͌AIA&$3C 4f YLLl#`ș ĊFS!& P"2eL!",!Jȉ &"H1 &)#$& 40$AMb"f*F4R 1#I@D!#fcPЉ0ѦX JF D@L!!e"I@a#)IL4f2S5I4%S%2aLaM2BL1LЂE"ILF30dD$!HJ$ĄbQ FA1 ę% $a@F dc#P#DLm"̐2IJ$fR LRMEI0$Jf(F S12E&$ dAA6H3%aJI($X#"i $ J 6D"30c(J%22S2"baI HQ1(h4&!EFą)",(cIJ (3$B@@$AIBb"CDDC3&RƢYb13`$LF)$̉DL QBd`d $тD2a1!))!) 2B`QB`` d TP!D!@ɑ(lbIlC(̲$ hR2 )) `$T0S@RHhZ L if!)$@f4QD% &11"f($RFMSL0aFƒ$@Ic!(e$M &!4"FJd`LL34e@$Ad0CDY1LD# b,"Xe$%Q"Bhi HCB),h14`" ,M(3"2L R$а"l!H҃E dF1H $!4A"Y4TC0YDII"c%!M&(PRdL$i 6@H҂)`RJ10X0@f%)(Y`@4,"Aa) "#&RƁHɍHdQ222 LD)EIe FA$Sf")R030Ef& &d2 )2! l$Bc4&l@i11ĘȒP!%R1i1PfAc",H,"a1)"d !2șfDFLB!CL fH%L4Ƞ04l3Ld1"EFe%(Pc&%D BdRa3VYCL21 4 $bLfDXQ &Ji I4HLCIDbI i4d@%́E0F21Ai aB3BSQHHD!&H1!Hfd2 X1ȑf!DQ$ L)Cf2d@b")DcK$ I#b$)MPl(ɠ6HȒa3hS!CD$MB)#b%$I F#,FaKL2e&2HHH4Q )2!`Q@I Jeb"Lb`"%## JbCF#(b0 H1C"e dFIiM4"P3i,FDPK)"$JJBbP@IYAfRID#D&$5ҀJL`I ҁ`$,ͅ Ġ&i "Yd JAShIR!e"FHĘ$$a(,)"4"RI `BIR&"PГ$cFH4`٘@R$HJ2e&$,ɢe1 cdc !(P0IFAbh%@i0Q҉2"A4ahJA0FM S( "d%)2 `d(3#&HIəQDDC 4 ɔ $&2"##LŐ2P (f!"@SIY,D$ Fl0K T(!a(҅$dfSHbHR$  21RE4)1EBBbFIfQJEfH DIjiD cDQED (L ( ,1Lȅ!I$i4(!Bh" EaYieI i2 2FF F$0be3DXfALBA2Q$i 41!&#Lc$RHXPdD1E(DE"f,f2& J$62!$X2&)JBR$*b b`ȍ"HA@X(!HK&H3,!4d c!H "%A2@$I iJQ#M4@ƙ RD1L(2@LJJ14YS6M2AS03 Ԋ D%AddhM"2l"Hf fbmA21ABfDȳLXbC,i `1,D&D$ 3 d!( DH`b!0D#PQ&%!2D$&4D1E6XQf3DD"bH3ФM C2"f2QFLȘi# I )H PF$bA0QI&4#3!`bL4fddRX$3F"4$& 0&)`#%@i JHF I@b eA@̒2L&"I#P@L5BiD(l"4!E&BH0A$ dbRLFH"F%)LBF`Bc@6Jf,fE#C HY1&"$4H$E46RI(2bA) D "d3 Bd` f $e) $f2&LHcJc$`a,e(D HSLHbȀJR@B`)Iȡ242R1L ffB&)$* $f##@J``$LdЌaRe$Pd2"ј&#Ed%0̔13bD"AD,i "F&T2J3@P#I(2MdC" 12HaQ(FQ@1i% $2$eQH!4$$0Ѣ4DM"əe,d#$BH),B1$ Q0$i2 6II "4I" I2A&DJIAƑ Q$1"Q(%$)!PXY322đ&0f 5F$IdLbifP)hL"iM"D"@RHF Afd00  H$ a% $Q"l2!#!DIM$Dd$#L%4H04&1 b!0Q&dІPIDcd f$$SJDLR2%%$4dMؑD E #Ȍ@S CIAi$D0BIIHɘ`c4"C&@H D2(IA`JIM BM$ԙb4 R&HjJ1E 1dʂd$(dK(Ra )`1I3!K0I$@)"L H`(` lhfDB4CHB&Q%#H6RFd3&C4DFLAQ4@(3 $ŊLd H!! 4D$C !F"LH A6Q lQD eC!L20$bDhE")DII) QLҌ@30I1L12D"*,DIK f30"d$B)`)2#"d(D҄&"HHF3 ̔L 2$4(Q01I&dLa"Ab&De(cfHTIbJ2H!$LP$3``"S4%)&cDD! F1)(`RFb@"JFE4(a2L(H 6hRJh#4aQ3#Q"LQDJiF0A L dR@FH H&"IL RJ4ad3 BRR̉ !0 &Rd1F$͈HA2$d1 4Tj`I!$FhHb`)!,H$,F %H3I$#" lPBH#(ƅ$)E MPQL04)c 1 Z&R` ɰ)P(DJ$$ M)͑1cY"He(P3hC&T ,ĦP6M0c2QB(D0(ahDe+b 3( 4d0j6T""HEf%fbHPM HF(Ie EPL$*``ƌ1F20F!$D&Q2dJB0f`!33(XRPRP@fHB1d2I4d)4FaDDJ%"$bBAbI̙$$e2R1̂bLf@ "4D EФiiBcDAfHcL 03%140L%,Id"I$ Ɛ̦%e  )d,FjdBIe$!$̂FLFH#Eф2 H d%QT40aD La #2 %2Q#f"bd$$3#($ LA$32bM"R F3)CaIHҚ4 "0 @1&LZFF J4D,lf dALFH) `H BD("FQ&ȃFa#K40DM HH0DDdљƄd)&ddAX!DfE I1h"J03L%)4f hH)قL%fFXbD#Mi`J L@AE&`Jb&5  0ЦSJ,CA1elbCb)IH!JhDФR`$ BL1RFĉJ@ Ʉ,)0L2ȒS$̰He1" 2DBL%)D" )FL4Y I6De$! HJ $bL2FHcHHHc@dH B͂02(hE,&DL@2iM"4I` HL h)AI2J($42)lR $D$f(d5 Ȥ04C$E@D`HZA&#CA,RI1 ĦD!"a b (% EId, "J IRh)E H`fI1I D)1 i!)$ 04e$( D0beI4Bf6!$2" 02`1F6HQ HLMdcLhA&Dh B&&RE)CbIRCIH&HFhP4B6&HVDR`Ll̦$ %E) @#a43#IL Bl""L $Fb) HDd$#JFf0AJ(#*B0XiL" C$e R14djBc B&b$!LHf&22aD&f&dRҒdY JDD4Bf!((fIH"LQ%" "a4hQai!%Y4#3K(љ%1LƂH3aH` (M 14PDe 4͒@1`LdbX,B0IA)b$Be!2 HbLQ01 1SHA2 dсЦ`CI 1&Qi&fiB$D$FR2$LQD3 i) D@Fb$2$HɘIILJFi"E Hi)4Bb!! iSB4JfD2A4"XA)40c "EDJBhfF4DLB2̌3#DDfiCLB`JJPi%)H!&dƄi2 f$I Lb&e@Y& e1 j &a$I2d2&L`"DQL!J-&Q2B DDAF2آYXeJE&0AJ&h`@$4"0bLd&HDe$ٌF!fLI`LRX$dbQCeBDdRL, 4$dfQR5)Y"!iLd$l 00f3&dEH҄1bE 2C FI2F0A H1R2&LI@$HAa !II $!($L ɛ4c$ ecDibdSa SSfI0F2d$рŌR fLƔJJAChI R4DMDbEaE!4d Ј0M1$bdF(dL!C%,,LL,2J`dB#LDF4d%) 2)F4I) #100LE&BĂ0dJd *4š $bT)#I0(e6IL 0#If)3TLD 0HaI(30 4Lfd2ALHɒ&!$iH! )$Hc"TbL`c)!3 )̴3&X3A!BbeHJb&C A2Cb$!(C`HEI,a$L!)L@MBM0h1lA)$$&A4)BHBD"1SJ PbașDI% %ġHYE  e"FJY(C!I"0F0ԌK! 0KJI)"0RQIDIF)I ILHM&I(PQ !"IQ@e0"3! M JCF( #!d $ &D2CdQ %"Y$h13$d$$ĈHF2biB$1&̈$I)eLBIJ1JF&,D1(d%bl0K$ A2Vb1dd),FȤC($d% F $HYi$64i #E4D"lDD& H-$"3 f$H@јb$F & &&"(@ $&BȰ A"24FXRI"LAHL !HFf2$"H4DQRRDP2B 2(@H#H#"dFLL$S,00&0FZ1H4DBJf`4HLɔP22$Ji,d 2C IdH$2XЌK6C)21BBQdfB$H)!e42$2L* AH!ba$B( ia%, L2 AFL#LR*I0 LRL212lC BLE`FĐlLbh l HL RP2QHCA%4R$I$RIf`HI)2$!h "e)D2$ SJ D%"e) dFhc&$,@BQB&&! Qb(HMI "Jd3! CDʆf($I d aHSQL&h &C2&jF $%14`&I&$!"2Q(1I%&2f 1!I2&3$ )F0&Fa(M0 4JR"H@"Y!C!ԥ"I$adA$"$T*%4قh ($Ăe2i1 `,I R2f%Hf)fD"D2`` )Id(  M2dd1& (10BL"BT!hPLS$C"(2Lȓ$@I15 0I! `TBdLɉ B!@IHJ2M3%P"l234Y,A2S4Q3$ JD Y$(F)&IE 00ł$&Jd f31Д$!#F&)C A1$D,42& $R RDh))A4`DbdLɅ 3) dSF͛#10 @BDQ)& ) 3dbhF!&P%f #@dPHB DHb%&$R"L"D``ʅ(R J"&02!F&ĄF0$ bRhЌ F1$HB4CI((Hd1fI&,c2Y4 TbRb,ĈFa#J$F(2LFdMBbaJI II$PJc)BdbeL6d(3ifHbdhɣJ`ZihA`d1h"#a#%L d460(aFPLbh @I3@B D@I)ŌbSĤ@)IJbBl a$,@$LPB$I1!2@V")@j622MBdhDF`LDXc2Ȣ2H "LLI64#bK %%2McJQD DFB)XQ K"F!JK)FdIa%I "c&d$! hёbR%1 RIM Q,Ē0(DJ!$4$,F2F!HhdPfhS `jM$3$ dJLI"B %A$d&DbHLɣ$@fBh,Fi1Fb,)!4Qi"c2c$S d%d2DRdH 4&JH@D DHB! cDH!"@! Q%&3PČF$0#`DdLA=7΁TQSEkJrNXADIB -r^Je5G$?i@ qϻ]ۨ[uvȧUPksm7Q˴a\8rwrvm jW[sn4Yq$[wV6擺3hYc6sqtlNݮ[Nۻiˮ[FܳZ%۪iqwmm6cc8֭˺vܵtʁuTmkv;vuݵuՊ;ԧFuuiafNmw;[VܝuwU;vkm;s)u.ꩮm\ܹ]-:n⺹蹛;;S\۳ڹ k]l+VfV٩mλZvۭ݁ݝ.ekLݳ滺5pը;붝cekwl]Wu4]m˵ݓ].qqV]ѶLrhiZvntXgKWsnFN]qvw7:;32tf:nv.KtY.[w7vmkn\Jv+12.λK:]rin 7q\ٓZ)[mʤtXE];պ[˲mnnܻ-;lh6NHlvs:T7..jٻڭTa]%ۺsls컺w3Z.͝nnkkvۭݚͪum֑km3V9v7Unk%eKQ[6fv3cMΪ4]w[ZM]Cks;uuv;,ۮXQu]e6n&(lζYTn90$թmDTd*b$Td*IUF@ 0"IUF@ 0zi LI)ꞡyOŸH(3d6D D(!I22@$ 1Di0FHdDAI )!I0J @`J!01&f #"P1DBb2@ Ȉ$(eI(Pa2PDĤ(CHa)b(ȉFi1$1a1L4($C0ILɂ! $`Hf4L2dB3 cIIHa"Q$!3CQƘH) EC)BL0̑dHl)bdPI !2(L#0@K I Q cdLf2# Kh0`,L c0!$H d1X44 I2e!DhbBȠ4Ɓ K,Biɘ̀ 4"`,LA) 3 &BL!JR1 0FD0T10bI),&Dى $ M$HH̦#f$БY! &RdLL&%&`HiDhFA# e&I0ĘIFH$`H٤EB d!IbP2HE#2"2l#  i2f1EdHc0Y&H$JPD(Ʌ2T134#!Q %HY,$QA"2)0i"Fb!ٓLI6I)h$#, EBh$ $,I0L12Cbd$@H4d DIIJRdiF̠JibKYA "H I`de Q""L L Tc2Hh42b FHi!(e”(eȒfI0h"aI04H$3&C,H) IH2 aP$ĢBa$L )Lf)%6E$4dH`SH2Q*$c#F$(F&JH"e $#E1 3I ,C3 d12FIi&L4PP@cEE6,D`BB$ 4)JHJ B1S HL R"`$BBd44 L̄1!QdLHeL$Ġ4 D 4@I)H $T 4"L !& QDde2P4 " Rf!L#2lId$d$! Mb "! dCR`3214I"@d"2XP5  JHID)  iS "B4C2EТQQљ$ FiE(J")0"aPFDhĨ1 & !B%h!"c32BfЈ6)PJHHRB(D1)6#@`̳c HX@hLL # (@ęc1IHY RK41)HXYDi$ 4fdFQP Ja2iR@4K($L0 H0Ɋ" Fa(  Db$Hȋ` 4 LD 2S0cfBSLb2CQ"b 2 Œ) "$f!LaP"612bB&(f JY)H0H3"dF DPɘb2fd L$I$D2 H !aF( &̐%B%#6" CA$ I&MDLLK(aL2!!3 MLBdR aDY!JbdH$dbM4 4$10Di 3CA #$ES&6SJ0I)!0$h " RLd! ")LBe314$0ĥ I 4 2QI(hAH 0A)PM aA32 I$I$ 3 dJ3J$4Y$2I@H&0%l)2@b̒#1@4Y3 D201R3M$TfLM& a!M!"2liFL"IIHH 1c1Ii1M2@`D! D)0HCQB!hHDDih! f @RĩKAcB %$`if$&L$%He3jdL$ "P`L(P14i44,a$#!M4E#4DLc" Ʉi2D̉$&Pe0$I A4MI$JHJ 1,ȉ&B%%!FHDb3 Yl E(! @@PL0$) LDBa(HLɒ0 #33 D LAb60L@J&*I$)J%0dH,(& $,)0IHA(j J@@R0142A)i$, L1! VI"dHeEDƄh$cHRpn( l@&ɤ4L fI&HF1 #f4h!!CL`2LDF 6$d 2D0Щ "b&S4I1F l&X( 6Dlf2$E 4`L$DRccHHRP dR1D @4(AHL0d,Ƞd( &XEDQd #&IbhI1 $1 (RiMLH21I1 B4#3FD!edhb2!1%M 204deiCL("J4$alA142ld"`3 4H"X!&)2HdDAQAA) )2)m(IH@ȃ"32PXIddBBD$a""2H RF)ĥ&ID1 RlI $,L d"JacB!b$0$46E2iA&R0!P̂S(ЦE)#2 PQԉ& 0)d#IL%!MdPH` (`ā2B QDYɠRC!)IJb&e ,"HI- B12JDP&hJ1H &4faa@DȉfIHҐ%4DM!S J((( 0c M$HS24RQF) (Ȓ"R$ %*Be!H2LHJ6D$6MBR&dfMb$L3 @؈,M2)F$KH*Q1IL $͘E3BQ$Lc!$#"LC"i)4JJ H!0)$cB(4 #`c(24e"021ddl I4A$X3%#)$)@be#L &QL(b0)b0"cL3 3&&DbL)I Ē 6 SFh`#iK3J!%"c (3I0iShK!HC4003@ha LD2$Hed 4F3"L2AFY3PHdDF$FQHI4H HM% $2AD !0i b$Yi0H Pfe0 id6$% 5 Dh$$e))I3HaDEM##Bd)$ d($H&D%R4LJb$Ė2Ac)@4!C(0ILi212(&M $AiRL D$&`%)L@(&IddH&DS) I RDJ@IF1H2L!2ȁL&M30LL$C$($d"Dd`JCF%P3b(A#1 )a0a)bB… &31 i!$"M(C DQ Xa2Q%%DQffL DdIR51"iBid1 2f4C 2iD4!&f &&(# SHYRcJLD"JI)4D" &HLS1Bi&"BLR"3 LMA2!% &DfD LD3)&d` AQ0$(SQ"e( ) `PH2 bbR((abI1I L!d BFfR3I &fi3P# M4Lc$2XCc M# YBP)Ff)BD1A $HS4M33!Bb6&"F$ "6lSJY"I)%"DI BjP hJ$ʁ(I2R0$"c 4RR"@#2Y(JLR *Hid0"A#A0P% ) S0$ L$## ̂lJ$), "IFP! (fPŒFE i% $ - &)Rf#LPI 4لaC"4(RjDD%0 $I2Q&2K!bHd )(Ě!1dJe23!a`˜щ`)$41"FI!#Dd`HH2RH!M ВFPMD&@H)iY4DDifR"&&hXA# EY 0ѐX!(E,01$%aA da&D!f4XT$@0bdıI!)" #DDJJ22 0Y$( "iaM2b#D@`!%Qd@50LlE$SRDH#D A 1ĖRi!!,!H($QBlɐAPY#)LDȐ0 $DdMa$ ,lbL)"dJR`d2((M$h R& *RXY(2c "bI$&l0̦20؀4D %!141$ɚEBe)1RA#E,&4JDBF(Ɂf$6"00 $M ( 3B2HRLdCE #D i$b(F$JDXf HDLJ#0Q"Bc6%`LbA&`i2LI$ i D)" QI"Hi4$4CEb&41!&$%J XF) $i D(JAfe00e6(Z2 )%hA(!M,I1dL#DHPSC)0TRfcII( aH cH0hba3cIљ D”2E2 1FIRL(M"IDSd D )))A@ ɤ*hhPD ! eBd a  BĤC L a@@LF#I E$M&"%&&B$bDA&Fd)LHIChe1fY(J&fB`$,` D6P12HL"XdFYH!#JFb,HI@h2d`d1bd6H"HF$Ɍ2 %) 0i H3&He4ƒJXR$eI"0H$P)BfL @dL #DF4"ك 3!YI6aE4#&#&L@1DfY&1 4TLF3 5 PDQ4D́DP!%B$#L!1M$HE2ҘJ%dcE1TIII@S4Ha E@ҐHQ LD$b L( dIB4JlL$2# IDQ& LC1`@24Ѡ)10EFc2b%1`Hؔ!"6fH`PF`"#& 2hH @@JDF)%HbMBbEiDf$Y(ƊAJƆ а"DdJ(@HSLA dS$h؄I )fC1"BD1E)!"d$Ĉf Lc"AM0K6e3RF*F4La@d#M JQE(LX!ɦLDH̤aF$ i&AaBE ƀD!CDQBB LL)D! a 4L L!"F42Y2LbL$BdI$ F )(H e Ā@E2ShAE)"Yd ba3$$e4&Rɢ"2 E2P43$&@RSbARèDHf$Fd!L2HTDE !E$I "QH-0M#!(٘$$ # JhL$ɤɰ̉0#fi A f $fS6M(cf H,4I2&I4%D"CI2$ ( T( ,IdQ`"$$I"(!)b 2DLbdĥ) 2BIA QBIh(ȑ4L d 3LCID"e@&F i01M(XRQZRlB(DHbLPeA1id,dK6BBAFC1a4"$"Fj$HCFe) DEXC$B$9\ $JJ"(I4P4"B`1`0SB"!dK ("(С%2J6&03 Ph`0 %2i!  )aDT lb`(,dHa4‘423BI`4$HI#$B24 i4$)HYiS$DI)LbY,$Ę#(0bE4I&h 2a)$ c2XK $ѐ̊TRjJhH*HAA!1 SC bXi)4%Lā,3&b%%&&0 4QE%e h)DDfI$LfH CL !b@4f)Lad)F&#$R,P1 &0IQ#&F)2If0DDP)#H0J4S#FI52J0IB01"(Bȑ F% Ԍe)$$dɚR% f@L11!QRE$1 )Fș$F lIL)"Q0L0%0"1)"c L(fT B#Db0MA)Bb$cFH`L҈Y 0I1,HDE "bHA" H&$  1H(PH̔BFEef(,IDDІɦ2F$HLDlH! ""h2̌#$dB,Bb 3 &bЈhR%$Pf@3,iE@fE M&11 M!!J21B  $e4"b̚e) I"6fX3L3)42M SbI)"`ḺLJ a"RIRRDQ"%$H(c 2i1f Fe DE"dlH&i$I"@E"#01 Hdh! I$APL F4$FBH32Ld( L4fM&"I R% RX2@1$ & 2FLDM#1 1!IF(H,e $CA)A$L)D,J11E3 $i 0I02D)CM R( @A(,!`IL&RHD4ILLDA"d$ J ,HР@1H(,1$ FL"e1L01"H$bCa@B PJ3&E2R d  f addLƓhM%##"I#LL2D6!"2`"&H J&4$2 Fhc RbLLL$CC$JRC iB*%$)JR0$R"DX21F#D(HBJdfB`HcDLȖ#B2he$C ĉHDR$D I4&f2bi e0DIIILe%2M!ɑJ26(CL0H"IJXf @bF3&YFɉ" ")!B#!3B 1JJI& ɠX iI$#2BEL4ddA1c"iɐLDP&2&CBfB)1% I2`(If, B$0FHB E26b#!&0QLQM(A!!IH,0BRL)!"CF"fcK%$bY0 h6BI&II)(̙$$RFJ "!IE4$L4H$hf$ MaɄI!J$QdĒaQ J$IehCDd !b"dȒ#`%b E a!!R$DJHR`B4,&b&i%"#0I,a2D% 4! PM" fdR (6J`&@H%"hfJA"$dZb$I))(ؘ2BdH` K$,@ 1ȞBJf$B&FE!e(JlhaҒd40i&Qd% dP҆M4&f@Edfa2&&0i$̔0L ,% ) ,YmI)&bA&FdP&Q) IIZ$d$IY)0̌2R eAL!33DB11K b2HQH% bVQM QIJ&QHJf"%"dƈ4D@ 2h!$"FI2#B, L̘0!#0H@L)I1 %Dč3)4,@(BH S6i!FL2dPa 4F4!4Td҄"H&̤Ƅl@`Ll6h&@ɂ$ & &C DdȆ @3e H)5DH4I%L RIcB44haQI)"f"adJ Pa!0S$2RI$ $`I# cM D F"& L $H1D$(Lh(R $4QeP&b L IS&LcH HA* Hb(02)H؄H )l0QA& AFb$&P 2D1e4MXdDIFSI"1,ĄHLHb#SSh!@H3#1D$dL1#!L $D ALHd )A0S@DY,h22,HHEHLH)D2#"QHYH) %DŒaPX5% i@̚2d&L `&RM 5"#)1304I0BfHfI I0̘D,(HPDĠHA L4a0&R$dc3"١LсDaD(*)`ɦ ґC"fH,IHP2 20" 1JL0hM @ FD ̓02`(S Yi$B#C&b @0dRabRS2PX)2lC%"$cٓ!$Ƀ$D&H3d#HD Bdi,#hRLɂa0& hHdA%hILH3$Lfdc" 0 jiBLR f@ "@fQ`DCR2PEFHFJS1HI ́ )B@cB!#2$"C03($M e$$$aHf$$Lbʒa6T#Fġf#$L"L̓(Y2Č &!̔L$#J)4RaI%#R,H)1Cce)D`2CH2l#!ņS "f&LQ 0 &I 4YȌ&SL$R !)(dA $Q$ƈfMc I,`f S$Pa20Ɋ(H2`HȰ)(IH$ 1 EPHQ)0 #,Ƥ" Mf4(JIJHeb2aM1`1"Rf3$0$#0@$P̌e fI AbB$& &) A! 0,̂44&di#2"fDD(B`C$Q P% IAfA6E KQdś#EQ&dĒRShTJdi hfH"1JFcњM"dI@` BRIJ &E$a& IML$ B1"C R!Q$)DI04͈6DM,j3S32 LBAAe1(d$SB$ b$ْM2a h&XBb"D0ĚI$`LJ K 1 %,̍$&L R0e$&LPI, &ĉL!%$& 3B@MD`0(4$L fc D0h fAe1 @C&YPJd) $I!e(B(bd"&DbI)DȄ@ `fD d3$K$(DA$(F`4L &1 1bM0 HB01LH)B6Ra$B1B3dDSf BF4 !bR&LfIP S0HD!"dPL " 3&FY42T2 4H4Jl &D`2bbRYf @c$Je&IHfb)I",l#&JR 2!`"@TP23f`H eXI$d)@ 3 @5 Lfd X(D3 $M 2hb# e`(Ƞ!dD$$H FP`$AA&T%$H A4"HDDJ@ @dC0,IM@ 6)I(@! !,Fi"Df1b hC,DaL%0`&h)"@M$11"L!A)1LŅ%S HȤ``1 &!HA," Y&"d"(i1#"fF3IP4R)D4adcJѢ@a&eL I(1L1 "D#*,S2S4(M#LĈ%T&RbRTdDRdC1D  IH P@fY"21baD &"0di1&`%3IY0Ɛ4)HH &" 1!DL& DC&LS& h@YIADE"cL%AJ$Id# E!"e#J@4@5B&f @i 2fI1$0%$A$QI͒cJ$R0dfbI dD4bH3L &4b`!@DA$Bic0(3&P$0"2 fFAbf,E d#!2@d &"5(A( ġL4 C L% $2,AAfD"DJ1I D&(Y3)SCjhXM0b#0 A1"lTL (%J$4XP2@@„a&`ib1bJ34I@(b43B1$* 2 !$#@A( E#%%@)R" I1Y,&k  f)0hI)b0ģfQ3) Y3#3$1F$ј0a2HJB2(`@DdB@L$DD0Ȑ%)YL&,RM) ,$$$ %$d!Da"i3 &&L)2aA"10"$a(!LiF3L)0("AEdd$@M٤&hLbh %hQ II"I&%P,IB`$ ͚M$ EDȀ46@P0dDhCIMe!M!b&! I02II"` Ai4͑ SIZD,BHM&e22PA4 6$ EARM1ŃHci IAbJ26(f)42fb  Aa @(1B#4B)6&M̌aҒD$1$#"R@!&@DILJ3& %)1! i%$I&QL c6 T"RR&IM!,d!1&! L0 QčH(`A 3H"F IAPDL4h2hA&S4Hl1 B@1bH11 `ؘ"!4R MIHd S0FM LH`3IDfĄ161K3I 3IHC$CI#"1E1S4ȚDCB"C2 01 4KLb2EʌA3$406bLH̐dPDe"D Б@Q##C2Q%D&dd#1M3Ji%d4X)fA0BJIM"$FLBAH$PQjID$1K6%$14b!h3e$FaeR6DRDF@iH$)$Ƞ L1idP1&F )DHM̐B iY0& 0@)$$2iA2`Ҧ#$L #SPXА &l1h j"R2!E) A $a %ʄjPFD&ҀE$Q`cĒ0 @D0`ёLJQ$M E&f3,D ɄlcCIDI@"DJ"22P!(T% "T J"c@LTQYb b`M DFRJB(A`L  I42dS HRfH00!d J)J lI faL23"a&!0!1QID`"iA&i R$h$(b 4S"aLdhRhALHC$Y)4$E4ёIaFhJDF$R@2ccd$٢BI 224hRL 0 #Ie$bPf(fLI XbhИPa0bQ2f "I4#&$hI4F2I@a$ $ *%%"hLi#$H""$bFl F@$$A ,$@RRi$T€$IL"IRJDH E)LQ #DiI`ɨ"XILdBa &C0&PHA )2ddFA(Ʌ1!d̀IL & $S )4 L H(( HɒE2 ,„, 6)"Q,II fD6&)K# BiJI"$ʼn DE(a,aeɰ@dDLL0(l Y#(h̃LIDH")J0iJHD‘“0#$ "P&&Lb$%H2BĢTe53(3$2&R"biJa H K% 34#4$h$R&!62R,ЕF# !4dF l$H x;nzǶZ^Z1Ƽ)&u4B&.!KAZy9o8W/G!ssr̙NuQE"F _3 T˖'kwCOђxz$m5MlX?wu \T"$z.~Brw elfN~ŷ4[tnIMvt u?/{M'ؓc7ѷ#~pkwiє&~$~216PbBϤ3.o&%Zp$oэ[_aur'bSkTD[> iu m?fs*G]=QJ5&CGZ"~N9Ynf֠_v8cC4}VUx_d% J P""<|ّUgw3*%:U6rbk'qKB/B4i-mΆ[zh: i&,˖7ffgm `pjū 3tZ.yg+z"v#J-fE8Zz'Ϫ^cR&J12QaJq_dx4%|)6$߰m7L̤^ҳ נB^t 󍘚FVRl/?wmn!FU7mCUigj_ƍ)kg]hNaR`!=RWM~XmBI]rT'0Χ^u^dFii@aHjwy{۸'@xGiTKǯT/3#[a];C1 N,`SjmHvʪt@uU2ͱ&~60$Z~-y݌"E|rՆPN&[49ٌ47zgC$N|&x7}(B^Haݼ9ytsGCMp, 0=_4ZA|,Ϻ)ȮP>eyԑ+E!n{j,ǡ ΑnƋy!˙} V5*怆0ˡe&^\3ZS#/qHHZNN. Ti ~".rk! 'YƼkv?5YT4O:i334/E/$D*hKJש*a/j2:m~۰ +BGG[)yPlK>iQ<: Es?~3aP)vζP[M%7JOl6; v5ⶒEIn2-SC׆OpV FFurP(l7-m()t?^FEqt7BKog&]#GrE'lrJ1Q,SGeibO:La) H:ktxP[ II@Ib)Nĩ,qwL M(w@~tz%ZOغ1 Uo3^~ؑ ?%v*}k#?f/F2%bby0ldrƻDg^phרKo7!hnN2oi@"أOjIwI1C,j @$g]Lq#чU=F+ pj {mVkҸ@\`kn_H7ϣ|Q'8{&s̕K~ݿ*&QyG)L _Cfx,=J lmLE!+OOʪچpp׺BaZ^:fq[r~-sYh<@ߞtMe0`ُ2<1+2,dgڤ=k]ϯ}MOLRGg>O}:nS̯I2b:rBŇ|ٯP_e}sݽ˝MY`lg`֣ 9^z8~^QI0TnrLXN:zm/3[!Ŭr ⺢/b&cۥyUQENJ<659pJoev&mm;ͽ=0ESPK*qBzzL˄y7ނy\ jgB:^Q &TOX#b mc5+ TDiU5m2k9++G Ն6Ԇn\!LFJ5QEü[ߝF-"m3ȧ mUB[մbכ mZҹX0&l϶6/O)?nf%&d_Y븝+x rRVb֯GiN參;vcnraҭ*X[u>#@NV4]kB);)goeIJE\+o}R%Q_L0gy}q2}9\70 ?¡!8ח([nY룧īdlhaJ&!fSp$*9oicW3u R jKQtzM e |卾{lUk( Xs:g4RbKQq 50n/!Y= uԿNIJwc&}LiV< ޮ_»X 7ruRu0Q?,d|!#``iKcLnCmpԽjJWhoxi}8IcZZ_z~EX"セfԟ(Z!YͲOtëIuwIH_lN儫yHj^NÁYv[ݴ׈ 1$Y#@> &?( qyﶊfrv惈 7.ю'PP֡4WyLwb}$=SMB/ pWuh>m޸AmƘ ĈǏodd>t <)<6 cRΩS s~l4<]f4Unn|xqI^Loz{yi%oΥ*KGT%Ue$,̭ŪUCkԾ||~X5yS^T|&nv1 ͜˺AVs؄wc~wK/m*cټDzNd́2гޓ•=/Ƴ5&;v>%ÌWR0\~t.pb5`)4b# )>(k/\£=/MwNcPfm8y5Eb=(ԝZ׷nٟ]OM̐_KWj ECq){H|aiѾVmY:g-hok++%Mu,L ^iK6=yw\[V97}6P硖V}qP#f[%dcŷ)kmH>o}=:L1p衉uz5[.HC(\6nFDIaqS̡%}=cqRt!XӐ 簫<=,lmyyRd۴ى S!jP55L!D4ЉZ30\Ngp㧭PS]T|J3#%+Ĭp2k#j]2F3ލASC[pGW%O %{i%_VC-n;-JyT!PuFGݚxB;U~} ,k8Qzc Y.ar-v]cZNT&ZzG?2oD!߻tLbj4a7rT1{=Uͣ.A)ӳL[۞-;. 1)^xZxV,qe@>ӚͼhW>Uj/cPS(ap؂@k..+v0'bKg|wٳ8.'^E xR8n~6uSEMOS/]T pog;mu4[(,RBD(&W7:8O z䶇4܍ X/mY <dnK+`aJJl2law ~}+:N&Vb9ie(Xqxމ%Ju/FNd>ezZKUR,U煓4?~Bzb8 q-0 ]T~Rr23Dl3/xLj;eCDqal_.ZO{}+) y 2\J2R=LηT Ԟ4|M#MR͒Vb>Z1+96ֹ1zobǚ橴ZbF)}XO+Y]g?Aonj}|=9S~,&MG\ziAfj-MyNۖaR& 4WUH֯*ml:QpQ6I5(K()2szss#qe;`{=/MBkPOr{pLQЯ+-oI,O,KN~>o_ScKt>ϡ~U*;/=x&+3**|{,{z|>UB7Β<|!%5gԶ;66B6:gLl:ɑ/ڸkCyEtW4JeP*v2H3֚P㯟RWa <߾:^Sy Yu(Y!Gި4:;,{\*ca!\o[AC\6ed(oP蟝 )y_{6_ W@p jUR`v44lÍw!׼g2IcւlSۄ '2U=M t_ qrH`BОuġq;>IsA*#>dn0K$_'/t_iEEνK]I ^~ t.s8~c/<|!Is3rW ߙaV6^>*-&=BW:cL:yK==o;>VhPˀ0gr9EeeT=d'{} ɶ˯b*,yf5M$#KɕeN/VܬSLqJ.w.`@BrV*E=S"Lqq*9^'Clv؜F^ mO zV1jä{i@ve sY#ga-/RtyϤSut;3ΘEEsBn!J;2 \ٙx|BE0u{{ % JGކHmjMn=PT[) -/ W~3rDjHggBփ%}6DDiALEfw-#/mJV9(׼Zɹoѿ~Ngߧ"Zp>2(ݼy$|(U榍!Ѡ<#w?1Q妁i_~hiؓ/+_)w" {mD~') 8RDG*aecyPm2l-$?r2}?y!ܯ(he';A!h7[lbύ`qtDCKPވtœO9qG2ȭT;>˾gF/=i/\ha7d1벥s8i|G-|4_t;Fy ۆK'`U1ðyhXӘD-*$/?9`zuI^%drG;ziKf9Cç7O_>2-o;R,$)r5MRs_,we4.Qv(d=ቛ(.bRTC ϼ[0h3{֑{VɎg׵#j`կ)ZDa˙MP+G3CC 6z#T7d,1_*}Գ%2⣢aBr{Ba˻>>.Wq/WD6j}*NMwƩç:e"ѵj 5d؋]Jsפޔ$#Ĵ ƕe-UNTO|NQrY6m{w1Y hga@96d3~3GA!Tމ= r7&hm5;(-㉣!ai栓q=;n*j ]]oVૡ,ݦ.LwsڛwAzrAhٜ>`C3'YS 0Yz0m0j83KY{*JQpI;sl'Cdv*eN#cF 5pO!%3%@YI.I¢itG=]H*l|xϽԤvcxku7TNPn-鶿yQOkOw9³Sj*`;o顈윣 ]G:4r1?'q߯BD2]]in[K|c&>fW9(4\:20SU&8x-2<,$+_Q{ ʯF!ɋ%LA'>BDg]Z}}2LDGc.gJ}@dM=hhi.: /; Ƕ6+r6|8Si-:ֻ\IkTjZ*H* ;oX~HT9[}4$\{Vny_4yBڎ6&)kSV7iZ]bUƋT\c-P8l2]AtWQzw(^ c(?iut*f]Gqu Chk!km.)}_ћ *SC8 ۖC'6k_l. 佅sa?\xlu$6grUƜ_t|Q_miR<6̵) 8) 텡݅@0qЈNeOIH.uϰ;dɵH4h>clM( rû=uϴnQ2KH?ꨶP8/+O%2tע>xisI"o5'BbG=P01b%w~aLnMgs¤W*{_] ,^b#TpW3:um8D˨z /I SqwuJ! y%[3TIC;(3%Œ !3՝1fU゜YL[>.6{1O*uL}P^T*]3]W&^e!voMݷ_Ѷh1bs3k+7㉣|f[M~[fwenݯ*>vLA:6n]L,5Ubv=;;D|L]7EV=(Ÿo p0%K˸# KN^~Wˊ`*+*Mm7eB6y;E6r]FwS VM20 3CIfevO^ )Ja}F929ʹТ[YtݺL}L+Т~JG"i'<|PhS*:".[1W#NNpbii{9nFvSGk3 ޔp]\=nh\5 Ӊ$MUEDq*5ѭݚnꪫ+QI _Sy(8;KrbLo'-o\r t=5lX/hE W/Oi& }EpGz,kVVfNnn 42HuES-XbrN@~ׇDGcdܪ KԴm:]aV2!K+r$ Qr$a`!B#/*Ó,euQz1*IKonHI1so.r 6ꥐrkb;:L +ZS~uz/v*}?A[ W* pQ,  "F/+je6K_;zm>NV[=TS]K}MTRj̎}%W.|t Fo'!JгO%[tܓMEէr; Ld@ϋUϭ|6UrXu_9a);yo&RGlBRJ`d:j[`vMʺE;zY3PLKEԁ.T} uMҙ.t`pXŘ%+I[JR5voeSq{M2l&e `ѤRmVDM P|;[>!5 T da[AvaWe8_9r k=e =,<-{ܷgid1:"[cU;-{g1Tv洄?>Ѳqxd]`ivq oC>\B?? vcKM }'N672`ɾTǬ*Lj^=߶9uK]WHrSL3ujiE-T "PWӊ}fLn ,syD`;bb[N2b<+ɕSdG4[giXe JGW?2숰eg pKE,fw3/>̨^3G؏"Gw59&'&K~=G~\A羚:DkxM/8[tW.‡qŖ3{#lmqhh-C$jkM=ɰiBN਩jl"0هep__\JـV-(_ډ/LD Zz q2NԻ4>1B%<-jb}T] |zs|eU̱CzMe; Zu=޿bֻSѤ.g藷T\,wѹr.V=*o,sa1x;%]iYp`awHTkn?KXUG]6w˦t=8T[ oҙ!SNGF7n`JC 5w3D[@7dEi`9 oO~Yxۺq"s{$:WQ۟EYACX4ՍXJ?ՎE"x[Fj j"& VᕒeGv6nIF2arY[@Lzdy)|խؖT?L?L?{ZI*d,Öb6ڪuM8*@SU!uʊoЗeU씑иK7 RS=!ybꁺHXQUcq\s3~z+}iU Сj a{>.EԳ4\˕f k`PN/ǽ>ˋBi}y ӵqBϷ+GKq"qy`Mi6=L{O^P`eSJY-_uw]5 @%V=E3FYVGtUK `8gmqf m9-#+w6ZBΪP0KbNTmdBح˫eT֐=gn\ĵ^@jt&WBN˨dz8{TK&D$`"cTwIl#3U.Xbh-sqtV䞶S *dj97({zX#7x4!:XɳTs=1Ae(iQ4`չ UF~a\RD]Zu2NbUm̠b)*Ԯ .,>7mc%4,jye>!CJPT7il|s ͙r6j:VLġX-e!}7!y zbŲ2"}y.:dWxx;oNVnySc}z1kBV(wec+;([Og*K^륉ݔYsvR ܸC$вNiu"bD:g*Nj7B4).~o;giE{ qb4MU-|Y'HhLz 2l]6Vn9&oZ!~-ÙcONCP2mݼk5 i& )PIT ыn ]9HguTSzdiT(_jV~vann(ȷX}NՃi8D`YGecV۩ ~RgY_X+v4 /!N}FK.+6TgGXHHW AUj`N/Xr9dBt=Ԣ ?B[[*]9a|K\DP,+'i,]=!.CWwpY[!jZ*IlR}g]sis)# d^(3ꭂm[Z"В-R @ CT~TrO'O+ n05?l/BYh}k8l1Ccxw- Ї3A@ 0J];:e77pL`XO0B/™N呯u?[N^Z^~;וWcZ?I?AQ{R5QjSJe1@fǦ ْyOa-q K={)˔U^TJ (h~;Wѯlg|N-xSۆ+гOX{ɅWYf+1kIZ\c91٫iH#wx(8+3ZYc/RM FmUz0JgA='4PEMIt͢ ın}1|ǩ|Ȼ+M_ 崵=hYDX9l0{!y e3h9 GKHf35}!4-A*Ds&)Ũj|ވ ` svZ(B}GGzq;8Ecs d/*'m?ʨn|@/;b8*ݐ׻o=fNlsޡjkORMgn~ƙa}q'AkjrH.S|5W@LQª' v}4 Te@KcǨ_kG8yC e`Fg@ +lsr7S-xW0Bga-_Ly=/ثSjWh-,tH3-SO")N-X''Mq ^LcNLȳC ZBث:ᡫ%21 MZ>2Dzx |R^*uב{*iX z~5O $[/[Z.9=DU/0bBkoJԎQ4*k՚$8ZeViZ ees6/ّ i >ʹN 8!ŅEoĪٶs`>Ӎ1>YOߩt1'p[qwŝ3Y|ox_eB7fJه=nK;/c:y(y69M$J?3TY ߝ-zn|5rsY]"!sZ`-1 E_ܳ$/Ppy25g^Euy֏0#S4vղ4ZW+gzpX`EuMkLt#2LTL0F>yϟ)|Jpm]{3p~06 ]%aCjH86ƺ]ަj#=/% {KǓr|Sv6B+X[m1]$7z}fu:~7{MhU9o )Ug jղH!1-z6޾l=Q˦P%3/םř.]jb(pnb6r@4*sC\_F\K~EcԬ8$[Hl뜡T .v%&X]ef;i _}~v7PLRI@H1Wd/nNs7 SꚘ.fX&z29ܬ$;c9o5oi%I|rLn! wB;͵G$ ~2}1XX1znsiD|,Zcv T~/[ʨq+|4@#6KvC6v2W(Y/N&B ;R9 yc˩]vkϨf|7!cއk%q`W#Vߔi0geX_v>'$k*73\e1&uu^|tbd1B'䓟Ox-`rQ#7/Nj۬:賭9- #߼Ngٔ\Di IYJھ+gZ4heyj PT]G8'͹= 0(M"*[h4Lcv}AQqmު͍C3>08g a -%ZPK^?޸9m$E ӝ'z!(8YW TZ?0D1gAc+_Ie0l2f:`c}-0(NUL^nҗhn07=!S'׶7uIb pSeHc"mV`ejDb ko5V :D'ioc n}ҫnWK'.>ŻvJ QZHze%C)$5yJP:|O51f NƷ?4ֹ.b<{c^x7m%SFD'˚8yl8$~K oZs+`ZBX@vr9la .ז4[G#f3ۣ֭9jiڵ6a * 4 aVZGF T ~|Yx[O&ׄk5-S= {L5!̪-*|}>~Ǧ~2Z?%er}OpO)cd_MϞ)g|A1t \b˩Tgxb3K HU ci QiIazְû~r1L4p62dH -W"j,%cͻɕ5UH5i7:be`D!Oٿթ[(,bT[,R';ZیrYg)RizxT)(M(LƻjJCGZWM˹ ?!OZݎ-ȶ{m/B1 ('ʐg1SKC4J$:{+?}VGiYպ: 6Ao8CrP34:r*xk뱱LB2?tŶ0Jhwg|i ̸[Lk&A 6`&\drʄT yޚ}[UmQXٮ$EW_4kp}.k 7>o\N[?ХT&s茷WWZLi?p<¶Ϊ% xC^ SuTЛ΢(;8Q62&Yp ZOo\gE6z'֔ s60w ީj"Ԥ2,Rܺ2"DmbvXkBK{( =QlUAd:*tP OwX8j \ gփl32H9EKc4di$X{/L/mh-&.QOdM`|i> Yᦸ49ܬ5N(1i0ǔ%t v1TqQ|0;f@h\ m_>+OçK}_aQ A, IuC;İL8hzgHNQ=p"ݩ4K=ӰiGf!Sm/NX1 뫽/2m*<&Qjh~{n/-ӷP)`]x6k^TsHLwVAg̓-鴬BXCs֍:l(ϝ1XTݾIG&)ژ4U϶ Wwabh)CPp~`yکjP/Pn4a^ ބdNSlqŸ W^5~_|nWn `照&yDgw+&Z'6#NY~_*1av1[W蟯' }Ć7uCT*Ǣx e1~hoohTiy=@(x.]f-0D'1w5RGiӫ_|c>I5D$b-|?GTXMf5"n n-C+X7ϟ+0Txl[k~jo>wɈo+Oz-t[7GsdfѦ}M(K_3uEcWз&Ct $+q.G^-5s >Hdq&678eC_{+ڋzg|ȼ4*L/C@]/S C F)ur_KWjkW}VBةpF>L }R6}:CB2(sNrٽ&wn0̗vO?dĊ[2fI]:$:nv]L 4SdyfKgXT`JvZU HU۹?k yt2|JFķYXݾiOgBA;`^}|2CkΓb^S]g(SyzN[<*Gd졇om o^LL+E㻖 hfƮБiŷ1vf Q]ּ"61oOm[ʏyL6zNujSdw<+SlCˍ@֞2,aiRdC熐c'XªX}]`5WlR|i;8'lնcy'eݿ"ߌyraB=yϵnm1O+O*/\p ܷ,^T m_?-)}(}^ғGڞYVvlF/a6-H##^!J(# !VilCy%(>%3렖\'k `1%m:4$P5 d1'6lw6ROr%Ã4$XC*FCe:-ߩ{!wnXt  e TNHav#嫣q5yZmQV8_6z`y}sWה}Àgsj/7I&> DP7fX>4|}ؽCHܮ5zDqAL5r_1,NKIOUƤ6HB KXi AH?A31`kG4(~x+ڟQ{l*ad,N҂est]ࠌJGKw+O%Y ߆ht<-3o P,ƓXSjn4 p^w°E<ڀ3VO4)JdaL (9=>pؿ,pS${ ͳ|8+fv ڴ3-l#ɲ 2}nM\N_hlpd{ e#-訾K,5v'b\{Jo%^L&6hՓ@ Xp4F ])0čخ%}gÛ(P8货=} (a9{BZCWMi]mgRǨ ٙ oyʑ>5ڵ~Kn) "ۈ-4" d$n0Sxx}y܋vqZ/p Q4c,2yWWyRƍg[gk8Ib׷5?qާ d^TC~i3e6;;_eۤuybgU˛ od}DZc-PɁ`ΨP}H?PIQ `.J`ImZ<S1xN!N}^{sT:쿭 >r_V@ d&GӴuij[^/~I*&b@}F)X׊?sv'uǮO qE6f9qM,@7w6-gWugU2wځI櫵S57!n=TT#Vjo'{ڞ!xeȟ(b^gZNAaȧ@AˉҬ.\]SCRm@&r2TS^Q>O+^ڛ^ߠ'W*eMqZYûlrOLc0u“^8kX[+Ow 'AnCI /[蝇nԕR՛X2v*'kG!k8rٙIU)c7,U2 Rh1-OH('` LlFeŲV#V~kNc|9y9G&(Sm!,LK(RZ޿aV^C3߅SFtl{yʜǏ"M3nu)ƨʡz8pWlcU+u"L۬qL(cM~â42=d2P.-__>1//d2⬧GV!p 4MOEXT 'HiBxך޲ g.>;;^Xz7eP:$cgϬ(&K%Jx1al߰G0wOa?+GL2ꪄYѱI=5u[coO뫭V ))|eQ3"1*=Ci%B.*C06=3R&]T+"{bl*qJ`x/o48(HhIWSfV1mwBQW b-$v,h{K{`@m@|h-WdO+xp&"|PaVb/sA5D]__ֽ+9#8Ļd"ߦۯjxov.!|OKފ3ޤK4y9b9zd2e>no/~cNɄV@ӗ]0-ON.2@LǗfM=5'YԄ;"C+65+82e]:zh^+]~\Iǟ<5!Ԁ-sPJKςo;".I1Tw *IO3Nmk"lz~H)i9@O7+_[t?B6b(@!_Gl&V֙1/!#9=(,؊h'!4(˳*6OY!۽U! [(3jG_01xiٟqNF-ޛn"nip)v@vt^h!CP-ccm2ֹi&J󴨀y>jR__KOn_j:M 9iAҚYIY )]9Nh T#sup'Ekg<-ٶni.=Wk- 5_QgB9FQ_;7ElC?ֵ>ZwǿLTr&VMYd)/lQ- _OO<[eKM;Wԧʧ2{/?u1C)x?d'2bRՓ_QĀ*rDV:W#W6?U;)ٲ6IgSw`P2w"SF^-L;o^7u ~oFU%YA[J6S4 %=>,o5;DoG27<A1},w}PRp'cu=Dh+j!fRGG Yhfُ1+ج n6濹29/*.GApАU x8ڽ @vsgN0j0KjBIIV9ؿ(FtQ)ϬǼ&f&M1":zW MM bWu]?~k#nNI ?1 m2ǰz+aO'_ׯ!-ɪ.Kb%~ &ZNAڦ%S[۪@eo`lއw4WǐY3sAG][neXutv"sZpPQ;T2TBDRwyx (ʻvhTU$c {{ǻۈ9w U9T!9=OLX{qns<ц8PS ˗x'>ܹ-T}QmP*x3ib}4FFDž ]Dk i?O{'_*Y<Ce[cX`기9kbl*y6z s==B fK9ۦ!6CشWN [e=[Ņdl钃 nL'F\Z=65)+c;70-ɧGo"rϔJ0Zt7. {`az=j]g"ںՎtM(25^rh^ː=8FxK|tbZ{;f,.`3ՋbMM =5/U.^Fro1ưmÜqPʒg!{+eے4JK86Ć9Sa.iw=G4gjj_C,oΧяQ])yLqgxC]0Y[8^oO2feCqGJ=߳hFs7rSavCdWl e:rfyܮ\s4b|t~d>|yIH]Gc[dyEw|"iKDោN}͒~X@Jvxv쥈/b='dK9xv[ˬ]L}%-CPp@<$͏>*56ݦ>ڂ0|TU\yJ!x@Ӷ$p˯mtvECJa#`tFt2jYŒs^ya*"^c`|7VgG)Ϋ*]M)>,5yrl |cXVXj2ApeMp~j& r#|ޅvlnzWv.#O&ygE4N:^W7_ *E8Oh\ GxB'wlb]<'Y(Te68akܱ G3duXKsm:ZT-Oޙ_dgms/Eeb.SpqIG*zUB9'1v[;uڈu]Qn+ynoN]lʭ|5߭Y njD#7*֊?cb9)C+_Ԋ+-oj} 3ێވ+_.wt&vO+`pLb$2@Ko*KUlٽНAm$$&k<&60f3+}򕪉󱋂aPm0t4Nǎc\뛳9uVDuq_*?3M]]0TnWU59pJ,u; g-Δ&½x/]v=w1@`Ms\0Fwnd>4ِY^'G@ű,wnOp.gƄʶHngAwdOO4dgojMCld,4U6S*4S:{GC]>m-J3#87 v(ӝ o=5^9 /bDscWT4UsiA[3[a;Y xƀ,"ű@⳿?UOdoMLD39  r^jכkTڱ܏Uڊ| "6zmE?=yHN ^| wsK+XKM5h 8us&QY®o%^JǢblۺǓ>$.jm`fޑ1U[uP#&)AYKYUǿ(T-M{vRZA3phO欸n/d`&u%jAk,緘NYw?wM%1,dWX^sl:z Q26}I3a P?Xl4}@nLg,&Tz:6 ɶ9~-cG%&ʟ;zAIeDp9}+vș zlmzeIgU,T=(diM F`5%aVC6-V:'!Q0,7&\|jk>o4qTp[[ZTdEK8I]To=CԮ ;eݓj/L "XK_n;EC|q[Ќx5׶je6032ؓ 56w\;T۵(vZC>/4دշ;dd)ouh]*u%O<՚pjջpv CxGK:0l 鶨I`=z 9P&^w7VTDsU[dPP6QSEG«Z9L_k|? ȄAiVdOn/5Bdf5ȣdnKSQnQܣ2bJCM5D8@w3&kCdXDcttT?i>Sg#89kg.>s9B8jL I݋E9UOB:ѲkF0Wūy !@gAIFY[zQ/k^ܬ毵oV<1 kԕt_l;>#xT혱Z(U,-N+(z℄*wMP۷B(PNʸ wؘod73b"ū%0Ugz7yBcQ~i($*Si[K'O;V1/&:kWAvHN2YROwCK~ՙom1 d4gսS$ T)ĽO+y]9M1 M՜ԂfB_t䯇\tKcvʋA } `?a5v Χ kHZ.!"wwa wiA$#d=-5o|eU"@oT܇ƨXB,0WN΍gƫU`CܟgQRXo$"*.]"OrMV(jR$; 5a*wZ{]E*l'kSM-X41IsLC:MeUQ> 薈 ]$Q>+>>V|.ONo3&y=N-ltj/󳛂4tv6=CHz\pS-uGê*7|]JqؒM`ɣޤ]Jh_cIrM5 Dcl|L[G@9_]w~HWVY0N>~F -u5D!g̺Ի\x0FM/'aeʀD'-jCK1k>?C@Fc/?f+B#|G٥ `@a,$9*{ L?*>վj Ӈ ddd󰯾輚80 |`DGW6]%׃U $)el)|~FoØJH0i8cHVHJK У AV$/ڸExaF`;M l+(ֶة oi#Iܝ_ƨ+̘˶Ce>o&k;iydlU^}nb Tk~l<4T"LQ2)bA6QR j'2uAzٵ"_[[6z^%sV컵܇@A 5< @-fMraŖ6\w|`o:%-;9Ҫ9 OsGvÒ]Fr4CKyD}} ܾ|X#Nvә1_*Tk;MQ&`DP&ptL?Uzf8('-,!Bحg d%+}WtP4-MU ғmUES/i牪.JzmXd'5:-X|z'U-)Ľ/* k#V3vw&7j\eeĤT9. =W[Ҿ%QYk9je4YP]m|m'vϠXMG;$&3f4ih:Da%sE&5U6-Hrb~àرL2 *45SUa$P{9~`{h_CC?mm0t.Oǻ6}{j/uB3» agUN޳5]']=Nnܖ< >ecI:祎*6?)ߠG*+."8)2haV\7.ag$;C JN*3ݐ?H̀eWS{ Hzf+ٜbLPf◹K{)F= N~6jt4ߚ6I5&^_*GOk#cJcOiqn1(?sz<0TtHU`UJ0+dQ:Ԇ^o3`_k1"O.;!i:y/ CdSyRu'VrgiºrL/e"Ku ~FU[,TTaҨ=P"oiSʑ-n]*u5ӳ@Fpˠ mLUGFz \ .nMF]T\StBX2b,Mo$fA.o*F>657q~@&>Tz|aKmm>LrcokuFwbxzL!T-?6rܡ"_sAU VaW6*/dqW \< >m\} k2.N*33JU`NfiC=)_Q/qJןrL+,*RN\A!}w#*&sYZtv{: ڷb+:2ߡ$>o5.ˢFgںK_K=?DdDOFi5c컶`Po a۵MeQΏnQNؓ2GUTnKňq$1K*O肄oRѥZ6]g{!HՍ©;̣TC+*b6O!^x9O^_`WHVrwhh]mr|6zШoMWT'ߑpjsX\Kr d[#(D[@ NFXɉ@rxI676KA.yG 1G~i 7&a/]4zS(Ҵt"=91@g*m_xgl ^Nƍ{%c8| 5GdrLدCne]ݑ[&bPxzZg/֗zRU2NUYQz#Ƽ,ml>>ZnMt9$Imz;f>] ` [Bf|0y,J팼gEžLvZzሱŇW>"_Jnkk܈Njc+3H>!p#{[+u^!YmC)St,Q5O$fYGq3U:f[j.gQh6<0gg?9nj5"Y"c#إ +쥔|;?O?_ p{!}n`Sϖ1nF,mA^G'͐+#G`=lOUc2w8oaxDj| +0Az4Y}lD[njeƺt頵lmXMܳZ 1"mlrI.*;|x}OYJYaͰ9];m>{~rE-OؗlsgCCuE(?fx~QgZ d{:&DT+;~fmV^:ȯNaEvPɞ W P^VBTr sKw lO#Rw{hBg>=x9w)ǪL QPZGv*Lt3Pp'34r 4U M Pl{5mYl-v/e+5O8Cm®7T85@,Z*e3&y }_/@5Ղi}@y*߈vS7- 2ۇP3Ҽ;Sʴm(лFr-Ţ PMe{MdnL5mMEC5\n9H hT K&OyPu]9Pu58mήJB4Yu$ԧ_3[*U 1NecCWXEݲ}O`lQ s痨UJ\#W hLN5E*4Z6|~uu8 p!fPo焗ogfUOXQ@Es"t/rs nhaB[=AUSuߌ f٦=̨)"/{^1'Ŗn@u; MƇbK v;!=Fbk-çx ^A\%_R<0qU08<ޣ3iCE)(n74R:;U U>VX軁).p`eCy jJDpJ ~92Dۦ<+ⱪsImNY<ӢWvjUƽuӭ5d 醦v"a]W2_aFJG 0F W*fUN`K˿rkĩS`?$O*SLl=5;tZY㰞GtiPir⥫cXaxD ('T#dpҡ"bcrI-\?R yz:W*lE0`~%&|)2ܺ"rj[`꤅Z=0@qdψː6umP|%* !l_Nc__ұ.]֌vdgAҿJ_TjߕX4RaUruo'Ҵ'XZHƘz<zo6?Q4szH*d59 @"\&r_# XS>^4)g/~QB8_(/D|F|t,,4/;(cҳjJ.̯xh++ fzWಋZ^Uڡ}V26?KJtnJ.h:;M&æQH?rHʟr.ϋYYgA|Rqi▞ Llb}zԲq}N-DTf:!S- YD.P/3EtBzpozU]!^#w 6{f^TbɹoMrisߏib5\1aPDzb7lWӊq(L pҚK#fo$}́|=:,b"/݉|<Y/vTvQQM{-%QIdT 1uMVP~!*TZ5> S<;ʓ@38hvInqa/ꀣ[Si{Nz8f6pVgP%dVSlINeQx$sHƲt(^R&r> ΐ"؜~YiATgv7ގཕBEw \U (2[,[ ywW8ɺ9Eü BC/ k>/%umHa\<KUѝx^^-"u[xƈMH<4SO7yB__.I!zFem GDj)˜<޵F5gռ^*i Q0w9{}3b*y>)ݸ1+ˢ5OƟUD]N-ZDaЕ}f#6fv0*b)ߺznފfض|~}7 = HmV<0X╛K8{V]{Rll4.HS0Fekt+ov7**x]9  ]LCV [\'1YO\hZjBuٕ6(-s/5L6`Zۢ*n/ٞEylpwo`O^-_Sj1``#vZ!#t iU y 5nּȲB >it8WVKT(R@IuTF$HMhU@>SR bEZ\Ȝc, Eg ]d6)IWD';ZߩU>(65`/T vscMd&):u[@5,1pY2G䰓t7~]Y7R x&xrMML<5/UceH Mh&YN Z>CܟbiCK"DMJo#ED0n>0GE@nYq lXPzq) {j#y nJu1\-.eƨwнZdGN*B +ml^Z{VΪ<1oBY|wIW=m52坂itGۦ!yZ*'DfxǬemb#oF^q`avL9J<̾Z2Fi5C%`t};_b^Q}z•b09͌S锶73A_ izƣ2C5xB4=ZעG'Lp`IS @N+^oMWGS}]ou3 e>p6 \咶j:{MYv?æ]AQ݇ ~xݦJ7wE^]D͗`Lbk^D`2b^;ي(2tDzBjaRzl%Ugu_哅}?ŕs0 cWIYk։DqF8- Kv R:.$3i> n_yR6!l(][(oQ\VLܷUKkQpTc1V )2ynZS0xuNmg`Ę*egEջbLPyv%Gk쬉G+*MEkUB[l;?j$`=7?ϞSE?]Z'fWWQCMӆ M:Y%ܮi^P? sd"0cWb)QXBjse ivN; Q x{j |&,-`TU<ŶC}9٢b+Y}dUwF8}?iBvd>d 46IxV/mr8lwݑC*63X54O}kC7m.ІƆ\2(`%zSp8З;E=FxԇaZR,Hwbuzxy@LNmKA~KҘy-9ّǶO@u)Hi#th:^ޡsL\J!Lk{fF; WB3ԍW$IK%`-lc 'fsێuت$tzR\nwn:鏩d,.nce9#xy8'O A&|@e6P$lrs(a;#äD`lۑZ?h5XSSspm,<m#4[]";ącKN2OWަݕ/I$Lf:^)CʭxCݚ}A6;hzZPh?ςJ|Ghi$% `3O=g$#9WBuS/"/x(,Q~uqy*!v'{Ng[@-{u,ݶ 5<=io؜Ȏ{6]mcbV֤P.MI/C ƖuC@ucJ<Ĕ:ԁj(v[ Ae|Μk 3h˚[ɨEf=;RLd0g$C[2-LfEaĒL8ƟTr8u^*,z?s Xӷ) cWgBg(߀ߙ)Q ezebfyn bBlxaY$< ,a}].s޹jUDrcoThg{B#I1^!k?IG}'(L${0| TWlWp mMkI Ӏcg0r7U{Ңq!wQ,^馒0/zGRsyӡSo޿9_ &Ow)R%ӬY"8,y77C*z;Pnq`E\?+7/ l[žm%E"avp92u_%luS'cN|{Wg7p6XT,!lN'E(i2r4D$7ocPTgK~T?LzE.`P6?=,j0IK[?1AVSڬ;_U0"oEJmbπhi(XbLGI-h6"ޤV$^֮e;:) pc+/5(! =$@H}XZj yCl_QF#m=lWO @kw8BZgM.eqSIoM$~\/ʯőwtYY3<#?쯂 '9U642͓1OX<$*xSNۚ_tƧbT0{%CܼŸƍR`pMW`E nCǵZml*8 [,9Xf1u|!EA,%ܚ2&n&,v{{;˅ )Ve (\;dF̆hl]TXƷL6K+_6{xmd[2Wr0(-lN*>: c"fp jkY74,Qɕ cor{0yNjk]'oZSE%RBKχEzH#-.\?Se˜F !0VD_ 嶘o 7b9'wyg9ME+X4ڪ;L2"YiJ.530C+)[Cb@NQީh9JdžJ%2l$ωhg-~4pi;콪03 BQ! j&Zc_J]^#eZBvec=z'dG?/7fY}nyfb4{F A~\%[h?Fa>i\xg:Z~ʚ 9~o-೓ry@ys$(vA+myxvӴ22=}/3C`16P|2Unhnok;;׋B:7;.ƔKp{lDP{튢k:y1s!;"lrhaNy_%Z{Ͱe%0FqqXDBl{eh  `mC-*-W>',\oy=!Wi}?)bjY=X4뎒 aQ ܫaĪյ+%^ZᮐY5\-} m]Fw 'Z6R3XC)J}ku.]\ȿ֕FPa|XSDzWI =ld)y/l[֧:SM&əU1_wbW]YN, Ŵ`W7GH?-,~9U.L$%N,<]{|!x~i+_0\dl'LӸxRTyC}Pa^Ӌ׍J_DJMGLNϰMn.cK;I⊻fTzIR*[c%ԂDSּ$)ruhMn&u)x$L- iTYWޛj3310 yZ, 9sã(VV{4ό{z]J 0v}JS¡5z11jmw;bf^$<<0m΁uTªb`s'}ԼEE -A%4 K|5G)yѳ^i7$u~Z(qڜ+MͰҭ7d)R{\P>?D}KؠD*~&JOO[nvEVB2VZ(<9*H_ ֦RXMW+5 @u-/ o- v6JwOsup-jgJRT_.hF':WoL`D$|ՋVLqi=! [I[sDbZMcepl+ǻu{᳞Ah1:llTW=~to>%?!,RZÖ1Z|F#׆T211˪RUQZtNj&exwh7mO=p d}Х3/r-$6oW j:4fJxzzRelqDMvRgY|!^J9];EcEr*]0LA2MltZ@L<[8`5=ۮYg AP.0CÞpS/Eu uqa*_24ؠѤ3JY>4ەU<74㱳jMd~1eN3sD~_gߡTo@s06J(iS`MP?,2 %@  Mb7w[J٦ͨ_-Q:QPѝxmy(@0QM\3;y/ڲgcPD݃Bc-&HFB>L?'MT‰+=EHrd6Ǣ˞V'٨fY9 O8Ww> Xhw]y.lew頗06zOdj[pwz# +2\zSalLB7PH O= sBPsMUJx7ob.8 Ե>&TJ&_ hKph:ue4A ^&GV܀^qP(t@| ^] *0.*XDF_zcpSun( [_eO-:advL<-b⥭@,~b(cT5ss_#w=ͺTPhJZQ adC=a PG^֋U7r^_ܱ)dn[Дԡ\N[o2cHexu x`0P8CUZvºL 4Yz 3F!\4u۶fמXI}v wՌ(̋N3F8ǚ澟ؓ9a50%N D'Tec{s_v_r.gprc+|a  (J?g'!ףclղxfh'u渨pW<)|AŒYFԆq-50Bž9U]czbë3=qQ8<9+p~nSzbrwxP*j*1zř-' iu>&(F7t"<&>M\~~>&Z~P᝾& 긤Z?aw{IjCC_,y塺F\ e{?t#,\X,K; ˎʱvaʚK%7d;%\YZ-x0O-<ޫtrde[\\;,~Slף^J.R5 M6`dVy4o$M+>wîeϛ:KCܴƕL6Dqjמ3E\n$f7Qi(`E.b Hy7,; Td:e[T\M &KHEh=EP3YPz9 2TEZY)2o?op:Z+)SNDpQ[岬'\V_頄QiZ;8H ^E!UvlMh; S<\m$FUd ʅVUdBzv:min )!Aׯ/U1~K_,gl,HɃ%7so޴bő fv6N}iiBք~rb"Ӵ=l~:EtOFnf1I8 d .;9~N.ZkItRa:qhtq8fmaLk}R9u>3x:zQn2XZV :'koT(8d*}ܨw9ttX;н94)uU<\ z02NKDGv4$rpۏiOLK0A5X>v"HwatMi=:PTC(',lDz[|.[?ơ.զG^*<Pӯ:n[Z9~lnzS!KOYIE*⧛+EL'2^z!tk>oGRyèM=I Ecl)69̜ۤn'uG>=!fa5zjQ[$G6U)4:Jb>[*-aKw$J}gS_m&1!u;MN qOެ1`)ԗNL!yg| /lm+8x_b_w}jS+%ӳMN"8+l^2*;W*03R}1Ua6%6XljaXhur\ϮFo~Y1tՍl5uT{!k(%,["{\ 5 ;3ll5EGU- x&$ 2 `EmJ)ԌWjlÆ^ uyWܱqJnyeх]XɃ]Wre,+f|VB7W[qoS|~S\JgYՀ^ť4S+4/go+S/T ̑FaEv&;gmV/9aw.Z$9\?_Cz| YyJz e-{@wj:<%&q^ɋU6˩%x6V֡; iIk>REK"w[i.#ȭ%d&^br܏.4ULmQP*H j. X3N/ygښ3|s*[*~ f vƣ)u7 D{ ҒGBpO?,a~}e.!jR­9 6\'kQ y\}5sÙƔaC=J]&4TPt5I];?>:;>wҮ#ȘOg /LҔ3l\,P1o5'E*ldNM130f2Qj cE{lcwq)e1ЙYos^ _`Z>~^)ψM~}9U܄8}٠sdC$Jyn(F,Zi[=؃}i xw+ygCO 3-izT ΒK_l0,7̹s#_~tdɇ OTq)gR+|@ţMI dbu[v>!x6q׾@CXloagviLbG/ZQ 7z*2G".͚r`l~g IsƒQ b~oЪh=i%mgK E<\Fdzbme| 6V?ʮpk"ޛm{zD4'8uEhA P֔-3J,Z}vUYM*d4Vyi}ja)/,_O}>ϗ$Z$JKdX[ Չ|p@ZsJayfFRU-qdhFگ_OR!_O~5u{Qo5&xl7V)&ե-a']8#A?EEO]Pa!J×XqLeSsxJy|,T]6hU5ck|ciE@,^2"&XyR];v[*'q[qBeҵkSre0DՔX /M$W㦣grfBݼ<0bHzGv0iJ\`Qg ꙍF:w<Dxyvp&WfK;35jJ}-.[L/3j`k.м1(&W^m%tۣClNrŸzéF1FḀNZ^=*f"qT3˚M~FH9;TCh evWWiDuæ֭rTΓ>1[!b&w̚dHF2g%5{=\Ywӓ", ߗX-Ǽqp@l};ZdB4 A2"U>F7>^X){o5`󸿆ZڒDqPM%>;j WH.-qеm x+ݷW0oNőhDmd#q&kkSͱL6jj[rInlUCS*)OO"Fj5s®ݢq/JT CXY/\r\˻9t,K}.ND_L^&DV;)&&}ݽ6'Y\ԝ+ᚍPuɪCCO*Ή|S iԔ)@Hܵ[ r>[|}? \tWej:y4SdGMOK[XT;Є5/.m@i=:e5o7ڳ4XB)c8@Rt,2omv>Y 7ribiqo,*<Ψ=5K,u[>Agi EGt$ݘI.7mz gL#%~"^WG&Az%4Ef/NصsTZH[w4-;ma:e_HkB: !fIv=@Ћm .L/ Y% hіp$B~6QJ)-zactPksn%qτ%3bg]\nRن)5j>ׁ,r\,Ѓ 3oq}NAƾjf2S^ZҺ`7wl}@PgFsI1c/H!UXֿFH z0F@i~q;w.| m3Iջ1}Z=M@A󝆷Jzv6|t]i5H9]ϰm[KԬʊ`շL]9#O hȫ[\,5/\Jhɦ,X B֛iwSuG}/W R(t\+kmDXr'"mcYi]~`\Mvkf*F 4Bh%;H %i@v !!ƴ3MU)75|PuNqjssZ:[W940oJG&'[籑I4%|v1.3tXz[^ЈqdtjѪrχ&p[$t%Hh >J;v) 5Gq֧{+b=E;@{L?4S2}fַ{Yd9vs([+@.F ί~פ8Ժ(qbkjAyʜ5pa7YqJ*}SR/)GyKYK]wim}(վdaɡPg1#y)Zlrhi2װwQ?{TUbF׍d4 q]K16[U8 CXE ]\W?[c zc`U%wͽ mY') ~TV&SWT2O\ҭ RbLdK:mAp ƶMn5<8^ٌ.UO~szq5.9ttŲBp.n>JS[RW?˃+KL'WsF }y-q~WKuǷ4Nh5e~jǺo ]Pi!C@>+ގdzؒ.QRb)mktLGh{& '"Byi_rS" O3Ǟr\]9LD fMxB@Z؉txQ+vPlY.טOS.+2DU, bݓ\tvt.# S؀3"rgrD3O?Oi{OGidiʡ˗D>{:˘/a>EeKiq. Pܡ[4Ktbq :խu+g%Ԧf #ѕ=1V OSGNzy,4e٘o핾>S[)$vhD1ʶsqfOcm .NNN6ZQW3j[jh_Twvё@#"Hl7lExlS3S1=Yk!Hދtt9/w 2zldxNM*~:H6<&wvS}T-j{Abp~-jEbZUQpShدro'KUDp0`iDE,q/#s_FlUfJ!,(ބ6˅EG#9YzJ{rݞ2quqqgctiE̜mVEG2rs c[F5%TyPqES* w.eXUZ `O!I.qw;䛉 =:&AhgPg[1OzQihɿ!gIǶTèrwFu>0lx@DDH?*uMZlj-z]1#!޽²ZO:B6n|80adV2G^EScGҕP1U%ݽ]CW_I2(0 Y}~Z7k6z*rFBŪདྷ{9gqvWpUjZ<~GCy.;?:vR9 }'zcZ",ժQ`fFζ dՙx5 opk{mzve1(/9vU|w\-(<5+UD3gV}oF(glǮMcS)*;?Y`bGgމ_]iLvgF&n@pi!.񥍼M.`fQT!^ @p7nE:Wnt]Ja y@>e熆)SK R]Ⱦ~ X0LxJCYeCd=*eT4ׁUg2p ol:6b0}t.`ܬb+Ǖ^v{!:zCK==,ǚ\ŚhbtC'6ZϹw>92(mFyS4ajv6avpIclh Gp;fLI0MXx- 3r@@@1l3nⶥE/)S!ˆDV v0 UW7?N\Q\憛={/bM"^ڥoP (gXw ]a I\wW_(u[e, "BɆNa"n2ū-ձCsCabqa?!c Qϧ]\X<ӭH(RtΝɦ)AYSxX_(JSqi3R0iN~2`S/n Q{Ikeő!%E_6E* ;Oz}@:~ZuZ;-q%& aI<- iY:gY34'> D$C=ŵkqBe'3~eϝ4DP9Up۰c( cCFݦ4u&n*,# 󷿍Qcr_΋]_ vP!0[SVznnnP沙Qu< *堜^ S@Y{f~"i/McL;i?h%v#pS;W# ݃;j0lGcۘ4 av. i)hJkyc* ?9CVi jX0hxDS+MG:Xx:-jCg%53-ݢ1A%_ks%f=@90ED*^S:27ҩ%ʌt<ϋ>Fa^-bbarU&80hsQG95h˪F-W v}Y l5qXZ5;U7  _Ӻմ&=%g`HJ? /kц(-zb5c˫ܡVrBujFnIrnƼ .q4. GO: btS/SZN&g; W?$?w %jCe#'i{v/`Rk_ ܙOޱ!j!y\4.,rwUeS).>1jy6 < [AƩ;o&=20zfd=^U2[tSvaZDD_ ik)jH+ڕ=P}}aĴK T?#YgdUTd4xGq#J>K@{N>`kvr$PqWm3Pvh|IJ{f0e~ަ!g q]یT͋ ֋U#(;+OR8i/@^,=ՀXL8"7*8IgyN0y Adwֈ̬(B+۳]KzعҲ ҕ%U+@菴h]\X_U#_\sN* T=1<>>PK[TRa3(zor:‡8ӳӯ1i_)@wiK# (mG_^%rd1DHS-aϞ,RJ@Qv<ЮԲ=MXW!B*DsB>C`&WqnO1zˬ ҚeuʶN3뷳 NcYq8YCI;oM^k#Ž\R Ȩ}D9 >c>6GR[ dlQRCRu2ag5F!vD7w:PP^L8u ESJG9oVmٷvN`0^,rHIφ8Q3%RD)aX '4#PiU&o%~}(oMe>_U "@IڦU%>2 #مji"e (|_;u3 auݹvؘ3e,m̺qqMxC%/R`R4X6l\ $@EQ9-MPkD";)3VMWRA!^ɢ%5a>{5,cȗ-%펢O4!y=K:ɠ;0ɆDByQXާ饏nm݁{4AžǯrPGK u;KggqQ$B8j=0]Lal9ſS9y(u.`1bZtX`odnAs-e$(Cjt1hY3mT"<ioRX@>6-YyۉF)2[׮qHwa<4d>B Fa >HGZ:Bf<}s Oά5zAfdXcrMɡNp{#},.Syb)ia7F܁\76p3'$Ruuɲf¥. G-|燍i&*& ~H (}0*( nn_=}_(+$k=C?/@" ,l׹UYmmUٛiwnݻuwkhdw9ݷ}͖]\˛Nw=lừ 9VŎӥǺvrumԴ,cpcv{;&7tHJm̷mntsuwse{:nq+swslcikhVg]w5ۦ*Ow{m⹻zwuw^=ܸ惺oLxݭWU:Zv۽Wn2ns{]6黕s[{svXunޞ뷺nEv2޽潥9ͷmwwNֺw]eݠ;smݭvWNӃ*]ƺl*ۓ]k{K{6۸qy6;]jNjm+j==׍vۛ{v޽^eW{g룹ʸ˥G3ӻyzgmݽu[o;޻siwn9[Ӟ*nw;VꍴWfz7=oyۙKƭ]ݹ7\׫k/{tkkuG[Kif;c]k;l+ۯ9zѶgO^]]u=.:XXk{k[zSosn{[s۶nsuI;qZ')ʛ|?#x~ 2=frIjy=f`a䐶RhS`m}_+`&sp>Wbh)Gc]=g+m@veBITf'#{G|4-j.%95"[dBjh]r7[%*]u')FJf1(ڛb+hڂ"ذNi_weig bX}fzHjJ-%Q݃c{v#*4^ʝM8agN8Jl#@lbc&|m@U( <[0ۅƋu4nAn}7.𑩥MdNiETA=' h[K] @wMq}}Z ޴ &{*;D"[蝬3P|hady_F؈37>ɞIVY\]}SA=s -NAvՁF=Dk,W^(2F8EG 'd0f6vXGD7@,Z>5ӹBkQP:{]6J{0gb:+t9wOxzCG㺦a?SMvU}b9U+`WN!IxcXaWb#NMXAPƸH{ر be:Kz2b ͦc@ mY6U>uӼH]|MDA#&T{x1#mjn^<;\Mo ǟųv%mX(׉KDlN?/}ƁSR+ B{#|wk +,5`|qGrfiӏIrb!##n6nNlj?uFx R `zny1;exת(YL783۳{&Lv{z|>'OAWJJ Dg_j\Na̠m.h&+T;{= SGdUzIl081;RepNi*Jlc[Ǹme,E`ŝ_@Ll!obgD L)"FTC7Y<$t#Ӓau*EQb+UmnbC($K=w Z4H<{fU" H冷( YJnDY:s~^6Q[쑂ɗ&< ۪SQ{Pq@y+5,~{e NJNq"ݕ !S=JyFE8T][q:@zQ$Ҁ$Qu8{chϽ{Ommǯ1;$Aӂ!ދ~ ]3$W^(-a5:]W3[>]M9V^L[ D`^ij19w=MIVc_»%_*ЦWCHczB\Xmu_e망|^O_ҝPq%=jy~Tnd"2%3Cͬ)^: Cd~kWCG(=*VZ ѓNp.Oգ(uTsx> &[j/{Lj=oݽFrS9] ~53!ۙrsOܕ\Ypд5ݮ:7_5SMSoGJO88Z)4"pH^>8;auX%H-duJ F"b?՝{[!o< k[cD8 ,eCaMB5>m;G;Yi/FY=ݰc3qwu5A+T3#S0Fx¨{KK ̎xe[)P}dfc`gEA4KFIB&;Tfd2/~7̻lLةeKa:gNmCXYR zeXݝQݙF1|RiMㅋ;w-$Yۃ:ϘM ?{kUvcGCe7N y\MTD%YOþy:s*wޫk Z~\\*c2BKvy\g*1}R JKbw*ʥi]0@l݌2CM?zp/8QP4ԍMIJR"4ى:Uy\÷ю`)ưhL 9IIeNyZ#W dH]˳b+TC:e׋Ʃ#<:Ԟ0N^RT%=>H j8;f[c5hмtܐ˪֓FV@T%snL5lClZcChL;]PdhQapL ѼhjL6 o6-ՏLQ8NgѸvab1rE~ ½C"bbLLצĠct^KQHs#vwr:J7O,4YڗGJmP<Ӫ[ '%NC?,P g8B}(,:n؍}ajn~ƒuBk7 gRmke֬ HibA Q 1&740C-#EDwAꙚ$9:?*-¤w{V;1g r9u%xZL PaVd g57`m8{,e#jIѧrA{8zdkXMHy!|"Ro{#^C缧ji|f'T/f2}qC;@ehCVϸ^n-p89,̖3ԡg.@Ǽo45Iy|7°E?c +Ճ}'P(<ӑarT}M_;dQ2ݨ6~g*# 'SXk ꐉ"X ur=t e~DKJ+q5Mh?mn,Y/t5jo=^84RbMA?j К+(S7~0[ NSѨ32fϥd ;AN#h3r u /[翅;B{PR}p@ Ӛ>c**ן74Mǫ^L??=VHbS:s(ؽˆFG[ZhcRuy[W@9{|kUPm(6ۑ60tT4[qyVXӀLຳU@`7.. Xڀ 6(g6s ?69 H(ZSZ%pϐs7` 57k Hj J* bFbǭxj,0l{1FE`Wovx˲4)0) ZkHtHj\]dP:v8G3;'5a?uHZsG, 9|IRL(&*/NLhmڍG72koE$$[(gހ3M30U?z# E/}=Nt:8UVAҠז4 @ Ȕ/w51}1O[tƶQq/-]Ŭ:^B@Hٮ\ƛnp,#8)m\$x51N]SŒzfzS5qPcV+11)Y^cOO9]wۦ>6wڱ"$gu*}S2]יfX 1wBfz\VathOrɽRJ(!*K'T2D>!5RY_?A EjeQi`ICX[}OFD4V>!M$I9-K'$8HN䍏.TZ1r9:{|k[- @lu"d`3nك\4N]XVE#+Hdsۼpg0훷Z cUX,G4_*wr8*Ka;鴋B &E 벉%r ֗ -ڻ+lo |rI11*ۺOFSnF22y5^]AҝymB*$:U\2[`jEt}y~!(.G\igg-CAQﻪ>=k[%ԽQ6Ț3sPJ*1KB;:C!2۸̼!\ yƯ0D=s oF2c=q@U 3ɼy)hևiO`*q;k 8'1~RKy{J{}mw7 QYq8njLMg┑yPSenjapjam}{U@j(-ӀgBIeBw.fbE "e nm4XqS(i0;l'ժZ,AQʹZ$oe=qJГrH #-"asn\ny)=}9֏YUYF8OR"~٪4w3 6FQ0|f!ݗ9@Zn|B/)QvhN_^:SYRnLL|QlQ(zTh50ɻ]Vk'vƸT9oWyUآ%ջ(qsMذè> FuAC'rncKSauP 3,^L#$VCQPr!rbA 'h{*.߆[0G&ђX(!beEZ*08Nӡ؅{fhQ3L"KjBHhL:jÐR}5~鏆ؐ ]5iLLGgcoD'!1Cjw>~iԐoȘSEuD v)}*o4;rؚD#5BWOHƉ1KylIda1@QT,#_!iu"hIyK± ΣgRga)UÈɂ:Bg^+]2Ertn0^2QUcMU/#$U0ܘ W{X2r[K'=&Ŕ/~&Ս hgoGEoP˗㣥 nN@3r7+ͬFBN6iHv7 :e[O KИX5xB+9}ꄖv{$zRœa՟];T#m z { RmUO]멌ɗ.IKXc*U1 &'V nty2KjXz5nf}aౕp\JZk45)hȥ7CD;&S s,?!kƽ3\ ;{@sN)Ri6ݒWWU,^aJF .j&3-sCim~Yj|J@o!fT.u}Κ76훮b! $tZt)+b/)SK#O,-,2}.]D3ޠٲ|G mZ-nX Gg=&&) I,:{kar_eY%vR$ugج` wdm=ۈևK?hOnd @`}aogE'D 3ôҩeW `Yj4љؒb _+;)a>̪rg4'|6:6ZW> qQ+ o9=&0zfo/o4DSd@H>zXQȧ/"!k#w.ޭz60.JyTvL2T[̭8E1q5l p+6fk-0&=/o, .YKmPUKb쏫ym2_jȡј>5Vg 5++i4"iVΫC`hM+ =[riP"a=ޏy $q&ӫ7*T5gڤGoD̙)VQ6DŎ=SD9U~]hޟ]-qPAVޙ v7u*LP#}wBbR+VK Lu].LjT5`ṋ*Tԯ@`='e7tzx4߻w.x/|kdXEu\sc?C)uџA&`lm0/yUkMJzՔrtG벃쾭R>yUbk'=pN/en#nڀ*qp)*2-j . 9r1|L뱭hP)7IKE3,ZlKסFW*R'f_S &|xB[7?ޗ2eG]Nr ??W0aSX)Ē \Loau9tFmjYfCnQ?E4l6]1gz[bK-\jfo 6/fc-,#z XM?GucǥðX H07+M*M)f_Np)贫*vȆO{sW=/w]CV?rޒ5Ǝ̪3-A6Ur#hEwt źTTOiFuA R$<.}5NWdC"uw.WMKqZoh[RgVVvzn1 # 8F`_^uaݢc1404% *ZzyMI U]FJLL^ֹɥHX R$/r yl=Q {,) ~LEU?V&U`N/ɘ֟4ȿؚE\B_l??V=bsB1VMN-01UUltgQB$!aB;/\qv7[)Ju Fx[qmXRKb&Oz~-pqoY*=)S! 7cpةfI1P3-ALykիZQUUDڥ?-M@nc,hbbÏ|cSTы42 @Q HdsǢQѮԢ= AM2dn@o8wt_u“ID\_byeef䷤5L8.!.|GfOMa HjS{%;Ap eᘕ5`'pJũ[aX7ۈg*mo|ˠhB Eyʲ`[^bL :ko[\0.qK x%v:i~Ul~2 1ek?YfU ȠUL7o"6Ć5ZěLfJ1g>5繋Yq}&d-;G!)]k%av"LC'l|qACR#bm 񞼧ExϘ4_W_ i e/77tcV5 x1ZsMƠ0r=$ƪ3bWof@{"ԿGx7驻oO^_}~^Ay!v }e 9l2/=bE5>4LAf$bH hktЮh | :ɹo29:64`ju);$۝|o*܄#Zj gP%2(J$OsT$5 t&}8HA cƚg2X4ڡ-ӣ%@܋F:gQeUXyapwG.oq6q@I/lBXoӗp'墅Lmkc.߆/ѼBz?qW;So )RAtMs"O#ƚS;evne;;M4)@f6J:KqR~wg.;"q=Lr6Pz@ Mtr3k!Ӹn #0[{ޓH lUv}epwǽ\sE态:0E_ =m:>aJuَՒTFwoRb_Ģ.Q΄ )@WwTֿxfؤˁw ,mfRl\n; e;IBE[0. d^4=~"~W~=ynkB܂ 't $1k?dPۍ@Jo !* ffhة1_N[v"n<[Cq/?#7r%O] 2t`'5mෂչ@gE<'ZO C8~yۼ񕨞ʊ 57aKNpPS׉egS>~uGwPj4a{:{ZhL gJ,Gz70axͪߐ~.(;yi1ӥIk N|>7{"-qSDl824cZSt4UE~!X`&m[Fҡ\] ʿל8<d j8^Gp;;X<=7#_(LZj.Q9*!.eqש0pyפL9 l bSNFY6ǼeHv6iSMa,:5[}S(.fa cC102RUY`66u{b ԭ+:=t2bf}5חmJf VSs[}yjV3ݘtzNP>66>|7GÏA]5ؖFr?ysPiPiٸ^}Tϼ۳AEs'm%Z$hntgܻmamplzإo{M*'tjذF1#C߽"ӈK:Fߌ%f8YF#oA$cir;E0!cэ ~њ&`uuOGs7$Tby(2Ge KleO-K绕.pܡ*4!}ϓ6DT965T8֍)l;n8%cߓ߱с}jlkh h?վTo4<6wj"# kd͆a˸c{IbѨ*lQ'<Ptڶy~gpKhPd[x5`.+_\2?RUѯktk)] i,2ysNS+ƫ`LiK yPFIV6Vf޳Uqa+zgK GwJllj@l\~gmY=.Xƺ=R[P!׈.~-oe?͸m`Eq79X4r4?Q{Mȩ-CfF8> dEzUy1nG$C,NCӼ 1)lÐ qOwg6$42$jH\NAEX9_( zKy..S5hP'ݓ (;M2ʅ_j%Uҫ,&͒/yc (VE~%g\F ړMj$jQG'P몤>\x1x7Ls80s /L ВDn"xoO)doJӏS: [~mGVd2gRzȾϪ(?F>ieb܅NJYplZuRL&օ)3By('5~aGO0lIiq ӵ1|@_4ѸF'ͺ7utv䍁 0%B;_ VdA`WRICƾg& *so@|1g ļzqi_b˨AaՂ =&x8vrx-2ήR]^ɭhɊ>,!6ӕ/ys:ѯUN92]jP]ZuڭND/'Π鵉ٝ,詋6xhTe3^M曳)HUŋ}glQʍ#n+M&Ȫw ͼSapn)-Lmd'qGژ9}lڄ͉uru$òG iLn[j%HȆҰ 7l!Br#`DLb8dLg!֬mY?8>`[8bru]\'KR ܲ_P!R Ӳfoaazqvӑt^享T}o}µAUAIY;Q99JVQMZA;趄@tl-MtŹ6'- 1CA<_7_sU0&HkԹ Ht#h)Z೫uw >i2SLi`;b> oA6]8磌+,Ee&h wqZX?O546~V$(s+zrz\'Zag޷!g%}mTEF37C皎Mp9 º=cLEqBӂHRrໝwmcy]hIf|1 ^ȩthSnT!Ms oمozn~e^xز:tzLL~FΩͬx?'UnaI-`3ȇAfH4KTj8&llBIC˃Zf& &VC/<|.Qm 7D>,쭕T9A%5 n*cHb_(j.eYMH9A#;T+;Ɲ k馝CHV3MP^ SM0* cS[]dmtqhxhFd&rIb8>0\z7dP5 $,- Vk0ơ_iFe24Ec=zy9tip1Ў#0[Y7|5Ln\&'5QE D6)0>}ďVCr+.wm3U˘d g0c0NA- ]2bs,F(s-?Dq2/>z|R`Ϭ"pk|KGDU.kx]DiEOi49qY^2)Q7 D\}` T1=;ǟ n9ÁoB˪+0xM_%$Uj!:^$ct11I$|9i4 uu-LF08͚\T=>KW$yiNbF }Eaf>V.gF*te>DPrw잝sFmHʮc_Tc`[J(V3n:#}p:TV~Iy&h,x{r۲K:w;w #SVn143TDl?7Ll 0^/ #-*pQ>;Ωɡ?}#|U& nϽƹ^Pfku;ɷO\*EikɅ@\,Q)`jwݕa{enCZ%E˔(V1Cz,$W4t8[Dn1q<9WDzto!n2ԯ[+76KJu&Ie i,iL^[ꓩO2E4[9%wCvZEH2n0l!KM$ _CL:\_\ü4lMLꟇ_V7uyia_E ݡvz|'6gn*gu+8pe9o% ]0W-}PxGuC(w.ԑSaV_Z"oϚdm:}i,@柚B@<  O lrrPY'5u}oh LqB|9'wnjS^WRĐb%y'ƙh;A<̻BOr-D*+0@"`!*FzrXy6qCT5ByX'nC៑9!;7׈nr~τx>_[V9$ ݪeٌNj$}GۜkNn(M$y657`Wm#nY#t.m6P"}+MpD7#r!c\L2:FZ~?6Eh5{yZ+vlT‘RX#И?'h<;}WE{}q+UvBpPҟizzQ% ?]/Qv-|Q!CS{Rk 7m?Nj1]$˞V2d*|yk^{%'Q1h xrojkͤG6zoMƩaTFg$Kl'3a]4| C-.."8=q ? V8}៼f`Vcע'~π2"~ߠXk>v͠vlW"=8~meK )5vU 96gA5yz_꽼 =_nn Ah7xCY'(}ڦWֆ|l \U}u8Y*yc>}㧣﷉eC)"Lh A4*W[`b7&C%51ʗE;F!LJ9 B lo6sYcn`ͭv61IÛDoy?ok? zeӸ"ڬlF/Ku[Hm"'%]JRMt}tCǐ]l5BwYs'MzY ;{@ ԱVc3' jHYLj#%c}ojGm#+|1y|gl֭aJL* m<{}囧>k'>CAՈ*T`ΗT@~-\ nTRU 1͏^AӺkk9LQW?bSUV쥽 !^iaެʌAc^e֧iU`_yXH ++$pkEa(ʟ?< ؊|W_?luɴ%ħ+FƞQ!~, G z Tyׅmt n~͌O}w. b##LH%;)fũk×iҚHuՏ1[-}jJ -rX?7@P3ׇ=[Mt-ٹ4z9Vha1 k`C?V"J{u:apa-YT*3xOm nJ<,dtO?L?cMXQ1tPt6+n'>:G2 =s@n$-l&IQ..|OIץwcB3Q;3ql&=Y{ 2H7?.jh-N3,d<L:u+ED.,8&"lxS$\[y-1Ix+y>c;9sO;pP=0e(5G<(b97P%q2dBNS㙵=\~ !4b8˲9RC}-pԦ[B#E^EwZαIT FH 3^x2݊0p[;茄ZϜzs=ACA(W4g&I)9WmyTI *aОjjT2K6hOhu[zuہ5fr2LsI g !O$Ee}=k!<&m@_w*Z*7Dʃ: z}ccm],mef 1n>d^{8m]22۷߳d/_*IJrٷriH߭7PƖJCѩhO`&=K!#Qݳ__Dq(E槦(Ee7iZΥ6E[:㤥fm[#^C`eB˷ 920DNg&wC*^3dwĠڶG_Miv>&H?.,L^ufȌxF9rݱI$1[aL=;liZgG4N2jan4Jv~ Lhtb5"3V'*`q@R "XD#W.d18UiߔH# eaЮG_Dԩbw=m+5erEOu_*\0gċ}TjP{Ȉv&"QQ(xܪ.X>g+.W&|듓-jMRpkLZ(J95 ZI";XĶ1X>폫QP#)j^7s'G m?gFl 梙qHGȞrDǎ=c|$@M;p0줩bLI2Tf.c;$5q2)hLaO&VvVZ 1S6kTc]oBh'`w^0Sxcz+GD]}9Sa-ֵC` bN6]ai7n&>~'c&Jx֕6L3j[o,8ۅօDơ8\lN7RN6ZإyY=m +*N{Zb_$_EK8~djJGo Ci;S_"f`&$Y]꤂Mwٱg-Դ pp"DkK,]Yy@'{fE}iǤbGX]c # =5!fFq<˫ `5N U!a 3;4_E鹵g^ GXtD  B<0زZ#=: _| 8&ESFޘG"ƪ_u&4W`% +$7`oncan)0 DePa+_y묝19FO3MsӢ2Q"!YKD Jm_{cU<&Õ-y}¶ v3d^bX(:yއ da!7<0q/}O*)1zRgD`=aD! |'HxAxヌ /"P2冟h!R8Rةtp;{XT@=Z.Cy6* <0#De0ݔdfFE^UZE}qrx+X/ݧLd(QOo|-S#%XI/IzAjKCG^ȌgI({j-Du9(.X%g$ ͠XLF,P*u iA#L&W]Ӈ^Oi ];330m$joinCʖTxuuwW{},t'-.=g8רTFoMo `N׳P}"(sFe /_z!\'+r*=aL\1?́F̉r$6C+Qr T2"0靌;x,qJ^䷬ű%f0}KƟ\WY/YC,(S1FkRˡ"(ZއAAshFEʌ;Jwsga]S=M|yMjS9nOOLuF{ڂ'Y@ *GilaTq;Y-kK9r4ʟ7]vZ%7lDmFp$o ?G_ 0<ХLjKdGN06n}v=02lPI\rh2`uzz aRHp |7F\#Sٿ K^"|f`:IzlJ_͓ljυhҰ0I겈W{'i|U0<8Laλ7ζ5i[|v]*0W^H74Op㜽>j+ A[4&|x6o۾~_n@[[cEU42W)1d0|8U趛|y ;lgt*&S'#aب<=S3qNO͚q[2"f"VgqTx 0q&mݤZi4ayјR~?t qAyG@ 0֝X; h0h_*&mla-9=eUTӶ7g?tӡZbt+oX F S[.>Iy|r}}߶؆jT]OcBŹ7dya(̈$3` QFs i" 2TN]2#Ϥ-0vhw@{Z2Z &5vj32Bc0S< "t9PPzע'TǭDuqY ,eZIaFipR\[q}9L0 @Un;AP3 }O\*K#v .{~dQ/ȓk1V!esWB4=+'ou9sD|FzMCVIQΎ]Pj 2f#ٯH E 2aMl }?ocaHԙmK"#AGA;PȹfzoD0dYT']٨kjy_&&:q K>ESSY#uJM+9 *r;J[J9򠀊 a~@`D\|žwCz%š](t @k|FMۦϕ਷*xq@9|(k "s=Exbީd,G3åcçՂ#R-=jK!1 =_RhMQyH?@`xxI0vbkt3u^DuZWf| BHǛ½3iK/s7O,39ڡ=LnlTr(#ǭ^e?N7)Tyd48x ϟ8N&`xS0vǷlщp]q&ȳփD #[Erh1)[i^p~OT_z3dJ~i2-N+4׸EIz,ó4:(smE E–}14uJE[n=hQɝ>L,|Qu4-Ҿv&mv8-Skki&gQ/ vO՞b?@? ~1`mT] }K->⡢n#B+Dsm{ )%^ڴ8^cƛn[l)iwmgJ!Hh9#n[+>.G;Eb kb%&p IP:y=k½sAx^ŌV~q<q fĺmm: d_K9 3>5p rdK_Ѱ4mF&MӝQν>FFq>v-~$w:.͘bLphj+<}EoU]cK4GO{afh oP dIt>M fB/v ˨q?a^أ TN314X1$oGnXS8k ; M3ٿI'hƋ L$ZjYUJHIL'NQB7=>tTAw,j2O*LҶHgˏ[KV+<{h1#Ksv_ԱDKsZk.ӿL]BBW5Wv5ipaKlS { FǷuv;JltTP;H̅_SmS㕢&MT,sJ ej-Rzx>>VnL25b(tE;UC#%Jg=,Q+6KnJSUPCkn)v*F6sG ^ՀFTgbavL)^h*nϱݰjM dNIm禲:Au}%& a-a&A'׳FQtQ  /m c@{Ԥi (vfa7g]U MHvSY멡߂T2[")+U뒨_xc.g('mgBaj@mzu2cv)^Bv'~[cKA04]ĩinĽxF2V*TTs/>^ 2X[hOy|Զ* QBCi|ǀ8HPC/'@a'׵ rMk~Au^cN/ =io0V2,^򊲜-7z'} hәt}0C|-\E+8DKgx{dz-vv/VlAeVQ"zV^r%l'@ƓhFއͶX[n} i6FR:s?H`zeޠSi!eHgTh3F롢v=;ͼ̞=W}gG4;CTU-M 1(Tуk AĔi/2l(Pf]صI[k|qT<کp9uqbrM_8SxRJYZ8wݩ17"ml0]ʻjiQwcFnZ*NY4:!L1oz=}|!MdNQ"CMdep7Q篼[;+Wmy,EAQkLg(&:kC]CZhlj6;fhr )[i75<\|Yj Hnxv޿:g~ڄKHYN6|>:ޡhF"&fC_u|7ܙ^Ea?"_CPj:Y˝i 0?I aiSAu%&lۻ?OaB[kmq%Cxd7C=تb dQ!MIUj7%>"?a\K_Mi7R:Ynu[M2$\5 Węp{Nuu7T/HaTÎ|^]ȺbܬM/g5.YdCA%-ڛ5ʄ鱬 x9u* !^|r |p<]QVzf*w _iCYdeE$ZI8Tl}3m!lψQ_JLz# wҕ`j:Lg'˅QPMg%)ǽw&ݙ1w^M >ޔ7Fŭyg߰L6( ƻtaڰ؎Rc ]Z-UucWV(*u,`(&Pf!{[\L]bb_:N6Gg<, tBx")V&I6 #}3jϱi, 0͚dcl>bAYN#(UC2OaƗOA}rIh W{tKR"m}8!]Jע@16!Z"0'Г w@HvV$mP^kk)k¦gUW˰gO^8Dzm-F7K]hsbdM|hk3)_*4A-AU\;z.)(1gd@Ľ<9쭇wsƜ4+UwIb7d-(j<ʧ> 7VE浱iZt58[FIJy9Us׶xesdIXhQec,Ix2s/mY*6ȝmΚ)bXѦMn5+"q@m&#urc2:-Վ' q9i-E)'NSTg2N;dPT^FoՖMہ3`$rזK|ۣRLY*qQr'pQn,a?NmPsjx:5@r#Jy_C *&Ӎܪ[t5)^"SxLۛ.3A`"2aʓ ѷ BCrhg~B\OBZavDzw7xa 6UB"/OߧMF`~pI,fp$$9:f;jeSS4v@p4OQAF{̓U3>5%g7y亭m@QfK9Pf#zI\@lS8y3ZNC(Cp IKʯrl/F_C]+? ~ӬJAGzZȽhgbSш1MDG5 Fg]շ茕1d  jVt UD0ayUyKkA?3Z8HPX+tYIp,a 񱱗b!\(4h{iP"34a6( A^ݪvhMIKk5ϴ p KDlPBk/\nkrn%B 4OJ0-d&Uf jo&"x neЕШ-T?Fiz=X$4MT:4S5yQ x` wX e{ޏI{QIayMPtكgxUkcl𡯕zXe,niayM%AgTdk |zNī| ΕUN-ȁRq.n4b8;@E =qD,O3sSX착PGֶBэY_OhK8<3shjɤZE0~b=LioL,bTWvS.ޑ.Ť*& 𙮨b'Vىҟ [Hb_1C9 +oWrDVp<.}q) nʈF[E+ ~.lK\ hYG1j L>n@0K.]hAJ*֟;?w2`>68ĝH'ffEek~Ô-e[qA9v10ɯ~Gͺ3&d:|.Z ̎max6X>c, қÍ}_\k\ e֛~AHj0&ZQNt2+p&Py)L+o;BѿLA&f}Mߨ?=u~hF?uo|dA ~-Vޒ;HCzqY/$7ܷ%'N_C^DTw&b,~Ȅnɜh~Z*Vg샙6X4kJb+d%537 D'z$aRkiIBJ+8#J'jZ3qW9@FSy;MU64?wT䲭"ڪ=+d˖WԤ;gOF }W*tR`6ԄGfPhɚt:L B:)-V˃Q3=wB̈Qg䱱2vhe1}q] wc.2}>OVnw23MV4Yns7&JI>dC>`oa`=J jJBnWU 麉 Ӆ6Gvl-yLyUKP|!;a#>Dh7 510E r#6y^mt,FP'Z'"ҥkp؝f'loH5taŸ$8~u dکAwR7R5\AciBmG]GKg|3b|=y`Ň 1d},g݆JpRq|[`0n{brZCZ˻ːf[Gdž " )3[l 7N:Ǣ%mF2e@O,3rҐ_g/t풲7a)jų@@hTҤ̒n+vaECHea5"..*/j k|A6V7(&bjc-W-:l}9 j!Hқ;ֻɉ{ BZVZK$ W}A;L;mdo/.̪cٝdL6Z>52*-?$)ND"xktIEu=l;VagaLb'tT?[{t:(6 ڠiӨdb)"LH-Rk1YWrEI~ɳ' a3H %;Oj}&X1zǂw . aMEsLa/,뺃Z q()Q$>TVZ*6޻l30=R1B&PjĩjvUpϔVE`Mr/Ұ؍MV c!\/ A*EmqÄOOԿ$79`vSmIfy:?i;gdaHEh1b%'e=vW('(˚k^繸\35 s!2Jjh\|L={g " U~|]|mVʩ{k߯ɹ@E@n5'ݬ*$M`LWEpVU8G7O7a; //n#rqJݢȕ߻k Rj- ˽ Wtg]l҈B򻼦.z#nEQxwKyH@^ݐ2q̼ FooVDܧbI~rMSxҦ!W]4?vot5@}<8J20Yd//яBU g_̼ch7e(y^hl2#AeH%zOyv$!lW7ˈYh mrIG+!ğ '$6NZa] 'YdU͉^`NfKM4gUGY]19{ś Ѹ/p /;"uݛ`47<}&'p1-2~d$w9d%M%4p5M2uf\}#W,q/n?i [MEu Nui@!Dal\41v\ pZRxC\߃Y=A*!tohpK&j劰ۋL8ܴ(HE"Y)؀(PgJޝ`x 8 LPQSﮓܟ zksm'U5- 87z֪o%K%lOAoHɛ{jh]UQA'ƟlƌH;:zJ9.+^ױt5IcrMcLTkL Ӊg:ME,Qm_]Gsp$.ſH4fCZ6XzcLC'غYbأq{޾?k kiW_07%vB9u&QYKq[ٌizcK{Y4{!fW>5,y OG(L #" `ʢџ ̾G厛/f@f@Mvzgxg /CI%i="ee@J7^&orx|zbݑ|l#.z$`zA7W[7q<|[ {AwάQgzF"?~#F?CQ|Dp$-mXz \# Lr@5sfbg2П ^LbgNZ/&cvʕv^l%]͇` ũF/ { bݟ3hMNl݄fq'֋o1zA7҉jQBӱ$ZFes[[~n`sZT<_A8?&:D>`> K.";.j)g|έ~{j  i to^Hl[_7re*Xߊ_,F8j^ʙh{8pشruIkhr&޻A W-*`ۀc[buN$qBx9b3m~ꮷi,a+(ud#qu9ifh 2%2~+)&k :ngp$:dv,nK CKd2vFduN/d숼8.Y.2_h{L/F'/x78IcN*.6Z7EY]l!EjdA.Y' 9#aڝM$7Z6SfC$Bt1tUJYـƉxwQy\ ??Wkn1n9فM 网6P nI!9@4-|]4*d֑>N, 7M@4`%G;>ssO% nOI Vya ߁3zj<_Mѷm臏6˝"J{K"}7TdV;đ5i`V~#K+P]H 'G6}n}­URSFN9,} Lm/aprhfA,ZH P# :rİjefaqjշ(gܸQ22*{BH]3*f1dd"\D];³?p-CRͪrڢ,Dc8Babh2q!B| BK'|$)ŪzZ1kߘiVh؋qɴ&dΦd@~B5f Yc)t5 _ DW+D(Sc7e*ߢ+Ǩ{ZJJ81`DT8+w R' Wma;7A"8Dz35Ve;.Ll֌0OM٭O(~`XD, mnS$VjoKb׫;ٰ59u(dxJ60.b\ۇ(Zb1'mֵTE K8\Q3RȒKεq1…֥5F'Ԇ,jܭ^>OAq橺)XΣQ Ԓ^uvI7rvs.XľZ78Ll l4 Ћg)Ӵ6݈Z{K}k '؉fo֤#`iILN *% T'2Rw'Ȁg4`s )Wߡi4@BN)>+g̲߈aeݼE/@S}m㇂S% czvWy@W;3Y'/ZwZN0շLFOPt{.hەu' + ^S;$&Z> my1nٕ'EoΡ-smF}n ]3`Q?t5v#pC!!dww(7.#YSb!-%/tW4c:ϥ]6a(ZՆ.~|tE9o->%&@㫚3DnƯ!g_8 8R3 d(E"ÚOI(oֵ3& ZJՓʺ.kCf iuެȱi>Xgj YYaɜQH odoL8Ry{z戠_-8'SL[u_ }^{96Mq90,fqY] |LrON'6Jj^ߢVl^<,6cx= H{Ɂ\5ˉy'ጧsnZhK@T~=e_oj- yRl-݃;G-ov%=c\ylUzY;B],L#]4ㇺ~jM6G:t!Zo4tv~IQ9-iiNʎ`e19kSg9 YtuһzTQ N!3/ 6i؎ W^@M7p%U KӯzӦ Ћ / RۋD`vnkXo%㳙gIg&ze sڢޯM)t?-( fuH]%N_IAt+lhb5 3ZcUؙMkߛv@0{vh1" Hj0MwPʻZ:hoX`ZaSt!MMnGY 6Sf3zkӅ|N}Ny>$DF\2SG(φF(q џ2oiz9K8 üT0׍QVzL#@դf9h A]3'oW>̩U!JF4O"۰ NJMEK/̠B? 1?x*p\1~Nks)Iv : u;Cڎ-F.3'%ahZ>RЅZG 7ur[fS JPê 8AwYKC Nd>'6BΓl} FHyRaѕVe6e8YbP0e@;Q5+n9;ڛ^oC@E*,m0Bixv3Ҡqz$4#`6`\beL Eeȁ>` lȶ1Mtɨ2Q6+.-h2t)9ٌ Q RCf|]gFˬ˻S\PgI%1(1#٬!}r\h a;-l\EHgZ"u>w4xH 6)ҸY^4pae: "FtkgpSWhs BD)GMaa 0׮Tb_ۗ6;OG y' aM:ntXj̍wF)?'+#jˊi.~ٮȍ {v(O?E` V6$H)`m3, @S%:C^E']i֣Uβwa0hj:3H`T"c`ۣv,c,4! d~Q}q3|nabڱhL$|[rWi2\~C*P='&,QO#!6%=7Y2wF|R Uj)ЛTsuOQ֭]v#{q+yVDjDEi`1 WMFu]a_[f 샗V\DwL泅;X(+NY)`4rԙԛΠe=L1|s`BӑfdQ my= cهBe7MqvaDh=x ; QL_E6F5IβOc7SXNQf3􅰥@N:q۰e9XMM=aheSHǼLiLL ,? zX1SxԏJPGlk@앷Q[ 9_lѦbLlv]'Mf'mkJќsA2*}Ehs-_\mػr ME&)+j%L>!\S إ9wX7oQKgQ ''c*j̢ - wB0 ;MҊV)a4KeOۯVpC,̦:o0^DR^E|h̜ML)4"e|jj?;Pa(o}9vmLj;?{ms2]DM[Xv4 IB[]J-$^"̕aBaFcS,N,گ[mU}.if=э3w01ʙ620d'`m /ޘ`PeG- 䥘9ܾ,MM1fu4(o62F@A0 fG':6yB(6)@|w!n4_]fjL!2oX-x+J`ۜD=n𝺊_M~j԰Rҟqdߒ'ԑG(:y"(k&Fh` Ld7^`ohtg.J8S>K(S(3'fZ*&8C)K?6 zbVV&]l-D -jvleǃnR5 B-G}ѡѓQo0KƳT5gv^̀&L2. [hаd!켘lRMkfOݘT1~dtø3gA]^r&;IE veF]bs3#;pק0ճ;TO_Ȟ I' IpR^v(L{.je՝p =iܖ4XskȽZ_c,,[ Atŵ gK{S.b6F4\7frG0o1-qelu k߄ 5sEzO7nbUX62^K-r'][ Ws"l&o$Дc &Ȩ7%2UYK'Us sQ@R0UV,Vwjaq1mk%9S+4ZVklkg-*rRZ>Z#cv+U)m~'滾қ T: o#:8tl%5ɓlU0d0QIxE(V!H;cLw6PQpݓS ҩ9觚  ϗ"*~Ź@o9f1'z}_wY-̬z~()K}pٗ_,[W֊[J|[ YtŐ F']nGTsxP/esYz[x^-S._>{$3?Ic(YMyewHѢ䰊rĖK=NZUnL,zm (E=1ZZ 6 4xy{,Ǹ5mf)SٻAL rD&|Xd:vq;D\UI _AAӻ*Xenbe}#3i_ԅR#4C)˼r$A͑r34҄E;{0a_A+}>Iʦ1^̨T> 2vTMd[7.8kLO\E4UUk/~|8mN]QcxS>MWT,=iX NW:|HBblr5Kb%B_oz2cĝDݧAn%_swK[B,Nn-(eI$X.b%m޻"&HHA( z$~O$ ^ߊh%C{F^I1z"Յ ~ؔ2j#!5cwo STǿׅR)>#Nۡ(m JT-0tU;Ǧv4Elv+q'Ba"+ڂ-aY8liɓcy30oݐk"@ˊ 0Dfϙ? Km>6lVi(?)ɧn#S4}lD}rV᫚=edW]2Whbb`MO$Dhp9-ZtdTL!II}dŇZ2ejګhȆO@Gs08+R/)cF]QGZuJʚA V&t>!UumP);lvz+]0^F< j-B2+ nxz˻ڪ,1Kċ#|@yO1T!wȍy޶7%{:D!7Ey=O=ףf u )#k4쩗[Q%.*U4釿tʐ*׳s c9'#Qʛ/1` M:%ūآ)ʕĿ&X˻ aldِ øiLѵ_DPe-;&Ȝ}FhK"2%yaoەn߸XJ0i_s[`Ay,$<¯}#T1&Is!b5mlnq@O\sֱ%c. Hs3mc3DV:[) .em7t` srdW2g$SIp<))ϡiK*g]:] Q!t .p#S]~yb[-|Yl*t!-;8ӵ@+ς .)9-٫$EQ[rKEtxǙYى6^ Ǣd5A9#mRƏr"1"vw |^O~}x`}2- Mmt)<ĢR9S x۽bZ YUjFZQ$`$L~MQŊv˧J]#,gD^lD k<;"٦B&c/̷uuyb' &C =g`?Տo6vx݇cJqvet 18zK hMY[b"q"ͅ]vt$En"[ŧ}ʿ{ JҼ Y=0`#ꦦoV!d=7JQj{W*ta2M]O2-Qx}rTQe+*!0"t_7djC^lkΆT#*R~ Tc dEِIQ̖OIcpB736k~lw6fzy1ǧ_[iz7Ě6i6Y]^نD\J=g·8[%Q ;8ŒiNΛf`$kZTbD\e6AHPN&`$CaՃ$umA`lK:lN#MhQ) ܆pH6ESCm8ekć ϓ,*3-w{Y[ITtyZP͞SG;ZW7- \- |dhq0jD6"\L݇nzvH-mYWψ%)X7oOU,R*}mr1*&Y pž`L0걈OpL? \_!R=ЁgCn;[MA2Oę)`8oGUCLHu̱Ψp>Co֫u - ӻ 띱qXGf]C3nqYj,nKCNȼJq0TϏDa:%Ӻpt 6E/L+;DGMtp!5t(YK&*0aOы +h5mHaeo WՐxEQXem54@LeTJʦIZuíaN_2mr`eE!CtI2\M,\&,p`[I@<8%Nj8u,k]у= )XwGlұFpE짼K+[hSƙ!,tZ+qh;r^f}^l-{۞>}ǏDI-H-bcGM˳٨ TL]ʛ3"0Y*Cv- w[]`n#;YD84shO* )5uvSrY-Y.ۭ?+a1kl[mJe+6LjYlc F`&(F%ј^7c<)nMظJnYqy5F6G6/Ԥc(I&n{!̢6Ψ5դK3ۜOCz.b?sԇsL/#1;afư[n}\3 B=j}a&a-{"fr^rI4>n^+, لzhE OÚ];&Bik5{N7Wǵ fwaɢ)\o̘BP-^ۛS3cDvƿM7L]@[z qް` ^~^l2#ttIkvYIotD3ٞ@yQ1Xf@ZcmM@ǺB/4Cߦz]ۋfeɺ?z԰Y| 먼 Ph\۪_meh&/H}^h&mqq䴼;b^Ie=diArK);p:]L1aYG l;a3h1e')uf]AV*ЙwgPFd:9TɌ P *LePƅ͘u`@L(~LuA1nay49EQ(= Zewn^+ iV]uD,,DUױmGV/=9X,B+eӷJ՜ L!Ga7!$Y9x*vyRvi-`qĩl0DU0\¥cSn1R=tӪ̴5Y{꘦E< /X q\L#NsBpMK ղ/[b%)`^/}@gY0^m9?wF*I|> {;2؃>z"Lҧju:ߊ~r{Vb8/.",@9)4y(UزҙuL}w `v'c2v5͸wO[j6 U3ڂ|D@GtVCrzh;J/Ly8*Y2.3)kl.l|*%R'&F* ffໝ媠Hfh3&żWR= H #a`hf@4-xR`ӆ'7+q$Jc>[BSgĖ}ػʀD/dz"ń bCzhWgD Gd*ȴ;nnBk@,ˎ\qݯ[Rᐃ9Uԫ9ZCZ:ixȜz  Ți(0;f؜%J竸[c@".1#9\eL ,S /U'KRl"0>tџ$krM:jNŎMdw|$cz~dj"N?%<Z 6'{|%ȐuGvWo16.&-NBm5);u~m;qaP 8Ʒ4lKFmRO:lEX-2-l4SmC jA `N״pM>޿Q- fn B59ΦYdsfGW_:?x g =lPNVBy5 OE\7[Q&/&Pkk~2٧zb6×? .; -Rղ̛vByKf({QB6\!)AmZBϓ-`굟ΆCn (E;JmU:}sf |㪢dۘsGkjKZ3oSc ZvF%#:s/Z=ZȾuߪ+]Wس (Dk/i UL4SX4/2։6] ;m=7d!deѸְMQl{LU;'+ 6>RZU1TzK"9͈ёt{N ˋ8~1=ա0{Ģ%vggDMqE6"j|*V ץm"x3FMXκPcﶸ43̦8 U"N_aPJZШz->=rS"z-p]/&sÍsOE`1^&<=tįmJJMWu 9O0rS[jNϽEޜoJm,ylj`٫ *7FuCS6*v_<&FCo;,znhAK).1w,H71GIQ$|Ǐ|!(irɰQt KGkW&`u1:!NxkP/A-u4==üQة̄klۮEEˆXNCߍ-'h+`-1L8l oܘΊ vnAhT`ZȧvC el_G5 *Z0dYӚ<CwL^Rli5=96Bߜ83cZ0݈I+DBhۻ1ɌXZ]6E f&uM=`B; U-(8e#{ v8kT4Ѽ`:(wT,`@Fsj ׼r?0O{==3&J55 <3S>DF9ZAHm $GK7vɩ1°D]D[($ 6044rD] "ehuqń?]U) ;QT!c0wBkK:#{Л \ QVSsx]Tu3eMACobvvc|_Ln7/SSIXu`hFfy/DFy?0ߨZ@cxh#E)榚SN6|ƒGDO0t9t.z R)"v#SSκ~nּ0>->U}'|jпNn#.3Q{@~WG>TXSog`n3ӏL&P's긆5 h6|5d7]ey10NCj ,L@8m dD=?j]jjsw㍦(yڟmŪ_G-;Cժ0Q(Wcym J9[X\"҈.eyeEhmO1Ln66{Dcc d#M}"l ~vs#v|~wpу2=^Ad]0\#rUp<BEN %QSU@us\8Mtm$O:{.ӂ&oʸ&Qt+KI軳$]lüۆ'mg/ Ѓk&!Ҍ5YTˇFl,.֖LHL54l\kur,L ךK54Z] `1WovYjqOqҳHH9L׀X-JnLyؗ\f%ji/,0_՗ X<+-_Cf2uuQvڌQñ9(lP硫pϗL ԯux!ht2dX0v]ݱW/)P([l)a-=[:*m_;TQ"TArЊ*u%Y& W Yc]7O$rFefJBV@ϧp~bɄAiR"bna7ϭXe,7|5#`TNW@]Zlu 䯶p˸{k8bEEd\qQ"}0D66Ʊ)C4nj׏&8wc ?F[D1QJmP2dT$]~FT;HHYjQ3,~߽+2A҃GPn vDR# ;e7%@p(cEE$vUP6?r>W} C%S^-O5)֚v GmQ4֟=kȋ{n BQ'ssf\LnSpH#FXD$d؁uN]G*ճ^\x DF%1 ?]mgP:aFFVI33-Ư.E楿jܪ;X/ьEww 6$i3[MgU渶w[&MDgz{QWkFgNK/ѓ9AMv%s@,6ssk004"}UĝY( 52qڳ5GnNaPO7e")>0̇RkŴ<4Aivaw;mzOx֓x`E.ki0פTzD&!!z! d rXצ|C>{pVsEiWZvXN |Y^U/t=9u^oL|&q4ǧ—ɏ (J`-;k)gXȘP^ČAvvKH j8QŤjQ&gi |>p5a+RGFڇkeyWV&umg$Xf~u,"I̗Ɂ7`_f.i'Sl|:cU櫐D8YE ϶e|lyZ.<𥦐~сN=s\8[?bMr2F$Y嗀Ԭ؆D%0nvRa52k>iF Fx4 jitE6|yE[w6X4m'lk^&IؕG`dIFSfSS)["g:SyPlZu_F,zU_CD˞A{o눡/ :ZʙC#?mW6TN n=h^rE4R1Rz{,4rZ\R/HnjW2%< TҠ%2%ۍ @U-O#}2 -Ѡ̒_I_P]Ӈ3r[Z]B^)Jbd׻)L|,G>nbtS#n6pnf<6);cU\9.~v4NO];d5ӷ~(%T|^lj9yAe "/BrS,-wQ3aM㯯1ԍH@KTިc$2ÙLjȸ Bp5qkxCmSJB+ӅU{R 댭،˴Z o;-ȭQc N,Ő^ f!~jnDwNßA G' 2,mƇg8K>C" $-Թ;ULxMNC.d6FW΄6v#K'w~f84mrT#4hmqUt&4XgW"BJ5:ݿmZ4 B/@~W) T>/ %P6>D4޾ nd!bn$tD7yڸ>(>T].qw";cɠjc-Z ܡͱxrO9_fߑK22:іLCןWђKO2xE|oJǽy6Ɉd-ι' Gz$Su[aTz.h;TK"Qzux(2>"y32I)>_k1+iJ˵j"ʙM5"`?n!2 FnQ=Y-t5CW5I{O{F>Yȧt YRcb7.B_\ިM-?^>39uOhI(x+1Mw*;\t|1q5iY8B9 bUn sY;]Xkc"U718pa"UXwN[ 8 MCij0F j.8LPb|= +EjJאc)3W9'L}*ȉƦp EPU< [,S |MNݐns0{le|Y }ĵ =BGz),F|o>z,ŬɧCH7jνe\M4xsߕ8[:P.hig+Z+Z=X<ܖ(9nx4H;ev*tzP JKڡ "S0!סo#Q7K tE pσ(HBbEn v1\.o?HmglJmTX(W9g{|G8ZH"uznpcM?oz j)18o) ƈ@h.av@Sߖ!n]1ƭcHjUh7"dx}UfuYvj yCD¤(42Ɣkc"l堅Axbq$" f6uQ5˯q~" OR>;rsN\祿 G p6f ,,R !~_cR*F2/z )-".A-DlHlO{(6VӎlkNN+9h5$!kcs-;wƏ Bē#5^VV3dމOCPjQ?Z?uh<::8Λ h<E GJQ^k-RAIv,Iv̍%΋FiOtJ, 4NJ#[j EAr/dɴS7kK|uCc:qy/nc ïk^wWk?,%Wb4SA 0mEhikiڀ?ܽT޷٤H<}bFKN΅*㫣u0l|1^ū,],4~Բ05yŠQɺN32>SG [,k*i `Lb wlr[yٜ˞mmGXt>GȊEJ:adS?<^j zW@¾Gb&y(sR;ೳ` )rN$e#jSڗ5#H 3F Wj#\  Rzzy} O cB? l;usM8樚d\11y(5,!87M+$T> U-l $gs"M^ᤧe;O e"%U;<3=Ys u'[3^B)L{PLbKnoƶ491ږ40g=g|(vwRS@{M ^vߟ3wũ+sz^ aUāPWU*fAlg$r9w%hʹIN`FCk|4L"D/$ܻ2zWLrZQcNڨ]VJIw^KyۓfEhwo?iI>XilY^3>;ѓ'S爍P贱3nX'tccZBǏAlƱ.XD}/o얧a*%A9Xj 1UbYS7S˱eMN>z3U3S]ߋ[wak=(*LL#"DɫV^S^e ..=akS TS*FCCRexw熰jj-, eny:DMj&F@㳶pj2]&(]@P:16gJZ#f_>oM3 nAޯ{%''vחތd#6҈77L_3cNsV +e1_me[7 T23vsFqvڬGITl'R.L;-vL1sq>:쬆*<9[HTvV6,(b[rAǫ Æe6kIc;m9a4; я-@n]'P{=9hb&Ey(cin2,kϷ.Q;uIի j+Kgf,z |=bb'b[8'})xy`ol37\ׇn7Hq1즷BҬya"ꀿ}!L9V` {C`i'd£D)Q, 2:DݯxM؏ÝJg.(o 3V㒙ĥ'妨ŸtEV6B$3η_ <_lFc͔5%TSkWu/R8 X$|Ǒda,\q<0j95cdvINUwU윃mytȞi/C2$68)H@8Mm:.eBJ@ Ie&p{(Sieb-WKRgG;86i/Q1TLnHM Bgd&CS "#\=Z\%D9*UGDzk7(,6cin <C&i Pzxl s2nmO:$Mv.;2!0v̤P ;X8JJbU[FCC]Btrm<-cOoi+T8['(LNjꝎ6˝F.ɖڠV[vQ^zKR!°st['II BV*lcgB8rP&6IukW>]6{EtCCˆOlݯPdfN7=CI6/A:$<7.拴.7 BvfZ~xӯDL,0)d$2@XKa`{㊚1FiF'q~նڷ U[kmmn[VڶZխUmbLu#DV8@ $T) D'Qhm"2 ̓@kCB[T ZVR@(6ڀ44 T( e PVP"6`@(TUP@l 1iB FhР4zh#&RbM kDqbm -lUVU6Jm3`+fmUjj%m%-U[R)Ce%mإ6m FM`)SffVem6fj Rle&VKd&M* R"CeITmR6-&m (*Cb6VUXي+YmRT&UlMl%Jf bl+cXM*SdFȓb)Kbi`&VM-mIƫQR[)6h6T6J[m @6(em6Vڔ0b%m El[$M-lRljl6$(ƭlXj[%M6-R )c![ )m%lR 6"E6-m(mC)SA6mRUl&lڋiejmTڑjjI*6[DlJ[RKh["hVUJV lR*m ؛(6[Mm-%6lUmQlj3S c R6lDڶ %6%mQl [RR%Xئl+6+6̣)ASSl eF٪ji%i6Em H[)Vʛml 6m m[UmAfPS0a[lMVm dP[RlʖҶJʦ* 6HlM6(lYjlVV6QVUbMImH؛D eIFʥmSl(0l 2ec [ṾaM(Qjj6[ L P-UmM[F+a-lV)fV1al#eVilQ[#i-Em"T6+jl lHԬ%VS0 ej1Vm l՛fVe0VelV6Z[Tؔ-ڑI UhSj65cmcTj-X@`Vٵ`QefV2X56[e`+-ll[[$6MlVVĶJ&[IVM-[(-U j Yl1lfՌ5Rbjڲ6m!m-bUmL @1[lblSQaB)(XڕmLմl[U6lLmm(l [AlRFhd+aM-ԶlQMЦīiM5Vm&e3VKjm*6[)6͙[ l)F€P՛6FƦllSe5fe6+mmj-mEcX*ؒ[*)l6"l[VjbSaX Malؖ͂mT6 )lQYY VțRFʭbضQlS5feV XjfڂAYMFըƬ)l+cV1Ce QKiM[EQ@+ ڱ[6+5cj)Ym+jlf+5le6ثъFf m66)6)23jlE[ lj6mmAkk*ƵllmbŰl(jAXjmA[2e3+1MƫmklZѶmFڴj #jͲLmڌe5lնe3SbVfYj`),ژSmMACjFjjaY[ l-jM[6+blU6ecmբ&hZ(35ح(أV(+fV(cPTk[hEE61AX-cmmTTjV+aM͕S([1hi#jmQmPVj¶lbfձL+j6ձX+`+l1MFLQ1l[5Re1YC$ƣQmmXSl(mVkV5U aYBjee[RDڒ X+1Ye3ئmBd3QSlV5j`TmckѫQmXjm*aڶڊV)YPPmTjffU+ M-FKQX6mZ(EfVmL*C`Elګa+FэFXŭF1hUH[[Pҫal++ bFbحEb-FJ٨PIX-Qh6 ͔lٔ`)Mj YB6FQVldZ6QkŢEmQZ6F6ƣh5F̭0V2 mM5lVJ1#(*34bXբѪ-DTFj*4[ETm+b6ZEکQYS2°ڴXV4lm-mmQTkEkbhōUlmhՠصEQ`bXec24kmlkIlj6,kFFV6-Z1XlZV5[ѶE1FMSaMe6VMmmFXڣ[[ض%MilVfch1b6Ȭ+P+elPjڱSQVm[jl)Vf)MQVEZ5AlbXRձ@SmՊE +B(mlCiSi6@PV6+-ŶИklbѢX֍cQh#Pmclm*M-QѣmEشchckj5+V6Rem%m+dTk**hc-mbQj-EIjѪ-h4mlhQX[h֋QUFűhhJEf2SbV2jcVƭXSV5-`ţUZ cmATUb5T[b6hƪ1XTdIa@Q1YF V+EmX(k5XѶj Sm)VMmBVVCjbXlcXVѣFZ6Ѷ6JXUh,hհ[IImcbTZF-E-E(SVQ Pچ͊3SaLQCVjLYDa+26je6$2A2)VcTZ-E6QFڱTQZ6Z64mFk[Eb #bhZbFX&lEA6mmjՍFXhcVhՍj+bZj-F6Edڱ2AEfj2cQck&k`XT[cmcTTRZ-EhF֊VlmccD*+3h&ԍbjb՘YB0Fhj V[eFj٫F cVSY6QkI5hűJl[chQmQAXՌZJ4bF[) lhEQjmbٵ[!E m@VlQShصm+b$XQE*65Fƍlj TPh&mcbmDi4[PX -[F3ZkXh,Z#b-RhXTkl+clXUF Hhص&B%lڕ[F65mUd(bVFM5h-i4lhcDXU2e2[ ),ڔVEQllml`ch1mت*EcXQFmhcbMbQEbQcQlTF-V1ƵchţQT[2bRVFmF FR!hRRl-FՍb#kFŋV*[Jl6ȭc(EZ1h1[kE+bhmTZfkEɨ6ѭF1F**6Em#2ƍ6؄5Fڍ5F6TZŤTj(TcdDZѫEQj6ac#m2MѶ(ch6+ejf+Fb1ja[mLfƬحblbm-+Tm1bՅ56( (iUQV65dƍj6ƨ,Z1*(mQKFhllcbeaL Z6"5EhQZ(*- Fja-bZ,TV ،m4ZJ EcXFMűX(6Z+l1XbmBFѶ6ALVj&mEj-F5ŨmU+hlZbѣD54XlZ4j5RZ"F*65,QjmŃFlj6-XJ6 kƴQQ5&mbJUQQK4XAŢتJY`Q6FZJQAhm1 -hcV5l-؛MMV̡ 4C(Vi6f[kU6 f6jmlj,Zh`*-h,ZجZرbj-Z4lZ,hUh6(؋ض-X5&ŨXRj"XQEbXѲV#mM5EDEZMcm6M1FE*CXbآk0TkF"bjJ5cQPb5m$h1QV6ŴT%TF$EjMFƍEQdX)-EX2cRVXQVѢV5Z6C)) 1Qƶ6ƨkEQ#mE6lmcF--Ihɶ)5cF+II#PcbhX6LEQŢb-dEűP[EbMj5ѱ4j6űcTkEbTA*iQEE5-E#6*ƌjhThlhFXb#QXljk6[[0TTE,V+EƤlX6c`(ōLm lhXhKF66ME%lhb5FƆԛU6ElUVқlQV YeQl 6[6UlSͨ2V6[cj,mkhLP(Xšk!Z MbRXEZŬh*6lj c#J50I QlM5dfP*ڒQe1Y FVšEj#RjM&EXd`5% Z( ZlK"jF؈ E165صڒI(6ƨI j,lAQcH FZ#kEFجmIbZ-!hXѤ()Vƣf+jbS5Յ3j͵5mYlHj+؍E-PU+C)X)kQTKlXŤE6XыIF4jł6ZŊАb1QX[b+FűbmQ6J-b Qlh6œ&,mQIQ4dj* 1*MUƫ(XTlITmFQT` l-4Xh1Y4l!RF Lƴlh"Lʂ4m-(ũ(lhi,QbQb+ ѶV-chj"ěZ U6ƒ$kQR[%hm4Fѭb Qcl`j6h1Z,hъTbh5FQ cbъJ06Cch X(M-#Q&Ŵک[ k`()QmJյlڌڍ XkEm-QbѢDmj5+6-%j5UcX5&lmj-Umm[EF!QF"ص*5E+!րѫ"4TQEX4[Ab(,jƱ` %Q* hmb*Eb2Q#bMQlTh*bQE* #lXh4+FEDlbѬhb#)b5bƐmE5U$U[c4Q(i"(!+cTj!h̚6Ƥ1F-@bƱ@-ERXcc61V V4j+jV L+miœb(ؒlX `kEI,PTmẸBhɶ6 X5*K2 ! clhT 6(M*4TDhƪѭFѭ[[-J3(+0[emmVElZX֊PhڒبQlT"*(6H+YbK5c-e(S1`cU%,-h*bEMDcdckѤXűű1Tmƴj-FFV2cQ hbM,$jh*4ƨ"1ERV6űQbEl‚Ջdh1T,K()elPh*6XX#lFДHEU+Xh"1Iclm DjѨlTbAM&5EFKcPQb6hű&b#TmhV6EcC(J6+ 5aLF(j C`kcFf,QF1EmbQm+h6ƍEFɢQchj#lQMHXThŴmHQԘm!$ت,d3`)61Y0h10PDTFE&ƀ)J6-IFHY6D)-dĚL@j-YҐV+5,mEFkX5jcVfjڔ mUmKbm6cV()XV5 XJfQl4V-EEj*ƍlmb1hŊcQTQV-X(klmƱ Qb6XQlQ56*6TdkEDQDEP[Q,TXړFBh[ X(H)&։m-FDFр@QEElTcdKEԖK QFX0hQDmj4Uc-F1RVJ* XE1IcQXbƤة5&œ &`bĖlIlTIF6 5cE#- m[5F+ 2hœ%DDEd+FQIYAIbZ,lk E-j1MIbKb(6 T!1Qɋ(FVl6D`b0FFֱ-%j*MF5cU4ƒV1Z5mcUdK%آ,1EFD&4h$`Bb6@Q0ccQFبX ,ѣP3IF-  LcFMQF4I%M!D+b(5ѴT$E,C56HU[(6`Xjc+Q bV”ءLcTFѶ+F-TX`6ƶ-QcT[cmTbţFب b-bcEb(DlQYJ,F((j-c$’V$(+lXZ4bmThQؠ ɴ ڊƣQƴjBTbI&TQQbj4`-64RQlh*IjňAmFQTm6bœE F+*+بhELTEXF)4[LiڍQPbj"6łRX64lF(Ƣ(ŌPh&FUlE FѬʓXF`llZ660lTDcD[C6fŊ3FlEFA $jlXڋ%a6*bLIDlbŨ hM$RXأ!d"b)-&(ŊTу[Emb؋h6QRhV-mFD14$mF%H”IdIMEEHQƴbMƄllPi)LRCAI$hHchbƊUEAlъBX-%؈bIIF&c щ"5e"5جZ b-01XѢbRhCi@&*Ih6$[&m d3(&dQIclTAEF#QX(DbDm&H,RQ hDAF12Qfd,E F+4 b"CX#Rj)(؃S2hmhFڣ [+ ͔dMVٲlճ (+ef+emXڣccFb*F+Fѵi-ƨ%QcIQ*э&4kD,F&Ũ*ŋa51E 2jbEATjeZ ZJ,kQ&FcFJ+11eY6cX#F5ETdŊ#HlQlj*R+F؋SM`YXlVdEd, QQQQXPĚ**I6($5b̢lQdTcFō%%DDTY6+bY1j6,hRD[$ A֘bH`hJ-űQkţ i,Rlm+AJ61$S5i*0UFc0jՍQkhRbc5&آh̢fDk)& hhJMlFIFT(T(Hō5jXlkFXAh"Tl%5jI4j#FbK"ơJ1l 4эXFbD1l) Z*SFV "̱hѩ$؈VAlj& m Lb#hV5#)F5mQY*Td)TV6ə$ Kc0DQDTFђ 4R$ْ L-jMj"b$ڋPmF Ahы l"Eli)*5&c $6,X 6 Jƅ bI6*2( ),DhŠ1lQEV Q`ƋQ!DXXQ$F1Iɤ6K%2bcETj#M"6S2ICP2BTlkE$ȶ$d* hj## `؊dlh aFdj!-1a Icb1RCaѓIa 3DFьD*V͓b6[Rk[(QXSV¶(jٔAVMla[Hkbب5E5Q55شm 6,Fڔ[SbBV QmPj5hQEc-Z*TlV1TV554QQmAQm*(QFmhITX`VlZ5llTi"XJhR"FMdIDQTZ6 Q-lVijZ&Z"F)62mQQEJHŌh$ƍIjBhB*0Q#Ed1`EKbƍXmRI)Q4,6L6cB[&,h#XFX4Lرm(I6(*-ɨĖ#i1 -QEc)c%,Q(ZJ(ՌEXcF!`kF̤Eb"DSDRAQ+ƠJŲD5,ƒcE,I`bJ&ţ $jLj Z#cELhmIcb "(р5#,mEli(ZŦZ(Dmbɍ)54))F61cT"(Ɖ6-!1EMj QIdK2ibJJLF(QhSdI&mlkZ2jƌ1"ƣF6J#XD!ED%%FI6 ED6-,T&4b6#66- jLf1SLE0KEѱJ%)IhѴ!!DHɔ[ A%&LĊJ)#&5[FD`"klQb1F1L5M IPi6FeQTh-EIQFń͔,4a2(&LaEA@R*HbLFIEEY!,YcAhb% %HJ"ň4Qk, (b"ђ,hQ4eM͋kbQFElVXآHZ(4m2$ -#T 4@LQREcL 1PHbm%cj0FQɓ1c d-#lE 13B%$XhAhQ)!ō4Z4kj+hDZ2Z$K0I,li!!l&Mf(dJ6#cbbXME"lc2)0k%&E3d4f $ń( 1%!2EE"1E `0,PIYMQERd6 JMi(Q)14412PlQDAI T f0ЄF(BK2bĖ5SQ 2Ql#SI !Ha,Q X&(4 D%Ii4A(L (lL!&'jEchEX6UB *QѨVMV Q,EFE,X$U (Xٖ5h+F4ml[4PhEQbj+QQT$mPhچEF(*#TkhhcV6JhBAccQF(RFDF*4TlliL$E66Ɖ,j+F) FAѶ2P(*4E4X6K&M6#h!5H(dfkY4#chѶ5%6J(DY c$b 1QXRT S*@Di-(l*)5EcFECRF66**L)QhэA%d)6Z1Ka"ؤEFLS#iR!kPHCMDc4j0Z#QHlb@(QQ1h#bѴA EED[$DQF1cXM&̐h$HHlF#F4j6J"LjHl&F$ɘa,ZAbJeEMX#X&m$B1` уh1ڍ&ciMQX!KL$IdM 4 S4Bdh4bC24Q*"AlQXƊ4 "* cLIbH1F,hc@*42dR6 5&ƂA1FM2eFl)"H%"JI,dY H6 h$QI1A$S"!6i)D5 j*CYIFXe%@H Ɍa"̂h4"iH4&4 HhFɑ &h)I4m ؒ3$I(Đر Xlb* XĘF$%,1Ff &F41HhЁD c ɍHQ$b&F0d,heA %)DaH(M I(DiI2DDHER RPE"ԅX$ 1BhD%4@+E4FԘhTb1hZ(PPc(hS2J((Ih4&,)Rcbh3 m0(SJKcc l&BAhI!Ab3(fZQdDc6A6$ II%Dm( hdم 4K**ddH!Hf&0RX"H0ƉH!Rd3 ș"@D2d@6MjHPE1Lb+FA$2Ɍb *I əQ0l@Q6c 3i0i) آ1  hA42 $!M0ĦL@f@F @hC%KI2"$1c̆(fbD0S6BV#($BVRP0cf$)I&P2EjFQ0,E&(bdD 6M $$52 @IH̠)%YB i,Xl̄DRThR%3 B4A(YJRF EBQA`E(F1ѱiHђ)-%`%*HjQ4i4ͤd)cC#Zf bI$dɈ3dD% "bicZ Ki!(S 1@Q$6e ɑ$h`آDSH02M!FXİi%0I*0$b f#D )`ȑ H)#1)) P&ДP Fh4 1)dQBB&$C4i"h4&0BHQ`$0&3#!4hdM(D  J$( LH%c!f$@B!!lL#P@F)3 X 0#(2h(2,i`1 !DDcbR(@2CRH 0Y2 $a2Fh4бID!FCa D Flbf %˜4( 0&aFI"F 4bLFe)&Hđ0) JED)4E)A jb0)C#!$! f!aYf0LIlI(02P"S h&3"1e&d @$L 2XIL&2l2431"ЌI 2e$$JPc$"c$IHHhhčddD12A LFe!"DI,&2R0,L$(M!0a$R ,L2#)JfQ4C!`C0$FP)J$LɄȖc DHɌa >7ۍ|Qxs݃6m4q{)_ >JBUA*J"JR 6 -E#ZSY* ֛XUi"Rf&֕VIkZmZmflPh46hPk@( PkAT(T dlP(PPZ(i 1iB FhIꪍd0MJ @ɠdzN8 i*BTm6TJ[Q-JFl" Ki"6JکMVʖl%*["[ R6LaXfSRl*m V%mE6$6[(b+ V+jAm[*[*MVЫbjKh)(mXelj06@m-SdEj VS5V6Vԃe%*)%m$(ձV͔˜YdRPثdMlUjFЭ[ l)-ղ6Pjʭ"Sbm$QV-jaVI[)+ajmL5c alHBڨ YTF[*lUʛ6AҶRچ6-Um)Q6+ce0(fVSLͨ"6[%-MlPmY(ͱ+j[D6ڨچQMj15c l[m1C e6̬l[aYaemY `jPcPV1F¶D*6lmTئ0X٫ e3V$ڶʖ[6[)l 6mm E6Rm)l) FʙXPհ5i+hKeK`[T%AڔBڥ6KfҶE6ElDilUla&E ب`j 0Ve6 fjcQ̭M+j6TL1ME3hJ)l6jfVmYPjYͳ(b5+lj66jثjƦjcQLl ءmMm*s9M50j1[m6S jQ3 ճcj-[)Tڬj+6VV1mf)MCmL(e3aL+e[*Ki%Jlة["MIV[$Im[UfmFPͨEl6UlV6d k fVe0f+Qh6Dڅl%[[P#j&mAM[[ S51[l*+h5c֊jbSce V [jٱMcjj+()أfԣm!HRVԩj%`j[23e60eVjl))bT6[*m mf0flmL٨j%bfڌjmXm@ٔCQ3Pb6bVR lf YfbmSaVʛ Vb)Y[f[ffQ6&ʩ&66lm(jQVͱF(lk bllI[6eXVE6YaF,Ql%[)ح ³6ڙSLlj2X ٕK525clYګb[iKjR3VUcPj S1LjaY llljcb-el66FȖ)[ JږԭIQ+iSdʛ$ J+h؛B51L+Q°3()[efj+aL Xmb+alXFFƬZɶQjFemPiSa+blJͫcV flVږM[FMm@V LPAj̦SjS5ffژKjl[)mؔQ aءڰիel1M)M6+`+hփd6H%ЭU5jEXEmFٲ6TՊb™1[ ض l+e#d[(ڑlUؖm(U6j̡L)Vm*؉lSdضVm(IYUE-Xڊ6mV҆6*+62*FVڲlVF l6F5Fmb[I+jMM [6QSlV°̭1[6Q"ڊRڛ ca[+S5l+b(E%حٲlEm#amQU -jVжmRl$֦b6[I[TM&l+jjmZ+XF([e Tj6j ̠ղSP)Em `)m& b4Tkj6̠(ڙMSkfmKb[R4ْ2e6+l5cPlmaCP QlkcmE[XljKj6TmlFVkUcj5!2› Xm[mXي f1Vjb͍ck+bkZF-6)#alQ کlkLʫX6+f٩R؛I6I6*JؠelڀV51Ec2jRQMf) ebFQƪMl(-fa ٩j2QmSmZm$lmSdfV1LVmIMi[mm*lJfjQJJX5FŶ֥1Xf@ձML¶Y[2ƱETU(jűXٔ3jbSVb[)CjB[jX(ՔJEZ"AcQѭ(ڍ532Q+Ƣcj5lmlXXVbFL(ژ(jaCVm[fMUl"ضEbl؛Jm UlDmEFkQlmhXTQHlVXmիjBKjmK`ګjFVm Ši#VY6mZ5Vj4%j#iF-j f[)Mj CjjlVa[mU[#jU-[)cSlQ[6KbЖVPPSlPQ+f6UlIlFFlmBjaEcj+1[1FlVfQVaXU6[TX֊شPVB S@ckbƣZ[FƋ ERUE52+ RU[fj[6+c(چ([JڍfڊŶ4[FcmX[5J6j[(ѭlXՋE[i&[l%lJmKi6d6[Ikb)QmXV5֍mVV6[lQ*4[bhQUŪ X#QXѱhZ66[BVқl#*[[j4QZm6$ͩ5j ضb6+*Ƶ ֣j*[%m[Q[+Z6mbɊFQmMYMZŵbډ[ ڍVj(,khZ6SʦefQfث%mح4[Fڍ5QH#bآ[JMjffؠVeDjb&lFҴƭؑjSfhQQEjֱVd)YXVd*)1V5Qdk"-((XF)e +5 I-mAU&l6+hح[ -UbQZ6ѢձlS5VѶ,jlUTŋFEZ51chTmQhZшEb Z4FVj5Alj+`mdaElm6ljb&ڊ2QAPQT#VѢ6k`Jآ QhV,jZlj4[bZŖ¶Ք+bll6EZтmQTV1I(,mmEUVm [Rl2j¶X+jڌ٥Rڬ *cb#jƋQX5P5lګVPbi#m[6j66TF6[Elm (Պ+1b FֈՆee`Tl0LefVZ*-`mU,Sb bFhhQTk&QƂl3Z6m"ѴQjj5lAk`QQlb5lV 2hƌkXhҌح66ͨT[-a[U6ؖ- FUlj F3رVXb"ڊlHjصQQ5AX)+#QZ+k[V ؕ)mc4iXTF6ƨ6ZKZbѩ(6&EEmZ5E1H hE*fZ bSicDXF-mEb+Fm6-E-j6Zmbح-*+XFŠMZ5hPbI[U*-أJ5Q*k-Qhhb6Жlcm664lbbc&ب%4ZDlب#k")(P[b$e5 @j5fj1XQV³S1XU% +bVb(kF-6ƍEkcTEQRY5f4YY,b0kdTcj"&ѣQZ 6ƴY6j"ZXQf+bL#Sm4UcV-EU[%[@ldر,h,QTcAQUhAlZEH!DZH"jK  AjI+iMPcm[J(bfEcV1[dYXͤQRX֊`֋ccQ#bb+i6UFѨ*"5EcXьjɶ5 hQ+IlZEQbRlIX˜VQ2jɨA*5+X*,Z4XbchѴcj)6Ōkbȣ-i*!FU&űX mF4Eڄd ,hmU1hdZE[HbVlVdah1+TF)M6m-b [j+EhQX5h*űk Ũ b[1kc[ƍDmF P e)-ecEQ`QQj#DjBص0j4@Ech6شUEEcA(Mm`QڊF hK%bL)XDkcTPX0Ţ *b(1mmE %c*1BEbjڋIMMBmPPm" j(5e5`ڌ՛je1XS6VږJMښQVQbefض-"mV hQڌF %QFڋ`-EIE[Fѵd1V-h6bmbQت*TTX6-cV-dՍTRY5QDh(Ɗ(i#mhة5&$d 3X#+l 5E*56$ƣcE%DFZ54Y(cThV1jI6b,cbh-Li$lUm%Qe% cY6HFM) &[FRh6S[IX4Ecf3Xm[)VVbƍj65ѫE1M1[Q)B5e2F%lԖ*--*Y6ѱ6dդhj+2EfՂEef"P6رj,b4jX,TmAPjbMF**6kDTlE5FXjH1Fc0AE6űƍFƓ`*"جmkhIi5c!m26( Z(h-E$1-(ŢF-&JmXQQTQllb%#A#+eQ#QhXCb!-$0lA Ɗ#d`(EcF ЖihƤb,hEXlkIcXFcQE&dhX6FFF1RlXḌFXa4h5 Z m@6Pm[5ffUV[Im!Kbl՚E`QJѨ&b ѵ5mZ5hmj5QEF4QQ@Zj(hFډh5QƋdƴZ6Fla6TX,IQD6ƪ(`XmhڍE X-%h1j6 &[`E&mcDlT%F2XmPhTF64[Q-`6ڣFTEcIT͌lE"ƴ ! ъTV4bƴXQ,E3cX kdTmb-j5lXdcZ#cAbbɍ%"4dE$F#L2EDEb*1Y,ZV ll2Yb b@(HEJ-e+$) QQƱRj64F(QTPTlIlF#EK(Mlٲl3Q a[bmA+e5c66d#dmUh,XlTj emmlhѤUѣB[Th44hccU5Am+FY-((SVKbP Q%ElRPZ2546֓dj J,&5Dc,j,[F66#Ik%-F*B؈6K- IfQJڱ M`T4TZ*ƍų)61X6LXETdEM6 iP5j-AV6TFhPFBbQRmHj2j+i4mFъ% PFA&4[&m"REE@(%Qa$(B4b*Ѣ(B(Ńli6l6ƍ`&2U*KEcRQljlcE&3"TC*a,QA Mhh#$T$XbcHJQi-Ƣ,$2MTi"(!M(aId Dmj՛)XCi6+mFDk1XZѭ Qb5j5++(Pj)MmF(KlQƍb*X"F*(kFZbŵIF֍ D52հXQTjQ"łJ(EF[i,&I(ԘԚ4F5l$)5KX-4EXƬTQQ(FF5QAA3DV D(֣ƣ[%((EX1b0Z cQQA&fرh RFhQIF"Me, 5Fm,ɨ QLb"K lXmE6 62bIEY I2JQ#@lZ1lY(dMI-h56`cDQYb+[0hbыP2%FbRilE0UAE"bM)5AEF$`m0 kF@lch 1Fcf 1d-LE"DX+$kFQdZe#F@Qd%clmIhэQkLHɤ1Ãj6*)6( QI6"1%#%D Ċ%))53X3HHX( 3 %(kcVZUA[H+f66ŨAj5lEm°fefEbQjfV,VڰVѬ[Q6AQjA6MZ-c5TF`+d(֍* Z1lTFAdbC[F +*ŢѤ-RQcb6kFddPY(--lY-V)II"0hجج!`(4b"#&֍J6HDjQcbh `DTFA"",US4QDѱlQ0Љ*5 BFh1QF ɱjMQhbŊ+mK&jJ,[Im6(Ѣ("(XT2b̤F1PS4j6ưi,H4hTlBhѣcF"F5ib&)5(ŴUbƊ,X(Q 6$lƈشRYl4QEEARldHb (ѲX2 ,Rj-mMdQlX3lTcIc"QccQ&B BE$[$lc`lDXTcdRDlFXcFɊ4& ILс4FJA FK4Q XHEQEHcE-bɴcFѴk!T26Ŋ@clmF5Hb)1Fhѱ#R c&Jh 1QXf5QTEl`"6űX6@3)h) V%ыDEhF 2h&%$E1h1 &JţcX̐M16(cDh6i"QEcLDh"e `Jl%hlmElfPee1AMT5jMmգDkljj6bص-ѰUA6lmYQ-FVƵ&!*-ŌQE4m% hA cUFbi+*1llj(LX6-FQHV4bFVڂXV)kb#5XTkAEQj+6H&b",bHEQbFōA+!Hbƈ5hb X1bьba` V6c EA)6(Ţ#c&LjeX֓bJX*HؠJ PQQ4ɴh"+FKcbX%d b4jd(UTF&5!F-bh֊5[*Y0 I AEI$(4h61HPQE-EF#3A@hHMF-F-Aƍ&"5b*BE%@Ƌ1ER@h"TPZ0S $Ѩ,X0i RE`RQlX+%1lY#DbR#hS,IcRbfcI l F1lQ`eH HMh2F@#$ 6 EJj%KHSbJQ&6I!Fb22[$hJ"6*+%DQh",i*F0QI4XjMd(Q &#QbI2Hf%IQEEF2EEPdQ4b("F**CL,cb%I6*"#$!Db(J 0Q&!M2lmL,DdIQ$FԄc&JF1Fll5$ DfFX*Hi1&DC&HPkLF"#5 be[HZL4PXɁ! cHb!M-j(lje(Ȗ* "Y"Ѡ4h,bdQ"h2b% #EIE)DhY)H2bf#f&hŔ#J61KQ% &(C $Dd2EM)1a"Hi3LDLE% `"3Ih`e1+ X#HIRhP̦JRL)!6PBQ "hD4`P"fbf 3FLj&AYjUhJliblV`%LaFj6XDlj,V(KlPZ*Z,[QcA-clZcT[Fkb Z `ڌH&Ѷ6J,QTXФEYE)BHhQEDb MDXՂb1hѬ-F Z1%bU*,j*c#&BmEfcIRU4Z-P,TZ!4͓ E+Q &b# dٕ-(i1T(Rclcb1"` cFH1Qj6*h0Z&(ؠI4ȢA6dX(1F6Ȓd͢dţh "* ɠ Ōc%L4V5&QPk Y&јlh51A! E2HȒV(1&+EQlllb@lѴmbEE2,jiY$4Y iD hhllmF1LFذIILX &&d Y#ZV A4,!&fX TT! (AFJiFFFc%2mb QTY#YƊeI& AFM&cc3$FTc QY2lb&T`h K "m 1)cR&RD3"Ebѣh" 2lM(I,i$a"DL(،eF2ZKR KPb-Q- 24DI5bA $F&$2MT$% SEFDH A*ikbb #JƊ0F1 ,h 1EFdS#$hب I2 2 I (BHQ`E6"$20ADFIL&(0̑4##!4&1 0QI@I 2JcJ IC4DI1BQDM)D44ccALj"5"mc؈b1D21e 4Z1IQ2%IFLIA$A`L2hfF&Mh2R )#HDai 6ň3)I4i1 fرɌd61D`M2e Ć$(CfS,Fd RPId))(JA6f&Х&C#E1Rhi)HɃ I`"F&ɒaĐl`a0PD"$4 FHb5 )dJl&Lfeif@Ȉ2l)E@$1ɡLR)&%HlED hѢѢMQH!1Q$ZeIDZ$ 04FT$&E hThHLędHl4L&MB ` "`̐" DlH&0eLdae 4F4C!M `Ђ!)2$4RBEBE`34"McMP#ɀF(La$F,I&C&DFB6ZF2Yf2Ѥl"I04IcE&60RY0FQ QHBРA"b3LPcF*#0ɒ,iMe 46&lSFLHdB,LS2H1 ad1%DD֌Qf` 1B ͢4cFhhѤ,)4Q$IJB2P,bL)B,!JLYF@3ld&B12)2FIHc%3&dm3e D$F6Je % i$ȳBS&Ih,A "l4cQYmAcdh10MLCDؐXɰ`$`6IIPDc,@L!di*"4&$e(CFI b I j4mL I@4Q0@Z4Rlh&(Ph@1*D*b, "4X*4X*") Q6Ė6- FVB,h,UQEbxv̄O/8礈NßIl6inu㎾/ 2Μ4OAX $ "(H$HIJQE !}mdViEZڦ٬ȱMR6mmeѭ!D6@k6̠( FA` @% jTPmJ4ҀqA `yPB*$$D-"$kRIKfEPU J(j ψ@M%=DM4h2RRh@ )*F@0 MJA0#FC0FM0SUT0`$_9՗H#A%|mBAS&-9?uIºQոqXWE \Bp _g][#g$+~qO|{;Ms׿u=F2s|ҹG$89녫Ѷ(cb5bI ,٩_i+-ոW_~nzDMo@\=OCFF̏#*kפ']xż/`ω)Ow"ȱɖnF~p37,jݣyO7jxW;ӛ]&H@E>shL6'Z 8?T W/!jsb<TH'2ʹϹǘ*D|F8(4+s?U{ZcCq:FCS 6y`M͎t~N,Ph9}:H9WWy#yix1*_Quҥ:ZoDo m+:i3yHꄨ38dQCMX=E .eUUp'^ETחgDWScX2W#:@sFb5hxmO72{平 C+GtVh4ÂrXqaXGtxȨ7I\0\"g,<[o5U:6 )rsؼಏiy{n%ҟ=7D#)e03M|KjKWn+6-s"$lK 9CI͞;vFVtޅ?kb#&OWV+´#Y%z#<|ρ]Qt I}3;P9^PRtUIxB%NzdЧq D`;4҂8 D%/+s6]2FNa:H}X$n>3÷--A'ZhRÁ48٠clѝ%(&l#!| D&Bd8h hKppd\쁔TfzU|3/8K+:|`t=!ЦGぱA;gggdΖ Kb>Dyg9(C3Z՛JM*}B%ZӪ;y3d313JMDN-(. 2%T5( b dҙclVtT= % /{lj(u@]*@gZhebQƒu}It|uDmWXB荶L^e&]ozcrVd+NXLM|}08ע4;/ݸ+ 2|\ v?i7CW, u򛃷7S6u9wB_lEfOOv2S\,Al ]fPG@ev *$6iMh9h*:FbB֢jȍ]NGl4%M[B)v)-g 90 <.t -_f5\{B5M3Su;75ȇ[Z ez ^]A&l9c)D2dBo:so7CvU۷s0{O)xoΥr #M4wa+x%'#>f3d4@jUnIS6(,p!`A2UH -Yn9FS欒yDw I ^-@dez٥-gω9aE3pf19<[#MQm~8SWi9Q#Kބ O@M|uDG9LC)N #|>c_JF_T[9j6)=ދ!cI[I OA]'#Ԇ-h=]|0A-~DA4 i\jX XPöIHugUE㽶^)+jB+.0,ޝG7 ep(U'2`3=/7ƴ56@qsyi-uj::>J} JVyS?rCNtH]ގIS5:ZwX%%)]iXBUaDȻbҰL'j K=o!h9=W\H͗1٥K;]^xrj oL_s!O%.kkѕQ>оIʀZ޷>{~x`%Oy+tC:9<uDX0҄K綔!?H> MA-^ eXVeh+xfS#3Vh`Kr}Ҝ_iSY.e-8#E 2/dx[6t&5w48 L,rξ&Wi7%]yRh& L׌3g%[#ռVp/0OK]ޢB A$Š3QBnz@{yNWˍ1D\JU7j./nxp% ~NsDrdEg>[6)H#W6 ;Rk~ B5{QZ-vdޙ 4T,N{DrJ4G/KqvU)߾g/zzh+ҪW֮=98|K.lۧmh=nҧS;.B^6F{qJGm t卅@Ej׊0졿)j?E kU_TI:.Y;n羽h&x˭G,W.\!gkpۭNfWXiK)tPe;Mc[޳ ꮕ7x̶wucwd*BRƽiVNOrLeM^xj8,mlb /R:+ƞ}L2$AyQV+2.( r!?>Vo[.=R˞ [ҕ*yzx@QXCbFەw?ǿ}>˧ٿf a`(`mM+e[Ul$UQ[ mjmA)6+fbmL+bjl5b[mJmChVTڒ6KjTѶSjfV2Ũli+i&6J6RV)%KkblVҍ6LհV ح-+dڪQfKbm-ڃhڍe#h[DmQV[ -V--V 3+el6-آ؋j`mlT؊UM[6%m%U[*؉VM[dFҍ2Lfڍ6DڊBl%i&ʥmDV6 jaiM"mQUFjƭ+0h[*BPڋjdڍlڔڤFжeFc(j0Q+mMm 56lfԲ*mj[BIUm&iM-MEm%lcPf3Q3S b6R-[!&%m)[)-MbM&ЋemJm 6+j6VmJVҍSe-)l%h6حVlFb"V jU[EM6l%%["ڶ- UmKdmSbP6j [RV+j"26Ͳ+`lf6R[JQ-lf51QUUm+d%-6mJUlS lVf՛ XfVlVVfX͊FPaf)6Vele5ledlUl ڊٲ"[J-+fmYF@Pڳ+mV3Y&fVҪMFYfڦlQ6#eE&@6h+fQlVVCbkal$ؓi[*[Vlmmճ 1j+3V ŲSdlثaBm`ڶŲ 6SbV6ضQS`lRl mZʶړh+((ڳ)bjY(%dlUVжFԛFQ6emVV`VV̬̠Efڵ4`6UeTMʕj ٵc(MmYaM+FʫeFl[#b5`Vճ+(+jQj`j5lR[ҖқDkaL56j+50)T6KHڋa+b[DVfe`)mLSmmS[6556ڶf Ul*څdKaLQXbVjj1mC5MM6 UbV[Rjm¶j˜Ơ(ffCjbVPڲf ٫ ٲ)[ ՙbSaVʓd H֣d MZ 1cjL)VͩXlJ%6 ؛Jة[% [%mJګhچl+`M` e3Sm[(jF+mثV6el#e[HQmѵ**6-5֣fXjjVfV jaCe YZ6RCjm)ѵhض5ZV4j%+eT(٨՘(aMKQXf(Օj4[ MղMVҪأjjlm`fZ[Bm"MSfƦ(5m̠b) lՕ[*+aMiPڶP[3S5S ڢeѵITklmbX,JQVfնe1[jUa6PVի bM)V6•Q6mViSbjV-%FFZ+TImKdVRVZjVV-+m[b[mF+Qڱ`mLFXVQBS6ձFVm[1Z1c(Q5hŶmb[0lVVڶe mlS2jf(JEAcVŶѵEVdi[l[+Pڳ Z l m[AbX[65cX+f) Ca[ll5-aEFVb-FFl6(fVVT6KbKi QPSXe2b(fX+jj͙@5`5 m-j1Emh[jf+lV1Iڍ[bڱcmEhXF6lV[Q6TlE`ڶ mFZ"Cjl+dmSj6m mELSaLՍfkVcUEmDli%lU%l&ʶmF["Eee3()Սhl6mJ֍hQl[TZ-F5EL)mLV1[jm L elڵQk֍TV-j66ڰmjFѶ-ZXjYSj؛ Umlm6Rm ړj[+h[-٬+dj-dh֊ذVF( Q[)ElblVFFحfSc([S5e6*-amVЬA@+VXV[2XڌSm6mUV[6hAe-l [ lMafVՍL[Z,bZlj5[ژ)MbUFQQX֢mhֈfٵ21LS3P+aF53Km@j b+f(ڌRmCVb6U ؛l4e3VVcPmfj٨ղem(2յFmmV-` jfllMm3S+Z"ƴm[ cP0aYm(-lM*m EŶh6ڛmAYb )jF[) [HLʡL5 [eبTmcjFأVѫET)X(VcSl+26ղS+6 FQm)[5353S* dڣd֨QhY+D[TDj#kl)-[I(jf1Y[bmeV[2ړe6Fڍc`RbYVQXl))ŒڛU)cR)(³e1XjXPjl)mL)@EmVl¶b6,[mZXlmlUlV+lUHm[PV VS2ئVS`[dmXՊQjͪmkb[+`ձY ٫0$l[eQbY3PSbѶmaf ƪf `m6M+ilUXmHSmS0PeSbl56V5mm[(Զ[ mBڨ%+cFV-[-&ڋV4QmbڣTkCFkTj-Z-bZEXS+2 SmMS)+ UjmZlQ6j*ѶcQV65h[F4Y5eV e6 LSm5FFհjlV(̭lTmFF5hkXQXF-UhڌVlQ65hƂذQbUlhj1رlUQjTZBQ5F,ZɵEcQQe [3jfVZ[F![clEX"XQ̕lm d-0mhcV- m4hƱmVŊ1ETZ5Ql[Xب1lmS*%Vڒ6FZ5jֈE[%6-[Ml4Vel+l+e6VR)l&6[bգXF-lkE5EQFLcbذTj5"b5FƊiMV6V VSjlV`5-h5m[6[*6ѭƴV,mE,kF,RT[Q³Va[elV̭AFѴmEѶjm6hŊ[Fƣэb*D&jLQ[2QMfV05XZ"BFjLVEɴmXU4VѵDl[j UFѴTB(jԦՅ2cbkIhQEklUEkUEQlm lVl[&Ц[6l#b6 ij6[X FV"m[*[dScdؠjƬem`mZ*,@ZQFTlmFXbVlDkFѲUkQF5hڱkXlhXţlmd6Mmj2LFl6 f*5FXm-aLV([Si4+fS 5V+XѭFm5FV5*V6Qm(Fh mQQcZ,lZƊlTѱdجmb#lmQTFؚmZ Ae#kEcmFcYV5Y2Mfef(Vm+aڍb6QX&EFڍcA5lV6XFhV6ض5jFjQEQlmEY+PmbMDQh65*6,VճQBYA,؊lbصdQEbجZ)-*Sj6m#j-Mma m6«6+P56kQ(mƨcTV6V1P˜հh+lj6cmŢQ5Q6 Z1Z6Ʊ*(jKX-&Ŷ4AmQ6شmڊF"ѵ*(+c[%j,jأ!h#+aYMLV(+JcRU[lSbm M`(ڶjئ[*lVj[ 4V[bXLVmAY[Sbm &,EZV XXF6bVX66Fm‚XcQb,-QQ EEYQETb,i0-h4X#Q14jTU5Mk Z5Id6F VBVVk`aMٶS2C(j3QXEV1El XV5mڌZŴkmmb+mhb-bmcmb&6 hElQlh6HĕT[%h-m55[mFABUlՕFbcITjѣjMj6mETUmmhcQUEE6AVҦŴ-lF-ڭafVجjjFVS2fD&ʭ-bllhQj+XڍM4Z6أF֊*-X`ŊMDZEh6ƍlVضQhZ+EXkQmEj(,mhEbX-F-Rd$(Vcb(Qm+bMbc jdmQ61FRb4F-Tl[EdđbFՌjQPmFVٲFĶ*U+AƦL%V6+U6P̭F2 mbF+5m[փj4FPɋ+VEmơXm5`McV-J5cPV2[EFmUm((Ab(EF DE(cQFHQQj"ɪűd DƨضJ6(*a ckj" ) ƬV" cѬDPb*ڰՌ6UlhJ"1QEh[aLPQjbնm#+2++l)@mEVMֱ5blm%m&eF UFƋ[QcIFƪ6&[F1+FIT`F"5c4kmX5cV*(%2lb5EmjѢJ5,-0l) H[ITV*c5b4j5cFQhKE4Y61m&Սk"(*EEm`э`MbX5&TFh55j٨X%%Qf[VlMXmSfb+l5f+b5+bllVЭūE`-QcQTmU&ƋhjhQlkj[QXƣEl[bEXMmhգkXbXبض55bb-cb6*ƢرFkDQQ#D[d#&(TZ*b6 QY5Q"+F[بJld%F0F(1Z-FePF56ɋHjƌQA[EX@ (H؈5EQ4cV6cF`6j+F5M Dm!AQXlY1m#FQLm[6b[HԶ)4j-mXDX+F-QcmmhجXF6ڣEUF4Qj-b65cEE-Xb+FՍV-lZXՌmRkcZQUEbcQRUF+!EEhƢՍ+ljX6رX5EE5jk(EشljjKDQ[E6V"QRlhڤ[ت6-BXJ(Z(51QXرh-X4chhKRcccbhش ƣV6 h (رEɵhX#EI5 ,[!#I BVѪřA[jb5fmj5kcXS2LVc(ڶSYe Yl5bEcZ ՛j%mYEhZk1TXѬAƋkEhءi$±-,V54Ŋ5bkj ьmƴUQd؃QZjTKRU`(ړFMZ6-FUAb6,X+FKRTlkbVƱd+IVLTXb-+Q6( Z4mɂƍE5Zl%lTEmcQZ TV(j QfJ(!4[-&*55%%hűDX`` 5#+ŌIj(5+AllXcDEIKEdSRmmAZK5)[ee6e3QH-BV#j6آZ$jV [5Sb6ѣmFTUM2Sb@ i6ڔ̭Dk6i6-H)5[j2"ѢF6U1j4Vk֊-FشVEI+EѣlQ[IF$Xص5Ekhт*m5,h1%jM$,V CDjE-AlhY-&Vѵ()65ED PlQjbѣDkQj*4Q%US1FQ$hՒ-@JDZ1ѤűhѬa,6* IFXF*!1EhRkKEcIV* cԑlmEjR BF*-+QdXQmF,شQk*,hѱ D`œƣ`,h#` Eac@Rj I*6`M6cQ ʭmbm*l%6Ql[AmVj+F43)ڶQl-UDdŵcFڍ-kj4l)" PFTjj-hkI#hm$jm%IAcQljFjFcF4lF) V6ѶbD%*-hPlbmX[+i[QYTLF$IQFi6ѨѢXXCkFVKQPamfF,FѶQHm6 4VccEF,mk$Qmح-Bc*Mci%bɨIQdZ- əV(Uc` lhQ[bPFQF,c"ѠՊXثěFEFh64TY4T2(1 J$jM0-hhɋl hbAiKFbت#mdi,5Z5Y(bѭcV66"h&JQ61h($jMb2ImLTDE,QAFHLIEFAĒD2,B13b-hcfTL&HX1 *={~|_oO{=UY)E"jN+rc?2YgO45K#PZ3QƔ9t:xa c a[bfMMbQ lPVVlSmXXS֋QF5b,kQ6hصTb1mjXѰFŌUţd1QkciVj eX)bQXm5X4jFFƢضDbQk5i6 5(BXi²TKeaFIe b#F5ѱōmKE%hlDZ*ITlVc*4AkQF  Lebl-bEBi (ch#dəFY ZXZhh h-,Z2EFQkQ4i#JƍhFEdƣ!1Q&آƱX3-*F1hQ1TbcE`V4hkъɂE##@dV(D!Eb4kh(fƨ6j6F4kHEE l$M4 C$K Q#6d@E(4AbDEƒjX$A1b4Q R$h"a& $h""1Qdj+`KA5&X(cD(1d`ŢŒ"H 5ƐCQb1RJe"i,h ,i0 `Dfl 02d E)JLo_XXɣb6TE &c4(̂($̤ 0h*$X AfhBBT 1 !2Zeh FFh)"FIF6K0ADJDD2X*bXbX L Qb R%6&Zdbf"&dQMLY224Qj`LQ&I!(ʄ1MII!LLfD,b3 L6Ɉ+&1BRFXS@]L4 $j|cs{yi(7ïBҟ\*m0S%Uv?̱31L&o <~*h!| @]wdߘ%x*y.6/1JmHwmD!T/1wuzOGFڏIf,62S(,z|+ND >1o@ih4ϢGԏVCk$zў-0J1Ŵޒ.-4f{D䅝Lj"əƉS7"pk`qp"7LIFjje9`Xipw\P3׆XݸAyw1q5J{)m|ZTٟm4ڞ3(ZB͝~"cFRNMO_'HW}nV}5ۃr< StҲv"O_'2.kPijnUr)ӏiy`q *2Qm=Pw d%-p)_:RV1PTQhitJifN߸{qa8G2%P{9(:!d vZkAV 3=NId(q"q&&Ϟ_BY k?&~8OxמD׏:uPAߧXM e2ZfUc6Nl"& x e|maAU^BoUMK͵ErtFjl1t~)`*3I79~ m3=Cd"R;g&'L8/t׃k/D.|ψp;|>@{sNyΊMpBʍjub'sX()BҔ%*Wx(@zutl޳Vvz:a1-4mqJ8rI6ݖ{Ģ,ܣisr^VUAt֙_>ƑHg>bMī3\J3"f(uf^=Vpwkڒ$±.&~(lg_ :Et\ tli&Yc!Cbrkaz84R/xOMe- L֤*g5̾/ͤH8^}^ZeVd'f7 ̿ $2#5s/i쟿HM[e}>xr^{8WĀ%Ri:tNn" A^Ve߇FFh:8z vNZ l6m*DD1T<94F_B"%r#0 i"i+PB9n  \;lZs L%/z27'7sDj7<[ۦƊl';MZ{.WrCC.2Mh"Pc \hҐJ'_m_/"@ μo_9`f7 skXl"38K$D՛emZE,t>bw)oʌ&QrޱUJpc#e-8hsHn~y.>JCKG]U\/n!0+}\p P(1XvyUzULde5ָїTmn9)t^JP>6˹f>>l2Mu.aX)ox{i^-ϝj@134zqKC2*Bnoo֦VY J+2ࣗ|wKTHd:#;L1/H 6O`N"ځ(b'W= q4Hͯ{=HE3y]Pr8_&rXU ~dB(4* S EbA:>!2ExȎ`}[cOl5| &lF Zq*zhXS3k:6v$ye7L;\҉ӊ8ggiyBƄWJ˴zo\5^3x{ۮo\D/5 Io$Ivs9tNtVrq ۑF#70U29n 9!@$B@Iꦶ&.H{JӢe#\R|c)2:9 (R H;)sͷ֨g*(22{ܵXX4t6/c(l:7t--Φ+Y=vToq|߄^xo;DN m\x1Èt⬓x#9Gj3OuR0•VNJKְS^ɬz._7KET,|.{ϐJ^#Z#{dou)E"N&HMT]rr腘R [?tǡV5[$ *}{St~`a;W#麩Q,29<=FXA :dƓ*$~% N;QpXq ]ϜbiCuU*R uR`TF8y=wYroӪ2y%óKF/+V=!rq锞\)NBtT; <4T JA4H&֌S/jOSflt0O8M)k/} t=c90j8V `Ide2{1o k :kIA)EGMͽH}qD:L.@BVuӤЮtG%tG Q~w'dU 7ܤR$Xe3rNΎߵEC5\UӬWw%+),$zsk°oy]Јg}|sÌDheq%TX.h/pH3ʠ)Spnme(#Isu".Jь}\hjlP:[ MGt*G){ _ ^5ZL&7߉qIt7v[_8yn@Ve-A<̹SNW cw|@H <慻Ȑ(,6{lNIKbz<\)q1#)}}cr1\:Tax2]J ϳ\^Di<ŝ\ǭCԟE0_YH ̭ܩI=Th  c&O=y "ܾ;F:#\҆fm:qmu~:io֬N5Sz3Ѥ?SUT ;+Kd{o\H-O! y`[q+1#~=۷GFyATfuOj!oKB[Ф̸u2ZU֕ 6zt٭XCcJ8e`j(\S;$yN.u}eдvT3JeYl+Fx:MCIPaUS6ALBj|nCiLZ| =`-tYPRPML*R)eN{z7{ҭJ`{:wmo|F633d(6e*hڊ&>űũ_: %!:$dx(wMGP"*.Eoe* #r6kHQ5ӵB.Q0's՟3 RpR"22/ti \FDOB:ЍK^谝"yfkjVU/Q%慿e.ZP:_ݶc f?~7-|_ks3?#B/&_NY\+'F3d}kf uGl \ݗ6FV*2OZe^m>ɛF ޣ79ޅW dܺ^lApTͫKЯ<["ZN g@J֕M) H #3sМ\2JĂ)=+OGI&u...\ mlD"uԦ8T?ٓH>6ZaRBS2{bYB!6]]ZfV`sr pC`~RM`Pw:YjwP^FqT\ M~Q?+FyT͆?5;K-T\Zz$FH+EBG nc__@Xf9 du)35JB k?$F|Ε[^Mo)ծʿ8翖PG֗`8J1-,K_i”B1Z1jwSsP*\#N5Q{2G2[uVyS:4$ҌKM$nO1eQ)Ҫ&ʕDJ8Hu*/\-*G/3gkd:EUDW ħU8q?0 ?=+yZEܢd<Ҧ"α3 iK$BKK-5'~xO!W!I÷lcI5b Sբ!B5ݜ?&AJf#6^h&.zdPDQ stAGě0kZs*8W@ȁu#tب㚄JúzL1:B jG U5r iFyD`D~m2/I|u(G]1FgW)jgcQ{lwwqC(xus\Ǚ}**)e,"*>[H[5tE:9D歙?WZ2Ir U!LB NME&Dq(I$O { Hk=ga>Bq=5rC0O9H5̓QPC*(z/_8&<*$0,Ez C/O\;(א\Sl.z^E[OjGҭ;Q(S%؟&wk}u荶䬂 +ȊB^=EM„WOCޚ=d^3Be'^iug=b-bL/$UK߽zO6AySޔ2 CENh]7ItEV1g"mRL( rI,O'exPM[lLJM`a1#AֹnCel8䚚g1zk}p~b*U[-^z@^GގQ>}$}jjSF W-8['j()嶫m$QQrd56B|jn.T ȕ'Wd'&3 +s? @-r4RQI(G'N&]#K'vyTU*yQ"xS9켬'vuvmrdjm2͙ͨFwG?"*KEC 6." `p5wY穋]\[$u# b-+H:ߎQTP0Ԉ㇌sVL98laad6g$3"" {Hb&DU<`q'Io<[x)"SM'\GѳprdG5!*eM+NWJO.\*\EB%U9Ώ$w{2,IJh.,+OEzF{g?/)'D;n.g&VB!nC*1#z牽p{Y2?'6z< id\-)J@VSUOc=(MUJ穼E IsXjQ] G䗾r$EFqRnﳯHu:X0KEPI>r yDxT^9i9q\ :+ؚĈwkFt{ ^TyQcȎIE9E^P_'E*g$vZ WוB8H׶ѰX1&s WXăRyl ><6.ϜAjBV|ICqC$h]M;g==/5!$<+~?k&. +])㻹,ۛty9yA=8ϪAQ6bbpq́=\O|xƆi''k7<'y"V&AB3y*^ɑ엛4倢6u"(.x"g-Hncy$3='*~g=ֻ+e ?'uHC9ˢE$I!j*[=騱S&-X^old'&^IaORI7w`CdĄRt5EdAO6lڄBK= Τy{Ǎ%ަ̈́;flr-z'!;kz=A|~c*(/ %oʙےWQkr5Cc]9\X"Rak/^G5fB2LNlޙNb$\S}r|MlV>R,=B%FzzF/7z:meI痤N]\' DEA瘇(d7p\~H⪃ u"ywljᔑk-JW"2a祂*%ZrH=Ff{pc\^$m!d vͶ2ǍE2QGoF?^A߫#y/e\Aٚ៦yظ dI4"07~oUF$Y9ee{$](4"*)'CL̂z-*R(H4,m'ꖉK~d< rl')lƏiHC!sB%̐ˈ T*x0(NT#\G_B#/Q@ψubɇg>26|}v$WݝKYU2 id/w}0N$EEo7RѼ!/mtMΛ(Ax̻f67׹G\L>{!Y.)3Ɩ S&z';>zckw4^E~}Jay$L} /(L=$ }ތ-HTdݨS 3Ҩs P#cRgiyzgcPPLKyA(r* xnO57^w eeEvHnK 3߲ `qN'TS`Wdi/>\L"L] 鮎;#J q&ICvW#f(\UIAkIrU1UW%j<鵥5Ip@ %HVaީi 52+5';7(ߓIRvDs'$G-ca:HqY26c\aimY9N~nE75dAWS:0ȃ,DP ׉kײV$5M9~uι4UܞEh+] }w;;3ߺcg8 }C^exr l\U@c휽$G<p L~M]xkһ f5~w#LUIQP\ xD}wѿsh\GH?zZAYL:oԶ )X[~LNݖqtڐ |[Bb:ˀᄐU*3<΃5e07t^s+U:6R[t}׺-0K8rթHikp:ꄦJ{̶B۫U {P'ލ3R!Y|jغP d*fw46np(' 85ha5NV.p9Db J"=:.jh!z_Ɇz͂uTFJn7mI."+V} feRDڃFΝ޾ZHpҚV:hm!hܠ4ȢM@OHHA4 PeqBφ1Q&vzUM̠! yzZe@->]0ŹQ<ӿH]SVi^ fY[Mn]:ղn ,6b6g=<~zr/h6iT[p(S%޿42>ϙy KDl!g{mwC3"#J.wK+vk ?9˦|[F3"K; *}_4Tekhwgnh 4"C(ƧX`:cQc)qt7W S}RW~>n3.–.XSjfM 0i;.Fp#\(N.S =R)[LK話Z~Dxe`ci,0n}%,,Z%! 4e'P!pewƖv[mq:VIP0<#c/jU;%a. sILa Ib_h+[_Qa ;ͪ)7}!=؄.=xoYGq1ێֹZ i>Lf'cj tl롹/ b uЙrݕ6Ww2N NjKFIaRΦc4i->L~ПIHQbw *dy0KO=Hp].\ ([cHPVڏ6#sp@G-6 !-[!coM •Jmm$$="s֙'}ۃx8ô#ٙ|˵ cHlp(BdS'z?4o#܎hg *. 1ۑM:̙Iߩ~k!|osQJ?Zs:tw{2VrE.pUh};č1kx OGuyy!TWBG97.XqFjM`a!!N>"$pe刍U⩫4"w786u 4J|rWV!L]dه]jw! 6x"ˤ-P]5L>!sDet>a.SV% P=ե-#$~f[z|kL<زÿ~j|CWn~ w8N ]d4v^ӂB;h\}(M}h <<ϦԲ/:Xڈ%Jj' ko/aܽ>,Vҋ `?a_]~4.Nak_,4rpVd -dDjd[۹"^L?UB^?_npq] zzM?c}IS߷Y86JE_klL2 nrp0"Dvl֕ԐQ4epq@^4(d<7 A@0qcAh|+{gHًGl?)%h;3㻢pO=o*ݝQe.5y3`T`Xȱi$@H"Th-glhH]#v5CDR#u'&U_kE!MQNk*yi}NژFql{wR;*Rh/d"pQj{ETS6'~]Зm xFi,KqH5" b?#0P.F([WwKPILS&] o~:9sXSB(e1ߣ&'Û'LW-":fuA3KDn 0?/`UKރ$i^#|6a}feCwD/ʊʏ>Gx=EG˜/:}F㮣n> ZklIWMIWc.X'EUXI@sӀj! ~Wװ' Ȕet2mi_dq^daO6"5*夸}ݬ48#jA=;-S7,}7+\9 mq*<ZrX A#M9O'|$N͛u#"Fhtae2w_]Xɝ¢7Kp~vm.9H>g:KNg;gii*3҉ VS dڭ_I,U69P*+$.NIk<:hWC<9^e)Pʍ܊ !wZ}!D{ċ.hR!T w&9;#`C8EV6yQ?v:I9gX+<[=Ѧxn7e~$R* &ՄeX%L~)P#m(ӧ*GEIF㈈b4@UhRVHM[KR^tB&"](.tBx7XЊI<{(@<3 E#Nv AkĐOZu| ]Oj-z#9rLS aKM, \uʆhLOz-~1ގ`O T"L Yѱўs4wv^nL)::s;= c52S {S+'f}c5SջJo0A`(Dw>bވϼ '] 3צ"ϹT Yvm"ۅ UZbD$֥["#5HkwWbCo kq#RͳKzփq $RS;"zAnjPN:Ͼ qѥ>!|2#,^8.2(6(ol_W@"a[(^EUcOyZ*0 d᜻M17r3dV;D&T w] Fts?;6*;q4$JptCO -D뇗Bq7>`G{ Po^(ϖ7"5{h z'vVb>}HXR&/ka_B)eeg7^NRq^+e`Twlƺ𮩸-l 5\Gr`lzh=nUE |!F@$w| ,3䒉· Sۛ$7#G;[kcH>odkNq8 / \ud-aOF@iVi-W]Ќ&񞀰畘) zc.ӼX)${zlKmUW+s>B\*|T_j6҃Ak%yT"dpm{^ JЫkv|o7yee)ժAcp<z+-/o %9^-Ǫ|;3{$ U0r$l({԰UvOeWjk3g.c va=&t]bgf᪬N/H7 }X5Dm=y3e;䒰cP{B귆fF[S9RΔ{||tL%||ˮ~/A\a^ղ@*e4ȘT9Uc+QV_aMWW]`j{K:B,dޡ8Ucˍɴբ%͖srY?x]Bg- ^wIɅ/ڐd 1_!b@}Xr͠6m1mbCI$$_-OKȚ}ỻ&Gn '`̬1[C"/Ff:)C Oqc9X<# \B) ]pe3pVoѩT $"!m?Q;M?LYj. O1bmk5iڭ{0WC_K(S9/; a<]l̲Rml&lxW3+ƿwI({5K"X~@-o*FRwKOt*LT/tB%rb oagT-ժJpi筰|ћ/4^+Kꍟ7eJ0|%ӏFM[]}^.)>Қ%"f^)TcɊX{٤ᯈl2ӛh&G[NPR7.\˟`n5!s1,(dvq5ka yXI޽&fRҙ-_W5FAudn[ R `rKT_7wwkkYfC;q#?í "ݪo (ò,0DO) `MKt\6( qq& )OQs>1'%@W-]s{sxew7SϪK=}J|_iX:"Vq*c[5|fs|=CYRQMo{;ࠐ.=f&E ewz5i{|d#^'FQDt"weD0Rh#ȅ)w64}555ZaQ;\!P ښRKA-'}T^D}\G~8xVní^BJI1m 0&ܓS5P3N!e+6*ˤ͔+f7jWr\ B1Pg3*pt}XѧmcQJ„>*;d˚ٽ<^hTyO?9jӦz쯔?davQZҩ|[YgtWaHea?^vJ~F>賜jeT)l德|ol(Ɲw š *Iչj{,C| 6j(|&)Pk:XMwfn*tY*gO2C%{[oьr[Y|E\Qn*W\,?=yb\#$z&~v#+B Rg-mF+6=#vqF[;(jYt4d"?Q7803HWl,A#1O [m2-GѫoǺJ90}᧚uSձ5|ηaZkHe'|s7ذ37\[4ՅB+2:jsa2ݒNzenFBѢ̞:gm E7hk/M'[4)e9YL ji>C,T\r 3q:7a}hPYo&Srm|x`p-B=AWTE ׌e]xqI2R5R[n¤p +22c &4nva]d߆GwhE1) H oWƑ. { :tkE3?pNIg-)~eM(_he5FDoV]N%S"SVbma4SVB[l0ȋ?kE"'4HMJLlYSrX,8l~ߡ`sJՅBqզ^8wے味'I2+~xE.\/r؎wiKSsZ7) *@w.Z-BU򖂰}2Bp% B§ŤJu.?µB';V~2HҺM=⥔C\`K4MGk#M m:ԞۍfV; l?&#SJ>;]$oT~ZYptO0s,]\!@\f/t)Mq+#ύOZߋe}wF0y}Q7_nel uĄ).0v}Wd͚,%l',gN`u`C;/ͅԅCSs/JRf$?1\"D`[+YN`W>.|ƑJn2tgC؇NQoexN ^$y5?YծuiX0dnkZ{b`")ѳk(>cit–{;apǏOq斸ҵyzPG$ v8*<1iˮY-yì-2P"5WyWO&oBgh}gm.6C) P|4<2 ex/1`s w|:?/ZBIvqG-?xDxNjQh'a YHg VQ ALF|z,ۄ`)j3Zz,~v{wefHQ "aj~H/DÃO} h%Pͭz2[_ן(_4L,jDD BPުɒW6Q?G[A#>оj{I  HߢJ({s"@2&!ǝQ)$mN&RKw?T2?{x!`Y 87MmZϫUU𶖁*!oâ02g E*l2DPsc%\$h~^2h,ʫ5d!,n2yܳ[p,3U砐nIo&_19:Kk}wıM(% iWR aI97fk"@Iew4:`h B`}S,ɭJ$6ϕ͢OV|>`5.[' W!ҒNS~F->}<$hPS7Ҳ/C.gZʿگz a^|H)ǬV*?]ɕ3a2-*mᓨr%/XG*?[ mX yf&#=%l:Yv8xgY+urXTlޘ%gB׹ Zp_3*tˤXϳx@ fhe2N-JJsYZ/B01GۂUzbG^ ao50ccI? %KRW/p: »nYC.o6"]^%^qdNV&>t +\e*r2+A] ް 3hq&vэ*v!<+e*G47կɥ>o_w;7kk D`;nQ-UD#xPZi`EjdI0$~!]@ETi~('egaƅRAjRLU!~رnCVd2rE\5:R؟O ~":IJC\a7ӋVwf2 WF<ַ$)a`WrD$g(ZG_.MzEᘥfP K(OA,爍98NȂgmӓ,YUҺJ~Jwy,Gel!j '<1h|WDžr| 9 Rj{4.AAac>՛@Έ\O~fm[Ʋ)>|i3x #vc 1\c"o?~Jg,n5Ɍ}84JAvfγWInfTF- -.K{?Lm0gӴOQ_%ܰFj~{H)"/-}`s9xdb̊/ڲV>zs{;x8m /+Oߤ\eVdѦP4yppDIZQ5GJ]95kL<]?5V dep.+h"ҥj^6뛔s*+%Qp|`?ἓ՟2w1HЄ] 5Kgv"mڂd ZtD7D h&F ~}).SFGoh#ZR d˰r5'zf(ƙx*4GFdD!$V6k+_ZhRE.Ǥ"jm+c 5+z;E !p417+5iYg['x,'I*+sCvvdE3SNXa9 ",\kT䝙b85 #2QK֔&q*:Pɾ`T.ыc$fȋN3*]ߑ"8s~pW]|H۸B6ӗW*"̮w;+%?gbrI˷THdKv,Iѧ“wtFZ9o\)Ȩa[yÀ 'ٶ,4 (1¬c+$r%# }P$"rɡCloܻf^/4'A*:rt4ȡˬ 2 D0]iWVjǾvW2wOz۵Fz6M+L}[h:}Yb|g5,3% : ]8TI˖fˉS̼^tFU#ʆXտGˋ%~VZt,`Θ AF/,lf=~[*+6req=1'?cw4(Y?"3k⇶]AǗ.ai\מ I$gQ:E/MiU xqF GЖqZl'H+!rqcU(&WZŗа(aߜ|U_&|sww3_ҹEAibG*dY-T4CCF;*5wX6)xƴ~Dc`!.+&KqRy+;lOl:*eD>s%8^e!Wi(horF"YDel&Z! Aʉp] #tb2&; T؋9N 4Ƚ~ɬ=Q]_k2W?x&LM%G!H &01 Un=ID\k_ +,G n^ [Ɣ]V jNYr4:Ȍ8+[$c{ l 2=w$i{P䝜k PzE@3VV8G$DA:F̩HT֔j9)WrT8q<[49i@jv$DOVތ:=Kv顷^2^ar1K5+:%p?"vtNziTd@ȉҀc?/Iy;K֓Wo&.\s?(r< )oL'.z$eζ;WBtbff C(7˒Gk zp f N23c?)ңƆ|ס ߑ S` 4Rƒ;4Vj>9V! 2y_1B3RaN<񕴒ՊY!Z;gÃBuJ dugȓyHUo uF%AX9h. pdA[c5vY*EBw FUm#,j!it amzưp^1;0ðUyt,kM5Z؞ip XDU%kiS\ק_^O X#KÄi-mZg;7(KCnRƤ*sR[ݦ<@NYLwƕc8t "wh,Oz2)lβ|n5{>E}y.HKzHe/2Ƿxy )ٞV !}[6mqSu4sر}D7=8gƦzQ uZ=c?;W5*4yC|O*^q{en^U"-67EXr8s-ē1ֿ-Go᷅E +OyԶJ#2R:AYpib* Nv I-EcBvC_[3TYlfQdRuJJw5,Ab`/Hz鶐]%<Tb\Zj0WCRؼO:[l_]/`_wCw4р}v<_wnf6;87cK1<юHQIa#U!dZ-@Ү|H^G<7Wdm #"٘*Ӝ K-u\*]ŕ1@,҅ 6 -|zsMp"XC GyY!rf,^< qj-$̿LFsN+){p1_ix_^_p6m`/ fQ^H_n.1P(Ed 0Tn8{ cٓd6w2iЇ*N(}{u=J F@*=Γ@{_*8j]4b6ӗ';!]]a[ÜtBY"CӾbuU3pp/[T* ex\qVhQ0 l u,h36qtۀE2Z2yͥ +9Jɡg(隯U.4r5jY2ܗgڗEfL%/R_ƎK +;lTHԹ\ E= w7d$ڏSĝp^*h&m-B=]ԐǥR1bO+eַv3ѿW)ٲfN7SzȀeUM w~屮z: yU7ꙨS&ONˌ_-VSC\bE`H\={8P]+2F쮾1ˎ #(aq S. h>#+)%lyuj’krjE5ޏ+ 7X^;$BT3d&֫1嘯B(tWTsv xAt{ka7!~+: ١q?\8U jW.EU [87slEqz/}_G~̅3*,svYՏ |W$4vb= _R4A|XEy ἬQK7ZZw ZeŽ jL\k q_ϥi[_h! KO36Vc"Q8;B(c ctcI`DJb2# auֶ!Yĭ:dr:%wRoVT/ % T/9KQVH0S8 \aE6/34o8ҳ :rr^V-๥56i%kir8] V=|[5{۔\T{AUj |$eʳ*)I1oj RZ`sݯcK~VӴRj 9GûG[XJj Zh~ڤ|H?L| > H|pțt+HW^B /ޖjMEݺ$_")&U!,J!<,A~./ʻJ`ҹѥIc gSK*Xl ;\Ձ,%w [:4<)UknII:Ń'&X$Ѩ0ˑ^h5Uz= MΧjXE4 )z?طޜ=2.\AZm#/XO/Sr(UYY\>!# ^l+lQ\d6e3.xp2SakSm‘UeX (`:ŔD'^b!덂iLn˨8+7fLѱ=B/{'~򢹺[ =!] 2,F*ZV#AN(Daooن(E\*XE֗8I7|ˊ =# S m -9pޚdj%le:D߮(2st|%x罯Nӡѷ׺qD?F  CHЊ09žO &O I*Xa(}wzBM%(栩m%߷]w%ZM65&2IP0sONJk3qF9 ɥ!o]@;n6vf]ߓtjSDRuWpwڬ V7͎2"OY&M f}(OwUOpg*X'"{& s'n 33п!l:ieo >mt!:Z}WBY x+t2.HG.+q ֧yyH=!4we8ZE;pjF$'3++ρ!%>=ZkHLŝM4xPř;m:" HWt}v+e[)vѾkc$ońewvbSIb-dp`D=ʜRNe1߬K2#uȅ qT}HA;{~VuQ߉Z۲!֕/Ovv}J>"ap.^"r1x{mϽT%K3]O570`.B.}sMͶ#T[_0k)pJou$ ^&)0^[/ז'gKaaմ,([k J{[|=jQ\bE`lxKF(@RfNECibqlĝi?L&%Tٙxs@´یS4-ogCW`W]Z@;b攅F_(@0j61(6aX* ӧgαK%p[RQ\C肾zJ+]כ:&KVtٌ5XL-zBZAg#ȿ9~s{ȰAxh>KM!fKXvck\'9; '?s鵸t/pAxz33"dߢڠqڛ\mQ;"*LYc|ʷ(-+,yօ2[l:v2ޅ;+ZnO>{Ͷ]H휚.p`87K ,Dam֭A)J՜6YkFo1]r0#+$R3y-{lk\Y䥽v&K.u[ |DRK#VdX3!ߥKk[[Z;H>~QX}=ts;[ :;~E(Ƭs;1 ::K؞M3/YhfR~)w7R'&=`k(WM?W>=Nqn\ K(T 9X)}` 0JJee)q<'H܃ҴO4u械1(3.`̐4f 'BCWj껳dDq&N>1qv7vmaS=YᚂzƖO֘qȞ;^~,:C L" pWr N\XfJ4svH˹>~/zgdFi7+p/4WpB2Jv&֊ E{{$Ƣq҆&d{ 秲bX͒G}}И. τ2j6drdkN v}]ogMV2fkVR+8dAv(B'$v\C$;aZsǠ2}lE*/h5Tm\YѽMpEDPS:e8c-->ǪQ;;?)52̳D-==Kl`}>-M(9Y/vT b&e&=f;aݟTkJY\=^ӞsmiH(qaSXtCw%x /~*E!i1mR[ 2Mg~hϠ,iMZ@zҘg7Ufw@ulJP홥2cyN|ZorQ3sjir1PKx-Y (<̙sQxOpcXbUg &GotVoJ'K]zHgCv\^*t,!KɃЪFE|9vߡgY0JM`8z#HYA*\^>,CAVڽc.b6ֵ?#S게{6s$pJl zf;Z*/AhB.]Zp$GE("_i -p??YKDp;6x2&ٖӠqJv!?lfR*HgXhہ>nu)ߍ/sC8(| vrZ'Jy!,s~Y=&5'6!d!FnSe9ryuȮȩ>E&aq@s8J]/Uvp TUѨVD㱯/rO'",ks{ d< ʶKC?O5:D@9۫q 4kRw,@;%AYE#$vu)^wqBs{- †&,6;e}m,a? $ܵuz$WH ~ފ_{nOh6-SUj&a༶.X0-kuuueUn+n5W<`jIuE& ] ٝݙMU7\bpB-znI(ocW^dlS qvnuO@'eNR멓tvז54Z3s&'m9ލIze6_Z^a-$u@&1l4qh,G|10E3تw'yOeT8Fu9F?. #2,5Scm9v[uKw̯:q)]Ƙ#iPK13H2K# yd6kDQ2F #t{ oHڮm(p A!S*N-[jggHKT! =dYn(+½d?unT=-ҵܟ2gt > I'-9 `ȼA/Jkwّ[al~o~'مM(_bd@E$WWiШ}/HԚګ{Wact@^_JJTM }H5|\q6q޷`yEN]+sd~$eo&[\,bph"S%Js]3W_U(X/o[; Fȷh͝؇vEՈl#? F$aKwK7SKݝa#)/5AnS#o0CDvo\ F@·YEP7APxG}_i]r`,2xϩ׸p{4hf[;7Hf\1|E0}.[gL?Sw3q߻\wsiձH'5@|myݍ8hd -CLl($ǭr#<8^[nJcbK"\d@ 2|4l|6A28Y9XpU%> 8:/4WE$fRQ2N#쵰)C)$(A;6&5b(=-sܑ,c&-t*Uօ1[0)WL a5cu7:F{pT^x.L RZ[DvMgL3PXCSsFF 9`.GɇMgNvSglβ m]a3i4&$(>/Kn>7)P\+} 矮|:u{'m2";L5)KTL#{ 6}2 9V;^\,0;t$/W5qq[.Ib&1$]h!!ʚ0`eT5 Q>r}OKkG 6`}~L.6f.\ASgQG *_/;[a:ko:,?/ʻf1\ɤSΏDžFL'ͯuv[T&{̓<֮CN23V_$g-F3j&b).OH0A@tf$Qf/Io=y?K,C|5/UɢKp79UB C ̢wxPPKB- ?T/w%`o_56N2˞*`maݏ&yRYTxuU KЖ^eeH>;YyCT[vS/^Z#-戡[8^"Ч ֶj{_Ѕ:5n+C3 Jdb.BmU ;^c7tya[6Ja`A\%SC Y]R&kKw{Ћti\-e(ܖ{3A4%˞fѾ]kStS;Y=3! ^NOkQw>m-n7 :0"'/]p_y%f茨ߌ!jk Ƹ7qH'T˧sL-/j.q" 'D2/K었wn* /3JhHP]-Uyp^׆[e wJ_8 ,>ÌMU]j@*tݡe-]!tT;x9$ ?O&TePs'6>k~eal%iyu{7])iV;~zۆs5YI!M6r$p>L2j,+m,sfþ] -Fj:S$lȈ%KCѪ৞;> a7`e<2w%se~fu"e.'dk#ዚs3%i&^yEB*tէmyؐ65: (Sԝ8.F:|F)kJ2 BBFn)Yԭg^JK"W.- (6oH"2Z4^3C;T=.e@K ܒ`34-:W+ts;yެ%56N"?n84s^m4rm e)wO]=, E_&Ɍ.3(adxtyEg)=M{9ٳeq) %nS2[1wKw޵ms>tlU՞k܄]$rarSrT+0X7F@v3@J5y(Tx#ܩ/J~\w*Ȫ<( oDtB.Jri7Þ zDzn`C.T &8T7ͣ$ v:e2uqt[0Sk1i ׫Ow{_h2} ~͚--s`dq%n'ײfVμ~İ^6Kfipc(u%JHRNwb%"]6}pH9{e21h~Qc{|edݛVA*wQQ),W=i>M\&rzzoYsZH H(2qֆqJ9 e5R.`8 ?G@ w^Bہ&2Jy]-zYsVk+,!j?7igk=8)MXI5I],焋PͼK~tC&bU;f7/6`7ǃAQQtE )#BdS1o mӫb_~׬u 9ޞ7yףTF;f>غk~m eyӥi9H$ Y35צ +%%#ip—E,Zo|^x%#gj԰ =F~y21$Wa;PG H"u]] IO!]qC5I+rye:C^?r{?o{,CYЂY >lD1_/a#aTLH$W(ϴ" {0K SH eezQDXQŹfH?#7ʥigl>_I$8dyγp0c?MF=Gcer";R${N;Oy8:س[H@v#{4KDq'R@^k> E,k偬(YL/ˤA-#ӄgΧ79ECmwj*d/}$2ECKaw~ѳ}t9_2x_Dr^2&iL\# O"+#r )%>Oz,,n>e_dWQm؁_$I[꜃1qT9'\o0/|a[H_H4\Њy]\mvډ`eO hpMo;R%6FP 6P:d^v2$4]ӯHAm+a(aV&ޡY:@?]z)ͭ,D8[&6"χ;V~yn5 ࣖ~x8s+ GŕX-ew'[2_]CP]^lv2=WNN S;[ "jH F_8Oc\R/p󖝱 9 Vb_{k㶓6D-6%tlIk-/skOP?$ e]cI̚C1هi[]a/Z7ҝ \)TNsg 뭊;Z}S|+TԝNZ5D | cDKvryaupd?O="r7R_shzsX#5_!4^rJ'n ߋRC*kA'JK0v?sCS#dӁ\΍-t uIل䖚Կ|rҝ\G%ڌUsݎum/Bd|nWևN:2Z%)&8'Q{!kh4N6ee9V鸙CO.Luw,!of$ r)X_Ve#BrD45wnA;8ԶFO u("3,a)[cfóԛXts(1b^2֡f#ǫNOb(D.(ɬe4¼Y;\^̅bBw#kwZdT=aMW@E 9RomzϥLfRf wMwcB?{aͦ@Ń{1Vۂʑwe&MDYF_B R ^i&8.P6SS379t= B6<㪖X6өvWaidE/Ai B&颌bѱ&Dգ嗊`mb "vFrB+q[K5xMMH/6nt{YP"8SݍP]:J*HVj*gΚodO ۾YAV躦(-<(0UesP/ݳi5H|FX*e3Һh($5tğ&2nHDnR h-e"ne5"ުGe)Ǩ< d6"(ldq"ka^Ho^{~fUen}3rCVk:7hӁsWwB3URvo$' 6 %MV)E 1Cd( :X d FᇖuK¯Ǩ"Yjc/t/g{Yj u h3y4ig4 /\eH;rGqVUoKK]zV#{lYKG~-nt4z1H њ6MťF@yu/륩 9}xF xr_Zx^vD޴ ԏ6iz+ش$ BϯU dL1UQ<YZ ڇ!TX ’v0*Kso/fY\h}) It.AaQMJ%ܷ\#_G"*v{<;Sbkk& >b·qIeoj-1C,5X<%՞#OrЄc4#kA"gIQwugI R+ar0@2z:Oc0X2%ٳx\zʖ۟;'/ o//6 ê^U]o_H< rW8T,#b`>`2|mfU:J$$$ϛKXXg]md_҃+R%ѧua7<@x}zyh@(9zH Zx?^yU Q8-)c&+@}|*t@udә`*MbEɨ e^婒b eI$!U<<貚YPlݝLMڤ }!5H\ƺ:r*J3: t!!;yʙ}ukJ.d_Y\l%*!hfYdcL9-Ryʟ 앸s脔$qV8T m@ts=QxuBqQ/xFͻrLFӣ4RXMV^fS©zR\ U:mxtD8j1>U{ߔ,`rd\d^`&}@nJrf'(㸡\%RWbq{,DXy ع.e6o5 K딨^vҧ)R#[=̔Ń*#4i.F vs-a\?XEx)\+7Dcɨde]j؅zi_p~W`̅!$IJPD]!&i^R\*guG,S#PGH06w,7mA,V)h̝&/C"c!.%Iu|[bW'_p.@.'F9{2w!=|2XAic?edukUY#UW[ `u7 0Ht^|mYusw➁ t)g7m7Et\=(s6ЎVѱ]:ςOWJ=Tυp Ȯh~IB.eO+ p& [W !V1H)x}zf9pDW9Gva3[3 &]zlϲETH;fFgB;ƙT;Ju莓K`eJg6HQ` fD>s(mGWo_+?t (7 Yu>EaUdN$,YRq)6@[9fcd݈Qҧh xnL8Po6\.Aqm3OVy79(n*H2τ;:qpSi'DQy_xH,f#1+lkU]ʟPk5C( I*}9c#BJ,iE G# *Zmkf,O!rC5^pzz׻~m֥&e.8EGn|S :gOL\daSj^þv0}3>C{e=P7o>? u K&S=6R dGH4GZ6|Śztܱz>{w kPŪ " ?KvƯ* 8#SU0&y͹rDQcEW=9vQ{ʅO!\.j*\ʤN dPI`k|o)L hRA4]ﻔ49tZ.eef .XϴsfE(Sw2/#ˡdG+Ȼ)|t6y6s|0k!8ߓu*N@ )BR -ǬYpu~W p}U o?V<8U+V d˻҅sʴI%w!|'80RAŀ>,Yyî?49yf0d-\̳q 8|)QaWȦl)_;8nz^u?"Y6 vG`>_vdl7;,u}t)VR0w-)?3Ѷmf=2d2,sJR,3##ˋr®=fՅZ#դeFK\q]pj rYfUpuT*a \Ll,?=sI Ʌ EW9~ Pi6uyq> 6Gd3T?cu&VW#VLZ=8JlɀxH;I -& ō!k?:m=UoeXcbG0_},'U;%κ\E>K̋YfN.6,^ƢD1(c-^=AtS\'CiW:?Gs0^mE?dni5#˙zD)ǝ*Є*wq7ln5ty'-o!Z;Mro C=OˤaKR#tu*BEE0ln14nDt AT/h}|AMƩV$/LpT2IXbfP޾w+m/CoF)Q"JI-]9@{z_vkXzjFD[C" H퐾|F5mJYκV# 5LTqQ6C~ۗTMj鲌W/'߷iKSd 㙺 L`s<[-a2Ưu^nU&*`ӴIJړE.ݐq0ӷ^jr~84-ΉGTkwM۔WKVo˿(e(Б,2!%(5Qi,)Bآ^աx 2/0dⷡlfҥtݜ4xoaTmǨmoj6׶Vqbaޓ \|U*ÙH_̋ [Sݡltxu"|8ڻǍa1l-c|,.v/F0̺l&`09ysɱq ]8?,w:Q2]~Mg XSdb[~V|ɲn솜{kڜ5z%h;Q U}ٺެtitMd"Qn xTü=qZ+L̓տŅ9ؑoCyFi#J][@e]!Zi=~ݺ0g2+yG5(@Z跼EV<[#E5N]s^iw6yiM@927 ,JNl+Me6BMŶ^  g-]遨A¸AOlK>oך;~] _SYz!)L|7F}M6\wxie آj)hȍ o{&궣&PʛzLWF~Ʌ8~v`7 3 'swu 2`T5 {Ten$XRA0 oRV8',٥v~xIrVנ"R8SH8V|m|gCcz.vI׬H(Ѧk]kgGXF^C4^zy_A#þܓ=|䐽zw?Æk|ѳd J< Aл\6lYX'UG\N/-ڂc(7lB#?' Uޞ2 ?GxJ\ xMzr:.2S KLIRꔴA<=# %QuE~߾$ )^6y2i7IX]"k'z̅BʛH7c'2T7k= h~3j>u>%t0>r7،8٤ f|Ifaq2RM 7)[RHc6z}؅2<Y|s'PO72-qYic#hKagFNf[Jݡ o 9:,Q#Y4IE:Rbj!ދ"1fϩH0oEGNv\^6urt)- dT~t>d֜`,@JGLu b=cPD#S<R?>  qxemLp6#{85BhQaLqޙ^މ@BS݁r}fTDJIҝ9,Ecӿ6AIh%lv(_nXzԕw'e&\84\XjC,5l=5 +^o5;KTT>yؼ}8H^\mbA)UgUQ3ڄU^ YW_Zo{8@;ǎcy>Xi sT֟G];_{=>a,9чU8/t ?if6o2I=]\2e) egY6E`@ VSJO8#mU.A/6NAal#ړsƫ8.=DieH˪2`?j$I-Pʔ?a  a0y$R ڇijt=jY꾈RJhR5wA_1;dvXFŒƯmBTadq1$ RcXvwE-9H(Fjo{ls=>kM C L\}H7\ewPhf}WM2?./xD28XvM/H5lhs0U]ۚ (8k^BlF3t;b[`7 -_w!$#PxMsXPrM[Eƺ-$a^u J ] V8nB{՛x,/&'8! )e -,z鹖MlPX!? Mw\e9V˥jZwO/ΛTUjLtWOcM.`) 6n5l|DFu~Ƃ$& 4)¾wcq7-tC۠LUY '[cHf?2ikKa3W:`i)4, C(*-@Bٰ,hyةѹYZ!hRTuyniD{C7P>f I}#zP;iيHe*{@,2kY%ˉl' [&ᢑ@8yrK5jL*Zuv>:e hJx g3,p~';P9Ѧ}h;|~Ճ)ۧn%$ 'yQ4\tVWWق vPRk0&<,0#;a5vйI!h2HԊ#ڶzF$݌d`+dHe@",f a"6 w~ĒݎNͥaq˺3!V!b02[A[U-2z})ooSFOyb C[=ȣj!"[Nވ%SI;8GAy;[{Ƽ^ok/g.2Z e>OZZL=Q cmo0Hs(U4T8Z((*?15ƙ!L3RwuNF@Ys{lR$lpx? ˆ_8wGc~570q lcWXeMPH{\د஢GB?vQ+aΆv(asWP7m9b\t,^#!v?mOg&\SJ@j]&o@U? vY]f`Z>ŧW[`TNrNqCk)jAsLo #2c/#Z%);NjJ!SSVt:U>lk]%߿:637 lڍ|r dաixBȣ\lt-YHh޳ ;꽀ƄR q6ReEۚY^M(mnzl I])VKMpZ#rLtM6FR5Gk͝,GcRY-bͰsӗKe@y](>8H[Nbk:wʒ勏%4Wb&`+UՎUkJ͑cL:"5a!4, )Y Y-z:Ym[S?qo.)lR fm89){DAo4vCSsWPP~ iPHH>]$A@ByeP*2oH,ȭK!3akIH-r}PLxoZ}wGJ3(P˛(-ǐ\ =d݁xC#lac1 9h&wW4Lte:ڑ`:y);6;dzA$b/c*5fd!3h#Q2AiW&ȗpkF)7!u+\ 5. B N_ؓJP@@CAMаNBߡ֝jO׬Lwdd 2{ aW:4m[R*U*zSVҠw.??_/eME1+$!.3'*%hs$߂z*>tIbHg6sUk8N :Te[$LeR MaQ~tnN'82xi;C>?69njRN<.(>uY tj/HłEo-NtL@-#jY@ Jy"%YqΝhi}g@q ҌLi}8iYƶm!]"glI8R*fb]GN3ˊڽ#yJŠH3nK~K62Q9?gL|0YɠfC5',vwⅠyrX'lG.-"nϜڵz8B(V{̾x}LNxȳ1wɧ/}n7h&$ѭBp>8t~хVi"B2xτh }̵Df}v$ c(/$D HVŢW  ,0aTΦG ShQwBw[!X^|?s} {w9 xOHH\8Xxڳ{DȳE/Q~PoaΒ|hA|NF]f[Z qh)G16'˔*dAGdذ])H®KW.'$ DWZ>RCxx?JihF=2Oc[[UͽE/:^ZU:ilĉKOFVu2dh#P,g3NtFe~f}PE`&awMѫcjIaB+hmJVJOO|{kFoʓ|fj,el \>&@_ǯӬ*",>\9fS͡ɒ񕫸F.dRC@I _>ŠѬ:up]iv`o[qjϷ(Rb9c)d )8v)dC~Fib.zZ +2HW5d2IYs>\c[J'#="=b;9]w|4Kwc{[oKmy/+rПf଀zHWy$Szl!X4(Κ92-zE\)Um ߤ  #Q@/}\LV((xti.*z]8I T!-oAfO?SZWҚͺߒ€WR0Pi^O.0V\Cg#Ih5X،">tBK݅aZܑ[}鿼#ϡ6IVr$,Ls0 r?b ̢{+ChI^/^>}rDƯVKɝ걬ɩ<rz3հgՖPŲ]/9#:{c3Lu C* }5ȵN#;#^:š/bj"7ioՅ2_u bIM8BXzۋf&Skxa|%;25#Nl(Q-׏Rz^o#6.O`!E8nˑ-b|\3 |XWncޓgUkT%Fmv/C\RzݥU:W%tlw\ȷhWL2{-nMqʓS< gf!]Q?=H x&*ˌsO߇yTg^}Yt2(ӧ,X"^_nYk-c!yS@g4OhYh~ѲX&jf-+9IC8h'x ϼv"ul I=0OkɊ5ׇ䢑˯/yU/~ v(8g !MEK,Թڿ;kDD+.=ZI* rz}i0BBQpOgUH*3ʵ[:C39~//"RXnWǍ#yGLm2"HYQ 0L70v_ WUr>պ"z9IѯEվ+W4|І:,Q"qho &v?] o׻uauxN\p5,z^^M~k'L,\ gɖHk$NMGuFsf/.AdAהswku,q+L[mM9|*uePGfs ]uK m{ mk}Nf4Zo9@440"y˗Չ'x }->~ 1"#ÀxV³Sr荠8PYf{ J/gC:aVix3 MM>Lg38iPSTA|BTդ25M!K-Dβ(0pvVwn4$U2;zLUx/^法#M[PP` !vRX=-mwgvɍ9P #GΨ\Ls"@  0E)жe i>KIoՆwY`i;_"ZC46[ t nkT} ǍmQnÝO Ujq͉P™"}gQ ;_'(x 8ru< yvW~굃|:sC)j. fEpie.ƒФ__II'L sdSqKRb\,G+Nؙ쥒AJaHbNX>G+CGdIkoNדfY![Q0q=o*<Ӛ~ִ.z9U{iɤT7Z[i=^(6#.Kn*T QWiUǣѝ!liVQl)o?y߹?GϪԱ|"C/a;[Y X)]|kveV_ן#P+D~ywR? {<&]U*w&,@ Fy,d!;ehnGÁ_#謼oV!˄j&+ӊ]F7ϯGr9|@^e5yoG?2O3maz^-mJk3پR/; x&¹Lo .ݱF@U-=BŽ-fbfJk:Bv24u~Ʉ)sJCA# vR@)V (ǟy\e{zx 4t)^!:W5UNk*DLG;Y. uX߆, 5ʲ/;r[ߛ&w& A+MC `{[|2O`Q +hr +ఒv }StqD6[3>T}~58}x,s#"BHwEP HZn QczlOL ^]$M'9~͟ XnZaK,2w 7~AZ&Ԇs2M&rv`栏&Q 8@_Y'{zx/K|աi/+>ˈWf3!e'k85M>P̲lgS%\"%sW@KKVz mJ]5)RLEvmV|\oe8,6Ĵ)b2F ׋?ж~A rjUd&iBX.!(p,.Jwg~!0J9MA%H6Jk ~z utkl*<)P^}pFdzjqVl+,"g}u^| EKϿk NⲴIGfPw&Y mf2voï68V9R2:D-9>MqLNRK@#6bihdbG+kZ\6B Sg@Q%EHXmSvY5Z]Z]._6 <jJ@}uf,cѨUnj %@snbo/)++S-Ҧ' ib񙽚{'Ib9˫|޽ʌ We"@Œ=Z  v` B*uڮ64X Dm'rѱsUs彵[/ ( Hٶ)FQaiPP*#wȾoXӕ6K_Z '4 1 Oe*o{ =vYc+e20eE D x}]q|zUpEFv;ߨ\qpW7dDŽ wTLu9~Z'oZp{$*U4ߌ*yF M{w eVuŨl{hh;2)"}?2#4 AZ{yY\Ҧ؅^o Y<vyc)cKj0N 'GW#baiI%-"~ki{36G{K ?%6#N `|^<}q`*B"g0HGu;d-kbftFs>;x⦘!ɧ|!frT8JEZ.LbըUR.HXxFk+vKnlY{kTw^v"ZH8l&oQ,[^ >,vG? 8Hƺ[5Ȓy}_cs}~C0KE{Hӭ"-4=y_:ޠۂ7x&=LxP*}a GMtcz|Ím*3ZfrᙧR-%󆩯{~} u  GdKwPY3T,XxN,5yrF&S1$oF]#ss+')( sw0J^_Yv?kD1PSp&"}<*^˖Gwq :|"Faby0ɾj32B;:7ix a)8<Y$:"O{\gסrϧfnk OZ`RJTk'=j<&Ed mK;f4I[_ůoGd.ڙvX8k-}`ybz>jh4ye1$ε6*h *'+q>%&pD)l3j /to/)a>ŕta~뒊/e2ߡ uImmLP*qEv$"ۮK02+$O$IQ3YFPI4.U8ǥ$:]k3 Ed.NV4!fQx.O!tv!6Ssspf ܍ ^Z0>!>Ah\qBeo3z9}bǩjdl1r=a*M׭QIBNs/@gj#@?+_ֵ3#_M6迥ImL'^ 9)Mv,Ux/Č*s7=Wc~=N ~ebaYoh1AS/DE}K 똃:]u.=e^Meo o 'VgQ [Ek)‰# c?ghwwI s:?1 @UW^+o#u>.o[WX? $DXIj. Қ |ʲ]}{.+lv?x>ڥh'VZߜXh2'~ nOiۖ?-E֞UM*lԁwhG'MNCtYT DrHǒo'ݳko xw=+`O<){jՃZfME Lq`Je' ɰmSk,rh3\"EiFlTFjj%QH8ڽŌ>3Y3In7V/C"ב,aj dj&8&ǹͰك- @;'T[}vdzcݾÍSraPmKph'ìHr˜47 >6nu֩,@Q_6B2W! MehcG0qc%9URaNo%(֌\'1O8[F/~]Z|_';kuʙ:JuB"YW<9[88*zsփ+(?viǕlZəxA#gŸ:xoz\eׯG,}/e+e;ѤOh {&dWR+~G̒s,B*rݦ*%͚W( yerg2|蚈 B&S\)T\ŏ}p]d (ʠTaO*IxJPz^HY+ǿթPOٮOnJ&y[ծ"%LMvy(tزuq7SWhb ,B&(:9YN4aH,07G5>hlWs@eX Ft魑\ﯟ6t|فSdpʗU8'MF]`Iw|:L{Aa>Mf/-V& bѭ3' gX,s?]LI6SgWZl5 $Agri֟!W׋ uQs Θ"f[zO4\TWk tC\G hM =@FS)YW[)r0EK9k% >bX=Dg%kMʜ͘!fǚtfBj*7 E"`]P",p<̔#HdCY+/A3NPW`}aV<ɾg`tk?#5!䉯/ 8Û&B>7ODV`ׅe͋Mν4Hm;49SqsJ-{qayRoD11lYISg٥hU2NJ~Тz-|NC{DFwʹ >O]b˒%%1)tC](1>gWia/o ? (BshBPihQՅy!mi5M;$JXO}!>VB3j#4Uת 2Σ곆ƿд,2'3.!Νa1}P]ehm˴c_f۷٘/jB^BM=w4<-Qj2dgeaB,%yOq>bLew潇!q{nl+(Q (VlepD$[ѯ]ѭ5噽fXy{rzhuD{l* 3$+K_tVALșaxd)NoJ <̗$+C#2}\?Jjφ-`Q^E]B,N _՜E )֙D<4`1"5KY:)dv-hdiE;K`ٕIn3Mb[;}UBl[#Yx4 \BpPvEUdQTlMxK'ɉ\^vẗ}GVVn٦Py:hɿ ws7`wm28'rl`;]V9O2Fj":Pѵ!Y- *lP <=qVF㇛qF^#;!ơf 9x_3,|GHraT&; ~ +wǷk(<͋ϭ͜LRih#hڦ_+mR,/'o({źGqIذ\u6 AH2IJYYk{m@kfHb0:W<⚈d|ՕSPnZCK]qP_TnaRoB .l;(tfA*y-qkShq ɆR-8bO[˫/'F>jqj4Oě/=:o"㬡 i6u4| ';ΙPb/5yG>>XOùZlf| ƙj} )u`eۋZzLH[ iL)$Z_J rպh{xd\\rF *ڐ 8i7 /BQzuBŹ"k:둕>D Jx 2? *o0ˬ\*XzWUt0] HT#8ٳ *fa\[GOF@[!끿&3+Y@^ƻ :0X|^O7tvV&#$lQnsX=Vn9\5CFOI/a/$HKk<tT|p]q3I8!NT~}Gڟ ǻuQ-wrt2Z=#won `]Br5dw?QR3xZO-\|Aͷ=_^{bx(IkL3wƞx?^eZvlfZsRo_Ea Whpl=_ ;(Yu% ~fV2.Y_:Y7!VǶlY~_{iKJ8/<VER,H`>lƒe/f~L&@)6Bsʘ_،CcnݶaM;S'7WmsS}!|BN!_D_2yZ܅֣!2,4S:EI]W(\Ytm(pqW[ QY;)BaX!29SRH;S#[|:EqJii|5m<FSCzC( Ļ3Ag p2f 3앇2%p7ɪl(EZ7 )̼5N+ʐJiwdp)7B=bGFe=@^QgQ"sw㺸ENNR0CvOM g#Js-e`py:9x",B;FU%.h(U, -XGgc`@S #vFwk-v!Y m)Ǫ2"+vu%50փQy3-Hr4X #%Ň"<VN) G'Jru1[O"hhx7"rAM$Rx>qe(Q=6/niAU CqV /,Aףg>y*cXҿ6pmGbKd㯻i*42}ϫzٵ9FdfS om֓@x#hb*8\InXUdEXbTW{|] l&=d¸oⲛ6~zЏr]]oKdqK˸:; ,JCXwua)篑Eu3L|<+l-\4緕 :2 Xdo/o5ϧ6Xֱ,cKTMFThV=뉵\SC (am]ZP?kv/ *%EX$dBvxN[ms2h !h>(BGJ/36=2. $\ jf'G /JOϺ{,G"ٍx[%Fp v_RNsGwL;q3`/ZK"xe>bC7zU mLqoC(ݥ5ez--t|Y+!a;0}N}x8Aϱ)5_ .(5+We*/L {-Q]A*+Z_y<~.J+qdt'KE]jw wᑵr[m3e?3oQÃ@5xB`)PZ;|WK&p]J=h m Ygg RPuHm9X p H :kkRꕖ iY0QcV#Z@ 59wF\VϣM] óK̠<68#3.Iq(rFJDSC-TE̖*2}< ҩ:z7pמa0K$cփI@ZքH_ݾ|-nfD&i@:pedM*=0YߩP/0pq\s2}+=XdAxiwwk T6Q#ALR+ʃ8G⇵~$9Vsf냊#̻*5"!@PT 9KEɯTT;gOSd:Fv ”k}Iwn3;I6iH@T5\=p"C롫q^&ˡr1}+%%#~\EO%;>ts߹ƽyXۓ0Ir RP{SEqLuO X泹J'^ -.Tn,6K mji-tY$N5<=nICfV% !Ei$a[؝ZJ\!Pħ]i܅ ~a,*kYюT)6q\Z[<سsv5꥖$OfQ%-Ҷlϧ~9=caOZuK$m\[ q j4 +u B^ 4 C:0uY&H/X,~eCy3vmK%9J Eݶ%ag-8fXJ6 s7+8 $dxZe !hʉHiZ+C+dlOשƴx4T梯.3#|:'lX`^ M6c c &UX_K':Aht#yYe[jI=OXV' Kq\_騾&_^⦑Yh;}6q5:%Fdԅ 8zLeapg}H/hwB Z j ~6[jliPhG\ga6 a$)>Y͒DVwg:׻$اDVeYb;eNV)_`L~Ey";;ŀ`w؀~N-c<֯YI_W#!xܗ>xYn,KX)ALaC]9GƼ{H3&i{]I@`WMCMVifҳsEֿZJx<Ч6Ap6mj[=%Y뻼ST߿6 |,99hCzjZcm->QܥO/fİ#iGR/DSNӛo*@(S =pȼcU,Z& ᥛމ}YNE<wڞ_|8Q(XsG2pf"i>Suƅk Ǵy@Ge/*}l9KiBJ|ψhXvjA :nUwrVL*OCHڜkS^"te[qivZSuHo2(E\wGy`g6cVʒWlͺD8'x<[R< X=VASor&F*),J |̛ !*Z&ժyO! N~bf.<A 8bcq0jlP*5Ce_0'Dc V7P&Ӿ0\t@۟%+T7l}BVTΝecbu?|RfRf2C(tC/ҷ+aZmU" &׺WeMJ=x4C mʋ;,T4bU:- wEx(ՖI'\$aU91E35w7+|'nwu"1bAwjy[eżBb u&QV=Q3h+rAZ ÁAnUnrS@Dj XuJeZ.m֛m<H"p"<,j*e \tZ P,罌-RTT/,Zk?g'UO5H^/6ԃk tqt:xݔA3.L&e" O(dݛ5YϏqv,,-u];ڕv>A]-*C"z7n=BT#P IK"%ؠQ ' 8D-ͩ! y\ PqV)A()zXaQ)E]걜C"9`2.Ϣ ZgFxNCi)RXquvE`j˜g^.<6ԗDaꃈ"t; =yT_!!23ZI]c-E` o 3̥( S͓jd ɝ15D6Ki7 xhe4}JvaK4DuACRir*PrWu1a GN'hPe|yirtdE'#6 oAU٬=^?p;Ddu[ulT~$ԙ%9ΗA+92'>oWCCyL,+}9ۛ0xy 漫̒7W,_OGGγ._0B!;_'vp޾G P%i6l2$wX)vf\7JMeb ($_QCKj.lf{!TR!Z: •g|ڥf4}jDp]}FFT7te626%l&4qdU^5CzΒ}7\~u Xs$Wqg9G3h%[9wV:7#*Z&23)0Si^XyK&1P42=aׄn~!TnĘcy!df-RlS%j~e'zEY0૙Vog8S4FW/8vySbD{FܐL^=H8Nɍne!h z %jdׇ`\F"aVBSq{r6S+Yf%Z D#ǔg~?;w{a))v+z8Z,&I4Lc߈P9qƜ3dPXEd<~[ /r 󡼔MdN7Bw"Qg6Xlw,l^A 1&b N_F4#(WkuƸֵ@⶝ܐD:MNW],Vy& 1Z- O  >,)a&|ag&;U? l89ۺ&U {w?ڒ2-6joC'Y}X 4M`|=@^e*LD}~tO]^.!5 irZ6iJ[ iUbMPEvoZF, {DW ,n>{jyu!ޔLI ]` <d[w)(L H9b\m24LǜBY 6 ^8GVhQ^l|YZ!"pg.KP*7 l$lIjj" nuh YQe[1)FR`>[Ne!utB ֳ#'ͻjILKUpdœZni=qutrYAEY/O5%R1ʮXJ;t#B\(@Vu ;Dn=mڣ. 1i<"\35D2k,y$3؀*;4.|tqAb,af%xoUSpzp*9b]-Uk]֩v$fWx(?zx˴ A_H!vzL2=Q>u]E`4+{u2g"zvɸ&]W`96.EsN ތ*w ֵ&yeҩӑ!ytQ)mDɢݛtz X#uxm#TwJy078J<gBwHxa\uֵ a( 1zQo6T敡gv\jS?V$g#6o;N+ڙM01F­xky:E67Rjgp xs4#ScM ;Wڊ#04]2a!Q@C\SP}଴xڪqYxU>INS?K?N|n•?aN[G@\6Rs8K*ty_ӄTb2 ~I \?s R?a< T)Rي3曒7]95,D]g悖Hj'ڶ O;PÒCŇP;6Xi.#(TBfJ2[R/NrpS{2[9"a(Y*W_*Aࡋ)&&Sirw: iIT)I>c3h ~6!.vCA~לTH cPD/ѥez\wF@3,umd89h(C2*!dPzOeP^)m8m2^}΋βٷaHpg{#*\Fjf_o:wE&Po}Z׾@NkUºimXPemGLxݎ>VDaŦN^(_򭶻On@(3k"DnײF"xУ\| u!'3 Eh⺾Aj­h:ހ|K{V }%R݆e0i $m9AA ?'WmW6_,U[b2B{J`ooՃ[ED@*`@M%$OO%慌{n& #M'8;^̍5]߻y\j=b҈P57l&D&»X{M=bT'΃[O|ea^ =)cZ~E߿#i{ߕx ΕUs"'4˼\i `R cIb; ^ee_OY{c 4Ԉ!w+yխߩro\hB0n_n*GΦ&OA5&28]TZdg}za*95a9$5$Uhbȴ4 Ϳ!X>eSTCKNˢ: hɯ53 o,n ̽]nf+ǯ$qvt};40̮GRahnR kDP``WXptS!uVZ p*PSgl /wtZK?^"h̅`0C/BxMu9y_,{ڄ]GVYOo3 oj*ZЩl(V{Μ亊Xp[eݍ%,ܠiW?L=>[&EC!_C0U` 4>EYbډd`f)3< |6xk]acnViͯLǡ6;m_HgFL@Y[͑/6i3*_B19v΄&n& ^|- 茌"̳`4sC` 3XTn5goGGEFi lc?=ӕN-=9,!8ZcM>ovt7ci;/oڌFđ>hoXy9]H%MN=Ir;H]ח02%ލfe)lOxC Le1T&#0"Md7z/7pezW~V`(#HFǢ8t)qul8d*߮S"-5Ë7/Gf6&ruĎ=bJ jT5t2*n2yyh !_v%E`b';2[MxNt/\ZZ 3K>QJWyr\̲o?ZaOjOPWY]§vNp$;jpNj\@ۗ8!GAÒtDf, {*$:Hpۚ %*PwTW&bL_Q\ 2 BT pJxAzd4| {=nInǴ,D ʅr̀|;.udÀP=T<+~ꔱ߆+R{+i*;dsq͢!)x67Sbt8C'${~ifہWߢ&3c,‚-BS3Ʒim]<+'ךoo?I $'P!C B ! l}'_MI' b1 dEA $@?VP*kkV6KjO?/͕3`UZVJ`@ QmjްScK u'):_v[ɶ5c"V)/Ɓ>bgYͳk!p[]7v(n`ؔIĺ?Z&jeDDO,?ﱨ kJ})6W.Q"S)jD ?,{`õ?{nԎ%N2֓id+u ɋk? 2Ib:dٴʚs~|؈yhBJD0!YJXʐKYRTLt%s*@a)98^,Ͷd;vD敱 j-9],+@n0MJPO)TCdʳ+)/ƚjTi`ɘO,&m2FfQEܜʅ*e)? ׊$:Ϗp$l(E`a-Ir @A(2+!̱rbBC+*"mfB,%LS"'4maJO;mru$ AeIhRlPțmNB4s4,{56lWf3TvID>y)\Wj퍪*Q _C'#>XUͩk#;y;n߼n{Țl_Sd+[zCN4ʩ'`ÉC&\7L4`Mg6`V3w.v\xɅz؋q+XhwnI`Vy⍵wd" `M2?j۴rhs,̆~0b4erHbdbR$ՐzQ-`gzgg JJ uթYI~8d&m7l M@Y$͐3O?073-Q&'s۲1+(mJ5X,A%-*;tZonD_|KG X7l33 EJN뜆e:[Y$X$|rKEPuPᙶ>f2~&5v=k%=! maz˛آW3%G#6}vޢd!VU2 ",OWՓ͙6ǻaQc\Q 5E2DH Jϓ#璶Ns_ ZV#ZîkZ 92zte5.>uV_$'";,/l)3j8t9nj⁂aI! ّus"ҕ>d=oSˎxv\9na lmr*P%wa*x5c] [Qdgrig Jűm-׻[ I2)ARɼ{+2\>S ,dh8IksfRIi`J!+W(@Ƕ&1POQ~w ֝j5qWM )+dKb. hRf-*P^MT:&T6@ށ?ʣ("aQőTWςkZB-q ħ'uDbUfd:! #d_)gXS&  kK޸TMnuO'g=-˔֕ΆBu)HGhՕiR#(! .xrx3[Sb5%kqs_N5e'e؊%X{(U) HD b@0onp0$ԕ<5Qy{\JDV3 `*1֢[۴?޲B K֡b!ɩ,XJA݉EPUA2s/Ȭ4V$@ ?XjAHR@˵e`Hh_R~\qIg?H)DAZQ=ӥ+g{b}epդ m042^#lV zSYLM߷gMckM6]bJd{[@"7h5S8Hݦ.Xxr$Ҋ@WrqH*m6L{eExavn-H`(BU=TUȐX"ee݉y!yuBRJi.RYU3xYS"ޥEQF=J* 'fov9 cvg(D_ b{}%, 8ݴ2% =g:}^oBo ]G"dmjYt!m3uyQf5wt?oƣL52|M%Jwu)߮25W!# *N1WmmDjQ[J)ʬͪ#v0>9L᪟;AJ*s6GŶ6";_zvO~' )YuCP0f[Hi'~/(B&"&{nj[ $$k'D~:yȓmdZ @DW B11Py/TQX- jO36ɵdjĈ_YBCM.=m "$ B@NRk Lr{dj!̜>Lݵ &Q0"DH IO6dk4ZP)!tI-,mXĢdjZAbĥ=zQs5(XTЋ +XB.q)ZbSXDPW"5L#bX9x4XYYP3Dtm@yrXrN%%BRö[^*E ʨL)E͔s0"b+s^ȹezNiL&}z#lDQ}K@'3[Z]L(SYIbkQ׏`H=zsi0"I&6+=l+ nYmBb2^Or\#E7둄V(ũ~q: 'wY ӊB"i4(XsK;c5-b:H =om'uJj|o:bRg5!񏊟69rjDZ:؈->NMl&d$dVҵ*g*@'_ZKm5?^$L'HIWV5ܳZujPJ 92ie9,Lό3&Teo0.JmJj:V$MKK\r&$ e:W;֓a¸DIWQ󎮢@)9ux_ēIПېX0S33m`R5 J5mTtvj,yw'X |fHEDԖ?|&W'!B"LK$M.ϭOdvF/ ٳT&9H/L!S["WmS[EN5v ;1AgWK/X,6] ɾ g5:"R rCdlj @&Nä6i.yW Шj[|Õ*u\& 8@D F_3!0 `%1:.bT*(J>&g0^ԬS&{y*'…h[6’E!T6tton8\2j`Ei $t{dbx D66&MdD2am~\9A,DLlfK(KEu# e=Ks咄M2hFFD$Xe9pD$J+2Rn*{\0+"3㫗[(&aT#V 4oe?-H,jUdzF?t5pDl*cm++cV2$ bhIEbLke&4|K<ȋiP&+*Rik.! \6mdmY92Ive&{o(NC9P m|pqO^LSZ3' @\2!4A^ 207m^ R>vQByiqԀ( m̱ ֡BTlw)ͭ uW#TOJAs~JO8d Ee"rB '-KYR(ssOQjƵXJ"y֍ZFU:fhVU^Tɭ05+;uM awjy}{mΝef s)k_yN, GxBT\ %\ݮ{MШcm*R@D 0D pR'Tgƈ)K=nTIL˶)(Rֵ 3ߗuGe*9v+ؓyeŇ5uma+' |tͷngkc!YI U)2gT8a>,H ~1.ru,m=}y!z4Xʙy^5z!TG1lUi&ӲQjyRԱ",TXrNrS [D?IF}nuJȢ(zbpTw2&娏5/#BBEl4XDI$ `qaܟVR*)!9e""IpY(m kĻXYDse"[,NS5 )YYm9 ɣ楥!\ YXeRŲ0LDYT±Ywg\"&5 $Y!yHU8l>ˉS/lյ jewX# ggдYޠQO66)U[Jd'Vk'! ѕ6\QS&2V~kMo(5iI N?LAr5+ǟuzINl#*TUܪ$ {l@G_9͸u|$HsD"bT-M۵"DD>y۩ gZ( '}TFze+vdwlJH0~RX=e%. g+ ?VA=s*]SX9 Y`1*3 @VBmΨu_Awu[[aQiT*^vJ$Jŗg94@1%m@GnZ[b" uX]/%w͛*"+  DNCǽ'>~zy񪦶V'PWPOl3Is)aH3^s 'T.-rZC2!_oc-eW';ׯ$qO`0Z!IK8y4ҌZjR!T|ީこěO!00lRmb$̈́ԉֺ)*^ cZL32 UV(oPDRu.9^6mDS5{f2]q2"Nnv?z鰖 `W#҈i[PPgdɝiN_ZZBȤQ2US$ڊX-Jƅ6?ԟuk "ɸ WI&Su:"X4I\X vͮ!iݳvݖ7땓ڬaY󿝕N_[HͅYoʓPҒ)L0@!*")~.FyL~ӻIX)X*dagvweT"2 NaϖEJ϶{VeBgYIRkAV10 aYg3fs,F=|Q>ZfUL(OKut*p}Ke`FV}_[94<~YgPQC8ȢrU\ֱ˦ 0NyS4y8A"$3Jm+ ZsI#:؍U=yeB6ԯͳRou*kݼK ʙ%H,ECVwY3Ғ֡03 Hsaq!œG~#@5a̎r_4y20a#..r^Cl8ufE\4 rNj=^3b0+JY( 5"fUBVQﻺ*zԫ'`h̦I tt))&o; &w9hI18P2 1PlDDD#z4G4 3Y 9+Gkcb>f';S"` "r &¤`!DԲ 1j׭&guXxv2B[OkXT"nJ޵(uQY HonéDPA*[k)L|& KrܰKʦ~9 U QmēOOP'Dk6T+h;v@ c Xؓ&8)"mtn`MO^y31IYC@9#Q ,SZ)&VE+D;t<6,)mʲiu3I &XTf택{vd0W@C)l3˜U53!oRfb`Pfݓ&LPkk,PdDf Ve65nϭsi2#F_7XDT!4cX+xƑfUAڙa% DlbS(y`HJGa=2T` q5&{%5SU{tr~36)) XRe!YH$<؛RI%=n ^ܱEgl=ckb[>ƴAy4+ gib?|u9𞳈2.@&핬e!v!udu3S%Z^qB}Jw;;BXW^23^iI5LݲP$tՠ" ޳3[%erLM)T'[*qcL@Mc57m)*X&DFy7Ny,h230MVRNXgJ>5&bKRe0 ァ# "<2u1f\d0XP6PIIj0mX ),6YY)lz[C% !t(DJ̕BT{qCfҕbrsN-W&Md-߷-v1U;WvQ[OW鑄fSR 0LCZP)~3(ͭonJCPn:g'K*"C*_md %eX~Ym鿛f+ f^BP`=lkDD&2-RJ#S'L:%eLnV6{s2rW"ɴE΢!'MO@S1ݿ?nyn4.H!mT_봢5h ֬)c#O-qBB"ij&RTۭ%e%ej$ eGkd+(9Z02rXm2PQՙ $X)%$g$6bL/>f!%N{} 9%#MBD"'KđrSuCHqB#D q뻷&[-PCY7hD\C=zVG{R0긬T'NP/PS(Zu?-)&{e Xa֩7j#pRNt{Xe?I8+[" =TLpse`sPTDcBZ:&N랸%$;Tjt6ѵcmEn(,'[ #WϺ[',\qp'OHXiEVٺĉ?iXCPժc0Sf"$81jv,O[mb`qUV%;ZZ,PPue1$ +@R:yxHPY_ xg<~؄v<:|eT8JWi TT\JZrf -rlؾӓmw.by, rji}D$u!xIۭKo2m%fBN(dA!+ZSnw Yu*([HF<U+)HBO8g;bq""a3[dD.K[ib@9$ XєG<̜>Մ"zY*BB ^o"Ҁ1l<5&t V)5 $XdQ󩺋u+V4+7ˎJƏRfEDۗ(lVҲܟ "HǛ%r-! %"c5UNo*L%-/HNc9UEeaxF 2-e21C (ʀG;b=Sa|dsY_ڄ*X%pD;b@myfsRBDHNjO,)ȱWld-pKŭJb a<.&O4>{բŕ.Ŭ?[{޳I r7aqI;x)32ޗ`:ӖU3`@2M$H9ɩɚE2y½U[iF RtńcDRճ晚D\!! (Zԁ"$Lf0hTAwUwP)b|r;G%&Ss*T'ɢe!TO<@XH+,u@% > N~?[lQg6DA~fhciC'=sL '4ۊ<^a{JeD֛\GƚB cdb$VDrP Ȧ>3yO.Z$|rhmebD%$"8dM#`$6l F[d.{s;Az}Ŏxp'Yg9?Ly´5eL]_ֆv6fEs&Nl\agcfC6jTe(JR$K²-*zZeÛO a`(%s֚**&#n=j1PAǚQי*۴ r'&VC$RD= vMJ\%Lxs'<iY¢iTYDP?I|g bYQ$L]2ZrVҒ6& <7f<h`_Z0%gZrUx$ /'w&lӗśE *IM qݪf`6𛉕"KD( AE+VKS$T3DqE% i(mMM($2/d%O˚yb36 !3V^pajY:f;k-;8u0! &ağp˟gs3Ҕ 5p&[edNvԲ)jl%VW'93Fk;tWJ.H*Q1.VԦA!7*e-Y\=re;\ ʬԪ,`)QvȖNuȮ# ')N:V+k2-fXj:ֲKI+kWoDE!YĝY4RjjY4Ԅ XXm944fcP\[+̮-qQ4/lkKblFlJ&&5! (9J]3+m',b`3($ۂ0Z&aԫɑKz̩®֘Bd9u6 H7VW4I3 ss* dYvJLF"ܔ0%CuP)Q ǚs%b`DoeU'[:qd6ф*ٱ{ta*`wXĖ$(Sdm!S[G1#DI(DmT%1 Gc]#c&oZ S"M}Xd|Ly8&$H-t `%\:S(sk^ vX)RNT-j*3 ii\Р#lT gvlU\zuS$"2F4lH, fZI2k~ڂY*fױ/duhϷnrN9%\^ka:wy6Qؖol^daX gThfq?[l䙟YRL-IFk+Qm* X Ĉag/GVunOt)/ZPT J,g5?<jf#m*1*'tU#ͻ~_))l]g[́cVo_tXT-YYu&WaNX]c@4msqc|Ҡ*)PxD&NsV+e!۾xV2*XmVs`\LXMm̈́V!E+2ʆO2#y$4a\Nvl{f3k/RRV2߷O%V[eD^.@bU WҮc&$5n1-Z"䱈16Ees\"ym[J-DD̷ٔH(LTD*T`d1"K5,")MmH$^/I:&ԽP ;6+iSRuSJm$zն011FR/[VF ;+  ,.:JJ0b/_[99ʞCf;mVT%F,͂Yl*PM;RwPԨgm53REPH#CZƳi=g19MIjY"CA'U-%"D'Wq&޲i! u}URj(噑Yn23 YQCƱɽt'm6G&r-VTK56s5&xGfx"zWDJ$fc2_*ͪFM^ h7S*f'ID%cDXBfdVuf`Av ya0*E)Z cmNqAh ipg]wIAV&{*6ҠfrNfK˿<ڨ4JO-N<ʨnD/RGZ #oY͏ 1AlnL*ءo@++r<yr 9*MDR!2U?&I[nyvN)tc͗k8bJ%`e3,*ʈ*d#֣l/ol.’ B$ AO<Sj,DHtkP3lVIз1[1sۖHXȑ\%Ȑ1,iK,.O-K:b Ucns}rnww{2PkSk(7o0c6~3]iXO4Y˶TKÞ=J1kJ8Qbu/ I?/&Uȩ˒ۄqm˟jV0|Ճ|E}Y3JVT3j֔:׬+QΦU UaQR~wiOҤjY塐]J7b*V"؋͌чKIb'W P`X12~vXdxݑ80?oSm sOR(se- YlIv* A.J R'DI*{jk*8KZW@ Hb?4 ,B# Q~1MeahUJ!(bo]츙ވ!6MݹdѕT!*-"5dJfq% 5auJ:ۑ&5,R8Dl= NW%L!jOϟ$l!*>qY) 3 /\WJ4_Iι>v:DEv;x4ˊ|I~v$!P9-b_,ûF|O#"[iUQ!8w>EťQ)EV7j+MV6̔=tHH 8L|ģkn9XDy* >iNBBq%2ŒK@V)Dj8-\J/2-,@2E0y>;[(Wޱ0~$ mXYEܰV%1#|{mcKRVD#T]U:=:6lF0U-6! DcN(@!VU)(wbNXMěcYRj[f|`e+iv=\Eb晀PG֐2Wo~n~=SUu8d\a42Z-f85In0PT, $Y9%Ouݳbw}տV<vϏ0Ւ3;ga?P͔9J;\+:M38H1@CZ*|oob3ǐ5YVC g׏Fe\YZ3G$qTi=Nk3B͕a]XLƸ  uw^.MEeEDNjQ%|3$m9<{p]J kJTUҲ0XP3<9fo|HJ#z:o.uLQ]5WLvNT❮N> >1*&TcnJ'_֥>k4ET:^C)yٵoP*ZN'Dĉ9\udN=3ݹ*dgle'fьdWl:`U֕C{R/mͭj^zypFQa:y`kdte4Vig:K5f,3Hvɫ;]4I$kJ76ĬmRql+6IF9]E7uڌS* WW]g3+5RQIR!T?C~ lgX(2SL V"Z`a b 6Ub|D2O sħ2gI+:ͱuBU*s_/uO }j5.R3<bU dDci1 gW) 1n::΢kUowt1,sY [)&'VFeY-Hf$e0 2fb*ګXfa93=jRJr!"gk%="6E*aڊX6֗:DτՕgt&iARF[! RBYQTɑ*1~X@0=בÛՁ c1`N)U͔0Db|YlF/ljڳ6HBn"ecӫjڙ~;Rʘ5<*c rdn'! }L0X)z&kiDNy!'*EY=u2TC uإJ 6YQnCYQnn',DMV0 M"AK+c cuW c|gTD{#[[+OE,aGӈ!\sR+d+]TzrLb(otvq [L\HSLL;lM"bud\VMp8z-d +"g{! &SaDBrE\f +nRCTOز  YFCi)KwK`S4hT{P$LIŮig"i! :ܔAbVJYa' |N>iva=:Յ6nQ ',\: -& ằLUAc\wv3_3'@"g$`Tueg.;BEl)б.(wXstd+\ZnX%KG%y۶LM2 2s2l?Zq "K-%[hqPń5hF@.TZhvշ``2>6G͌4 ,@F,jXI`YKz.@RxbX(Ų0DUAn>Ѥ &Y9s4@$@,`dIIJxt8k(UrmlK6Yv"wjƑ2)2>%)$@]e09-N#]U ?<c޴DE -`JgʎKe"% nJϮ󓋬k@6A --pwZk@>dM5e׵+$R"LS(5H&!$1P\b@u)#!~fK=fj.TY;y^2mb"D2V [lAl8^p$ FTVRmF#!'i 4E" Rk90C+^SmgZĖu-Yւ"v2hKX0} Ոc̰ FKVU!V$EK뎰"0c*b` yNU5;m4C}3 /ۓRkKw7Hd cͦ_axC*f[EZc[wMe"? ŪgQTY{DV6Vʩ-k[`R" Ej,mŲ ,2%jT$P{f$`؞a`J%<='ڨYPIX&u"6Q[ qIPS%byLUH>ZFТ spK9Q5QXKk' c-m+ C vΌzRy&LșbgVJ E`[(XZFi02O':D Q6Q2=z$ChRrcQY2Ļ[ε̬F2mʆR?_hAF*J$;`QBeU9-V|U'pal(bd4IXi""b Dqj~ 9c'5Jɓ\lõ(,]IF/3'xr*~Tũix}Nqa%X{ 4+L#̹\L(N: CN[5d'0KXJ Zu3b2dPmC̅S7kLlv/6dأH#nr-""if'ihϧ(V>"Д!mvs؆,aa]nXL%Ĭ*x䨼J`Df֮J[I\N3 1ї<~s%2lM)U<7٦s"Ӓ;l눬3U[:MB++n2u*rl/l%0ĉ v\:5d8 QAhYSGy<0:miM$ )eGګ ma[ (-n5-*dD#KKeY,A!1$CU>IO><PG8qME w3O =tLQO3[Y\&AR&$ EseƑO4VZZKzd;m^iH5tEggY'mȏ+$wj)54i]rWܒ\iE!e &@öv,ϪIevOˁ y?yNg0KDXK)jaglL$LrsP̨;JT*w±:5r8"@Ȝەmfr&F@Ʋ'a|b۳)FW%d11WM2\5huƱ"ug3dĄ0~\ßGY~j,+Y8pX0lG-9QNfc?mJ'%K;t榌YRkye ew/b^P)o83>jjJAYCɺTċ"V&Kmp6[K2Mg9u,5VBy ec UPR[SVc5K+emÓ׻"fe ZTsD̨ VOl00C)y২ɺXrvHd mh20"d3TiG9v/JBP" IeI}pj5yBO%S&[Wd D)İ DRYR4״9hV6T)OaꠕT3?qdv5#)7$?ZφYsBeHIX3<AϭrmʂEJ*9DJ5f^o+1NؗWRlͬTrX[I YDXȶE lks.Km(5V ҨKXVGh\_ 鑇JĐ&ep\ǩ)*U- ;]&H(?39muVv033RV@+$֋cڕ\F_(wXb, uu2?quƖ2N; "q3IDBF2 5XlX(.**{ ŜʶUC5~N$˛J M4eAu }u.U>$Ll naP+*QjN"rmR(ÑE QizJiS^_-mf 5,w^Yaɓ>9x,NI?9 E*MJ 됺Ȉ~%^ A@Iͬ@\VYKO{{Ubc4t)>c-hTXdX*TU#RlVEۙ ɵZla />W?3X4#^[*^ۣ2#epV*;wg5 >Q>NxԿ*!ܾ O:"Yn˹#55~E&?;Q FK^aDtkT 2l#, GlRZ$ؒ'$,"60"` ",opcg%Ʒ7l[PIXtgmcM)A?6b R_8R-ƣT"R9!uGo풠g( _YFeFѢ%mXmfruf)1P~{$= ]U3V8 ޡک13S)03_)ŠVKhUDC>3;QLg.mw͌AXS֎TQ,Luo,-`,1lL\`s D0"$3WaMV%~pl!fn}&!&jknWt ڶ3 &u\- =vHgݟR.Yq!5_*H gm~5hJT+RB(ImBT"Jƨa# ';X%}~<*pJUd@'Ĉ$ 5= Ts?xj/%f!(RX"_ۨr0FQ ]LՏVc VJ@7ٴ_;2.:maPm"İ37Y sΫcl*%m?}g߿t͖@9YWud各.XKiDuNoKL1"Cт: H.+kJmg\WP32,[Ag2rs\bwaUWAS9G ɛ<^oYQgk-۔֓Kc80!F? ,ԙuZ$wXLLHXY99A@T&rČ0HwFi)a"Hk8Y['KE XVb+U-h=hbՕuo2αۼgg%L>S'gVQCR)ȦVg cKcLF|w O8zԫkVZvZ -k5II?8d<Մd+ہnI3F)-_9|-LeAW:wxqH"&YL!9墇<=Xg\&A esHFHkbsv,Miҡ+X*Bi r';(I@!6 iM៕L=hX*,K*%ό=טQX_4VfQ;h[Bfb"z>-%K,* yHe'x'VC%:+~ْӌKmsoZ!hO)L*5JTCr#uANv%" $Y5b${̂w]4d_-v1g% 1b*Gò WJ 6K{留B_DIg[h؉c'5Y]OXbOu׬=9sv +#hVJPAAE(+W a/6 >,E!l mJ1SԗS ɎV=e5%!JpXB9.keڔkOtΖ.{Sdq3YtH~! +Yg7M3QMBg6 XD\"Jʶlted  @򞦜RySB"&GRZXwDczRmOžy{J$/v6 - r!^ONƴ@` )nXҍ'Ȉ=c  ]LlJ&Dɛi}01##!,IT焓N@xb_[<Ī{{~J픆cm Dmz2݊G2DFdsfMHTl0T͉airy᧛M0085II.UX`0V˗ QK -,^(v m8HK{צwxl2e"S[Z9F,b#$:ċQ3J0ؼS$ LH/9/,>`b J؈BtsΌv剭DL(.,[iHA2%U\DILY\a XBm&m!e4F+d %&Xi m`8řK\55*Lj 7T&u\%T ̛nzO_)Su0ǚEu(TS"< 'ɿOT>g%7w`F/g+V(1-/1j1g2iqt3E;nl[LUnKs>tO\Iґ)!\`ƥg4EiXI&RF.! l~?)ȐE#>U9aTv5C&pAQP4M^'80 lt L{iX82Þxl9L)ڐe)L!R!0iܟ1eώؘXxn:ey0sMg]d^#mG^DY,ujrgF~50yzj^ECw(8i*ɮ3o]Xta0(MX&킎m$kex)NثvE/S"DDIYӺ$C:UUU.[I:>n#QaXʖ$Uoy#X1~X^ԖۮURR1i{rPERBVB5 ʊB?MŇlQp&=#+Z c3ϝDg1i+P56*r(f[C咨[5{_݀.!}4 YRQiW^2.gDDU3nVUU,fk, Fow}3~j0w>"~뼄r 2=kZ0\"S!#h,uS#R`M7!%HX9Pg2 =ey.e$eDcrdX v_%bo0!,Y.#mf'UGθyFx*[|fv32 ffp=r]^A oVB&="6(m}5͉m,)[F$jW6ۂzu ER4qXw'1Jdx+ wBE3̿*#ט,`0$+}!P&wJCmNZl,z:͋cM&iD zXu,B vIYvY6hY#lѐ`(Kie)D$!~D, ~ՆeDK OV!\lXb%^yS89"fB08br*YIi`߯bz@-uo]ԁ2(ZdR':NY0ۙQAL[T^ڎg+RJFbAr&2L`>ecRH܄[D=-#zY[G[hp`]&brR,Z3\\r[j"Vzei5|8Ð̢ 0 *.Tg UUJ1fYbJmĈ$|k!n!1QaJ- kzEZ30uibP;s3DPH Y乩Fy TȌڦNm)@ĉ&DH@$>w{sm$#mk'6+VII + c#uYP֨-4o8Af*EB ڏ>&[CMg3meg*g2e*Z+ev8.' HKl3SyPDXu-ywr1S y#DLIS|A9%f3 dQe;4vvξ#K<)NeX̮K@ A`53dT1 NYu2õ3;Y *"-Ia.ܺāblZ%&P^jh,09Rp*arTj̅‘䵑%mR,S ZZ<9/2]M-fybhawX=v!5bǭ, -6 [Nע2x ls\L  Μ53}fE>'6(cG_fA]HJ%NۄK ibR'0X,l5ݒ“΁ `qR#*E헖!\0Xf<FfdڔFm.{grO8"Gfͱ&*,iX#e4jVDZԚDtLI e*ĉ#y"92ۗ v5R tIι=]$I4>z(*W%f((}Gȋ96$b"S!bB*Q|͉ u%b0MKJ/Y.$$ -@_nk,C#P>4Jc{({Pu,E+Z,IX#P)(EjNB^r=RլRu]!|b9eA1aX,-Hs-/]VT'yB+0G&2+8 ń$ 7wʕS?Ha 1PQԢ*)] >&WEW Eϩ_姖7x]Y- Hy&bi(XO%z3nrIXk/E̬$FY[ YC+YTy ܨ *+-6 KGZUX (uҳ3m 5A-UDSbQ{%]ʃJXX;P`*Jݴ.eO*uBXUO9vԦ"-O*nD]ycBy3Y9̜mYXngM;( [FC""Gd7b"#&VXYsp5 ĄT-uByc;nu%"k<4blEX3MuF)",rx])2S%2$& H2VZe)-S*{X 5J 'MDKO6v|q E-R*r .MwG$ﯽ,m_pyLbQ7a)Y mAkJg,EB# kF#&[QK)zZ F*nRu" bUeAqMچ/*zpsQ6*a#%jVwZ2c,8IN.s-Jc ?mmQ:X7N` ֏[s0TJR@I0#h0ڦq0z,5`ȫ fNry<L?TN 3@z@ʇYLRk=bIڕ_-2 e<)eb٣"eur䣭eQY,n4! VBUP#3F9e):X740ň+jVےgɆNLyJ*>MZdÓ!"I@`BZےEE'|+F?_w"TQ:)-~9"g@^f82+J%a 0nVu^XJ0}{ݰ[iv`3 eGf)K(CeeEQN@ڋS&sXǬ *YfH c%G%&& lmm,U+ Jc')1$ 4[_Ҋ)V G,``Bs 2QR#HgIq1b#6m+,DKJVS DE(Cl`!>gjYg_v_)bωqʙT֌C?6ϓ[h5C";LfCW(WS;YUIFV,`2^N Db1%j1JڒJ=:!ʛ㪔 JD|V1 UCVx+wNͤ7(Ӑ6~y֏G5 SXeas1T;_͚i1?p+QJűJ̣h֊rdfUPd'JZ[Ī8L/JeJ˙lX( 3QO\&IF͏1??T!t~7 !b6V.-&U J@2omq2]m$THl6sD2UMjm('݌*K JTs|eXs̈ؼ`B"5H`Ŷ&yH5BqH\BCY expQ `j"/õ,9)}ΐ3,$- d\[++z3 mE9^)F_Y扩J̺\*ZQCYW{ry,L~i/.L% 5Ȼf&1K\R@@i3XȜ,1'2mcđ2ZŪ f1-ۭbFdGaPma3X2-Q-Z9#g XHHq69o;QO5b*"\J¼J͂F$dU2Re"M$5m'+R anTYZaS-مDO@z0g|Ra92YwٹX(1)i2u9 c5Z9{媩SΔ*4́u =BqWYXHknau"y*JfDeHHW6uưX!D@ݍS6IZY@V)BtkTglɄmR*֚Ո,F |N3)of^TmjD-}e0YʢQqB;ww}mb20}yvm_ TuUiKY$*LJug!FX jt?@>B{k`SQ f/ܑN$ @AER/data/BenderlyZwick.rda0000644000176200001440000000111313616365110015034 0ustar liggesusers}TMkAGv&F$~$ K7!'S$]ܝHdFrߘ Sn'o"Ş6̼W^WWJ**g) UR5nt߽8V*_ٷ~"g'2zqٺٹ\-h*} 0o6bdk֡wy:o~8?:. &ַHuy/O 꿂.%ăuS.b&`O,CgV0 Ma}t KGϛiYkl?`D<A@q`~},=x\gݑo\}'`s=zcu9/>Ҩwy7C?i:Fz֋u:y,潿 }XYJاQ&-% odfNuMf8y֋w-muK7rhN$39a^(ϥ1 3HZĈbjAER/data/OlympicTV.rda0000644000176200001440000000053513616365123014161 0ustar liggesusersuQMO1FCnx˛ Jt zox_ZMofk: b\`~HGu~Fk0>*}=O^XEdW ,FR3_-y&hT 27*; @2"K֊tA[.6k/L5k'_kf =4*khջ#OWq`lSr=E5\!rw6r-q/a.naԈu'5'2fm5QjũńԷcd ']SŸ@ITjϥ7TrO#؋7 AER/data/CASchools.rda0000644000176200001440000004034013616365110014103 0ustar liggesusers7zXZi"6!X@])TW"nRʟKMd[_;zĮ:_4) *QȘk_dQZ` dGnF_DE10ѹ4N~D^hRlՂvGr ;ĹMUT+5OvXH.͋uq\P*I=Hvr fƘph6ͽ-c.Bvul*]$1奭;PU6K^$ ,/i.MM[k\x,&9RЛq y(?6u{Ϣ޻QPƐBU̡rMta}>4`+mvmuz@jq$,{^])i\~Y!d}L STDLJ. Lr5В1lпX&Y( 7{B YϟBlqgqқuG,{,YΉMa\BGlh3 l>U\mŠE 8zaAoD*q}/ȤU`׫ae:kLXu_JߓFuτ"xOW~ˑAi,G岛K6JN|VU6(7fSΓd8Ic ٳ` ˍeh ڜBDSv~@Nke.Tm56{3f@ ])jjmBn¥Mi."î>i q4L7{ uφ&zd߰ K Y ̋=7G?L*밵 Z2gzKx)wC^ p9oZT_=؟HbQ6/]QN@~Ir*B"'>~rH NF[`2yci#ZXU^p@dy;R~{{U~Rb {z6Tݱ[%{,7J?O3tvJjoi;!zQ$@(`7n[Dޢ( robg d!ViMro=d 1!k]~0bv}*ash]8x@"/ధۢ?rI).47?Ǐ>+e*nxLzA} Dd*i&1P} P  hx^RD9wAM7>ٲ 8%N%\NrگbJ2HEypN#DqHpp, ?PJ=yd0ۯ;Uadz+:O=ȴK(4A@+ʼ6bQդ<ߥK/Kzc Cp,u mdvGaR[ !O`p#Sc2hKNB"5E1EB2fr3x>SZ~Մ+ CFmi~%RT5"_׆Y~AwrQ.c٢ogl)C͊ȕrHS~PE`h7! 薽6(` WuD}^ig8 #} QNyPRGu\F\f*`aV}\W&~2A/)eCZ10)xM{ ߦ\+~^klj{ 56}ITS }YVzf)!'j C1p^oretD6{4; E^̈a!~|vo29ASJ{lA6-dm]q~r n6:&u 5 _.;:wLqS/U1~L(N¸1a|:ۤ+I99 /|azxXcW:'D ^_E_oUQ/paFy?2m}'5uR.jv2!Bo[Ȫh0>XE FJehӢfM6pNHR! @TߤkbL<*Qm"Ztdsz:uUt3<}¶7~ϊwvupL?l)Rd nMԺtJPKPiMDw%xmYp_L9Jfᝑ @@cC|?va3e)%ڢ*eh yKu3T*ɓh߶|+zv'*^O%f ?H>Sx;umٳ$ qT*deF ziLcY%H_Ce)izG '"wz[Zeqd8S $iyJsʰ(_o!b %.Os |P)GfާqNPvNDG@xn3rAXrfYCr_ tgxKt A#Mmc+E/͎ilިERok%\"nxRč̎$ ~D+޺Yל0*m6^C4R Af--fM.'X<ҡ|S9`98TS|v'ƿ;:>x?vVc~ͺ!ԑ0A&\]3{`-G*ξedUǓC,DʣodSKT"Na48;;ީ)ZNqx]zIac *m$ʞ ػ ž'N!;ϒ\~#A)UQ  yyTh06]AwvS“dc?u4^Lmݥxꌅu$\˜~) ODz:?zxD79/X"sx [þ1PWM]8vOA7^JcGkGܰ7qWhƽjK@"7Ge׻SS_st~݉kvQ:?'s뵟`;xE\ t $j'd8%?=6DN)ᄊ:l{nrdvB-+ Olsm2?SK0XEfaikpp"u0 .D:a7p yݾry(ږaqΖXMPtC|,Bc2ց!gut HK36š7rHY#*z۪p;  M+)&5"@;*)YUqAKHWtͶixK[+́R./G8vsaEɶ`} J=shl?Uy߮qiƪ_mBlSNk+O\YN8\ENR:=>#{OGz&b۴&ɇ{owkʆ oigWȪP6'tb6) šZ0XRJ>V$r1(3w=Q~Qa^cM ݰ-A3R̤g 򟶃}{N~HZ6~r:sðgi&'C7/2ODQ.jxsN2qzIsvȊiL)}&\54qe ey,s,S;q096]q;7ۄ&Pؗ*Z Ŕ@K'f"Vak[YQU*0,kL%">x( Wڣ]eT9z>l[U|0FC+cJ&h],O MB$|1| 7pbq~5Di#QWvݟH4{0kgƪS&V:()KwަZ>qyr0 `u 5v3f!wIY,V&gk U; ZkڥKzi}@⃄E~{=U3 sJ* P]'EBvN_lsns "&iE-j2)MQߠwGj{0$; Blx0o .+!Q2ts(4ƴqin|%qSlrUyz4&NEw):Azc Ԝ)`E)ii{:ÿ]sQ=U~ڌsho0u'+T dz8eZC87c2USozt%hU}޵]!A'߷eW^bFJ"ghcZ4$N~9{"ylƍiNޒiK9^&]wkdF ;1{a-,׃ aQ9P;s竏@Ѫ&0GcubCROoI\u-tJ:/( Қ]l{OP n~ذSv0J$ Sꟗ0 ppFp7$]I3c"ϖYC%IJS+5D2#$ԗ(W#/D\K<.ǬLbeF_ >u9Xw׮Uܣ[ZN9/Po'r&+^lyV&Ȼ+~0CGo`.1.=Tꥱ&ڐp&*#!Wf?܈!;k؟C)F*{Zzɜ8<}㫾Rm{'uZ'1(YM/++cE$dHvuJ[u$uC0I쮂›@>e4(' qp3Ԛ63|:. /a, 崽9Ǡ)IةPQP & ]U8p41Ni&h..r1fcbMfe Hab½^ 7 :E14'b&NpZs] fUž7=I7 @i2_Xp)݃_IMe~uoI?_0=\v:ET#cX̤XϚ@p"g2Oa֓,e LwYvQ6h,Yx#_;u}wv~YG(S _0@f4 9+a߳ ^-ihE{_U08v'iՁtJcvVä^(ac2ISݩOR&o.¤؄DX#%q e`7bʂyĦ*3-\g27l`+(:/ $jz\x+3;oz.A؋VIp'I*U%SAcZ1hO uz63iANNs^ϴ!@8;@PzK@G0x|y E*vPp"rܔt՟ؠ9 fPj-?ɬwͯ[ NGwT6+Y4Ģ~B.g L+Фy5.Z597lɇeޕړ׮&3Y_F|-& T4,XM`u-#7p k`8l6 HH]I-f_day?s߆-$'H|/ 0FʼnL#+H̨tz `n!J2T{=wA,^B;ru} ^]7~{'~3Tr'1;:`U /GGKEX)47C6 Ö`C^\5+ "n.7ϋMsH1me'RjTo̭x}_~sh0@cv=B.ZW†UgCUi$sTτ:? Qֵ\ũ5Z xَ~Xg6,8Cbw|hRнʙYZ$мAz K }{ J|iSdI%6nNuHZ:f+a2>#,Rlg9 u < nǏI76f՛Nt{}T[kY&5Y`/)Ҩ*Bƅe5ګ t OO62,7E 䰮h4vW^0Bfx׈&_^⥡-1݊AѦh0 p$RbЕMr+*[, Ş7_J6. m8Wޟ2VHx;b *!¬3e~lxj7|:FzP` |SJ9M?2}"dp,>]^ܬ\!ݣXUy9 Rb 6B<}`=L8B{_Lh RQi%q7V]s x'S7k_P=]a]7#[Fzb8J%ԃ'rjg6` l+Kf(!L/mKc^R_U;NQ |*OQY =J) P ; 29Kki>QJdC [5ğG`o|-ۡ)>'i1:A"$f J$)͏24 ݾ8#s|*Pqi59hqm_=2TظݚLl4"DB`ա)[VCъd|ri$=&d$SV.p/Ҵu;*Zw4l< ?<5K|9)]*eY,ލs|QjuTef]-'* Lr B" lHݍKpd 6-|_&uv\|h Ř=*ބ%K=?o]nHї馵<繠FkX9e"@yca ^"3F#Fb'7 T[?7{r_"$:x]x6^mYX%Piڑ]-=C꘴ @} ]>/O}<Tئ6 Nkzh[d"K]2;@ Z3ά|^,UӔyoRz/irښdDZGQQ2 uxl( i.m \ R=\.ѭ])娧FةV?7RV /0Ll*]"#> ZoзWM  (2{>YBHQ/ C' |ta]X]*OS*ހg}@P!o#σS7= R]\nH2fGx7JxPrJS ދ]O:%rنBsvT *ô @O 0?tՋf}ZnZE =sgkӗVD慛mKuH 9<:g2%/5S&Zv/?ÍӇ&xô6]t:s}4i ξl0 Eݒ5eln,>֑6^9֦k۬GpGXZ58* H69Roڱ,vf =?̴8 \hrJ?N*`sЦ.ppq[AFX3h<+23l{9̹!aGcp혜PY8ijSKl?=acM$65^H2F"mv 6$ xbIEDH٠T{cžq1DF 3R$A{)ٿ#{WZlBj AOyܫ#'tSI™Z?_G;^STC8Z PooH}n/V &Q@iNj~ =ϛj &R9qR?AJuf; e 0"ϸzO[]Ov?-lX4jQm  . P/S;C?>uɎ~DD_FPԀ^}uRA{5IE(6&WqUE6Bdn{8 jFYC d)wJ4X56d>Osҏ*_cVUq:Z] c? ?Eq{( $f1I.9ʠKY/fvo*M1A^ArnZiNŶC8d)[2Ӈ5\eWDR6\ |#t@_:AS:X&^\+c,O'QlXE 8UNkx:Qg1OUO)_ׁpxl{pʳ5 g+6L%qMM :gKQ[3i6wJaR 7e6o_L˴qj퉶S!{@,FeEgUO9n%DJee$QU%$|(y 1N _UTF#›ڗ{c V2=70hOU"غbr1 %?ҥ|N/>)' z)$S6%w(_XnA"H[ƀCb{!!҃{'tK*kP۞YqjR rjV";V9X 8dvvSrK4uv)jj.%6HF_ y*ʰ,Mz*>8z25﬽2F>H ;:ؕNt}d(y[7ȷ ڜej2x7 &3 'PLichRJjz%fB7U.CK̭l`q''x}iS$O+uMV-m,Z»K|{N<JwںE=˒G"AA7Aȴypt {aSnƭ>,E%Td,ײ _vVUhZlpW/SqUDl3lGfwJEN׽~Hj(5S"%{8h% E%1΁~`Yݸߢ4mGҨ2EGt(N .c댔&֭N-#z$tZ5&R~߅zA4gLA_`H8ZI&ZEꚖ +՚BqSњ>QrSMD0[" ZZ$ 7t- Nkť7 df#?.%4˚q.XvCwZGI gZ;L UU?&BKt+Ѽ4Oܿj3L_QQ!vIҞM{J@Mrڻoۂ4ĮC%YhcڏC=жUT.z+tUˈDIn9)cX'iO1Q= ޚޛ9X' PXcn[̮l36G7qX(kJr&|-7*+t~&ŗ!L+XZfBw~ؖxH7=QދR2݁hM^ts3EY\괯LqP`V-/`h>$]¯)j{!%pm2hOnj`!.! | م,@6_sڤrwh&r;:sʱh,5'Q=i{|j~p3`LMxuV"lZ1͈V$\#fyX+ Bط @GKk=kRDOK2=ۀ(qM*ZzC`(4@\FGN#a k4߀:(GajZSs51y4On2acFE(3OTɂ>O׃F2cKwvYNϋn3"t jt(w$ؗ޵aVI}rk%݆bj2kLemȨNw6C4lKYUZn^ER61;0jvV8k@]`7e{O*)zq⭈UEY)X.W~c50i '6bawǰ{V4 NxlGW0(IU'Z!,x_4W/[s51ƅ3Wx"e<T׋>Ou+z[N݊_rwU+EJ_X1r\)ЈD`g>TSYB u꽖IT&0";;h=CY鋃'qXȁR(1xb6,Q _0ߐcԫv.^Wni~Nr%Li?էHu4]ν:,IlIH-^5>.Փ!4(P'n%;@KȉG¾4ݹ`4ѻwl~0Ç!`)':I a5ALBx(Sq@.mKh݄ =j5&Q<GCj$q{&)uۛx 7f5GgA {/)Q,\̩]IQ< 'Lob sIJ=ZC?sm(XtPDN$/]̑5ĵ^3ŕ^),Z۳ hG~LF"LnlBE- {egM$!hbhJ(rauc{<%IܩvAyV ^#F:UB?W".0.o-yySAZ~2;GNXI- w0Tљ1s(ηe:;# t,n{[l͙f9q\ɣ~h ϙa2+8dYXD/dzY<#2y [;]vj 0rXo*GH}n0j푃Hd/2O;u P;1aUZ:"0CѲ &E\b~=oЃkNFrB2AMnn/Ra&pC춝}`kP?t4r맛;mSl0_O8#Q;CjXU~PvdW[,R*҇~,Y+Liuf=S/"BYg:by_UDt[E5cnY 'C2XxFxOU#H)Г9B5չӞfvW0@B >0 YZAER/data/USConsump1993.rda0000644000176200001440000000066313616365126014522 0ustar liggesusers]KHQ?g|UQDI}_GgRP|@ qLR\ ]*w]ԝ "Zp_,݈ nJEWuUąxgc.|s;sϙ;2, fa7#ҲxIۉ@7ez'MKq5vSGPסB4/вm} sFh|gHGV$wy bJwߡ_߫>'B|1_%vL=J7Ucy*{O>*7踒츖VeقaeP .ZirL\3w';RXضUnߍw=X$n[ZIXh*rK9 IyBfduwRY;N΁rH9[RuAER/data/USMacroSWQ.rda0000644000176200001440000000427113616365126014203 0ustar liggesusers]W Xg 0XGqŋSEѭQUPWPktծZ.j \pZ<(*"MOfޙ;>x11r&WS0)JE شa`DŽ4CJ@ʎ "l'5UT6<$A؎ԝ5ϒ8%b 6^u3B 1z8^Fu"f /3?#? c {Qe@^MJxVLv6*䟚JCS'T8M_ю|m_yWE[UPZ.gJ(H%Cp<0@}l:xSQz6v8%јW+-\/2>_kS [Wq+W\z,sv5j_kuiȾ5&.fԧƼدDWC^UI 瑟Y5YMz7UȫE5^诙y5ȯ)&1.[Ƶ{q>kħ&yqX 7YMԲɰ^]2(މM&#Oͣ⁘[u;8ա/:qط76:/)>^6&s+JLSE22:yʻ۾BdUѣ΁oƹΰk\g<%l!MB|pXҼ:ϻFq./?)75س_Lk*}WZ "\o9=lp}@Yhkgx?\b9/]Z.mOV+3B>,_yC>|oFvau㿏1Փݱ-+ ܩ{·K(A8n0V 8 ݒaNTXuog7CcCjhLxϺn< RYuU}|X(?>rHW=t1vyDͧE0%Hz0ގsާi<{`NHdr|3 H~q M: j6Vw;(z(]Hw?;ʬHfq|8L5ox͜* >w}_P,.5CM wE1(l,1XbCMa_oJ/Uc?r+z#675)6J.˰m +)q{Sy^<;*0ĸ4vZ _Fk5g03&Ilg]X'yqѯrrY=T8C̳Ǿx$I9g0a̷v*`ly>o αWi! =97 x7Ia,W١]iN0)=pnȾ@|]*|Ua5|Nb=9 sqcg{Y゚ط8 /KߘOGa?2qȣ3o~C?c? xg͏C_OaMq;潉q~Ǐ}g6-΅L>E4sF`+1[w, Y޿bWNB?8E;8t˫.T s%[(\>W6b(;)KܯY'W vDlj\Xa cY n5kIWlqjФbj 7,F o&)Flf(GIX&6w%\/dfAeLda=UP~w7 )˰Sr PKӻ }?_:â8gUᰉxݠ<&&k.01G/XhO4ZPGFnGlD?HZԀ"H`w;Da\4pfzR;}X94vII7 /xp*0aK꫙׷jj;bŢQ",;5yG1i}aW,B#5}<}g+"UЏnbևzk!zŤZ.D͔пS9;䟌Xpkozd7vZ^vp]0P i;/F h3!=Aꔣ%D}tb-ܶK{1}F6] w8ng0$<;g\p" 2E$ߔMHTrĹM*T 5>i@xrz6v0ͻ@z.Zaॹ ł~ͳT#..-e$œV'n!BFX8=I>\5Mv2vQPj<O(h^XE!_9'Za?7ß%CǬܷݛsw8Ƃ1[H7 GC'zV1~IdQO&Q LJD&;-jY9f:MŨUK84Y8tyI& :o.ma0݋3E3ߵr*?(f ߧ ԏv%fX \[~j˱B/7?Xtɔ7}F~,>[!W.`EHYCC$(86T2O9,'*B|&$M1g=MHFTa;JclL':+{ s*H#l+ bh vi;BRr**mskP8&Jܮy.3 >Loc0{la?;u1vYWZ1J]eGaJjb yz,3 K ̲תf_.RU߭]q.<9r3`lVC{W&kSzlx ,g"[pz:zq G^;˦~'~ϞD2_5?矌JnUDvbOh1eCPvOZRb*)_ &FNQ'dUԶ q_sep脺 ,ґB⿁mJ fl`m7TL ~/<[7{s< )tcY b][^ TA*Ţ_ 灦AnP,܏ vTTp  KWU73"J <햢Q@ޞwݗujVL5x aLp+s&(Wm){L! =՝66p /. )'?V݋?~Ɏ\w%uنh:s;MrcI-Nv &X0P.Esx#OvŪwa^|~bg-*e%-:sP!44.+~TsxI5mgN:ͷmخK12 <\kQ$S`T &ԅP4 @\DbŽ^ji@%CPe,w<<džZ/ ?5޹`L$4"ei^Y+%DIzlzˮ}h()~c="T‘H|DY';i iƾ&`\|6^{w&+SfS?9RIbF䳊tKՏ]>tzzz9 n! H04į_$_K@h2np-cxH)g1i b=<^ `\ʆ.Z[2(hW~zǺɟOOz!kRYâK֗Э@ěo%TArbf_W828ȂcC۷ (#2`+iu  v0\Z0gaJ = A~=l=rC2ԝroQ^~ei#+LM yz4əެ(HXm /@pxY 5T\Ч<œ?R 1$ ݯ6X4f}^%m*q8%"ĽfZ f L+}Q;mvjVj)P?J 39ߢS rHNa#gu l%3ʍDХ b(| j * ]b8La\O+@\.zsfkw1jJlL{>v]<_u[uގM)]Q0ZgR!Q[ cY)gyW\23.X?K ،xl]=@` ~~Mؕ ä# }B-襕tb˹Zrx][71!`r\h"u&EHZ)y/ww>G/^"O *01$ |ID\4~- s*7)&H&KYS_Z%xI_$2 )Nzd1Pth,L& Ac;5vՆHakX+l6tVr>۔s昢[ZSLFp''+ͽ޲G3naE׊Ѧl⫪T ֖ =¡M6<١D鐸)V<5Q xM^Qȏ k/"Ěk!X)˧#~ZQͶPtYQ̒HL[ !327xy`V^>ˢ1*Z{F)4otXgu,_DؓSU]ZFl@%"~kvl̏ qutMj=DRHpR)V~ˤt2= eo}=ρC&ŧC=v ,r[鸿K?˜)sg<]#pG89"x12t_5VyL%Ji/\%`]Jұ* / -Fi7*JDtT/)K+tMK|L'_*} ̀曪V8H:5ʯʃ~+Q9I {qxss8Ν.Xy>hꜳrrߵBh~v G'sk).#qT*1&{$t,a9:Tk?.@n䨍x?DWd48:K>U䜲\WG:jS~ʴMXk2%Ĭ \*WA#̽Hl[`8 Rx U^c5J $"Slmׁb"1oPB}SO}̒9EOkѾ+"`Z\ɴTD0tq}U-;~N+4 ҧ񗤇ʧqvr:M&,IN~D;?:0~"2΁?bòYK5:I@^Ϗ#-A=A,\7E {+GhZNr?pl#Ą.}`3sm/)A< qyJ\Ӓy絥-sPo Z,ڏofRvhrN|vF /( /n@0#N0ohvʉѣ;{\GFBs.bt6w2 -@_Aj㩻{0sf}e7b㲚Q茦?P?tQ;,{@N-w#f-yPÞ MĴf$\L91$YƩgtfqvnR_ h;p &JNucJ5"-vb+J1лC^1No-RTQQHDdH;Z PlEgްw0L;Ҹ#٢>yTw 4\j)YL}7"Ε,j~o=7N%ç/誅7>­[L ͝ DK76 el} &0:H<_VܟB0 9ң0][@&Ɛbvn)`< ߌFr4.C*@pl0~+,E8]՛DIzzᵓԗUnD|=)VZ%{aж?؈1& {{QMi肫YQR0*u m"nEh@i%X2s"b& 0:k!Jjf!iLAeu|{e`mBӖ];.ֻ䉑L'Jq[fdat܎T`" 0N~Ƒ\i[,8SO8h;JĜdWFbQ|m\Y&гk ׽sb|bKiˡ*H8pϩByja<߄jΘ `Ī/4+/OGz^ik!ԓ+jH!K_\aؤ6<ǔ q髄=R ⩾ $ĵ'>P,s٨ҏg.-yĿ;1ɾ %Hm],铖<%rQTwS 0}Y33>s(Bħ(-%AY_9M.=:  O;NRxOPmqn {7$]XX Ȕ b^y 6 ƌ"ciս^)9#C|umgl|ep?':N;I>憌QL^%Dϑ 8N 1_ )}4d;Bѹ>$K^CX,uge"J(hUQ+T먶q+y2tR>cӷzSg3%*$2!!@\Gm=% YA}0[BS\"|桟i]!AgI-T0nDģ6ݳ j/psGvUqVXgl8Z6Ћ:@/2J7f7)vک\3 p:a(7Ĭ+l7V) EQ_U7tX/I@_ˇX'sl{w*1SU&=;F8O>/0l"dkϖz1"ė(A1NUVz+F ,@>SWzQǮEnLn*&W(^'f^c%}rōnLҲ<A1˹mjw߸`z~G&̚Xlo=2EB {VF>y\Opr9M$('pOiQ3z,na)jr=ַF#Cue]ѭ"o `]CPKj"*ZTѡ BB/ [Y'Mdʒ?Ӄʧ>ǯYOy@5ҳxnRwJ]a~:^I˸|+N۬-ѴtqrfUz= #P̈{6 lUIK?Z(@^M$Ev&H0g XY#X,)K27êU~Uȝõ#v,/u}eA=#nFpU AdZoi,}v N-wEQ}{uD0Dȍ*qtG!͠;:&xT:6 ;,cfm=S'ޱ=7J:qWM]db#rR`u.UE_DM@`z_%5yḲH,qU45'lƺٯB=}Sy)r4=mZ1/P !8XB9ah>b'/5/Tl -V Go"2m-̓Vo^łUُ,3^ЃhU;g!6WI)!px%s mӵ?5=M]*EXC?z~ޯ5N!J,HZ$}ZΜ#$y]Td^.{?{锭ޠQ|M"Xk P\hr/&RdZ|j`2i]N<˚Ʉ =$R^`#ZM,`NI7Uc~v؄%68^J|vm0䋑:A[}-Yo>$ /\\ݸ);BZqmʢg'{ H)V6Niu¢jϨ{l3WKT;vTsZs@~ lu0Wr@_: b}4%FP_U+pB`4g̥m GMԢ;8 ghD1t *bQϢ Q:x7q;etl%mn{{pHq+^ӀEuLH1 ˬrSxlڟ0aa{/TEanms0{{2L]fuv^Bz$RHm/G b8/y;sqXZxyq#.#́E2LX`p%Q =\q'bry<~1tJP F|AVKaW  -7;в̥Pڇ M*nud>$QSnp |1 K!p Hs-nWv蜌MX#D#Uh[xgd^ET2_)] m=$yD&ALԻ)J0]sl\U0?{.Ƕ+x{ݶu@,7fq胵 Hhޯ;;&Fڌ:% s\Ύw)XYׅr FsQn Tyɂ<zF8ßOe)-cF q15çLrG^ݫI1ҦZҁ#(AxmƱ풳_Uhfgب4˵&dh a:]v%&)"6Ux9pSKM}ԯ1lt KƳ9$6rY#k:R0b ;~8 M@FX+d-+$W.DT4|!""ęY)J7{Und6C\G pGeP@&y&_o1+[>Nx?ب5PAyU18',o<9rE O0@ocgr Bv!\<1P]k-U{1/1*c1uAn 3 9*[5t" X`4]a8"9<Mf/b 絫b^-ntcӆpО'm[i #XK}\qfpGg--_|qbl=9h@AZ3[X L)$>872,}7gQ: ?KqD>];($=C ѻ!*GK_U7*ۨvHcIfd앏-Kr)qk#/Do|w+h\k4t2k: rM iA?GT;]tZ]u(XڝV3ѴÒU%jE4v𱁫!EFecq⫎n4<:f?Vm|?^57)0aM$Q{K¤68/4ӿ(`g,D;].W1'q?r6~*ꑬ ꤷVN )1w1HxivD?'tnmzLmq[99xeLX363`U _<0zZ koYJC!_?fvciW"C3UO 'V! Zhv'p^+6Q(~%Y's:Ar!U-R({)+hѣsxF'*--T8#G]t4qO H]a#l"In%r|a,aZ;3&TS8=HV/v6>vPܧ`* 3ӞO[ЗMTQ> :N| /r^A 9n~f p2 qu!IksZ5p;MIsŹ'@t^HNxu% ߭zY@3$s!S_fGW < ,O⸻Ĕ: Tv-_*q>E# #-+ Ƭ2?cf>J$㒁1 M6c$P汐pd?`ҏ5Wp@#鸑H7OYMDWD]u*ĩ#$$+ԒԅNQSQ'Q@Ki1 L66l8DO $ _c 8% K&LtX/g^K) ~TflX&\N2bl!7,zI_z56pBAFĽFB I3圝R{ĜDʼ%&QO޸ m Z*3u{Rms*[YƳ88tܙ; NVHKo›@!mD#㜮 K])Z>칙ؤ<8_HD3*lb[II}Pɴ`PU% =JhjJ,F "|*BWoT>% Na+g_`Y)yHt}F=G6|_ŬUm ~LԁgTzWT~$hJ|ӫ(\X9cK{h 'w\*y8yͅE|sf2QǐUҢ -ɶ,iZ+oC/;m}$@t1S=1|edڟ7;{צ7)X vڪPI@RUr,w'pk S;V?$7Yo*>wN7Z]!U0G sT|CV̚:|-SR!>bω_cY<2RO qi1Sp D>JwgT90+TR>vzZ.EIy%!KingJvTL=lDǎT L**VpIO"!YL˿yDG_S-y=YP}w% HLcFײ uS7 :^$p׶슕MD4=o2yT@T誒Xi(4{^K݄mz_p"O50؅υ<PKqv7/f eK^hJwH俿\w8]yFqV ai^XM*_`.c]3h=w7La+GqCҧUt%t.DQ>zCphРqiN]c 8::wkeoMYuU_%TnmI,kv)V6vsny7h(@wXkkf>T&$OfiGAs/ N87Q*$1,\. sYߕi_'"DHHHDmUX56pSǻy',8qC#q; (<]b;f@}ndy*ʽ _>3Da Rk - חA2XSZCGnEbh ;ڟLll$,$sNG|ES;Twޣ߇%),u:sP\L-8ϝ"h@Lh܂a֋kޛ<`Y)Qǡ`FP`QEg0BlhȲكjHw1Ҷ!ճ~v\cGI8"Z%gg$IgMLl^dkj.DI7K;ٲ_yӤ;Uc`s8:4q+ӝSxa]_Dv`V[F"=`)p W&^I8:RťyujQK0YgvN*`\EB,{9jUC33 WT )1A8NDއ]ӇlqBOmX8^ 07u`5윅(9˖ȅG6L;rTm?AW' ·ِ#"􄲌{ox/D6F+ 8o )vÐL?'JpsUZQyel5aa$|tLTF$'nVNphT(lRmI ̥LD3]XZp܇j/Boi/G E #+Ťɀ;eě2-K1RT7|`k@ΞeZ- #u 6O*~ q[Hy8|4N,<U|yn_ѻGR,eF3 rH +^EIyZZxMM A<Y25^=Q߈h;SD[۶gJw>muO,~+6a#wF^7+9VM# s#T}xrL7GhA3?kPS v/g*naNCGv6<\ Mِ sTwgbiBj2M#VX?"26ȞASiN8 x\+A\:dKw_gzg. QXo'(LP\Qͽaf>u;oۺ=/σ0`<[c935okg-U`6-,OZưlCU SYBhciqCqw);xȫF=jtӋxo˺r1@R&6 $ 1A15A0 2`Buʇ@!ƦPE"u<RaD]0=#a^='J]#L VL G`0 rUQѣJ0\cb4Cȇ:Dk+49䈃K@4 U-4?5r2Km;7@XQJ|f.Li(ػkږ=@F>A:X̵{?S@TzWJ*02 RUYViKE; K'-%L!TOwP &$]o3&%X 7\{%)$ɦF>^N},n\bNw˂vIlɆv v73~ lo)ڂi # I_Tv?dwQ6b1!k,dY:vf!~Ms:EejQٍB]TY[ulkˀ.Zs#'0 wCeͬ 7GJtT{Jd\cGwC 7ۧEy/{/S`;HFօѺ:tԃXkl5p=p Ƀ)iogRm. NfD yNhZVM_KNvSڄ)Z0teFN3k#g3|ԪpZhЇYz(!f_x"t)֥s/"C>yJuc&wo2I &wIUGXȘ6`m3`9XHMőPB.YF0(DXRk'9~ox-RbmUx7v?7}zbvhfm"Y=KS3*.;?gj 0aſ?ۢd2W>+[F)k1q@:s=oY4hݬ6#62N08ˡܤMT/`Va2iȴ斚ΝB7$[:$:ŒN߈_,=-Ǟ|MZJMxc5a,X ,Po#^7!x7mZ)`V܉x>1"X~o^ٓpΝj|l1dhVjP.ȎƩԡDd(Z'Dzn2&)eKeQ[O[;W(U]HoS!xuoIͦ锍 t]lѵ.wuFlL1 ĩW8R%rK<)4 мf |2t c `.i+@242['%©[Ow,߱7fT-~ii6oчG5^+x{n3Xc}o"YE)Rv2,3n/$ճ㣈#, GQkReʽũ{#n`?]f8NKPj91SU@`mD :qH[٘WC(58F/k4έӨ#F,y~ư^l~XavG )Qn?QigS-IIAY8(`VJ{Ѷ< !1e?l1cW\Ac害w2E-b9? UO"% 74Hq-MiLkb]L)j[)#/EcZ/suy](%F\Za=N<.|[\݉.Oݼ 'Z% !r])],RT QQ-U5~0(܅7o9~l=`(~+f ]3*c7)%SA?+s A|63=vߨVڲ87:.J Sky lBSZtp.ӷ2BcaP+ϪC!g'ɵ_g,87C]Mq"ب9T=CU;sخۨtus- qNE=Z١SيV`Sb6ԡK#ݕcx~Yx dcr;7rI!O Nd9+R~ΕrErL4YCCF+74ёPdb,pmTQ pm9870͞@8~S5Nbndn"`*hɖj[F4-/X$0AUt1tAG&69OoF"!8 =m.1+&h$elɌ7)5N.Dqs\4fN̞_F*EV;0b _+,]ωu w8yNI.EȔ.$ c.L~%„CFhp(vcjSPdᑘNT"+^g (hgjp?0 WC8ǁ=L8̞j{x>t'@Udo*t08wYTV>ZSMn1g A ZO$:63_4n}"h̪"ֈGkrY|_v\~R Nh5;W/KNP> |! C\᧭ /ifCb/o{G'Ȑ<: HUU)3>$cx3{ +F_訁| ["{0s$D톑g͗8 '&]1uTx0RO=k]pgP4֨$ 5ݰj}}بCm[_']Vr#ﵡ.ƚʘ̌MhVOiaY77qCK^Vd'3H Pq[̀U|>nE+!bX]p6<; 8LVXaw)鄇`N;;L`| R/A, +)H6*.&u%sTlrɎ<(uR -U&3GrF"9V=wBbX400O^&cd?2%^'ܵsds6+^0iTv;x.<)d*Fq or,:pQ E+@!_N,n gWX2~MC76SkӜPjWWmWr2}fbAJ63rOǪ|f9O!MbeճD ^~tƽTx D{C5P&,c}a"H^rqCHC2I[Q?+kɯ` / .wϖDD)%@37F0SylUyorF`5%-mޞcexdȶ5G%k!ݶϥ`8Lӧ/ 5 H ÁUj8nu#VS6Xw ;D;EVGTCx^&?"dqP(=:WڸhI]pf`Ӭs/=VƵznIfڦV)l܏XP@_ɶpPظ .|pcC  E{(`4-/LXE4uΥ;x2[VU7H'Vsqr4z:Ͽ ξU<~ސIӟȃUlDW², ePV\B(~T=RH]ԓ,}ز}uxl6z);<{+D|Uwٺ1uM!'W(GHA ".ß7/SC,V3Ŭ{ƚl?zTscA*_녯-7SyҟD]է!^DŽ-/S_Ӣ~zBYg3;7LV_jV$ J i3Iߓ&t pz֮pE挡xn@ E)Et` =9[ !^.*=uCWZ6u,"ߍ4wgiv6(b iRVKcD cw+8z& QX,L[?0 fVjxVLI=D}?Q8+`4ܟ4yL3ZAS#bƪ7[A m!s~6NTNg4m /&t']|eSҍ05;c](tC#PuA=M5!FAv BesלӰCA R[5:y6^;o"9mZ'Rncf4ܥ ^OkXa/,G2+xJDuu[c i4tiFMO?^yl=7&]TKNKe*h`7C lĥp 7$vA^5t <5Y(^a} %m62ɩ݈U ZԢZTSm6%m4]61bj)Ӥ$u{ OzdRBgx'ÜsL%{/\YHJxB}$wG^loGUK*wNx P,مVDրl̺G|xη]GosvVU}ԁ {@ $g 1PRFPa ut~0iz50VN}'E-9bkP'%687eNJՀR;?!XK+tFß|Ϳϭ3h-@ʳ@XƔf ^sb t[A"/?H$ &ָۚ> Wa]rs h.z\w@yi3SaR $0By nhq,H(ē+A5`wɑ:#6 w22L/$[#˱d m>&?f z%8o (_ e+9e=y%n(p}!4"c&0Aqd F,q+\qjEȶR@YH!ygǂ?<6n,+lm@ބ}0뙾/2D!lG N<ܧ=t`Y[r!(>H;"N'T2^g! 'w̑j,q(w#! Ɔf_v;JCL'/-&[@bf;̶U#$),4k+^9O^M'y>r4-S^[̎P4Ì$++C;#-vD/˗ F|Sׄ1&(NDT!(Ve1 VU֑'rmnp/s <˗VyꂅH{ϋ ZNq|/2G9#=\8^>~59T1h./2|D)J*;A߱^ AdDlďp YR@Wg/ήճ5f0|j)Bnt7 mV; J-.Czп8CM,avfVPzI,F)/wCXF-$Or1B~8 1@"sS'כ-3~=ǹZ0kFٕPe摡'EGRJ^3IlUBr NO;WBp/+_NJܺ3DéTwm"3 t~=1S0B];Q`Lǥ),:}zvu7SQX$2;?؁ݿbaf+|B1VR#`YMx*!EvK9koydN Q{ >^mʛf%_P+ϟrA׿B]߈FS|Joo`% &85]:'}tZXE&S('c`%FpC˽Bj\GɲBۆ5{Hg!R5xiQd6p)w C3 #<sb댖L 텙s +b}VM-flߓCf2{yz_کV&]s;4h4aϤmF>25E5 ԔG)j:[-R!',Kw PDDo~Cg7vveŠhP<[5:' d:/A @[W?*$#*TU0 nA(I>Rn}0 b=B26S;b 'ٽ鮆l(A'? `s¹U' C;M;cÅw0q!VQ#jj9)w5+7h#0dM}3]t"T(/c?sT<֩uerƍrhR<#=+mfX<Ί2ºgdk/Ohuf- ;*>T>Re<_aSkd զDEH+f j/Qڪy&Ʉ⭼-E6V}Zu-gn7z0 6/qcHȿC>4 ՆŜPjxb>>+.KM Ki6˩6:NV{kT̾[;6xꛁc/g#Qҵj{! F\i^xcM2~tA,`C3'@E0Z# Hb{qNf wsE(׊| @{w-hAI*d`VF37qj C׾ sLu?Z2OV+NbdU*PȀ9[k?(5=;&Z{4W .->AC^'QNdccw=c߅ =8J.)O=eХb^ÞSYD\آU#! Ai/Cجg^q/]ޜ_m<ߙ>Z{7]R3D1͆ԅک^S*9&h QPCU1KUw@O"Z0ͩ\Y_Q۬x/P%q (S>5F5(vPZtkiu⿚D400F/lZۛ[__Bp.dː V*&Uʠll͈/7yFR3b^i]kcF/LS="ׯJ M@#INHTR$I$/r!vyW&5$t3<2clb|R5 3S׍\{;}:xݗn{7ke >x2@ XК$kKh'r\[zS3; |r0lL@CS_:!i bSpwo,I& q22,}q~E ! Iz ~}S!'%9i~vRڳ5S,Z%dC\vS嶖ECaTu" h*8JT,l' oGm.bjPpL3b}Wc+n JK`B$9dp'uWVbvY5ZWZs(>8N.3J'dGwI9+@+pyQT~.:Bgا-,9oVYqCz4f )ZT9X5K2bPpL)x'STJ'%n*eNTx6*dޙb5ɡ;f&R-$WKz)Z`N, E5sÝpH.~NPR,av_LT5D-T*~=8A3߂$3<&`xL귣 X{45ROijʃgyI3MWLZ";"+-ʦW=qoPyףꞇI>%3DWd#X%'rH4e Z,ޚgլbmQcaY D \& 2[ƐrӒ}<#%\zpc72J*ܿxtE&׳~ ʘdg=dxG$X!ަOþԌ'SX`;J +S{1߿ T> W9\I`0tTcMOYP4e[Ҥ&aܐƖ\Ha'ܟ>1ݚʳv֤aCB f!g@;s{nަgVP>xw2/KJ_o^c]/)HM\ ғ0<rl,vSӱPCr XҾo>ڪF{DI!-D9NRm~M/]]KR>f  hGc@i綕if\!$`vum ][Kj[T^K>sGlzJ{J!M /RA!HzYIV!{r3Ker× Ƶ2w#K,}N,d1jWȚP6 H96Ti@-d Ee5[\N8ۇ=lȴ1q \4ᜪdсטﻖMUa_Q6148"9]@Xe0Cyqt,px6a>ډ#QU&.^xZ~,1s=|JAJAmj,ć!/S$7mtgG,+'#֩sy^•;4"mXec?P8RGݴW*8YD3H](ma%6k}1R$DE稭FVR#eJLQw x\w?*J31j RN8.w7z^{x^ ԎFM HI0.(n?Ș7ݑm D/r\6ߍ/@_ x޹zVWBt9ۤO\ReKF ^{9qx.>(FW^QݎQFne1T؝mf{vZڢ q}sp?( ו4S+m|k/(rrL;XZW mCt]hcn#f-㾍.g3N_7sL&^؆/x囱_n%~̸5Т-z. E0+opa Q I0rq^yq@D_~d{.{W,7)hFF6g(ψA[FdP3JDNİ0Up]@'RZuSDdr|EnfJ͊P㣑0_Y~|`עH"!8Zoܲutnhwׁ Xo68a^Rj&dqʿTK9G=@3~#?9m d{eˆ_^'gE]L^4@t{W=؋Sd /!D/C} ҧPKij*t܎W.H?Olŕ[!߼nBC${4!PbxEq@gģ"88;L 3vESE7$,zhп\D}8UXVf,^2 MZSҁ/ppԙ DzvJW](zڕrw:8k#:r۫MmM+;G!lֲX3,{=l- jI9w\ tpl%\I^"&"5tJ%*i;wv6ajZq RT@OkYn|证UO%ٰw! ܓ+YI8_).?K0QGgy]i"v)'#nYr*"37ρZp?+|q{Vb8+;~%WO68쏴鄏/<1&A;Y:.CibbyPѱ`4~x3X0d.7LХ6Q06WD$ȮҼaJtulBJcw}3){: #>m0Wx.3O_9Y·(zz3⡢_)ƒ}{2 K"~{9>$fkN^J4"BfH&:^whHN8 Y J$/K0+o+y+: V5Z⌞ b񠢏$].#kdsq:+ ^FOm@fؿl#}3<x*4r1fqARߍa26d7!]g_ՏZm{ERe~a(_s]-bkn,u20֜k.%zn.90lAy{8FߓIҥhI_.-[ cv9T[E2n\-z1ՎawJm*`Ł\},whMU F hڱz+u+x: ]6 BDȟ)}ݕcLM>cؤDJ3kg=_1`8"iF3)v_9}]"&q]Б1Q-`ǎmҥA*-<'Vfx!4DjɲQM?JxGW&.w>8KAũ]mGX وg{OWr]Q&IaʯbHIY{zpv[{<`oBcC'#aMYxM;O3 w xS44ʳs{FԀOk{3YXq&o_hF#1<è\ U]`qٰʿa4 1Ӻ-RG.B!TW5Q8bluQ(c('\7pbX@H^*yp˅1ZR# t+i(.l2X",Se5- ʔJ"r3 m4Zh|SUɊ3"ΥL?#ˏAYevNɘB޸Ilĭ7.|&/V` "}Q gh@7?Pj{uϬXP J,L}ʖ,35C SBKh8uN.Բ1 }jJ>ڨV䷚;Qn [ů(.SlO1{mrVh; ׹t|nO"+umQ@C91]nā+FF:R jP* )8| //fPQםWdi,cfY i9n #ߑS+Ou+]<`@z‚KDD-%DU†cZ!SID=7/Z<_hnR^%y3yc:)]@6|b4S&홒\읏@~cx6#g&Eg<1#࣯>C[?Yo#V- u{]! s5 b Ź"p 2 4󈯡ƅIsi>X| ;C JWjԃeLX_T9V% ҧ?WifZa wYyt5%]iu2dH fYA/d1UM;װaWH<Э&`j6i?c͂"{t3 LhK"f0ƚ@%搙f:2~lUX+/Em=Mkw-8?X'HvCSQw"'?(pvGN^"G]z8 "vdآ wuW+8D #Dag_P Zut?Xn) GϳԺsZwv_Y ?SO}t})aE-=0L Rו{Əg@y:HwġMu7eBmPd^;؅6ړx[}l%hx !H{fi_vB @Z.RP8ϰ cLck3p`a~dPYĂM0H5Bw@PZM8G`KDܯ=PQMPZuqi"\!,j)iWY.8T7К[ϺHe!!-H.rM.xp T/U(Zi[Lb]oӸٔ[.6 $ycdSvuo\Ӧ{C֧y: " ;D<ӿSL@E@Vj#l۴𪌋>WwX0AS[d^)g_&K",b;h_Gb&LRsoPCQQy..7ޱ_-2z T_CknIc{o`pIhXaa1QF&$ ]) 1Y~ψi5vAeA73Y(z\ |= $Do,h `8Ƣox רTpw8y{PBƦ ?Q#qӛ7ô((5@"yg!Pۥ34:%xxg9s؆cRR46RaF8?HĒ37([@=OAaHbNSB8f1I5~ʺGMSP$Iy$CIO2%z3NJ =(y%N5MZZl\1gìzz\VaԱSo&iypN9i;ﭡƅKu7arKndSvCȠyR*r.h4r.In|VĵEp/ ]\)3jr_^Y60ZR^Lt ]2OӫN]K.c {WmD kzIב ԜoPQ ! o4_gew.>RQSG曎S+$A5/.Qhh Du\bq 2J rkcsPPhBAg-쓞~{ 1eEUH&pAU4`#yp^̢e4[O$3 - }vqB-] ~490RD)68 l~5:z1zFo-+0pX}xE_!/4ejka+ XwHma>[tx;f fØz$Uiƒc $=nO̭]S;PCJ`dr߯3ІmdRN/&}`Ib-y俭Dnt0 nɘZ˷}n1[^U$xa°x ƞ\d<gQ J<ݛd< w^8 ZK|sKQ8bKՐ`1t5&-r)^;@iH6-C/vbo73EX%7!`1NpT~R n(92#$W:"d̉G0'W;&d2q|fc`K"Ó{AΠVtiGBN-r ]-9t{2;#oQosTlcDz Q+c*z:, _nyW?Aw$98v]&LuETQ4/S,?MPnn!!"FL jLBPW4@772H@{VyF,xr.DLjeXXձFZդ9KjGqHCYYk(m9JZgpV\*Anm1+0\+&IXန.?BkV n@8;2b\p_W_"Ѱ-Pq ݴ?ywha8y8D?G?~L]+%DKUONug+O*8Gt9A#Fl b%lGY;,4ڛwNCwb14ڕhiSX2Ɋ"O!FeAm4dȃ~=6V~sl@N-+DmzJMAj;A"GSN?)A% )Wr5dEUO^u9]s9: sabҀ+#*%LhP^L0%R6"KA O172J"ɬ#>3-姿J6c*aKCiך8}N3Bb1DSe}IRBH^O.=kamKO* Ee\~B06oUr+L1Ո{&x>s׵P*y"Xj'˅8mSƩk#~YQzgLü@a/%& ^̵MDkҺÏi#M5>~;3.m5o/ÑF d <4>60ϣCoӁA&̳bLأJ?I0ss.,%h %@ҡh+_&wXe*I;:8ɚy>ܷ:wGPH;z zo_-g^2@Iܟg^/YZ+ u#1$3FSUŘ| ~YK'4'B Ѱc})}zR>Ge~דqʕ!y6Ul\ML-Y>y&HuE+|eeN Ng0jvh@є8٬XhTvAtoU:T՝\PBPQd%s'N|df\"J`v/@@>(/\YE=uc+s~dzζؒʿx^I΁lz7)U;o-j H+g0ʅjYL۷mBV^ҚBraNnƹQ^t2$c!6Gv1h7/j. DRhab0]H蚹PP;. mۈg;B \ DT&J{b/2{.e KqvY[f`0|l QI-Gc`Pϳ6& (dri FZךAyf SECjA'0wpא:pe?wfhhG#E;-G{+u; |eۡ\ 7D{P٘) Ҟς L'?5.zL/.ON3۾pq`ӼHYo*'h*|zݾEW UX(}jJ]=IŞpZۮxXM x؅N܍K~Ib!b?)aO _m~~3S}σ U ;BJulaZ͆a I/s"`&fK^&Vlܪa6͡sў';#Wٚ+YJ)tɤD= VIsh-:`Z րP= سw;شGߴ1lAnp٘' _RE!a8NӄR'Hf0רhp00pD@>̩+Id`6#ZGѺ]&dE)q7J^ZDj,D1lW6 *a{?qbw@@X&.l[p#$yRHu4궬Ѽ&8D>20#GaQ )DrhQU&j4fc0؃#z_ CaP:m<2R w 1}L?5] 1:A%D&3?J$ 8W:]" EݐQRwvЖRk=( Gۍ++6U#誟Dׄ7\f%&8ۻv:wnk?T伟GS*QsJ՗V}PIRiOڷnQY7'xd2 \ G) +R%0v@J-vn$)Z\6bgLzy;< &ح o V0f<& "SO ~T Lnm+a%bWcLUʓkmhP+A<,8e jTް#&h($>>0-ż0&n:fYkQs;^moSR|M1'x9͍&S=)H/sXk98nJ'Xgk2 #qT`C˧rZpVb#7@ ߺkQYxMT,=yX9~AflܚG"u8J{}t'hWl2'G=1ʗPџd+B(iX)rfreQQKMIpXU17b쾵v< Im-ORv{#x )+(i5GSڦZImY2U2Mp s;%P PpNtq,%evJc޴3 b72oŀU*8sTbwJ⋹K{T}\~,Sp J|Lee1(NF@M^VqE֦z8).eT eXXD'h֢)uY;trɿ4V+RKcO )Q;RYɎ CKp\C]H$*|oEwL4Ơ9;Y_?z˼0*_Σ\@8WD \˨mR (G(|64V!y_G=ӋC!߰x,)\T4)Fm SAihQ]f˻T?/,t~PO9UXor1 ~@M~%e{#\J\]lN;!$"f  6#sKa$?uaa}Qˤ^MW.4ױe `̿}DQ. UrNo03>EQ*/}V3,/8 6W(~.#6aiAOd'UW,J9ۋ&͏?`T ~I:(GwnG>v!5LE`QE_@ݛN7ѽC6 x_8s܅pqb8'}Z* ]UL#2c X=aÑwSJP!!O6GR>_0Lo}p oD(`Uſ%lz~-?-[sd9ED nT+QuF{5 QxEA:yUMx9Q=^Ǽqy .Y Զ*:nDBѮw4C\%r`cU?\'4"C*Ai5pG x]ۣ; 3Ѣt+fgY޳yz{ }[vve W= AH9l!הmB*C^| 6m\f+!_NKaTON$ i:oϓ"]Σ\;yk8c!U f>{B=ҁ9PNjV oď26׺1Z`;S6JGr#*e2 J< T#WGj(r`k8.C)j+g ocrgiF@:ޠd ezF7׶.?a> HTCu[QZiz$0ndt7 `jHCun or_DvuZ`;l]?U,9ٔ=Ѯ|4iBG[dMXt͡ C=Sf_I, >ϜPq9vEiT{(M@;&ƤԈ42q/X.6]_S S&ẖF(cעk*7x%כouJξ}>Pεȶydt\! 2/osQ3y3վu;_N'W4oQlX9g^om_2PNOYQbΫ714qaw/D~O7c'lœ3}A΢?]K;h%#!Wi3(=W7137$ IF[= WqBS'6$B$*[@U:}2U_ec`yД??p꼼wp>cևn9\lR$οC=>rB[mǃf Tϥ&d r4$MVNZ+l؆"l$vx U Ll!h'q9b* aÂ6eՊ&zq=_;ݖ?g8*i y౪i L^ÍEɉ^j.Q'00,a-"‚&!=- NF{9ꜾRO:gE`08 Mb-V>m=Z#ףObmS)e3pvV-xfŽ(&tOU>TUhh[dz!8r܆cv<]M ْMd17HMF,+ɻ^?-AUSVT ìnYાPp;K˕֟u:Kn8R&<娹р>z>Z@j&Yb@_xM)a {7UbRg^݋EK;b_w̆ժSY6bGg4ŶEf;cu`Nf09Leq cNioBkrSDQn]a.>'k${8I A:۸pnt-ܺ˗?;:}|VP9vڟdVZ'-\֮Ur7ą!^ /Qj"E$3{[γ7yS՘(u G#"; g /Ќic%r6:KďI3d!XUDab-4t1e?$|gb'*(ġ pL#P/w=MYclckN n['4  ˁc)v+ %- GJJbGv(:Uh@Ѣh:_5ˁY &Dom*ĘrD)MVBkHgmCݥ z #t7s3g2$6lط-X<8/7d,>&hЄ\,8 ?z-*>"g?tlagQ5 Y\Lzo]Y(M Q[IX0=#:aO4mS_;Z ׿Et ڟ0bM )=d/wI?Ȯ߁XPإ#0ҡofqG Ԕ;xεsr-W@3,K>pc)jf]WM "J&F4w>S&~MvHD7<{%b ׹|7C "ͮfƄ>IWߖ9I-\]_]YI>J "idQ^;fPR'G´p IHj#b(bBU R%$|MOUԤSN$qQ,megĨJ_D(kŞ۲=t@:6h_B`,+v 1kchn]Ob,NCo[DU)T@ת+^:\i/k 5*qŏ٬j?Ҭ00دCCc{ᖟ_uFhGlqOS(_DCk0S$e=Ak=Ag-:?0dSB=b_.reuPdU5՗K lf/tU*i i\&< zE{.y;Ij,f^$Mo`}2KUNL(G#/2oJ8:a>5)$G: s-CD _6Ry-L(( C ml =;{Pi`9S|R]bniC:?pQ&ɚzo|EK]_7BI!OI̔Ї|DG[jCJUE^T53!8F{ : ծmOmY={H_UhrkMt3X*LܙJ.iDum*Ϯro9,$#{\7 g8˅@)$Ot(l0!ĆgB9dq}#hjYS:M^Ou:yE;ztr!)>aiPbI]Z MV;"{ϧ@Vnp6PPܚ=(= vD]m.#Nm0_&Z%jV(4XLGhiF |쨃2yh|ëY$W<'>8[\m] zF#.J0hd`w^8VǞpޅfOu%DRĖШ60 IG>tsxX/r:kAقIAƅV1<&Ϣ?r 5n:  ": koQML&5EC s|ˏY{ˀu/ڢTMbTLyY_AL J8IRz%m4?P>.k ;Q?]GR.̗ByAN3 P[Zm!cysGmcȢ^3KJG} m2,fNWcԺk6Xg3,MkX>>V-XK&M~T3dZ^TltuגfZY{8LY*jB 8Q@J rCe-? \IN!]>ɛz$&l_Y$6j!Rq 8f>M̘Lq\L|㊧17|y_Lcn"IT i6: Y}U$!)8o(oeDMS- E4~F4SfZŊXiLZ ASY rryNO0IЬ\sebL9ܐR3рMg|5χ&B-nOD~z쐌gzl ftXHfngv)9XcebQc82r9x?A#vuOg ])@; 7AATk""w: `k煁]͈%pۚfbK{ dA7Ǿt{a؟c:fMdq,e!{f$ř {{\W )%5i.FCiM",EҚbTih5]SQZh}Ax3mF 7$wY xp4%:trݳ%*IV! 5+bu@lƭ 'U1MyOF &_tgIOJ]d8"Vo[{KVBOKNļw1ů35p͖N$BݢhmDF)3VtxPwœ3Oٶ.Tw-&Qȷڢ*70zq@%VNg?Y!lr$xH@jj~ o0[w9=| 'SkeIb *Sv6[c0'W]#zDy< Qc3 8&J@dw9Sqb\4q]o^Ӂy|v^)`.RHDU}ńbMkNj ΢G~h)A3"hxmgP,$H`za_9U [{0n9R 煚x:K#cWkՀ4Xg\p%c [3U ͷ_1q; ݸU 9|L'teDҹǰ@,E؇ ~b\!+&);vP'*`|owgNw;; [8eS8<%k_D!KUQp M.|sWJǓʐxkyQ:(!ē .,(LYK`Ivg}iR7&usE0j6*,.<58􀑆!vA SmޭLzNYZ5(( %DWk/cިp)}`/<ӥ{E ثC1|Xn _xyi}!'Vl$_PkPb=&Ԡ}7hIP;Çv~ע^H/÷H ],IJIwT[)m55admll:zaGJut l_NJh8 l|iH'' ielG6&j2^q޳йl.5cqR &3Rو  f ?l؎,`)7c3el(Jp=]iP uȶg$3Jxqut1Hv1տkC80m%WRr3tjU`șC D ʨ?9;'v(HIՋS˨p~Yz}4hh[^1+Y&܄XqtP7KHDz(y]fV(s}W>=~47 rF(#9O ;ǩi 'e輆`GZKx3qJ+T` GsJ\jKy *XA 4"!?Vp#1CϣDPFS-چ5X,J'(SA$f?6I)fI.PHӖeQF-v~=h8F t!"q_O5Mt6 ^&1g""Kʙ@̽v ,:ֻ=[w |@jgV,֎+%"cJko[=ZrQ QhBC LUz]4CA=eN% 9 ]svzF@W6vtIѴ"Ad.+)=ue!sWF$)hr `H zANGmb X[Gv竈2J0{z{6 w Ah +0\q 4fh0Nj$sm*]&8_:cAIF"G'ђW-ֶ0nsoJ9!8 Fs|`%sxlkNخ$00є<`b됇? VL66ek=79^+!u \`΂tAy TWLęO,t]Jɿ9'ـ׮#lDعz;3B$ QHg&0y^ɼfřo9 "!]V i`엱2%[ , ]>?TmXj麈ٺgZmC>ksH;c(ֹo)[8-B!ʚ^C ۺky^>Ɂ# ʮ_g'ԠW4ćP98*\5>ߖ&`8m l?rK& IxOoDeH8.%\bB߮-+V*@q9 ,M-U\7Z#}LAЍjl\ F@gDW3~6d_pK3af| 4˶>_)V'MW:JqZO ƪaI ⡰w Yc$hQ1;Ku% ;imfZ w-݄#3}po<:sHA;7_ǓVnir ákPD8ca^C4bK Kx[5콭Vm˫tL^l]ÿVOF̚i"B )bcw.y;PaKT\yǘ J!)yDeke;9Ajq?+4=MAka1hHQƍWP=\Ex5Q.c~ٍɞl8f4TQP&iUtcF|Pa*C,pιitO;5/=n{:'QO%>Jל'.d'Nw&*5/lûuap~ 8 련8 TU4p7"4H joTQUqIc˝R,gMDiZkU\MCBDgO` rh l9nL5JRe}Ge| y1@ӟɀW4|dckF XK*a6>Q4<8E#BAsƎu44)bջY 4`AȳsxjY:`ݮStœ")W|L V&x%ozZ{ $_95V ZJgL*4iys] z0,NkJWQv5&cɀrB4xY| tu̴-mEd\c?_Ue߉8,@60!ytB+1o>8UNAY{ ~|1s r& ւɷU4PUB]G\@⸨o?60 T1Avv-^c5M:y[f OMn;bQͳ!~MLWU&W~khyć:!2]"/ yP39s5b! çLh֕p"uy8GW8tM%KW8bo ae.klx20/FmIYiL,O^'yx$LY`+B2hSN7ܚ [y\aV-8= ]#t$=&Ft̰H&!XO2$;K2x'vO mML ߀8n颶'?݃2YÓކDmB aDG =w 9^ߡ$I VB >}@:hU9$q7Oi$r271.l^7;ŢӠ}"V*-vb5F$MW[im=bjl %3z?;3"v gc늼U`?LR3c[1lWݴy+**YAdVG&F*~ ~N+`c3[ \:$Ǩ]|l a1QIIW~V^2Y֨ <5(tG$G^S(my9򡗥U8G$Rgeq51|FX)$Jc5Q>]AO7#sb wvJÆ?ל>zGo4/R2l_-AuǣmހEQ [xRIw#+N_[r`?}hsԌGȊ7,4쑎_W#\@otx}#PdVzBXŹ|YA))T]25]tmP,2V,YW|ӈVYs`ə\R!Hg|&z/jHgd= 5)8Qq#*Ks@q%^ nm1FzJ]HaI>\|J_Pw3mm) Q;B ݢON|8|Xmvta$$k(]SiV }e_Xpعn0$eܱXxQ5v("N?:P ɰQ UO#($Yy\`xh$`x1 6av:W.%K05[MpWfHXr1JJ5Л,E^+| cES?h&]Uft؎4vfTWּoORuTP9.IG)9R2 j b$sRȢfDnv+}cGOE(Ϙμ2cD.qwx#SOAǂ7ַ5.K8ꢾp z$7|D`Pwa8@%Oel*FRRxy,@:Gt+IS"tU8H͝hĂ,YHd{33!65t>8B<~kꎜ9}F_6VVt_鉥jAJL}"1HPG |Ci*R̪ʮj֠ NBCtFM"UYv8DËCEyM(W ūNap{MWf峝e+YhV[q_ IcPZNDlm7I$/zݍ4ם~]&(vl>ar4S.C c|Eϥi1><+,V^>G'ȤSY/=[~a/Sve0P(鬕jPk,cm;8n=6ha7`-G&pċ8 Gz0CV (.6,:eD߱]\ri-}1GRz]u[)Ϻ=l0[fDF.2Q:;e C%S)#;dɻ?Ť73K8񚱈ۅsq1זD[gx@wn&futMP WS+@ mm',YajOE3!oxr \'W&* !i $5.hKM|жeh ZQJ/nGXYNED% dN/E/8k hK>c”KMBj>+}׈|=`鳛U:]QJR;GsG*T`>ʐ|M.yR6jEz7mppVdٵz %UD*2Km.xaQu_$Q<Ԣьm?myfbt*GNYP lKPʙ< UrvuU Jq/ha@?]p d16h si&yeў,m? >Z":zƋxpPӪ$7 `oTdXS7jSG͆4s-2nPK25 D(&4gD4 LQՊW#rX:< ҕp6l,CaSF) a;}6>Q++PYrN?Ι@8WXXa`tMʧ!N-t/P17WmRΣewX$õBYMz ;ڙ\J&+]PCWZ@?J|rKĨy%I_č+947zZhvhG@awt7kGiBL/R7wN|Χ< hY!t j9IM PpQK,?(˖佱 7)W?D`Yd>;3e,r=Z}F4-yc !W&9Ce?4܏WFmI?1,;I"!e8r~ClB&m/ȇ\^z}bH]x [y\d0{n]`z$Z&Y}0 ]2bԉ_2;X[=04vbBrQSR#ed#z"{Q2K-`c[^*P8:'c$B&?ߞ,̗'s޶tbOt-j7⢚s!#In Pnm)wm ֥7CZ/q֟?&Apsw[Vf򝧁-?*2?Y+ۣ;=B(m ɜtjДUނefWA =FՐmfmknN5OXFEi&5J[2XKvcmJV4ɭʩ \ mM<(R<]pTFֆZ)%..k{N'!.dæp@!?p`"urZ/*m*Ƿq3?0;ME^UQJ8_Kܛb*!94j9Yi8T|C6ٲ{^{^|Ԉ8p6cg4lLfb 6V,bkG!yZ-M곶$3RL|R ?*D^;Ts<I$'lbAN?SSs]XqMJ^JF'ѡ3Czjh7빌-b0 w)d Q;kg%&HF ^x֊{zcjz P4+wjo6;hc#(" WAzZE)$+?Pr+]n)쨯¥SgaT0ƆnjM$Cꪻa[%BkkP9MfS ,GՏUW5WW9!E12+I)n8WA/ts;%Abd$wX”&l}(Z쫻dO c 9@%l =hqC1&_KcVGp=߶I# AƘئ8[KVWjO=$oss3/b9`ь2Tyl@WHD耏}tJ@YBg6RD=ί~>âV14]0=3A * ˏ%ApFB:z`/lэN¿GwaWbCc)ퟨUreanyE3qX?a'5įo^qô 6 >Kuq?}=!L{ aH(]sk8Y>S>P&1 +!]p!AmIR{plq .3}`x'D$c``ƄY/O}Kx1&XZyw{J\,)#(10[\I$*&Hh- vW̑I/t'VCSgXA#jҖSq}5(ۥԣ0JoB}؉r(\,df_@LYIa29qTEbUPs$zQb-,M,ONJOiIWN@ +%;t^:JFŝ X¡%d"Rk$^ <pHeM (j>)TR4V{;6ǸT"SE٤ ݕf(e;OvI3Mn in)1`Ja͈2(V #trs_?ۑ1I3PG9SG1sbokr然7jm>O@n-0dɨ=,*uyظumr dK u^B=>)qdRxrts$FfO~xhh7pڝ3sG\&,s;~'U7H|b >*ŅwL0OCcdI9u%In%n$ۗC9 }[&Xa~`_yY_)'k3M;/ߪx*ȑZٛH4Н6NYĽȽ#lg#EZ@dڸ0`/gwP/#5ot$8N@n=&c![;E.t&)b3~qҊH&/#}PSPD#F E$ٶr;u4a63Z!8ߣ7::S'#Okv0Hx3XCv/͸.]6İSsI šğDX {W&ivnEҤ6AZ'L-}N"p/StVkly Ǯ!^"''W^6weG`D;*E*Q>9I~ %q'TΎX`_h-..C 2BA6"ch;2v0 ~=kLϴ@ XS=ow"JP]~*݂[Tĉh<6wwԦ+6Tn+N\צy!YcB+n N}쟣dl2ܔg(rm^Jx\Mn٘ձ!'6&dK $Qo@&?$]C,߹pFD+B zʏS9| E)ENfzԭ~۾;-S]y4`{Qi3|!bA4JbѾݭm}L.nnm{ETXV/az[?~>pLr_ w%bc=kx{)Ԃu55̦"ޙVڡ(I R3f-Y=%韠tfwt2`\ g:a&cd.%ewm>=a/C\%upV2gGkLVx[]W^ʉ4j UWVө雔5t ƪ <&IV`\t xڼUm`ZU- A,i CkBCmAUj@4NU#X㰍'U7p(5d#ҥa3j.HElj_Ϳs5ף̓Q}O K11S0mAHF̃3_-3##S, ,ʖoWje77&1ݺVb9I3u$_M8v;^‹L]%([0d5pI $>J$tZ{δNH {jw*~8XE0__"R%L91G hu|FѤSg 3+⨁C|8O٪no[ {@s)rxI'.%_180!q*,w`^h{.*8`a5.*Lu! lx3oӖ+ދtpI=h-WO8fG~ʣmhJQ &flę!=XuP5 pCr=b  H{_'PxTPӧ;y.5',t"Dң #=Wy.2Aw+kv_{)-FXJx[~3?6#ğԂ4 C+$=Ν~=|]%@N|ڤ+Jexvk|I ppIq V([ct/b n_Jt HƷ"+ .eDV7`F0O0>B [\37ߛ>0yR/''m̹0/Kaٟ,[ ~\µl{&'|< /aKQ 0/ ŗ- g=ooNHR?xXNy:fW{`b ϤhRt\#.4]"7=kDQeƙvÈ^ğncϭ&M2ԙ+ q6cm!G#o'Gra/DIr&m }ͫnZH:G8K HH0d9|Z)Tno9غ=liKͱ^dHgU4< w|ݼ|ɝس42Ⱦ"Fۣ0 YZAER/data/NMES1988.rda0000644000176200001440000010466013616365123013373 0ustar liggesusers7zXZi"6!Xws])TW"nRʟKMd[_;zk>FI"pv9ͮd]lqJRiV>kln ɭ-m`l;m>|幧j`d@)QssAB7fw0D"zZmuȺ r$(|E7G"~(q WYmOop{6-ތϊsΧz+{{Ac7)߀dR̴u}V&kƺb xD'̱,-q!.J%#tjqgٝ{` Y W/=g\r빎"j܁ojk?ձٺq2ltuw>NI,c"w2.xMxAdz~n 7K{$~;G"lL˕^_!<1?O(k%Ѹ&^[xie}ԪPdGZ֕kڳ֥])IbI{6ǹ.ꦮ*^(׋7C yme(w*}ކ_"0i$$2ios]L͙ $iNӐ@3h+I?@_ohk`GDקuom'\מ(p:#7>1ȱw@ÚճьP{^hcE8n3r3S-h[+3_-J/|H:i`)CEwR8<iv Kl0]u"p6QBK ]&42lT^a<wvM%%<)4༟M~"H";i?N BZTƛ O jy*1X h#ݗї?e@>O[F $a/=Y3>nsiCh{o#u45ī96cYg7Q50`m_GƠ-xIJJ֢;h#}7˓;>8k#PEǖ(GI,cPT,%P'j@zuN"0"їڔ @}0An;1kwLݚ옜Bpj~9LiQ {FPX}EkS-w4C)㱌m7-趸TN 꽛]Kal~{>Cۍ\ЮPUE$ d5C(j{`.b'2sl|l݉]L%&Jx̎oC5 nQ&s mc3sBE_f}wSYʵFgƶ0i|z3@h[gƳe5(HUą(SؠfbR*<:M׏ ˠ{O{ d$–VKc `#\3S3hmGfFF2{Bn<|Jr <,`o<ș(!KGB%hATGAGD3O?c\ǃZK&23* =!L,]ܝ ^k7 ߻13MvU`UOx}$6J"s)\;qvv4:űLtU= <_R #Rqm=<`a8u'rz/ڢ,yUN~:Rrs!e;P#w,bH/~(~U(TvsO񝌺ޖfwlw\e+Eq?1cHCWEci(w;8g@_L"{^|$.,F`9*aܐJ#|_Q5\yt\U2OK}"L-@M/fNvdzk jI֥-x+W//V0yQNu*FHAa-]VلUa%81frM.&iHCl5Q렿_bSbM{1~ٙ Ws'*ՠj#8W(/L*76aj7.PQƐ,qBD28'W1Om8=Jhk 7rۃ=B4)Saa-Ef7k|SMG:%šӝ422v沫T7Do<\/>.; %tUpڠ-ƶHbIxH衶)7փW1Wvo?_Wjb\yGGK0`hHuu2e4BE_[>Ȍ,65a,ڥ,,~ Y>${x|UYsY GUM!7IbhNx]vA!!!\#7}ôh]g`zyQqk,`R dy;ϣAwaLbО^}Z] ;JnOp(9R]j\;gl~ F5>a OT(,)8>&+5qtmVF"/dm&s9vh(<:@quiJrCRm'+p`Ж8"C:3n!AZ]<=gRx' ZRW}2R|nКT nb-n<@)rH f_;nu S ^0ONˋ=F7xv ԋ۶{**$BҪ$MDPgחWWxy81-#1u~Ǎ[cyϖ_39S*H()'A<].6? }0Y0+SS }6$Gd wk타%Hhwhf:d(ODE)e |ɽ{dH-yJ]N(x)Cl,^5АADT-Nyfp[TvͲ($o@\F;"ܻ?sh ; Eʡ?@G$@Ra!V;`FT;lv#AaY.9VYZ Pn.h;3ڈ+p?Q5l:Ů/.lUtgh5]/uQ3a=Ewa.#weD# "Y{Mx @+$c,}'+":iO-]4w {%Q|'#H![C#ѡ z6$d_ޛޫ /.H漧&V=|ˠ>v^ !+We G5֊+vR~c 1~;\V%YfF; {9zI[ubF; $mAo2֏a2Tr1ӆHd/] b{*JjnG(; y1^ n='v_Oy3cU,em(`zwI =9@i`n8N< 灅q&=kİ,tC!_*V5RsI`:j )J11Rw߈v! apm3)3Y0&]#ګ9^[סh k0֭ xB㙛Ol#ߣK͝k~:>O_t\ Q`V|YeykRcrv9rllT1y߷ɨfcZE7` ^݂z5ư)sݰ.}aWg34AE/ǭ^BC~fQbS}=F B n14e {-KqחM]ÜpcRtGfש-7mlkqt}du]Oy{5q=ZD+ Rk|N!6m/[y`@fAMձȍWyC 1elT3[++@ p:" fD^:{X:,n>OFr%I'Ysb~6M-odcarѲVF[knlQ} ,_guv@ ,KVraC9h.iw%%_Kw.{ήE|zΔvS$^{8DY MbaQɳV\bVg]zfjVD]-uZ3o[P ĺf8rS/ލ7K@qO!f۠ k5laۉHҲQ޹/ϵE$2R#ۓJrS/iwE녽g”yx4..ezJC{{sZ8J=iKKOrVLŐץ-@ 5'Kdx[u~8!NQf#H?ۚG-eN2QN(=ٖC'OM,뒬.d xbK|<f{o,-C^F|~Ur\[ÛMH)E(zA>uYdLd_3? S'1<}-49^b>.ΰ?{5ݺ#x}-ybAA#ao}Q wqf-FΖ}qN5[ziɜrqֺ0M6?,nbpbۍ[Ik}t<``;t-s.?OhqCg0MoG1(ދz4N!o5Z(8o7aе؏wsa_SJ}:EfKt>Zb|FiĨ30)TmosǍ R{xp s2:!#"/[< 6-8NOe^%[r' iA"&/3O;P RfQḮ ߣ31Fza Pܹd%&[??ZjvHGV{ o .9aiz4mЮy[5%,1ІA7G˰7$ ة?(׳'0A<,-Cv5U*:/Y~6+>b0sRsu`>Qˏ4\^ހ'k?%^duEJ酬ۢbF."ޔQ] =t5'IqĠY_1G2VYg< Q.///0 7MIc'} o:䥱ߢj}(# ą9 ~Jg8&K >+[îVl,L@lF@3l1WM.*Z$.!}Nz0C-CM}h"=͘QN79?'>R~qG^*`ڳ/ a ja40b? )p,lu"҂o ҇<5uj$(ק')'滇(NIje=q$2(P{y 烍JX'ާĬO*m|w=^wwI4w`'RN8ԓ/J3X1E~UzǧkNO)缴#-JcWА/@BÑD3I y& Vjꄟ?y4M ;Gj  @/-= 1 Nl_M\=#+b 7dH<.!hJWg#@P+g;=g$X4Iw3y&^.k2K&[ū=ZV/M<*IF4[<}v9Zq#SF!{ t@;l)}y`7XZu^co ?% GxP-eO벇xY`FLzN 5lL$Kj&ю%MDwDƓF<68|(+Ã;df6"ّUM+H0*.`@beMQ?xFD)U=WdE>KPhujD5*İ9r 0ۃr c EMݼ=A [/I=*Hh]ӻ~e ~$sRܴ1yCH+ITm>@ ֵģUه@i$TNdתPQQT2gW꜍/H?d "׬PӲ&MCyD6$憹'X $$g?M>"#xKˇ.ST NKuq#,7q.pCndE<|^6UC: &Z~."4Ơ ܺ|+%}G/gxe'M>>e6t#ؿK.Z lDQ( FL|th4{@Ы79R,S$dvDB <qMլ3n^-Jdz gcl+rXs7095w=MMDc&Nqt0~Ffˬ+l-̛fo=D޵Da>'PɻS0FJ,=A]}ER*q?M $WTi*1M6*Cc Q./lKGFҌqۈ+c:/c1n@M/S b87`z3N:)\u^ \ b%ۋ&`WAxa.W fz0 ft;_J?D'<E/kU+bx%5ObuMQR#Jճ]!xe=]"<[%2Nی+)W&z_J<Tbm>X'ةVAr%νmw#ŀUVs lMT74~AT6SnA+#w鹦9WD(GPV%0qpS-Dlc5?gkնAv+ 7mȣK[xP-\"g_x8Q'6nHem?(4>M)^vӍ< WBoOպ @Ÿ՝.W},gm3ܘ϶9VVclttٿK,D4rV 4kdiƗ,!}o{&gW`&[C'cⲜY8b!:T#DӦ` OLͮ$pW9B32v"vqx?Mg`nV_3FSe)߹sUWUK oGH]wX(-Ta} w3:f-o*a3': ߏxU-2{̧Y7Gݎ) 8ߞ b[|7P3·/7b̭q?xЌ> 5$2YF awφ2lD>x.G#!eJ۸!#/MrMoZXMSaM;}́_Q\ЬWzxO$K, aAgj;rZp_Ox-#d'V'Ib#5]M~ߠkPm:f)-~ޢRib %:&x32-VuW\WJYN!$c'(Jx̴F=7#3!R# + g [& DzM~m?k$SK:q@u \ f T{Iac]X#D?`0hFn"Hp u?O]԰`l$BR6N]M-J D]gA:f3ln{W'm Xnh>[alF9My9*TC7!3Dc( % `j͌I=`U@B=sK D܂4gQ8/@=v]X0Q |VAe(d71NEjj+`޹ġy9quSJV08yn {(^{clʶMs&Hu1#7}׵D7eFeeLG $mKVK(L@M'"d6Q Ga`1ńDEzx!7T6Пt\̩2zbb$p4Ra8 &ْ2V'J(^]P@MEwx_pZDSNhIcan*5|PM#s 篹'1b 9A `e3^XP][2CLUC7~~~2HaX\*׏׆J*yՅ}M d[{zO9t%7AʨS N$:V +:ϴvߦž]z;a|(?%UsQ  85ZRp߱^m iBoUe ̾ yg>(.._x}s6J*;{땅o#EAOA$EM JmJ06Vެ@' |Lal$3c_)+sg^5'gqI@b`Nk|&o}T{Thלd:$CIr;B/vǫYzw>_Lę!C3!J?MnIBhv"*`c G`DUt?p+Osm+7T(G:չ=38+}sc89몦Ki9QX"8+-3F}ךE)I DO0@`Zx_ʺ0۹]@{ W&r '[)5_Q(W:OLa 3:#ݯ!oqtMY. q0saʰ,g} 8 5OhGKT},o\e΀@oXd|/0CMJY.ypr|:ٸЉ߻IK)RӼ,φBɄ$"sʚ(zB C 8Z{T3txJS񝗢;@ ԝLS&v?B h9 {V<0e]do%qI<c_ZO+del)\=@uBtsFn*"%~' Hd!(ŒxZTz`),lqKhb%&G1 KiK8OQ/.wNL<<<(GV|a Z=7nLH-}FDy9%c!qZ41ߢ]vĆS갦3 +\@7& Ѝ,05٬?;J 7NF~e,c3-/ A:mO]ztWJ=Blӟv-GqA1/ŨC͍*ފ!_xX1Ӈ BV5xO\VBbyNGňqaҌ{㈨G@C.fj蠂 s EN?"#w PH">̝Dosꯉ'(Wp5mhX2XfU28sh6,{|m:` 7Q!va:<D2+_.k,xźx bJr;3c48Yn;Q%z_'|t2U뾏Dȗk`#2g4쩪P58$w2Z%$гgiAi~0U5%z#Q!*}ms~c :K*;4 #o~w3:2DYˤ@JnLb ]x-Sծa=6wc@hY>'9kOEMERvF)ݛ'\@/o!GN1~4.`35mFYL[ʬl C!yJK5;lkWxYڤH >cHIHF%rs*NNXrͿ  +r@ dB@I}k8[\:޶՘Y4<4u]ըH2C6Ptg 5.|BΎ|?:f@[9:^Ka;Kg -'/b5#e%i0SE҃[zfzwȼq^O'rh{{R:NmoWc]&sʞMJuH֔ƁXAgw-sh1u! F QZ#CqW]f¸ TǮ'8_&MWg @}~F7Yul|DmV1K^ q6;`!-aփş|TI # uEa[umc(O g#umn /r/0k;cKSw@7M=+oc-ˇW^ꗘ]ES-T_҇.nr7cBSqtEj"HGS[#CFQ&=g`?$" }XtF:sp8䧀!+= > +Aژ0<ОT*_^L/'+E;1$Ψc^?_A1T|Y0c4BC2 i44JR) Dpȁ/,qjHq :l-ѭVІZyQ0g}[q'Yl bf4u12 5%J̒^46Y x g"X_TNj!_G hJ}fùb`&xvKF3gVRK઼ t~*cw@g&2'wVE(r;e_&YR!0J0h1wۑg𳆊ۿwK~dt9-9] A%D ^szP%D] F@(:g8#3h?xcU9p,,Bg6rkPG\GG$4(T\ߑXȴLQ#/_6H6P_|(.)=KZZHx]zRnk5;#"Ԣd1}$JMػO;UUJng'V;\^ TVT=BAgԦWg$i@`+O@uO _fw8FOc? bɌ%([LA!_Bz`H~qb:naB/ZlHs <[ihV`>,:)a:qj7V'K#sׅRW]jXFLœd8E=K[ZM,ލ1w|7시 Xa;Qj(EߣTu~Ft։mxbRމD.X77>PmL0iӂ+qK:k9\k[RG&Vc sBԟKR?=:5y*XnaOd*H74:,ۜ,(vXGytMnuZoŬ{*ԍ7&I ':~Hc򒾍`fA`)|tIII G'C7'rKa{Ł<U#Y $o`2ϡZhM+U܊ɶCBՈewNc,P$GL*g`E-qϼTMEKiܼǨ(>;މ=ޭ˰g?[a삱IwX pQ•^/^VJaw?v"\ora8SrLb3Y߉>+Ho(r' 6,-~G bLlesMUXM3*Dx2mIv2BbҜQGT+GZBONCdҟs%@}qL c'/)8KQ-y(׊cx|&pVzìizZ,IJ-hH}. FF 1a>T ^Xֱ3h I * ٞa_kk}؊}C7"(&AٯzFbYn^3א9b(q%%_ԧSp L3wuYȉ 0\s% MoӑUۦDžr 54' 0[ i~$7o؃b\쫙D!j%!CBMi 6C6g/]E ?( SD͊A^R"+-E4}ʽna%^JM6 *ZA av4+sC빣o>&D(q0ITE^B_qzA#F/Jd~Xl_Ǟ@""(ҬsCiGɸq"GoAp=#8ymBIءTP_.x9Z_qYH>pel+޵N-|:Ͱ _>:<H,]-gL-U-d+b/Iٺސt|Jℎy7HcgWS=R$@ 4E}TVSbIx-t[oqĪ(aM9ƪOVa1̨~mb fg w/3]sCFy$kHiPxj DOPW%7_SOBi&-PV0`e}]mZ~t;|Ηm/|%}vvtw7i\[3B湎5G"4X㽮ͲI<H"FkXlKfMhtT @2=<`V1F{uKM% x9LJ 54a؈DZV0ꎄH{ـ!@nwVFʕBaI2Y/? dNórO{l'R}==l1m{@*DRj-b '`F>4f _Ƴrsj02<=o4n/20{[ ~B?iJ[O[{24{WUڟeoea79aÓF," ma)CpY80.^oјrHѤlwQ\ڬLKXb㝕1^8 SƄ`pe-᤭0-nUšuR,xX 9cb.ODžfTPQ&s&cG?I,[EFv%oSF\MFCv)A<2KgZ4TCot7X\WZFH $ĉ 05`-R N!>f?@qE` ?$!eX=Ī7޼&*,cp_U8y mKЗ}/ Iת_>^_xYx#wʨZ 'W/QvoAgT`B[D [aĕ1ưRiFfcy[8XoAMT~>GEID XrL@O<|< 9Ⱦl,؎5EJI6A:wBf'x=R|弮#*$\9^wjϐxp kRuӃ6vQg1cOeyϨMhqpJ ՓxnEVi~֐֥ C^'[(&= ]Ì/"YDQ[C#lo 7|J%,034,S'!{}TМ0r”s&,U:]x_):k|-(hV %yz;3(?AyqH_!Օ OP#"W+f~Q\ѪS8:TD蕭Q{ѷ~#;z ^k';zUa:%*lt^({~L&1r摥k*[¡nMҵzK/Hͨ?ғ6#k2%-[Kn#JfmIy;|w&Bp.|=tڳd;BoVZ G$>,r iI+DJ&f@}\xEp߇y7 }I0'cCnCi%$mwd]}=0iA <)T~CF0hY*7/X7)riybT,<~I)LY!UUvw5&:1\ ,\XpkHY:{CgaѯgEeCI!.+p.5V+f > @ܟpn!F$oyBwrkҶ;@6 J/[S?tNe0lwnBR`G%w;%xc,@NtwPC*$ }gSi |:Ά F_'dR0#nusCuJ纲fV/αqҠP"/д/c_-sc4ZDi (vνLeWhf.OQst^破,c}\ٸ*8I[9Gq YA!wEp)gI q*UqP=g<!&~%NS*QsRrE:C:mO |`kWQ֞d=/CJ)K,C=T14_+~ J,UD}Tء&M%oћ1bԁO7l!+Y K scZOM#.0A4ʀ<`2"Řr`NM-jl`FPP6/۲;>y,WYw|I `gvOIKE ƸP$7#) Pznq^\'$e2:wTX|mEkYk 82m^'}\핋ЮMոh@ͪ]C#TLY4VՑ,ۖvP87=<ֽ8 Q,;xn`* _7nG` *bf~˥8! z8`:Yv_Ӧ+4yYVY?[b6U S|з;hF]ЖA aG4cᢎ#k6~ Kz2}H?9l]k;FOwy `4ӈLAKOSdԢ:7/ajUNru<4TQ_^3_2mo4$<y:_'~E6Z,}c"iz5+5[LQ£y uQ4%`'OܑB25gI6I@>Rx}V4"Vl u䞻7N%"w6#8bvA[؄fv$9EUNAtzEx8>B zX {eȺ⹔Y>}0Y}[x"ұ .Ix'+IuY\ǣ/ d#f+@#㨂CfJVv;Y`,v#m~klsDh RD8T{kӝlRA9C J($y_Radj"2Mfr-`?u R=T YaoV YD|F$AzuqNE"O<9T&Ee$g8RV^۩(yzZ#$LHr4Yޮ?Wk[ G׈ pυ+JUٻw^!jM%|R@- t fL쀥U&,]ՙJX8rPKMH1haJ-:w*];Y+MMo@j>| 6Q(5҉Su}LV=55{:j v75%BʲYSjb 9ҁ=}#A[dJ'kuu-=xI AYnm4[FۨhD}J=93}'`I`^0,#mBLCuݪ !tolL U߱ȌDg |_R˄49mj& B0s!GI;gO PVK? [#U0} @E:6C6U篰ӎroaMGQn#uZyȎǦHaqRѩT==8']HTR.*ZPy`S( @ ՠ!NU8~P z,%2,&fp."{C=$v)Aܒ͏ I0(Q\#ŋ:!i53_^Wg*/DSrQyT!qaPUȗխV&WJzXF[qMЛon~qJU)%/o۝S93a` */bf(N0bÑ8@mz9/WREx73JQ[n)o$8 -)X)e-ǧҏ_>99?Kv-Τpfbn0*^:,Ԃu9Ѓcp&ޡenseh u &pZ;cw d emYi1ڼ`zE$,Zmi!A(n3t,N I%ޓCUYW/C%WJxX MyaNNlc!YNXnmaK/j.gΏY͇*TY2짷 ѹɗ9R oCZJ)MsqNqJ`{WS Vg:u1>V}/6FE&Z`BAoThWW`A:vlp@0AzD&ŠttˉUxfDzjd+% O(bՀ22&/R i'ٻmXizP)J_RcqHyћC62|QjmɮLQ19b. A^h/S1=eDo=cMYfK2w rφ }o!-#hX"ίqI/@ya6=Ȇx$"3@J ,9B] k0՛K$JW[ St}%9 ߖ[ZZGj}*zjz>\v=*x5N7"S'>pH432lkGBKaOɧGe\buY#WrcAY=@nR h aAyە֗ҠzQm#~ހw93F~JYnh]ewfrC?8]d&*/6Gɤ|{ԣ{z=V ȔL+ it/V;ekӳ6Mɋ9qI/V4$T~ ( aVN_#AC$,^0L*Vf9XΎ(-=,D E6\{:-c An2u&9_KUK2 =K Nd{dԠͥ;d^JPe)ls}c1K6x-'&*Akf+b@/&g*6z8;k_"ėxBU+ S T/AW*tS'ZyCj?'Sb{2 dMylKz&RZcTL`n&y%߾+^g[ȼl ӝ8 Ut1^,m=L n9RXON!RFH(p%F~r[X.&: H=Lװ tius9pcoJ;,UmuVYx[vbSVӌ 0| nq|Z#7Jw 7u1*NymK5'S2B;/}ohk@aX*&,9*6Q4 A-*iT٧[&!Vxpm饹ǚ@BL]x8Ip)nU_[jÇ=6T|]^8 Z#Ɲ`!R$ʴ .ZI*H}ԯn)PWd[/M)y\0:~A8KI\5 IztM3ONOYWtS8!'Ӽ._0$+#Y&,AQ*U%"00.Ѿ$:-Aw{/eVcf _YDYلYNdW_.hH G8"p݇ ܩ6AzכI7]ECÑ[=e<{S(7*_c7?d2N̏nRH֢gɆsD?'Y $u,d5ѝpZx[4$],n9Ik@ ΋aFR})yH jY}qL)WGw]2H>&}3+ì"mnXsG+^^+TG]mJEev%OM*hDē`rz],fK+KzO5*7淅):3K纱Lr7V@B*$)쵬O~}}0ڪ:B>GăGQWf<(:!h YAHGaIX2c6eiZd<) wC: p};EYtpBExi|E4 oIUBO{p0ٮBfWM (!N›"oXDuB ډߤeZ׹s<=?@%N=mkp:ֳR 1Ak79sU:WB@xD Z3GR>cpz!r`wą*b'&)&Q&3v$D^qL7-/i4>PFQhl G7@Q><'}]Ij؆N'wEFը*1 ''kA-}{S_ԾND*<¦G m.Yiat E?٤.#W%P&eky`|4\mh߲N>mU^`}=r-ny'F 9RvJr69A.{ cy0_}u\ Cmz@k}rŽh+%]=zOKy~ l;0DjQ:'2Ag ]0eCOqokl`Ns*t9񼤏tf|8s~i| |ThKA+iKrLCCFKm۩#V62HR1B0"Zlu2ǓԱ~:gsogKI<Rתg#D@Q[؆/flfٙ=b"j8ލJn7λF ϵ 7/TrpƲR| i ` /kpW}c>2_‡Ju&ںSӏ l1YMk:[p1% p1HXJVJJ& [ZC)G`@E:He?{zܗKJz#0Q88RD?{=b/$b3`6g ;kH䪷P @Wu ~ψ1!#><@MP@  0aow,N}ɩ88 DrbEL(_n[f6"3?t͌5_nz,Pgx!~K%^C3Gw+7Ya޺e$;(d0+5欂24EBӅU0]-ptjýQ(V&K.CA +˓S;Sɰv:V~ɍ'Ӻqtѯ^m3YRW2"z.Q #AQc!gP.[Q`(^ P̑X ƒ,nD~Wy鐎,2gՒ +(Jz1W0ʾdgHW2U1uJ;-EL$a L0O0xtcbO}G 0KO]Գr1qǶ J{HpX%x7eEs\NALx0BN[ً~KDesPڴÕw!-"v -bwtE$'U(VHOJiQxA8u*Αnqڿ]D2f0~t v$*֢={:bg& #&=,چd`k s_:,AZnZ*TYTcs0s# <}8c'y!`E))K;ND%!}˜gnrҹ%:7Yc_+ŢJ|[ ƌfpo@vtx3x ,C3@@>#c?ޯ<&/*-V}":0>WtC4\km4-?J'me|lܤB [ )>(ĮY 9ȄHB#&Nz/3BLEf Z$g_ ¥i$m!}} :mDZ;GuM߀Cӡ QY!du/(BX_Yo8m-IOmP4I*^J%"sD/_ǻa4VĞ4wxhJ%B3 R A=f3W>6Ut:]ɦ U"%5V0 ) GG袟zeư>A>xb&XObL5b:zѧؘ")Quʤ^Ӎ[׆)$[v f #aHvɩx1ӡ 7p%R T]Yj`vBKeE mw>e*J!ŧymJx G?/iuϪ8C)Fυg2T! +ML_rK?WC¸F|]/^wD=| Z7b ɯN,Lξ4Nؗ"*Ф}[0{$%ᵧ9o," k?fe!2 iG7 V"?YB,kwDD;qt_66TUz2­I7񆋻B~hDۏ^;g*<lo2 M_Áeݟeġ;w)5቟Wy`f~"PL$?M\8}Vx/hLIIK F#ismh`ˆMJj#z\S t n͑Lni_d{w ݋3'rӘ֫?W݉E]g7zַ[Dߝ& abjJ>{#r;Ƌ*@Pkb+X?zr=qV[}t(S*VԄ@|~T^hivBO"DؿfCf?(XelNJm\ǘހFyL$ $D07\ y'(? $prK'./npjvl&"ٷԒY^L&W|F`▭q.T|11{zƚ!w Kfh'jn6c҅:R%" `RҐ GB@wɠq1C"GkfNZh0E {yF=qO`Iud{}. xӳ5ڪ>kYKFc';ۢZk0,ADIA90 ,k殯._QR%ŵ'(!٢]lYϫj 7Sz mdN^Af8v]c~XBRİyl\Ҟ Ijn  WY'[MJE0a9uHƽ[ 'kSvWʵ!xCq8ngwcLN RK)uqU/$pT)G8f٫>`^Q1 /i[: nZ:p.sCI3+n)+݃]3DwSra3vӸr}|%cl#kLV4V!ڲgCcS-LҭRCae]O=nLG^๛ Mp>hEE5YC!5ߚ˞O:!@yqe6@ zjaֆDF| ֑2-~4J3g}G/#gQGX1Ih+̚Znt]j%gLOs*ю V_~ڷқAl4kyV('˿ ^T# &e0QP f7L}IaK$7y$. K耑av ?\J @(o?M 欀ڗH"=ud [WU!,nO6s2W-ZIth9j9>Z{m6"\Z6dE/;Wxp2uz}p] Gc2 Sa7~4a䔸E Ll&U2pﶆKbv.ݾH HCH.0/|$^M87Lˌ_<iB 1++q@۬Ci$4(xt~nؤ6,^&d29a8f| c Tu.Lg#=:j}|3HvV⛷FtH>=zt67_*g ym"rJbqA|AbQd0Go6@ =[fy3@BEҩi{QeD ["07K?N[+Rw ![ά÷u`gm R[;vH }7G Hd6M]i3]1]0^MiliNQK-SKb( w{L7wS'/=cyE%+ N(vK+ƁhVՏǚ,^nn{{`0M Dc}E>u!Xy&뿆@rʀrwi&zO]pKX[Su:)(͸Aeß |~x!4"#n\Kry% ]u-qZok~ESdo`A-!!dm!GAG ,({ɑJ=>]'Z-k{(K2Ch ]]^wvld;F:N_XccrVJ.| l#ș뭂Mo]|0B-,a)zm ,$V[jWĂ\ypPxbe$+,؋'v̠;̆M*Ƃ6 @]-[rk?¢A只7i2y$`/}{" &fk)y =znd-((Nֺ6it/Ӷ+pzr3=wsEM"G{Ae깷5e;[(?*]`9RGo[î<DwdFZa4,I"CAHÅ3 w2TS&@Aż3ݴ["J>b70<}|ءߖc,W"PqX/*&}e.`ZXC͠ tin @YK=e:hZO@qa͌u҆|2Qhep9!% s76B۬IM|v) R^SGX'@;pQ,hOg[ i^/ɘGYݒfxjZ *%|>+ϯO T|L Q7iDXN_x(aJ;j݇gV`Lʓ ~ 6g_SR:FoTw扥Yu{=7ǙQy$e?Eb$Յ o. dͱr9ˉ7`"$T~7EWRk(5|Tx ƗN@ϰ]Nxp|Pо)?O4ĥV /= eKb5/:GVKƪ [%b6@?.bcD6w <^ 859\(׆o̮w2  x^m!_ ';5g3Xl҅pQvokHفW PwyZ9oqNu7Qk~MI4m ' *6P0%cC $?LL6t| LؕވG Kuoep鵈b1$zM27C#E}sװZdN)'jʙok9!{kɒK(Nr|Fk_ )MGNe';ɪ[~zE.aؐ9X#KD"Bi#d"athڥ¹\ÙWM|euLuÉ%ɷ8pyiF{|PBU< +Sr^'hI=tsU\WuWCNLnx?~_8Sͦ7Ȃz;<&[s`b\ ,*(yC3JUp{4D7Xtw % G-."Ҷ . Ǯ̈́:NZ[_l8ώ*StPhąmQYۢ)_ZeX+gm;S.&ūM\ɥ,6nrf pEpb_.)G|*!_V2bB5獨Yk.S y;-d-\.+½ΣAd?j-K}Mi9z7ƝK#Z/mg?soE ҞV QoBD' |+ʊQq/6F,''|cN՘-N?AyYݛ>\ԁV8JSg~U ŒWRױ<H+~O{jt5IQn,87g;%09PC!kǂʯblAގ:zN3<;@NDc;uGT.`LV-CYGfx$i5 jx`3K-otb!B${ x0OQ`Hbɦ@Djk)g:>0 YZAER/data/BankWages.rda0000644000176200001440000000224513616365110014131 0ustar liggesusersBZh91AY&SYP:Y?>ТK*hL44444iD?#U& h {UQOR@d@ ʪ~TO@4J`@L&M"SRiˑb#I,k26MI_4 bchh-Qcbb5IB1cƱl EchJ-qF,E)jPPFRUh>(Ѩ%*J5ca*@,XؠQ6ƸD-![ZJ $*iB*+ʌFѮ.5!Q5&QcmFا 5XY;%BXK [TC$ l-cyqPsN׏ mxu2m-[x]spرD9Qrs3a=nƥv]7M5Z%M]ӗ w+Nf@9is<7TCz:n#< Wm^d<v;xW{;8j3{z$<4a!z<IDv3^HXE)iv~O-G81c9~:_I "   L ;0 @?@|Xl6VR( Ѣ{bPiv! ivmj]>m)#@@tT(HJڄJP$tt ; ;1J)ѡ֒cT@Sk 0P hUn&l:=S &Hi42)miH)$) S'0@ z"SS 4!TTzf qAL)mXҭ+@")@R4R"VS(P(5*شmэZ H%41(%")J-"*G(ZE(PJZF֊Zk UiiZPhV6ѵՓ+QṚj+(ձFԕ+l5dm+2`ƵV1h-QhFcbVmb4[cZhjF"I lu#2lVVaF٨b5emY3V1XUR (j-ѵlUűVbkIjۥXjFڍjFڱ[4hr PVe1M͔[P((MAI+1lh#F؍ eZUh @jaX[jBe-clTQѵhcZU(-FMhXŶ* jXƍض63j2Qj)6q3PٔŠkbXL0jaY63-*ЪPBRQTlBMmQ1FjlcVjmLe2V %(QXэfkFحF4(W)+c›VƬ+cPj5FzZAlUlbUAX)6ΔەZ+QZ Aj6(1BFVll*5Z-ƴTmh,Kh,lQF4[^72MklUI5e1X(bZܶ5ѫImEElh61k[cTmPEcU[^DXFɶh%m(Ս5(m5lՕAZնUbcFALV5XɰcF&X$ՌX- Hj1ѫzZ [Z+FTEŌ2R4H1%*4"R*UclV Qj clIh`cڊm`hUՊaX)l,g h hj&%hڊ[Fhj[rCrխQb6#chFdh5Ab6bhh4llV-E7heQlmDhБQX+TQ`*4IF eQEHEQBISIoZ`Q.)#\3Lն(ef,PhQVQZh,j6#mF**ƨFCVc+mLVն3 [%md-mEQcckFCmFmdh d4X1QQFQX-Ŷ hƊѬXcX5chd5Ϸs8t9Ft[jDhhV66k01K(ƪ5FE`F1EjM4QDlhmMF0lTQi"*-FF66-ƴZ,mţ[%h\±J+t۝-b֌UbM`)1FFh,Dbٔ6*E5EThƪ"ƌ$5FōfhF&mF؊6h+IFF QFE`#bQ&0RX`ډ Q65lc2CfBDm4Ij1d &H6,h`2&5xSzj1zk3gv+McZ,RE,[cXƶ6lDXQ5-lmcQDh6Qʴ2EE""I6d6$"Ѷ-[!HID3REX lXQZa*3c`#cY,VV6[k_,j5j|7|*cZ FXQFb-("6*+I i4h,ɲZ4FKQhԖLh15EThDmPm AQEI4Ze Ʊb1He!DAZ65(ciTSJ"02T1f3mJH4-TV-QmѵAXQEk&52"тHDV2Qk3m̱Q+oi//t4F4U%ت4]j QFW-slQEKh&IkbZ,VƤQ֊6$XT%XؤňhddI &(6,E2EbX0iX[&̤- M&&M c*6l`m% 3V RVJ Ƣ h%cJ+ bPPɂ,mQZD`hk1FKaQsbÜbP ij }1zm[kWͭ`Fi $lVZ#h+XY6+DhƨIAFh VJEb * )lcFbđEi4kIlDhF&m$`cDf!V( F^  dcbRh%*1&ICJ5c%Ji4b1d Ѓa%%,P6HE ,C@i""QD`((c(P&0EEEmE-I&I!LY4A2 M EHT)LL(0)b6HhID)̉J*0L4FBD ,JHh&H !1 6-ѭ`h6Ƃ,Q1FlQ$jK!1fB!4E44H$fbDhRL#J&#Y4Q&e$)D!A3L0HbDġL@L) !%I^<r#P$_w_8 ˾pF ]kaZs>m^ONj)L N#NwyHddԢ0QPA20{3b]'."D33B~NW|)bB $MT5)PiGks _R~҃"g]PI؄#DC$IeE(+Qǚ5_^}&k曱 j3F NJޜeQLx.\ ʨ$%!~M4`)? }gGپmߎKG˲pDq…I𬚚`*"  M&X6KPEi|Fzxk9swy!W% $`ҫ"@GF*~Ot=*Y NJO !Z&=;gvn870 SO.ש7E@;pkj{-h!b Gx:-*,Mш!ʂ[2^am: ȐA  JBi6`lDLY0,f%4@DL1R5FbD!03D$ Ő -&djL66MlEH4b2AIBidhL!dc`2K"D T͌I$lE1 #6ѐhF2˜ыFEEVUfSHQ)(@L!V&dѱERH@0d1iI(J(P (Ō$I S"  гAѤ Q`bRJQiL)Ȅ(ɒ*DZ*S!"dэ(bh$ DeCLl2h1 C"%S"(̔LAK%&E"(ciMM0,IYX[ML,a"؊شm_Uh 5IMIŔ"SH͠fQIRIbcB!3ɲ "3ȌC*VR01&SHfJ F 1)!*JhL2M &i $@6 FKIBM(  LD+" 6$ц)2BD) Df1"#6 $4&hN2řJK 2bMYFd b$ FJ4fQIDfDJ Afh&S&4R2"0h}]6CIFS4&4&La4(Aa, # 4 4$ٚ,FbL,f0$B#4F&e32cidb$E#)1DD1!P ELD)3,LIM0Q F@d$Е%aeh4؉()II20 FZXbA& R$ZDidŦhQR44b$fedȂPؙfD&df$0IFS"d%K) LdXfCcB4-̩4%D1IIHEBQ! S 4HD)%"JFi M"@b4` M (Қ f41 SQJh2DУ"A2JH,f"P2FM RCS32#$R*1E H2b1X *"Dl (BeL*5Fo"K %e,Bdbb$0T3Rj&e)3)$bL%M L3K2&e#II L 1@HhM$Hdc!%f2L 0@fK6(6$dJBhAIK)%!)30)2M(6(  e$"VfaL% QE"$Qi- ,RlBP J) #(iL3(eId2 !L"IJ  Y0$) dI4eE4 J#fK Ѕ2L@dLLaIDF́M4e2HLA@ 6 3R(h!di$F,YRb `DF4ɦ(Ѥ(0DDk3lcR4%F*D%FJbDYFf&e($D`(EDMQd)M RBdјIM,lXcf$&faL(Hb)3ĬK&2(EHI1J 6I&MLJMIa$Ҋ2 lđHB1D FF#cP36$Ė$1*4#L %% 2 E2l25l҆mR5mD(Ri4JM1i&IaI"Kh4D% ˆR% ,h0QA*h@̓,h2[4LBa1i"!(I 2LBI&fLRJ4M#0iTDIh1HYFh"T&&(LCbB)6,`6DLi0ICHhɢ,šLEDlB"I*E`-&F&4Y-bDM 6I"!YHJ4#(634Iщ)1ihT@1RhDD41aFLRa)ZM6 lPD[Fɤ6M$!QF1c0b խQjF'd˻#hbƲV$,b 6H3RaSclPbih%(IQ`ƈɌXaY)*eAa 1b af,($Rc162#$iLDT 0e4i"LH@Mb1%  &"!!e(2f4 ZD* APhڊB"E0$6Z%4`Y(@iS(&%! 4eDMC1A#D&i"( & Pf"Dh%)H#bb`$HLRIc`RFf̌#AFɠ  $MhHEQI1FJe!""2$H&H Ji%&!,"aAF(ƦQc$@abA2LHY $I ŘRD dLC*$$"2jiMff3DZJJ2@dY6dBE"QA$H Ŋ )(LLҘFc1(DĤ1i4 6-E1a׮FLb͂%2 1$M#dM6+!&dIQ%@2 hFBPc%1L&4Ҳ21E ʙ,hI&h`flbldٵLVXі2E2JŋdbKdHH20͂H`XBEL!HLRF2)HE"QQ6 ’i LZ4Ɛ Hb4T@&4"Q)c6B4 &$F! b2Qbi$QMIl3a S$BJ44DC,F IQr3RIHlL`YAQdɨ S,jI(bZMIF3D$lHŤie Y140Hl )" &h&Y,S#5PaDBK$lhɠ &6 H0l`EI-#3 ITl-5lkF6QcCZ5a&0#EEjR&4i hD&b&F,AMi10&j4PZ!CbMe66!-XlmF BR1̌EC CI6R)DaH04h#IZ& b2$3 LHMR% M3K%b3H&SaLơؑ iMșRdYLcI$llR(&JDP2h`iiFaL)#j(Jlmk0FbC%#2TL؉&41PidB,h$("hh2AhbR2i0@J2Rd"*Hd̡L R%Hb&XK1#FTmQHaRe)B1LD&Hd&(Hb bM )I%)F bBlE`ĤJQQDcE`Mz\HƂPhHh4FLKH`dٚ #2"Y1AEF3(Y2lcb* 6#EE&$AI%8/i[W׋)6?cRd3Z4DZ0JT2EC [qo0:ll ` &P;UtE4^#Zx4 Ȯ1>M<}|v+>Q;Cb1զ1S:f7cKj=lQڀgD'R XQԧzo:ǧI ĀͿrI@gɄ4lL&qcF65h&q5lGhWG ؠA"21H.L2me5廏[Y,sjFMai8r ST-z4j*ժm`65Wf؇E!+o!ꊚZ;$Ζ[$TRMrVfx6f\+yq>4ś.ؤfS.glmTX[݉2=7[6'1Phs^Lioyry';^[:'G0c HV4'elME/ n=eE\Y}>[UA%M)|je\l332lmQ&S"$ }vs1ilNki UC[^W9&ظ%5Exq3*ܫׯ~A$jtŀ,-}etbf*f =O._v|Pa˶B9BG"x+yz՚DFƣZz}ɳ>'`h)PwVc:-;b:b4 .A. b-ׯ,SF1Vhm =lS|=z)!CHb ! w8ϸ v1~~߷ͅU56X*qo6'1L4qth=5À(8֐F0fuI4. 4趼A  9e|3E.Պϒ“vApŕHPhb5*&6x3Zu m4Ru1&JP&` ߋ0;}bfLۃz5Ga!h1>019kGkʶ!Vx6; y9fā wޭֆ"(:tAb6/į'oUP P>QqoˆYN@GZUS(.m\xˣsm_5rAkP IkL MCn둉tY1 I;!n]&UMq-aS8n{Ő(zҪ+nkbKgLSWjz6ߓPz%O)t% ,f/ljc~q "lvi,e:FafI4bt _9\c^}},qyjlH0Pf]~QIsEkYv۲oEi\N|ѻTjT$\痃_µ+/ K|T}ٛۡ}MYF^|/%b`"_/Xy)Sս+{XGw Jc} =*ܭȾdOd:P^n085b3Im󾹢mMchК6jڅ&(kG{WԵgtV\wnLi oI4mn4z-mjZ kSG|O @Lq '<1Byb ҨĸfQ$5U縟5r++hB; Vs#f;! "؝ v½/>_ atm6=V':RȀvb,5(űȇj)$O[9dy{cOP*<4ٹG, ty}hxy=Ǣ~7֙j#a1zxcwD~ 9~ԙL!nj&b&PIQ5w&{D ~>n9}>D5SȎ9v;taYuY+iqTI5ؕ(l_]f:t03:֞D|ߖĽ0KS1fdF,Ȅ8v2s s|VUs?M=!UHAUCVc4͵+٠9fo UZLr_ ǀZzyduOANwS1,5X}$iˉ.F\8&>[On{j#Gӕ˖+x2xLZpTvdG6١0["G ,!$B"?f}{P~HܑChϑ>ǶxzB DOz"u#933_=Y1<3~Ș1jA~)Wi =bˣ kE5(0ue(0VcA}WM{꯭Wޮ^*N"I8x׬zRc{5F&^}Gƅ0?7F OE1DHb4Zk?W C4}⹯4qJ>h }pǼ IS iM7kCJ"J@ E3 fPPVMD-eM@L\ $TLoU01><@( IvZ++),F!C.qz\3 ! $gc`Gġu*[//M6o;g|dGq{W}a|Vf_wjhmtQkeCQܱD&@88{2|-"3:/&#ыąfcc&Ԡ"I"wt*Lm1'sh랯)!̦} q6Fr H^(>{1J#!4v1>Ƙ/kЈޏxb胯4JCd縶#dtfQ4"ͮ 6j:I+'X8k`8jj f/(5-odggǤG0 㗖fjL==2W;.BJt,<9)l=jjiZ|];T5thW<Omk@V5ZMGW#OƓwXfc3V(e<2Vы:bO%kB]BYDbQFt|:yZYUadM!0@{pLC  I!f[J45ΡY^Us!:Ή74T`*#\հUN鷲V3vGSO*iI+\Ko\M]3Ġ, )΋ 396J5"PEk&a9hn7k[X;q\ Jŵ]hIKh:d52q.{h;kL?K'@R[r@Ό-Nj `lv |_F=l18:+4L( D2寴&щjqv~fMQI筮{u/Dd'im) @pΦamTWwa60oJ32d1!BQ$ I";7sui-*P K]tU%J)dq;/[_O9Ȧ㺢Th{OzUGZ4j/4~l3("M]pNffl^QY(y<0B7TFs Qti5)6hΨF:)V:QVК\^t3BQ}:L麱ÂҡX H׻ ̷VFU䄐LҠ51Uguʫ-<~#|foH,2j3眝`hI@'M]-yY||H&F{{a.>N׊L*B޶*{(ۈ46:UL*Zbzي E!PHIHjGX' ~^wD ZδQhʪBU A蝔 .ga sY7)Go`n6Ttmn'D7e.DWWN)y_kecO5b6DR({Ӧٶ'SRhdE S| 5zBz,]}|͛(16ypMIJoP=n OO~}'z1wqUYU!z{UsHD2$N̏D⫺A rn E}ʱgRa i5̦Ɯ#2c&f髅dMRqpXΘW`Z/[֎דG6+0 IRS\І˂Ց(51@XX1j ɤ ka MBVDO}ߝObEPLV1Pz?ϧ<||7?Mz]֜7'|?}f7=w6u FKZ$nTeuY[ַ> $o:5Li`Ɛ!} m磶XI-9\݈5{vBe4''~/&Pa((-bUAD&V`}DȾԮ&[Ӵ6NM6$t'&Hd Gg]kv6(+m,FEV=## k]pd@?"ŌX`fbךO9nmx^{lxb/l"\|xDN\7;s`\v޻%65EYI!"JBJc ,޳K-<2:7rD UňZ(0u3߁OD %4$zwQm<}+;i^Bк@TeGdHΚ"BCG>DOoFʫ.sFg G S ^Տf=;Ŵ( :i;̝t~sz5/j~kU'2zY}¢"#V-cY:Y]&%2!q֨j~O;slCgPOHfNsFE4$rz^ ߆eDlQ[߳S.J^|' ~? ^g9Afd-I9QDxN*R34I"쫾|mow0ֻR3 WZDcӣ)_\m(%/Z|*[F}vN uNU!׊MF\gF,F()<}:"ߧWS2:\׳''%qݴN߼SV膧bQΉ"psڌC"#yn pޔxk󻌣yǞV[a1tk2Hr( \1vz?)}#pމ{0A ۆb{'3*ҡ!B4XrqiOa* ("'73Vex^ 9b9!!ߵcIiV:u;@uc'p$I`U_JX[HO1h(7SZ'xFT^J̏/s)P)X#ғz(4t+&7cM=fij鱼oN==(bdG)K=.Ѽ"\n4˭ԤK3P, -[:(]Va FZRkJ'D*Y>ƶ:jֈX($CIQ&?E0S;=fibrηxR#ANTYV<"6_15yۑ ?w~*Xq~M>?<>Gڐx??8b # &n;05con;!C-ҿ^OvIG_6I> |U}Bk&=(?vomsaTqZJIB W^GLho{" :' ~Hy'2~3@@ ?{m@HO(j_?`lι?4"Ѓ.'C@1 샐-o\mUKk N*޴ $}[^-/BPy_.P1Bxfe?@) эn|w$S {"AER/data/Medicaid1986.rda0000644000176200001440000002345413616365122014326 0ustar liggesusers7zXZi"6!XG&])TW"nRʟKMd[_;zkҝKcK;1Y1g   g 1)Zrw%݇cGaW@^24g6MoDr<)q.IyAhU=GǾuY1K@F޹Y~ys% _Iur_N0x1>J9'O`>ʔdb{1,e[&]mShPq@-ܖHjB9+W91 ';ΜO'\ta; VA]\$>Є' P!vFOjB77CgDrm^Sli)_t@T!x[%^ Nw6t5v 3̯7scU7b44Wx6(gߊqck^|?K|1x+-1 FZ}E˶tnh b6m$;0z,R'Xڮl/>/8TCEP8hqwj$e'\@!(v d8>m/7z:Mj}G(S0;uĀ_OgsǫUҷL)) Ͱ3VQKM E{8F~~lLĉ/?vQUVXyNBIî-ڢY z=E;M N˹d)! g^WΔSb>{zOxg(! T]_g'ixm*BB^ň (*W|k]9rx9e%?G@k!c k.fDoTws.z{+j&+̿^t%me$^YÞˠZeԋ_?YB`QZYij8Knrzp?徟 ]>Iaxu V 4B%ғpC͢Ul,T paLk.n BԤvcE}? x܂8pw3kFwR^頑.&8f fnD|pNyUw$Mc>HiYHI- Ǽ TXpYw2O9qzflEe _pЯnfP%E^qYt^S/ `{䛵r2cI sC>kVLW/T֕[XAm,\"C`dT㢵GYQ>-$v*&Jk.o*t-O&B20˖|&{A:fgb"ߛh}6v%!%fVuR(˰[~`vq`,1M\Prt{LYs]FY8v!B#~ܳo =9V`5H*0X^uV~#`¢ =M,7@m@]Rlolk5d5lʳ$* < nPm|ByUq"x ^UqN&@I:[&ĹQGBs>TI#!lr1a4 6(|D!E?{`dI;/F o&T%'50Z^Dl/h;9Ҡ0֥^$|u´[KZ\pS|l?y̳_I|#WvHr:`_u,>T+gϮn5*ɯ DžR*ԏ'6nyX݉7B]?EmuݢCrP1" +akVA |@@ˆ麿%~C0 4"$Cr ϔ56Ć&|Ä񩷙!˼#}RKfw(/Ĉ;Ԓ]0`-G<dQ㐩,%cL"uf3ĩ?P37aDwZX:Ⱎe)xVkw?LGEȵŻCp 8q(sޔ}$ y#U*ϪwJq#BtG[x3siR%Y:@T}r[ E3ĈKn?hsA>׮cC9ݚ(sCcLQ.JᱩlqBŶT^f;Q4(VNiÖD 7EJyzl NLn$J*n)*H[fU^0!g̅F .KissH RF4!bekX![ǯ| ;E\Wᐧ;[5eɔv9 ^6J7O N2R.Q{K s嬑`V,Z0&4#[٠,[XKŎy&N{p(P"x}&^!%R>4bu dD FM̮"\#ebPVHa+ꭿ H c,IB\j).ܭg'sGrt_ܱ*OM˩ظ[o)4xA~M\*И`@Id6,c ]j&ML$)~= ]Qe*WBy2d9.Es:`9l3}XjH` V@ueWO/tVV_-aYL ZSa rh,Ij9k ՓsƙiB @:@YoDw))l{ @rmNz`MB;':Cdƒیa˃[>ӽrK>>B"e4vkTR_&cҊs?~ROzF<vxQ_φn#fmY14q21VB)1Ⱥ ?< j \đx2` i\"u~QBMx_Q3ppȸ=yaA;)Nn&HL m(/;BB ENsx:S-,yo="-St]9s* #ɟ.畴*˳ %ڂt?c \QL(M1gF-eo]CU-A_yb =ew2JedI;ճK0&ikYPFMD*C|Ѝ+ZF _P5||D&1v a񫩕C濾XOPh.9rhS*ACt -FƱUƣ\gl&MrH3Hй֜=%c y{eOƢ>?r:w׷٫L~%>4hH^zXLGl \oc7,Q$Gh᦬,َ%\,T Do HDYsa +0KMM#=`v5r R{-\*[u|c㦪·`Pjo |Ʒx>T-S1w~_S6"y??%7ٯ<77QER<(ufcLI뎋`;aC./DsC9}źvL#"op\G#vUwKc}@I|iQbK#SxR(p l/N6iEGk0Y-^-"Yx6}:e'-T2{e5),V:9nit‚ݜ|׶<}i2+˩9 DsW#A[)&YU*NH呩c̉⟻B SG4,YZܙm 㑛{Mm+gd! oskJ˨X1Mg\lJo>)AWIyURXB@G$5qf+xd02CMyi9 z ܠUctt%qW8mPes勥LzØ?FޓwX ܀bJP(7C1l5. ?8)OVē.c-f2aLn̙Ex $Sn#܉I^bXq0atyn1Q u*n1 ;aG2ք7jEY޳Dy"t,9=ꃐSd"yT/6'KeɛZVylTtGfHɆ: t ]>@l֬*Mൂ_cT)D|gMY" Is\.bCO>b9z"m_&f~Y'ZQdisDpU0PAi,n-=k\-mZL8zi3{=9i~{,l+lD~=0i>hLp!Eiq^pJߩ],;C@5g/K W:idާk ~H<%x&G0} !Ȯ*T%UБBz6ݶ{ZdAg/utk"8!U2h@*hמwbWaRl ;}A0Sf)Hyt}ew4"O*4:\XT~nylA$?1ЃW| 4f% T~{ / Ӧn=E 1i>nco-uCFKnHIb6Mn&|/aQK~9M0mv/\RG>sD wdJ1,Cu%@qc sj2cP!fe}`4#X}D|Jq% !xh̤u!ُ5IT7i@9j=KL̪j1rјҌ- P 8cT7spW)rtng t[Ϫ^n1г< H #o]e@.y$QiۑIGpcZ ;wN _`x^] '$Y6|!,STp5Url6xX཮oe2 SݐЕ8-"(QUPRo&`7%xI9lqi0Y&[nbH2Ezh*͹^; 1X~K16;Wk d!U}U-eRR\9G@LQuYQQ&vrYIQGp*UYf2-$Z\avf=|_Y@;DW9s_̜iIOc,X9dOCMohgH٠vv$03'<կTdq -μYzTiG89|vHFOo0($6onSHTEenΔ1k~:-=zODRlc@vz-lFW2{3`٘of""UJbkgwQd"C?6XI/!&U&._}mY2təxo`jN~C}R$KUlѫ"SZ ss"kR{Cu>XXP& 89 4 Otm'Oc3a”)J5"beo왿# 1kR((m [ ɸqzW94X4+T w%wm <*R}Q!Z>ZE|w?"Uن2ɕz]-4kj|de-BUlj v>@}S{(<,[b˅9Mdʼ,b4Kbaqu7D|+=Rʡ-:`^H WP4 ,SCz#/_mT1/Hҁ8[w9ʤ1E2E|ɡ mzi9 9E"fmh&F3շ+xY/g_q6 NoxQs=ܼܣh^k ~1_9b\܇pک#Ѐi `$|8R]Mb6< iw}=؄KQQxV/"F׮}S 4:]R:<e4t__u}lԯӯijmZV; °K\+㮐JPMS;.x5~Db[c[z@.7DÃ:ÉAYI~_! :n>|YlD\b9~eo2:Y/m/#:yUS7}G`VW-8Wu Dfzl؜fݪ]{"V?2N(L>0 YZAER/data/RecreationDemand.rda0000644000176200001440000002030513616365123015474 0ustar liggesusersBZh91AY&SYV8  O—֢li{I!$WlQ*a롡l֔,͵ƖBLFh4L hzzIC } "0…7WD~,C|Y@^ZeuZ_M1I4sQ'SAU{:v;t5oau:MMfw`/qg# MV[p(mNO$`4E*i@ÄOX o'ш)+ZW4[\N /Yk=} DYG_וEzBсLƂHP=ߞH!j.g4I[OK_`(CCE0[X$ l"bf28T{_FD-ݞփ-qq俬&Q<ʊJS|# !@[in_ᨂd$hF>Oӱ1:>Ӣ1cĒrI$I$I$I$I$I$UUU s*LR>&뮼_)"!$I 2kI $H0I$ I$I ڀg뮺zmrȈ(NQ $`I$5{AI$`I$$I $AI$aq ۷l))_NX4QQENQ`I$$JCދ YgǽpnlM7~9bqCr·nݻw0Z(/Jr(%9QEUAUUU2I%AUUUUUDI$I$<˰lԒY1c1cTSi$I$I$I$I$I$I&iHsN[ePa0V+ׯ^,XN:tʪ$I$I$I$I$I$I$h' Xhf ?.\N~~~~f1c*A$(UUUI$MUUUI$I$I$I5YÆM ̻v#:Hff 9su ksR%lS<=KKz6qfB-b7,kh/*1Mv^RAM% M>Ia]`BY |etw+r2XEE",?d=ۜq[ڑ((BUBE$@`AH  T q5mƎ.XEȱa,",aEQ$> #k}kHlMm lHlVD"$H(,wnődD6r"4(S_:(h_M;MϾŒ5FЎnm O.pW>ugZN$:m0|#-1δ0T?&??6R| y|K+F VeBAIig3)XIqp3=D8~Tnpo@*ր$k3hv2 ÔfDs?bƭ;l! G#kJsB\gE-V67ɗ#Yn1=XӋdPrP'lX94wH&)jZ]9T*~tE1!9z'QNxwÑ/xub}W$Čެ.N.~|Mݠ B,~awDV\$"CMޑ'Cr)f @we[8ʉ)-!,ZP2P 7PJn W0_R$*:@I6&ЂXM֣t`XmOGm^XkK8q7U\mljH.:mfi&p+( Itώ _NY]vі\k#]͘v|8VM;ފ.UXwU Qt)y'R&(LƔYw`K9C+jycU Wƛ9:΂%]b5˝^4*H^_P.&u;H|Wiff3s#dlr:`5ArŜjF-V1F 3r F9:XqD28I@8_C"H` 5d~AToEz |݁K`%33=ԷQ8?<*jP!tMcʣ~<ʹtT߬&+@$S$Ŕκ)m ?v]-n KGeK %U29T= }M>S-ttT[z1Y*jl'm6?Ei#]mC4J!O੢ }t~.P:q(%° ]|jEt:q8k9-E%ºt9vVLGF!*u D\ǢfՌEWrIn~`wF%x#!dhžUnr9iN)w >B)V>&۴m^1e:TkPN#\Ihpd={n!BFNP14$OQo֪*%HX -;CɬҎf|nh/;JxƘ$CaB kÜ $ٸZC$tӴ|8 :oasHiy Pw?U9!>?'J1%/G<.L" 8}A*$Dח5BZ6An%ٶ qf>@c0PI&Z@& άHqh-Yp <(wuPVDe9 ؗa/^Wp?k #bWVq>\9iVbW 1akQH$3[e.%W)܈d*V]s0᧥ yuիY߅C齭}Efu1ZWsbIX7$>Ў_lb1 k}'I<c4A hiK٪L[}jw(.aO|)ten.I {H-7n~:4`@:/7G˃wg{7"Qa/O99JyTOuᇁ̮ihHino/CH&o~[\)5 ifms}J|q~~dhX0saLt-7N]3G 38I0oxjDhPZ?A{ 8'1Si](4@{~V傔fÇUl[kX!4OVq?އe?|oܖ%SD%e14>یZ^n7G?cs4OET"We–iow~3υ)RہuO0pc$3󍞕ØB-82߼Xpƹ@uz|hjgS #%`AWR8 t -Y-{sc5?C`EZe\٭%}*Ss"։308GzB)Wd缞}6>.¾+-'BQ̽^CzK(+秕B`]EBH]Ƞv*_ѿ+6}UOdYKũtkqg$`| *M˛~gE1 UNy#~Пŏs#Jg_)F,R"u)i$0jL8{F:5"cD25?w#[|(-Mߐt{Pk0$ ZKxÈbieє"-}{7b8 (RǸO 8̿ǫPZu)'[x343Ҽf2] @Dg=D;)_rR9EX8Ain^2r] =BU0sN?$Hn!;6'\X%Zr6k~ؖNh1 ž݉A$ )x X9 rew./; 4 8-hn(.9|jwQTV!BnVxb *fM?-oJ 6{w߀CUIc3~.(*"HMOI!e b.qH  50AER/man/0000755000176200001440000000000013616354265011447 5ustar liggesusersAER/man/OlympicTV.Rd0000644000176200001440000000127013615674673013633 0ustar liggesusers\name{OlympicTV} \alias{OlympicTV} \title{Television Rights for Olympic Games} \description{ Television rights for Olympic Games for US networks (in millions USD). } \usage{data("OlympicTV")} \format{ A data frame with 10 observations and 2 variables. \describe{ \item{rights}{time series of television rights (in million USD),} \item{network}{factor coding television network.} } } \source{ Online complements to Franses (1998). } \seealso{\code{\link{Franses1998}}} \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \examples{ data("OlympicTV") plot(OlympicTV$rights) } \keyword{datasets} AER/man/USStocksSW.Rd0000644000176200001440000000406113615674673013736 0ustar liggesusers\name{USStocksSW} \alias{USStocksSW} \title{Monthly US Stock Returns (1931--2002, Stock \& Watson)} \description{ Monthly data from 1931--2002 for US stock prices, measured by the broad-based (NYSE and AMEX) value-weighted index of stock prices as constructed by the Center for Research in Security Prices (CRSP). } \usage{data("USStocksSW")} \format{ A monthly multiple time series from 1931(1) to 2002(12) with 2 variables. \describe{ \item{returns}{monthly excess returns. The monthly return on stocks (in percentage terms) minus the return on a safe asset (in this case: US treasury bill). The return on the stocks includes the price changes plus any dividends you receive during the month.} \item{dividend}{100 times log(dividend yield). (Multiplication by 100 means the changes are interpreted as percentage points). It is calculated as the dividends over the past 12 months, divided by the price in the current month.} } } \source{ Online complements to Stock and Watson (2007). } \references{ Campbell, J.Y., and Yogo, M. (2006). Efficient Tests of Stock Return Predictability \emph{Journal of Financial Economics}, \bold{81}, 27--60. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("USStocksSW") plot(USStocksSW) ## Stock and Watson, p. 540, Table 14.3 library("dynlm") fm1 <- dynlm(returns ~ L(returns), data = USStocksSW, start = c(1960,1)) coeftest(fm1, vcov = sandwich) fm2 <- dynlm(returns ~ L(returns, 1:2), data = USStocksSW, start = c(1960,1)) waldtest(fm2, vcov = sandwich) fm3 <- dynlm(returns ~ L(returns, 1:4), data = USStocksSW, start = c(1960,1)) waldtest(fm3, vcov = sandwich) ## Stock and Watson, p. 574, Table 14.7 fm4 <- dynlm(returns ~ L(returns) + L(d(dividend)), data = USStocksSW, start = c(1960, 1)) fm5 <- dynlm(returns ~ L(returns, 1:2) + L(d(dividend), 1:2), data = USStocksSW, start = c(1960,1)) fm6 <- dynlm(returns ~ L(returns) + L(dividend), data = USStocksSW, start = c(1960,1)) } \keyword{datasets} AER/man/Longley.Rd0000644000176200001440000000271713615674673013365 0ustar liggesusers\name{Longley} \alias{Longley} \title{Longley's Regression Data} \description{ US macroeconomic time series, 1947--1962. } \usage{data("Longley")} \format{ An annual multiple time series from 1947 to 1962 with 4 variables. \describe{ \item{employment}{Number of people employed (in 1000s).} \item{price}{GNP deflator.} \item{gnp}{Gross national product.} \item{armedforces}{Number of people in the armed forces.} } } \details{ An extended version of this data set, formatted as a \code{"data.frame"} is available as \code{\link[datasets]{longley}} in base R. } \source{ Online complements to Greene (2003). Table F4.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Longley, J.W. (1967). An Appraisal of Least-Squares Programs from the Point of View of the User. \emph{Journal of the American Statistical Association}, \bold{62}, 819--841. } \seealso{\code{\link[datasets]{longley}}, \code{\link{Greene2003}}} \examples{ data("Longley") library("dynlm") ## Example 4.6 in Greene (2003) fm1 <- dynlm(employment ~ time(employment) + price + gnp + armedforces, data = Longley) fm2 <- update(fm1, end = 1961) cbind(coef(fm2), coef(fm1)) ## Figure 4.3 in Greene (2003) plot(rstandard(fm2), type = "b", ylim = c(-3, 3)) abline(h = c(-2, 2), lty = 2) } \keyword{datasets} AER/man/USCrudes.Rd0000644000176200001440000000161213615674673013442 0ustar liggesusers\name{USCrudes} \alias{USCrudes} \title{US Crudes Data} \description{ Cross-section data originating from 99 US oil field postings. } \usage{data("USCrudes")} \format{ A data frame containing 99 observations on 3 variables. \describe{ \item{price}{Crude prices (USD/barrel).} \item{gravity}{Gravity (degree API).} \item{sulphur}{Sulphur (in \%).} } } \source{ The data is from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("USCrudes") plot(price ~ gravity, data = USCrudes) plot(price ~ sulphur, data = USCrudes) fm <- lm(price ~ sulphur + gravity, data = USCrudes) ## 3D Visualization if(require("scatterplot3d")) { s3d <- scatterplot3d(USCrudes[, 3:1], pch = 16) s3d$plane3d(fm, lty.box = "solid", col = 4) } } \keyword{datasets} AER/man/HMDA.Rd0000644000176200001440000000417613615674673012466 0ustar liggesusers\name{HMDA} \alias{HMDA} \title{Home Mortgage Disclosure Act Data} \description{Cross-section data on the Home Mortgage Disclosure Act (HMDA).} \usage{data("HMDA")} \format{ A data frame containing 2,380 observations on 14 variables. \describe{ \item{deny}{Factor. Was the mortgage denied?} \item{pirat}{Payments to income ratio.} \item{hirat}{Housing expense to income ratio.} \item{lvrat}{Loan to value ratio.} \item{chist}{Factor. Credit history: consumer payments.} \item{mhist}{Factor. Credit history: mortgage payments.} \item{phist}{Factor. Public bad credit record?} \item{unemp}{1989 Massachusetts unemployment rate in applicant's industry.} \item{selfemp}{Factor. Is the individual self-employed?} \item{insurance}{Factor. Was the individual denied mortgage insurance?} \item{condomin}{Factor. Is the unit a condominium?} \item{afam}{Factor. Is the individual African-American?} \item{single}{Factor. Is the individual single?} \item{hschool}{Factor. Does the individual have a high-school diploma?} } } \details{Only includes variables used by Stock and Watson (2007), some of which had to be generated from the raw data. } \source{ Online complements to Stock and Watson (2007). } \references{ Munnell, A. H., Tootell, G. M. B., Browne, L. E. and McEneaney, J. (1996). Mortgage Lending in Boston: Interpreting HMDA Data. \emph{American Economic Review}, \bold{86}, 25--53. Stock, J. H. and Watson, M. W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("HMDA") ## Stock and Watson (2007) ## Equations 11.1, 11.3, 11.7, 11.8 and 11.10, pp. 387--395 fm1 <- lm(I(as.numeric(deny) - 1) ~ pirat, data = HMDA) fm2 <- lm(I(as.numeric(deny) - 1) ~ pirat + afam, data = HMDA) fm3 <- glm(deny ~ pirat, family = binomial(link = "probit"), data = HMDA) fm4 <- glm(deny ~ pirat + afam, family = binomial(link = "probit"), data = HMDA) fm5 <- glm(deny ~ pirat + afam, family = binomial(link = "logit"), data = HMDA) ## More examples can be found in: ## help("StockWatson2007") } \keyword{datasets} AER/man/Equipment.Rd0000644000176200001440000000542413615674673013721 0ustar liggesusers\name{Equipment} \alias{Equipment} \title{Transportation Equipment Manufacturing Data} \description{ Statewide data on transportation equipment manufacturing for 25 US states.} \usage{data("Equipment")} \format{ A data frame containing 25 observations on 4 variables. \describe{ \item{valueadded}{Aggregate output, in millions of 1957 dollars.} \item{capital}{Capital input, in millions of 1957 dollars.} \item{labor}{Aggregate labor input, in millions of man hours.} \item{firms}{Number of firms.} } } \source{ Journal of Applied Econometrics Data Archive. \url{http://qed.econ.queensu.ca/jae/1998-v13.2/zellner-ryu/} Online complements to Greene (2003), Table F9.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Zellner, A. and Revankar, N. (1969). Generalized Production Functions. \emph{Review of Economic Studies}, \bold{36}, 241--250. Zellner, A. and Ryu, H. (1998). Alternative Functional Forms for Production, Cost and Returns to Scale Functions. \emph{Journal of Applied Econometrics}, \bold{13}, 101--127. } \seealso{\code{\link{Greene2003}}} \examples{ ## Greene (2003), Example 17.5 data("Equipment") ## Cobb-Douglas fm_cd <- lm(log(valueadded/firms) ~ log(capital/firms) + log(labor/firms), data = Equipment) ## generalized Cobb-Douglas with Zellner-Revankar trafo GCobbDouglas <- function(theta) lm(I(log(valueadded/firms) + theta * valueadded/firms) ~ log(capital/firms) + log(labor/firms), data = Equipment) ## yields classical Cobb-Douglas for theta = 0 fm_cd0 <- GCobbDouglas(0) ## ML estimation of generalized model ## choose starting values from classical model par0 <- as.vector(c(coef(fm_cd0), 0, mean(residuals(fm_cd0)^2))) ## set up likelihood function nlogL <- function(par) { beta <- par[1:3] theta <- par[4] sigma2 <- par[5] Y <- with(Equipment, valueadded/firms) K <- with(Equipment, capital/firms) L <- with(Equipment, labor/firms) rhs <- beta[1] + beta[2] * log(K) + beta[3] * log(L) lhs <- log(Y) + theta * Y rval <- sum(log(1 + theta * Y) - log(Y) + dnorm(lhs, mean = rhs, sd = sqrt(sigma2), log = TRUE)) return(-rval) } ## optimization opt <- optim(par0, nlogL, hessian = TRUE) ## Table 17.2 opt$par sqrt(diag(solve(opt$hessian)))[1:4] -opt$value ## re-fit ML model fm_ml <- GCobbDouglas(opt$par[4]) deviance(fm_ml) sqrt(diag(vcov(fm_ml))) ## fit NLS model rss <- function(theta) deviance(GCobbDouglas(theta)) optim(0, rss) opt2 <- optimize(rss, c(-1, 1)) fm_nls <- GCobbDouglas(opt2$minimum) -nlogL(c(coef(fm_nls), opt2$minimum, mean(residuals(fm_nls)^2))) } \keyword{datasets} AER/man/PSID7682.Rd0000644000176200001440000001037113615674673013035 0ustar liggesusers\name{PSID7682} \alias{PSID7682} \title{PSID Earnings Panel Data (1976--1982)} \description{ Panel data on earnings of 595 individuals for the years 1976--1982, originating from the Panel Study of Income Dynamics. } \usage{data("PSID7682")} \format{ A data frame containing 7 annual observations on 12 variables for 595 individuals. \describe{ \item{experience}{Years of full-time work experience.} \item{weeks}{Weeks worked.} \item{occupation}{factor. Is the individual a white-collar (\code{"white"}) or blue-collar (\code{"blue"}) worker?} \item{industry}{factor. Does the individual work in a manufacturing industry?} \item{south}{factor. Does the individual reside in the South?} \item{smsa}{factor. Does the individual reside in a SMSA (standard metropolitan statistical area)?} \item{married}{factor. Is the individual married?} \item{gender}{factor indicating gender.} \item{union}{factor. Is the individual's wage set by a union contract?} \item{education}{Years of education.} \item{ethnicity}{factor indicating ethnicity. Is the individual African-American (\code{"afam"}) or not (\code{"other"})?} \item{wage}{Wage.} \item{year}{factor indicating year.} \item{id}{factor indicating individual subject ID.} } } \details{ The data were originally analyzed by Cornwell and Rupert (1988) and employed for assessing various instrumental-variable estimators for panel models (including the Hausman-Taylor model). Baltagi and Khanti-Akom (1990) reanalyzed the data, made corrections to the data and also suggest modeling with a different set of instruments. \code{PSID7682} is the version of the data as provided by Baltagi (2005), or Greene (2008). Baltagi (2002) just uses the cross-section for the year 1982, i.e., \code{subset(PSID7682, year == "1982")}. This is also available as a standalone data set \code{\link{PSID1982}} because it was included in \pkg{AER} prior to the availability of the full \code{PSID7682} panel version. } \source{ Online complements to Baltagi (2005). \url{http://www.wiley.com/legacy/wileychi/baltagi3e/data_sets.html} Also provided in the online complements to Greene (2008), Table F9.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/Edition6/tablelist6.htm} } \references{ Baltagi, B.H., and Khanti-Akom, S. (1990). On Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variables Estimators. \emph{Journal of Applied Econometrics}, \bold{5}, 401--406. Baltagi, B.H. (2001). \emph{Econometric Analysis of Panel Data}, 2nd ed. Chichester, UK: John Wiley. Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. Baltagi, B.H. (2005). \emph{Econometric Analysis of Panel Data}, 3rd ed. Chichester, UK: John Wiley. Cornwell, C., and Rupert, P. (1988). Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variables Estimators. \emph{Journal of Applied Econometrics}, \bold{3}, 149--155. Greene, W.H. (2008). \emph{Econometric Analysis}, 6th ed. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{PSID1982}}, \code{\link{Baltagi2002}}} \examples{ data("PSID7682") library("plm") psid <- pdata.frame(PSID7682, c("id", "year")) ## Baltagi & Khanti-Akom, Table I, column "HT" ## original Cornwell & Rupert choice of exogenous variables psid_ht1 <- plm(log(wage) ~ weeks + south + smsa + married + experience + I(experience^2) + occupation + industry + union + gender + ethnicity + education | weeks + south + smsa + married + gender + ethnicity, data = psid, model = "ht") ## Baltagi & Khanti-Akom, Table II, column "HT" ## alternative choice of exogenous variables psid_ht2 <- plm(log(wage) ~ occupation + south + smsa + industry + experience + I(experience^2) + weeks + married + union + gender + ethnicity + education | occupation + south + smsa + industry + gender + ethnicity, data = psid, model = "ht") ## Baltagi & Khanti-Akom, Table III, column "HT" ## original choice of exogenous variables + time dummies ## (see also Baltagi, 2001, Table 7.1) psid$time <- psid$year psid_ht3 <- plm(log(wage) ~ weeks + south + smsa + married + experience + I(experience^2) + occupation + industry + union + gender + ethnicity + education + time | weeks + south + smsa + married + gender + ethnicity + time, data = psid, model = "ht") } \keyword{datasets} AER/man/OECDGrowth.Rd0000644000176200001440000000644713615674673013665 0ustar liggesusers\name{OECDGrowth} \alias{OECDGrowth} \title{OECD Macroeconomic Data} \description{ Cross-section data on OECD countries, used for growth regressions. } \usage{data("OECDGrowth")} \format{ A data frame with 22 observations on the following 6 variables. \describe{ \item{gdp85}{real GDP in 1985 (per person of working age, i.e., age 15 to 65), in 1985 international prices.} \item{gdp60}{real GDP in 1960 (per person of working age, i.e., age 15 to 65), in 1985 international prices.} \item{invest}{average of annual ratios of real domestic investment to real GDP (1960--1985).} \item{school}{percentage of the working-age population that is in secondary school.} \item{randd}{average of annual ratios of gross domestic expenditure on research and development to nominal GDP (of available observations during 1960--1985).} \item{popgrowth}{annual population growth 1960--1985, computed as \code{log(pop85/pop60)/25}.} } } \source{ Appendix 1 Nonneman and Vanhoudt (1996), except for one bad misprint: The value of \code{school} for Norway is given as 0.01, the correct value is 0.1 (see Mankiw, Romer and Weil, 1992). \code{OECDGrowth} contains the corrected data. } \references{ Mankiw, N.G., Romer, D., and Weil, D.N. (1992). A Contribution to the Empirics of Economic Growth. \emph{Quarterly Journal of Economics}, \bold{107}, 407--437. Nonneman, W., and Vanhoudt, P. (1996). A Further Augmentation of the Solow Model and the Empirics of Economic Growth. \emph{Quarterly Journal of Economics}, \bold{111}, 943--953. Zaman, A., Rousseeuw, P.J., and Orhan, M. (2001). Econometric Applications of High-Breakdown Robust Regression Techniques. \emph{Economics Letters}, \bold{71}, 1--8. } \seealso{\code{\link{GrowthDJ}}, \code{\link{GrowthSW}}} \examples{ data("OECDGrowth") ## Nonneman and Vanhoudt (1996), Table II cor(OECDGrowth[, 3:6]) cor(log(OECDGrowth[, 3:6])) ## textbook Solow model ## Nonneman and Vanhoudt (1996), Table IV, and ## Zaman, Rousseeuw and Orhan (2001), Table 2 so_ols <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth+.05), data = OECDGrowth) summary(so_ols) ## augmented and extended Solow growth model ## Nonneman and Vanhoudt (1996), Table IV aso_ols <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(school) + log(popgrowth+.05), data = OECDGrowth) eso_ols <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(school) + log(randd) + log(popgrowth+.05), data = OECDGrowth) ## determine unusual observations using LTS library("MASS") so_lts <- lqs(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth+.05), data = OECDGrowth, psamp = 13, nsamp = "exact") ## large residuals nok1 <- abs(residuals(so_lts))/so_lts$scale[2] > 2.5 residuals(so_lts)[nok1]/so_lts$scale[2] ## high leverage X <- model.matrix(so_ols)[,-1] cv <- cov.rob(X, nsamp = "exact") mh <- sqrt(mahalanobis(X, cv$center, cv$cov)) nok2 <- mh > 2.5 mh[nok2] ## bad leverage nok <- which(nok1 & nok2) nok ## robust results without bad leverage points so_rob <- update(so_ols, subset = -nok) summary(so_rob) ## This is similar to Zaman, Rousseeuw and Orhan (2001), Table 2 ## but uses exact computations (and not sub-optimal results ## for the robust functions lqs and cov.rob) } \keyword{datasets} AER/man/MarkDollar.Rd0000644000176200001440000000170013615674673013773 0ustar liggesusers\name{MarkDollar} \alias{MarkDollar} \title{DEM/USD Exchange Rate Returns} \description{ A time series of intra-day percentage returns of Deutsche mark/US dollar (DEM/USD) exchange rates, consisting of two observations per day from 1992-10-01 through 1993-09-29. } \usage{data("MarkDollar")} \format{ A univariate time series of 518 returns (exact dates unknown) for the DEM/USD exchange rate. } \source{ Journal of Business \& Economic Statistics Data Archive. \verb{http://www.amstat.org/publications/jbes/upload/index.cfm?fuseaction=ViewArticles&pub=JBES&issue=96-2-APR} } \references{ Bollerslev, T., and Ghysels, E. (1996). Periodic Autoregressive Conditional Heteroskedasticity. \emph{Journal of Business \& Economic Statistics}, \bold{14}, 139--151. } \seealso{\code{\link{MarkPound}}} \examples{ library("tseries") data("MarkDollar") ## GARCH(1,1) fm <- garch(MarkDollar, grad = "numerical") summary(fm) logLik(fm) } \keyword{datasets} AER/man/GrowthSW.Rd0000644000176200001440000000315313615674673013473 0ustar liggesusers\name{GrowthSW} \alias{GrowthSW} \title{Determinants of Economic Growth} \description{ Data on average growth rates over 1960--1995 for 65 countries, along with variables that are potentially related to growth. } \usage{data("GrowthSW")} \format{ A data frame containing 65 observations on 6 variables. \describe{ \item{growth}{average annual percentage growth of real GDP from 1960 to 1995.} \item{rgdp60}{value of GDP per capita in 1960, converted to 1960 US dollars.} \item{tradeshare}{average share of trade in the economy from 1960 to 1995, measured as the sum of exports (X) plus imports (M), divided by GDP; that is, the average value of (X + M)/GDP from 1960 to 1995.} \item{education}{average number of years of schooling of adult residents in that country in 1960.} \item{revolutions}{average annual number of revolutions, insurrections (successful or not) and coup d'etats in that country from 1960 to 1995.} \item{assassinations}{average annual number of political assassinations in that country from 1960 to 1995 (in per million population).} } } \source{ Online complements to Stock and Watson (2007). } \references{ Beck, T., Levine, R., and Loayza, N. (2000). Finance and the Sources of Growth. \emph{Journal of Financial Economics}, \bold{58}, 261--300. Stock, J. H. and Watson, M. W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{GrowthDJ}}, \code{\link{OECDGrowth}}} \examples{ data("GrowthSW") summary(GrowthSW) } \keyword{datasets} AER/man/USProdIndex.Rd0000644000176200001440000000272013615674673014112 0ustar liggesusers\name{USProdIndex} \alias{USProdIndex} \title{Index of US Industrial Production} \description{ Index of US industrial production (1985 = 100). } \usage{data("USProdIndex")} \format{ A quarterly multiple time series from 1960(1) to 1981(4) with 2 variables. \describe{ \item{unadjusted}{raw index of industrial production,} \item{adjusted}{seasonally adjusted index.} } } \source{ Online complements to Franses (1998). } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}} \examples{ data("USProdIndex") plot(USProdIndex, plot.type = "single", col = 1:2) ## EACF tables (Franses 1998, p. 99) ctrafo <- function(x) residuals(lm(x ~ factor(cycle(x)))) ddiff <- function(x) diff(diff(x, frequency(x)), 1) eacf <- function(y, lag = 12) { stopifnot(all(lag > 0)) if(length(lag) < 2) lag <- 1:lag rval <- sapply( list(y = y, dy = diff(y), cdy = ctrafo(diff(y)), Dy = diff(y, frequency(y)), dDy = ddiff(y)), function(x) acf(x, plot = FALSE, lag.max = max(lag))$acf[lag + 1]) rownames(rval) <- lag return(rval) } ## Franses (1998), Table 5.1 round(eacf(log(USProdIndex[,1])), digits = 3) ## Franses (1998), Equation 5.6: Unrestricted airline model ## (Franses: ma1 = 0.388 (0.063), ma4 = -0.739 (0.060), ma5 = -0.452 (0.069)) arima(log(USProdIndex[,1]), c(0, 1, 5), c(0, 1, 0), fixed = c(NA, 0, 0, NA, NA)) } \keyword{datasets} AER/man/GoldSilver.Rd0000755000176200001440000000514513616355012014007 0ustar liggesusers\name{GoldSilver} \alias{GoldSilver} \title{Gold and Silver Prices} \description{ Time series of gold and silver prices. } \usage{data("GoldSilver")} \format{ A daily multiple time series from 1977-12-30 to 2012-12-31 (of class \code{"zoo"} with \code{"Date"} index). \describe{ \item{gold}{spot price for gold,} \item{silver}{spot price for silver.} } } \source{ Online complements to Franses, van Dijk and Opschoor (2014). \url{http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition} } \references{ Franses, P.H., van Dijk, D. and Opschoor, A. (2014). \emph{Time Series Models for Business and Economic Forecasting}, 2nd ed. Cambridge, UK: Cambridge University Press. } \examples{ data("GoldSilver", package = "AER") ## p.31, daily returns lgs <- log(GoldSilver) plot(lgs[, c("silver", "gold")]) dlgs <- 100 * diff(lgs) plot(dlgs[, c("silver", "gold")]) ## p.31, monthly log prices lgs7812 <- window(lgs, start = as.Date("1978-01-01")) lgs7812m <- aggregate(lgs7812, as.Date(as.yearmon(time(lgs7812))), mean) plot(lgs7812m, plot.type = "single", lty = 1:2, lwd = 2) ## p.93, empirical ACF of absolute daily gold returns, 1978-01-01 - 2012-12-31 absgret <- abs(100 * diff(lgs7812[, "gold"])) sacf <- acf(absgret, lag.max = 200, na.action = na.exclude, plot = FALSE) plot(1:201, sacf$acf, ylim = c(0.04, 0.28), type = "l", xaxs = "i", yaxs = "i", las = 1) \donttest{ ## ARFIMA(0,1,1) model, eq. (4.44) library("longmemo") WhittleEst(absgret, model = "fARIMA", p = 0, q = 1, start = list(H = 0.3, MA = .25)) library("forecast") arfima(as.vector(absgret), max.p = 0, max.q = 1) } ## p.254: VAR(2), monthly data for 1986.1 - 2012.12 library("vars") lgs8612 <- window(lgs, start = as.Date("1986-01-01")) dim(lgs8612) lgs8612m <- aggregate(lgs8612, as.Date(as.yearmon(time(lgs8612))), mean) plot(lgs8612m) dim(lgs8612m) VARselect(lgs8612m, 5) gs2 <- VAR(lgs8612m, 2) summary(gs2) summary(gs2)$covres ## ACF of residuals, p.256 acf(resid(gs2), 2, plot = FALSE) \donttest{ ## Figure 9.1, p.260 (somewhat different) plot(irf(gs2, impulse = "gold", n.ahead = 50), ylim = c(-0.02, 0.1)) plot(irf(gs2, impulse = "silver", n.ahead = 50), ylim = c(-0.02, 0.1)) } ## Table 9.2, p.261 fevd(gs2) ## p.266 ls <- lgs8612[, "silver"] lg <- lgs8612[, "gold"] gsreg <- lm(lg ~ ls) summary(gsreg) sgreg <- lm(ls ~ lg) summary(sgreg) library("tseries") adf.test(resid(gsreg), k = 0) adf.test(resid(sgreg), k = 0) } \keyword{datasets} AER/man/summary.ivreg.Rd0000644000176200001440000000650313615674673014561 0ustar liggesusers\name{summary.ivreg} \alias{summary.ivreg} \alias{print.summary.ivreg} \alias{vcov.ivreg} \alias{bread.ivreg} \alias{estfun.ivreg} \alias{anova.ivreg} \alias{hatvalues.ivreg} \alias{predict.ivreg} \alias{terms.ivreg} \alias{model.matrix.ivreg} \alias{update.ivreg} \title{Methods for Instrumental-Variable Regression} \description{ Methods to standard generics for instrumental-variable regressions fitted by \code{\link{ivreg}}. } \usage{ \method{summary}{ivreg}(object, vcov. = NULL, df = NULL, diagnostics = FALSE, \dots) \method{anova}{ivreg}(object, object2, test = "F", vcov = NULL, \dots) \method{terms}{ivreg}(x, component = c("regressors", "instruments"), \dots) \method{model.matrix}{ivreg}(object, component = c("projected", "regressors", "instruments"), \dots) } \arguments{ \item{object, object2, x}{an object of class \code{"ivreg"} as fitted by \code{\link{ivreg}}.} \item{vcov., vcov}{a specification of the covariance matrix of the estimated coefficients. This can be specified as a matrix or as a function yielding a matrix when applied to the fitted model. If it is a function it is also employed in the two diagnostic F tests (if \code{diagnostics = TRUE} in the \code{summary()} method).} \item{df}{the degrees of freedom to be used. By default this is set to residual degrees of freedom for which a t or F test is computed. Alternatively, it can be set to \code{Inf} (or equivalently \code{0}) for which a z or Chi-squared test is computed.} \item{diagnostics}{logical. Should diagnostic tests for the instrumental-variable regression be carried out? These encompass an F test of the first stage regression for weak instruments, a Wu-Hausman test for endogeneity, and a Sargan test of overidentifying restrictions (only if there are more instruments than regressors).} \item{test}{character specifying whether to compute the large sample Chi-squared statistic (with asymptotic Chi-squared distribution) or the finite sample F statistic (with approximate F distribution).} \item{component}{character specifying for which component of the terms or model matrix should be extracted. \code{"projected"} gives the matrix of regressors projected on the image of the instruments.} \item{\dots}{currently not used.} } \details{ \code{\link{ivreg}} is the high-level interface to the work-horse function \code{\link{ivreg.fit}}, a set of standard methods (including \code{summary}, \code{vcov}, \code{anova}, \code{hatvalues}, \code{predict}, \code{terms}, \code{model.matrix}, \code{update}, \code{bread}, \code{estfun}) is available. } \seealso{\code{\link{ivreg}}, \code{\link[stats:lmfit]{lm.fit}}} \examples{ ## data data("CigarettesSW") CigarettesSW$rprice <- with(CigarettesSW, price/cpi) CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi) CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi) ## model fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi), data = CigarettesSW, subset = year == "1995") summary(fm) summary(fm, vcov = sandwich, df = Inf, diagnostics = TRUE) ## ANOVA fm2 <- ivreg(log(packs) ~ log(rprice) | tdiff, data = CigarettesSW, subset = year == "1995") anova(fm, fm2, vcov = sandwich, test = "Chisq") } \keyword{regression} AER/man/GSS7402.Rd0000644000176200001440000000720013615674673012715 0ustar liggesusers\name{GSS7402} \alias{GSS7402} \title{US General Social Survey 1974--2002} \description{ Cross-section data for 9120 women taken from every fourth year of the US General Social Survey between 1974 and 2002 to investigate the determinants of fertility. } \usage{data("GSS7402")} \format{ A data frame containing 9120 observations on 10 variables. \describe{ \item{kids}{Number of children. This is coded as a numerical variable but note that the value \code{8} actually encompasses 8 or more children.} \item{age}{Age of respondent.} \item{education}{Highest year of school completed.} \item{year}{GSS year for respondent.} \item{siblings}{Number of brothers and sisters.} \item{agefirstbirth}{Woman's age at birth of first child.} \item{ethnicity}{factor indicating ethnicity. Is the individual Caucasian (\code{"cauc"}) or not (\code{"other"})?} \item{city16}{factor. Did the respondent live in a city (with population > 50,000) at age 16?} \item{lowincome16}{factor. Was the income below average at age 16?} \item{immigrant}{factor. Was the respondent (or both parents) born abroad?} } } \details{ This subset of the US General Social Survey (GSS) for every fourth year between 1974 and 2002 has been selected by Winkelmann and Boes (2009) to investigate the determinants of fertility. To do so they typically restrict their empirical analysis to the women for which the completed fertility is (assumed to be) known, employing the common cutoff of 40 years. Both, the average number of children borne to a woman and the probability of being childless, are of interest. } \source{ Online complements to Winkelmann and Boes (2009). } \references{ Winkelmann, R., and Boes, S. (2009). \emph{Analysis of Microdata}, 2nd ed. Berlin and Heidelberg: Springer-Verlag. } \seealso{\code{\link{WinkelmannBoes2009}}} \examples{ ## completed fertility subset data("GSS7402", package = "AER") gss40 <- subset(GSS7402, age >= 40) ## Chapter 1 ## exploratory statistics gss_kids <- prop.table(table(gss40$kids)) names(gss_kids)[9] <- "8+" gss_zoo <- as.matrix(with(gss40, cbind( tapply(kids, year, mean), tapply(kids, year, function(x) mean(x <= 0)), tapply(education, year, mean)))) colnames(gss_zoo) <- c("Number of children", "Proportion childless", "Years of schooling") gss_zoo <- zoo(gss_zoo, sort(unique(gss40$year))) ## visualizations instead of tables barplot(gss_kids, xlab = "Number of children ever borne to women (age 40+)", ylab = "Relative frequencies") library("lattice") trellis.par.set(theme = canonical.theme(color = FALSE)) print(xyplot(gss_zoo[,3:1], type = "b", xlab = "Year")) ## Chapter 3, Example 3.14 ## Table 3.1 gss40$nokids <- factor(gss40$kids <= 0, levels = c(FALSE, TRUE), labels = c("no", "yes")) gss40$trend <- gss40$year - 1974 nokids_p1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "probit")) nokids_p2 <- glm(nokids ~ trend, data = gss40, family = binomial(link = "probit")) nokids_p3 <- glm(nokids ~ trend + education + ethnicity + siblings, data = gss40, family = binomial(link = "probit")) lrtest(nokids_p1, nokids_p2, nokids_p3) ## Chapter 4, Figure 4.4 library("effects") nokids_p3_ef <- effect("education", nokids_p3, xlevels = list(education = 0:20)) plot(nokids_p3_ef, rescale.axis = FALSE, ylim = c(0, 0.3)) ## Chapter 8, Example 8.11 kids_pois <- glm(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16, data = gss40, family = poisson) library("MASS") kids_nb <- glm.nb(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16, data = gss40) lrtest(kids_pois, kids_nb) ## More examples can be found in: ## help("WinkelmannBoes2009") } \keyword{datasets} AER/man/TeachingRatings.Rd0000644000176200001440000000534713615674673015030 0ustar liggesusers\name{TeachingRatings} \alias{TeachingRatings} \title{Impact of Beauty on Instructor's Teaching Ratings} \description{ Data on course evaluations, course characteristics, and professor characteristics for 463 courses for the academic years 2000--2002 at the University of Texas at Austin. } \usage{data("TeachingRatings")} \format{ A data frame containing 463 observations on 13 variables. \describe{ \item{minority}{factor. Does the instructor belong to a minority (non-Caucasian)?} \item{age}{the professor's age.} \item{gender}{factor indicating instructor's gender.} \item{credits}{factor. Is the course a single-credit elective (e.g., yoga, aerobics, dance)?} \item{beauty}{rating of the instructor's physical appearance by a panel of six students, averaged across the six panelists, shifted to have a mean of zero.} \item{eval}{course overall teaching evaluation score, on a scale of 1 (very unsatisfactory) to 5 (excellent).} \item{division}{factor. Is the course an upper or lower division course? (Lower division courses are mainly large freshman and sophomore courses)?} \item{native}{factor. Is the instructor a native English speaker?} \item{tenure}{factor. Is the instructor on tenure track?} \item{students}{number of students that participated in the evaluation.} \item{allstudents}{number of students enrolled in the course.} \item{prof}{factor indicating instructor identifier.} } } \details{ A sample of student instructional ratings for a group of university teachers along with beauty rating (average from six independent judges) and a number of other characteristics. } \source{ The data were provided by Prof. Hamermesh. The first 8 variables are also available in the online complements to Stock and Watson (2007) at } \references{ Hamermesh, D.S., and Parker, A. (2005). Beauty in the Classroom: Instructors' Pulchritude and Putative Pedagogical Productivity. \emph{Economics of Education Review}, \bold{24}, 369--376. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("TeachingRatings", package = "AER") ## evaluation score vs. beauty plot(eval ~ beauty, data = TeachingRatings) fm <- lm(eval ~ beauty, data = TeachingRatings) abline(fm) summary(fm) ## prediction of Stock & Watson's evaluation score sw <- with(TeachingRatings, mean(beauty) + c(0, 1) * sd(beauty)) names(sw) <- c("Watson", "Stock") predict(fm, newdata = data.frame(beauty = sw)) ## Hamermesh and Parker, 2005, Table 3 fmw <- lm(eval ~ beauty + gender + minority + native + tenure + division + credits, weights = students, data = TeachingRatings) coeftest(fmw, vcov = vcovCL, cluster = TeachingRatings$prof) } \keyword{datasets} AER/man/TechChange.Rd0000644000176200001440000000252013615674673013735 0ustar liggesusers\name{TechChange} \alias{TechChange} \title{Technological Change Data} \description{ US time series data, 1909--1949. } \usage{data("TechChange")} \format{ An annual multiple time series from 1909 to 1949 with 3 variables. \describe{ \item{output}{Output.} \item{clr}{Capital/labor ratio.} \item{technology}{Index of technology.} } } \source{ Online complements to Greene (2003), Table F7.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Solow, R. (1957). Technical Change and the Aggregate Production Function. \emph{Review of Economics and Statistics}, \bold{39}, 312--320. } \seealso{\code{\link{Greene2003}}} \examples{ data("TechChange") ## Greene (2003) ## Exercise 7.1 fm1 <- lm(I(output/technology) ~ log(clr), data = TechChange) fm2 <- lm(I(output/technology) ~ I(1/clr), data = TechChange) fm3 <- lm(log(output/technology) ~ log(clr), data = TechChange) fm4 <- lm(log(output/technology) ~ I(1/clr), data = TechChange) ## Exercise 7.2 (a) and (c) plot(I(output/technology) ~ clr, data = TechChange) library("strucchange") sctest(I(output/technology) ~ log(clr), data = TechChange, type = "Chow", point = c(1942, 1)) } \keyword{datasets} AER/man/Affairs.Rd0000644000176200001440000000652713615674673013332 0ustar liggesusers\name{Affairs} \alias{Affairs} \title{Fair's Extramarital Affairs Data} \description{ Infidelity data, known as Fair's Affairs. Cross-section data from a survey conducted by Psychology Today in 1969. } \usage{data("Affairs")} \format{ A data frame containing 601 observations on 9 variables. \describe{ \item{affairs}{numeric. How often engaged in extramarital sexual intercourse during the past year? \code{0} = none, \code{1} = once, \code{2} = twice, \code{3} = 3 times, \code{7} = 4--10 times, \code{12} = monthly, \code{12} = weekly, \code{12} = daily.} \item{gender}{factor indicating gender.} \item{age}{numeric variable coding age in years: \code{17.5} = under 20, \code{22} = 20--24, \code{27} = 25--29, \code{32} = 30--34, \code{37} = 35--39, \code{42} = 40--44, \code{47} = 45--49, \code{52} = 50--54, \code{57} = 55 or over.} \item{yearsmarried}{numeric variable coding number of years married: \code{0.125} = 3 months or less, \code{0.417} = 4--6 months, \code{0.75} = 6 months--1 year, \code{1.5} = 1--2 years, \code{4} = 3--5 years, \code{7} = 6--8 years, \code{10} = 9--11 years, \code{15} = 12 or more years.} \item{children}{factor. Are there children in the marriage?} \item{religiousness}{numeric variable coding religiousness: \code{1} = anti, \code{2} = not at all, \code{3} = slightly, \code{4} = somewhat, \code{5} = very.} \item{education}{numeric variable coding level of education: \code{9} = grade school, \code{12} = high school graduate, \code{14} = some college, \code{16} = college graduate, \code{17} = some graduate work, \code{18} = master's degree, \code{20} = Ph.D., M.D., or other advanced degree.} \item{occupation}{numeric variable coding occupation according to Hollingshead classification (reverse numbering).} \item{rating}{numeric variable coding self rating of marriage: \code{1} = very unhappy, \code{2} = somewhat unhappy, \code{3} = average, \code{4} = happier than average, \code{5} = very happy.} } } \source{ Online complements to Greene (2003). Table F22.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Fair, R.C. (1978). A Theory of Extramarital Affairs. \emph{Journal of Political Economy}, \bold{86}, 45--61. } \seealso{\code{\link{Greene2003}}} \examples{ data("Affairs") ## Greene (2003) ## Tab. 22.3 and 22.4 fm_ols <- lm(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) fm_probit <- glm(I(affairs > 0) ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs, family = binomial(link = "probit")) fm_tobit <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) fm_tobit2 <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, right = 4, data = Affairs) fm_pois <- glm(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs, family = poisson) library("MASS") fm_nb <- glm.nb(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) ## Tab. 22.6 library("pscl") fm_zip <- zeroinfl(affairs ~ age + yearsmarried + religiousness + occupation + rating | age + yearsmarried + religiousness + occupation + rating, data = Affairs) } \keyword{datasets} AER/man/BenderlyZwick.Rd0000644000176200001440000000432413615674673014524 0ustar liggesusers\name{BenderlyZwick} \alias{BenderlyZwick} \title{Benderly and Zwick Data: Inflation, Growth and Stock Returns} \description{ Time series data, 1952--1982. } \usage{data("BenderlyZwick")} \format{ An annual multiple time series from 1952 to 1982 with 5 variables. \describe{ \item{returns}{real annual returns on stocks, measured using the Ibbotson-Sinquefeld data base.} \item{growth}{annual growth rate of output, measured by real GNP (from the given year to the next year).} \item{inflation}{inflation rate, measured as growth of price rate (from December of the previous year to December of the present year).} \item{growth2}{annual growth rate of real GNP as given by Baltagi.} \item{inflation2}{inflation rate as given by Baltagi} } } \source{ The first three columns of the data are from Table 1 in Benderly and Zwick (1985). The remaining columns are taken from the online complements of Baltagi (2002). The first column is identical in both sources, the other two variables differ in their numeric values and additionally the growth series seems to be lagged differently. Baltagi (2002) states Lott and Ray (1992) as the source for his version of the data set. } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. Benderly, J., and Zwick, B. (1985). Inflation, Real Balances, Output and Real Stock Returns. \emph{American Economic Review}, \bold{75}, 1115--1123. Lott, W.F., and Ray, S.C. (1992). \emph{Applied Econometrics: Problems with Data Sets}. New York: The Dryden Press. Zaman, A., Rousseeuw, P.J., and Orhan, M. (2001). Econometric Applications of High-Breakdown Robust Regression Techniques. \emph{Economics Letters}, \bold{71}, 1--8. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("BenderlyZwick") plot(BenderlyZwick) ## Benderly and Zwick (1985), p. 1116 library("dynlm") bz_ols <- dynlm(returns ~ growth + inflation, data = BenderlyZwick/100, start = 1956, end = 1981) summary(bz_ols) ## Zaman, Rousseeuw and Orhan (2001) ## use larger period, without scaling bz_ols2 <- dynlm(returns ~ growth + inflation, data = BenderlyZwick, start = 1954, end = 1981) summary(bz_ols2) } \keyword{datasets} AER/man/CPSSW.Rd0000644000176200001440000001050513615674673012645 0ustar liggesusers\name{CPSSW} \alias{CPSSW} \alias{CPSSW9298} \alias{CPSSW9204} \alias{CPSSW04} \alias{CPSSW3} \alias{CPSSW8} \alias{CPSSWEducation} \title{Stock and Watson CPS Data Sets} \description{ Stock and Watson (2007) provide several subsets created from March Current Population Surveys (CPS) with data on the relationship of earnings and education over several year. } \usage{ data("CPSSW9204") data("CPSSW9298") data("CPSSW04") data("CPSSW3") data("CPSSW8") data("CPSSWEducation") } \format{ \code{CPSSW9298}: A data frame containing 13,501 observations on 5 variables. \code{CPSSW9204}: A data frame containing 15,588 observations on 5 variables. \code{CPSSW04}: A data frame containing 7,986 observations on 4 variables. \code{CPSSW3}: A data frame containing 20,999 observations on 3 variables. \code{CPSSW8}: A data frame containing 61,395 observations on 5 variables. \code{CPSSWEducation}: A data frame containing 2,950 observations on 4 variables. \describe{ \item{year}{factor indicating year.} \item{earnings}{average hourly earnings (sum of annual pretax wages, salaries, tips, and bonuses, divided by the number of hours worked annually).} \item{education}{number of years of education.} \item{degree}{factor indicating highest educational degree (\code{"bachelor"} or\code{"highschool"}).} \item{gender}{factor indicating gender.} \item{age}{age in years.} \item{region}{factor indicating region of residence (\code{"Northeast"}, \code{"Midwest"}, \code{"South"}, \code{"West"}).} } } \details{ Each month the Bureau of Labor Statistics in the US Department of Labor conducts the Current Population Survey (CPS), which provides data on labor force characteristics of the population, including the level of employment, unemployment, and earnings. Approximately 65,000 randomly selected US households are surveyed each month. The sample is chosen by randomly selecting addresses from a database. Details can be found in the Handbook of Labor Statistics and is described on the Bureau of Labor Statistics website (\url{http://www.bls.gov/}). The survey conducted each March is more detailed than in other months and asks questions about earnings during the previous year. The data sets contain data for 2004 (from the March 2005 survey), and some also for earlier years (up to 1992). If education is given, it is for full-time workers, defined as workers employed more than 35 hours per week for at least 48 weeks in the previous year. Data are provided for workers whose highest educational achievement is a high school diploma and a bachelor's degree. Earnings for years earlier than 2004 were adjusted for inflation by putting them in 2004 USD using the Consumer Price Index (CPI). From 1992 to 2004, the price of the CPI market basket rose by 34.6\%. To make earnings in 1992 and 2004 comparable, 1992 earnings are inflated by the amount of overall CPI price inflation, by multiplying 1992 earnings by 1.346 to put them into 2004 dollars. \code{CPSSW9204} provides the distribution of earnings in the US in 1992 and 2004 for college-educated full-time workers aged 25--34. \code{CPSSW04} is a subset of \code{CPSSW9204} and provides the distribution of earnings in the US in 2004 for college-educated full-time workers aged 25--34. \code{CPSSWEducation} is similar (but not a true subset) and contains the distribution of earnings in the US in 2004 for college-educated full-time workers aged 29--30. \code{CPSSW8} contains a larger sample with workers aged 21--64, additionally providing information about the region of residence. \code{CPSSW9298} is similar to \code{CPSSW9204} providing data from 1992 and 1998 (with the 1992 subsets not being exactly identical). \code{CPSSW3} provides trends (from 1992 to 2004) in hourly earnings in the US of working college graduates aged 25--34 (in 2004 USD). } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{CPS1985}}, \code{\link{CPS1988}}} \examples{ data("CPSSW3") with(CPSSW3, interaction.plot(year, gender, earnings)) ## Stock and Watson, p. 165 data("CPSSWEducation") plot(earnings ~ education, data = CPSSWEducation) fm <- lm(earnings ~ education, data = CPSSWEducation) coeftest(fm, vcov = sandwich) abline(fm) } \keyword{datasets} AER/man/USMoney.Rd0000644000176200001440000000136413615674673013310 0ustar liggesusers\name{USMoney} \alias{USMoney} \title{USMoney} \description{ Money, output and price deflator time series data, 1950--1983. } \usage{data("USMoney")} \format{ A quarterly multiple time series from 1950 to 1983 with 3 variables. \describe{ \item{gnp}{nominal GNP.} \item{m1}{M1 measure of money stock.} \item{deflator}{implicit price deflator for GNP.} } } \source{ Online complements to Greene (2003), Table F20.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("USMoney") plot(USMoney) } \keyword{datasets} AER/man/CPS1985.Rd0000644000176200001440000000556013615674673012727 0ustar liggesusers\name{CPS1985} \alias{CPS1985} \title{Determinants of Wages Data (CPS 1985)} \description{ Cross-section data originating from the May 1985 Current Population Survey by the US Census Bureau (random sample drawn for Berndt 1991). } \usage{data("CPS1985")} \format{ A data frame containing 534 observations on 11 variables. \describe{ \item{wage}{Wage (in dollars per hour).} \item{education}{Number of years of education.} \item{experience}{Number of years of potential work experience (\code{age - education - 6}).} \item{age}{Age in years.} \item{ethnicity}{Factor with levels \code{"cauc"}, \code{"hispanic"}, \code{"other"}.} \item{region}{Factor. Does the individual live in the South?} \item{gender}{Factor indicating gender.} \item{occupation}{Factor with levels \code{"worker"} (tradesperson or assembly line worker), \code{"technical"} (technical or professional worker), \code{"services"} (service worker), \code{"office"} (office and clerical worker), \code{"sales"} (sales worker), \code{"management"} (management and administration).} \item{sector}{Factor with levels \code{"manufacturing"} (manufacturing or mining), \code{"construction"}, \code{"other"}.} \item{union}{Factor. Does the individual work on a union job?} \item{married}{Factor. Is the individual married?} } } \source{ StatLib. \url{http://lib.stat.cmu.edu/datasets/CPS_85_Wages} } \references{ Berndt, E.R. (1991). \emph{The Practice of Econometrics}. New York: Addison-Wesley. } \seealso{\code{\link{CPS1988}}, \code{\link{CPSSW}}} \examples{ data("CPS1985") ## Berndt (1991) ## Exercise 2, p. 196 cps_2b <- lm(log(wage) ~ union + education, data = CPS1985) cps_2c <- lm(log(wage) ~ -1 + union + education, data = CPS1985) ## Exercise 3, p. 198/199 cps_3a <- lm(log(wage) ~ education + experience + I(experience^2), data = CPS1985) cps_3b <- lm(log(wage) ~ gender + education + experience + I(experience^2), data = CPS1985) cps_3c <- lm(log(wage) ~ gender + married + education + experience + I(experience^2), data = CPS1985) cps_3e <- lm(log(wage) ~ gender*married + education + experience + I(experience^2), data = CPS1985) ## Exercise 4, p. 199/200 cps_4a <- lm(log(wage) ~ gender + union + ethnicity + education + experience + I(experience^2), data = CPS1985) cps_4c <- lm(log(wage) ~ gender + union + ethnicity + education * experience + I(experience^2), data = CPS1985) ## Exercise 6, p. 203 cps_6a <- lm(log(wage) ~ gender + union + ethnicity + education + experience + I(experience^2), data = CPS1985) cps_6a_noeth <- lm(log(wage) ~ gender + union + education + experience + I(experience^2), data = CPS1985) anova(cps_6a_noeth, cps_6a) ## Exercise 8, p. 208 cps_8a <- lm(log(wage) ~ gender + union + ethnicity + education + experience + I(experience^2), data = CPS1985) summary(cps_8a) coeftest(cps_8a, vcov = vcovHC(cps_8a, type = "HC0")) } \keyword{datasets} AER/man/USMacroSWM.Rd0000644000176200001440000000173113615674673013647 0ustar liggesusers\name{USMacroSWM} \alias{USMacroSWM} \title{Monthly US Macroeconomic Data (1947--2004, Stock \& Watson)} \description{ Time series data on 4 US macroeconomic variables for 1947--2004. } \usage{data("USMacroSWM")} \format{ A monthly multiple time series from 1947(1) to 2004(4) with 4 variables. \describe{ \item{production}{index of industrial production.} \item{oil}{oil price shocks, starting 1948(1).} \item{cpi}{all-items consumer price index.} \item{expenditure}{personal consumption expenditures price deflator, starting 1959(1).} } } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{USMacroSW}}, \code{\link{USMacroSWQ}}, \code{\link{USMacroB}}, \code{\link{USMacroG}}} \examples{ data("USMacroSWM") plot(USMacroSWM) } \keyword{datasets} AER/man/ivreg.fit.Rd0000644000176200001440000000555213615674673013651 0ustar liggesusers\name{ivreg.fit} \alias{ivreg.fit} \title{Fitting Instrumental-Variable Regressions} \description{ Fit instrumental-variable regression by two-stage least squares. This is equivalent to direct instrumental-variables estimation when the number of instruments is equal to the number of predictors. } \usage{ ivreg.fit(x, y, z, weights, offset, \dots) } \arguments{ \item{x}{regressor matrix.} \item{y}{vector with dependent variable.} \item{z}{instruments matrix.} \item{weights}{an optional vector of weights to be used in the fitting process.} \item{offset}{an optional offset that can be used to specify an a priori known component to be included during fitting.} \item{\dots}{further arguments passed to \code{\link[stats:lmfit]{lm.fit}} or \code{\link[stats]{lm.wfit}}, respectively.} } \details{ \code{\link{ivreg}} is the high-level interface to the work-horse function \code{ivreg.fit}, a set of standard methods (including \code{summary}, \code{vcov}, \code{anova}, \code{hatvalues}, \code{predict}, \code{terms}, \code{model.matrix}, \code{bread}, \code{estfun}) is available and described on \code{\link{summary.ivreg}}. \code{ivreg.fit} is a convenience interface to \code{\link{lm.fit}} (or \code{\link{lm.wfit}}) for first projecting \code{x} onto the image of \code{z} and the running a regression of \code{y} onto the projected \code{x}. } \value{ \code{ivreg.fit} returns an unclassed list with the following components: \item{coefficients}{parameter estimates.} \item{residuals}{a vector of residuals.} \item{fitted.values}{a vector of predicted means.} \item{weights}{either the vector of weights used (if any) or \code{NULL} (if none).} \item{offset}{either the offset used (if any) or \code{NULL} (if none).} \item{estfun}{a matrix containing the empirical estimating functions.} \item{n}{number of observations.} \item{nobs}{number of observations with non-zero weights.} \item{rank}{the numeric rank of the fitted linear model.} \item{df.residual}{residual degrees of freedom for fitted model.} \item{cov.unscaled}{unscaled covariance matrix for the coefficients.} \item{sigma}{residual standard error.} } \seealso{\code{\link{ivreg}}, \code{\link[stats:lmfit]{lm.fit}}} \examples{ ## data data("CigarettesSW") CigarettesSW$rprice <- with(CigarettesSW, price/cpi) CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi) CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi) ## high-level interface fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi), data = CigarettesSW, subset = year == "1995") ## low-level interface y <- fm$y x <- model.matrix(fm, component = "regressors") z <- model.matrix(fm, component = "instruments") ivreg.fit(x, y, z)$coefficients } \keyword{regression} AER/man/MASchools.Rd0000644000176200001440000000577713615674673013615 0ustar liggesusers\name{MASchools} \alias{MASchools} \title{Massachusetts Test Score Data} \description{The dataset contains data on test performance, school characteristics and student demographic backgrounds for school districts in Massachusetts. } \usage{data("MASchools")} \format{ A data frame containing 220 observations on 16 variables. \describe{ \item{district}{character. District code.} \item{municipality}{character. Municipality name.} \item{expreg}{Expenditures per pupil, regular.} \item{expspecial}{Expenditures per pupil, special needs.} \item{expbil}{Expenditures per pupil, bilingual.} \item{expocc}{Expenditures per pupil, occupational.} \item{exptot}{Expenditures per pupil, total.} \item{scratio}{Students per computer.} \item{special}{Special education students (per cent).} \item{lunch}{Percent qualifying for reduced-price lunch.} \item{stratio}{Student-teacher ratio.} \item{income}{Per capita income.} \item{score4}{4th grade score (math + English + science).} \item{score8}{8th grade score (math + English + science).} \item{salary}{Average teacher salary.} \item{english}{Percent of English learners.} } } \details{The Massachusetts data are district-wide averages for public elementary school districts in 1998. The test score is taken from the Massachusetts Comprehensive Assessment System (MCAS) test, administered to all fourth graders in Massachusetts public schools in the spring of 1998. The test is sponsored by the Massachusetts Department of Education and is mandatory for all public schools. The data analyzed here are the overall total score, which is the sum of the scores on the English, Math, and Science portions of the test. Data on the student-teacher ratio, the percent of students receiving a subsidized lunch and on the percent of students still learning english are averages for each elementary school district for the 1997--1998 school year and were obtained from the Massachusetts department of education. Data on average district income are from the 1990 US Census. } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J. H. and Watson, M. W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{CASchools}}} \examples{ ## Massachusetts data("MASchools") ## compare with California data("CASchools") CASchools$stratio <- with(CASchools, students/teachers) CASchools$score4 <- with(CASchools, (math + read)/2) ## Stock and Watson, parts of Table 9.1, p. 330 vars <- c("score4", "stratio", "english", "lunch", "income") cbind( CA_mean = sapply(CASchools[, vars], mean), CA_sd = sapply(CASchools[, vars], sd), MA_mean = sapply(MASchools[, vars], mean), MA_sd = sapply(MASchools[, vars], sd)) ## Stock and Watson, Table 9.2, p. 332, col. (1) fm1 <- lm(score4 ~ stratio, data = MASchools) coeftest(fm1, vcov = vcovHC(fm1, type = "HC1")) ## More examples, notably the entire Table 9.2, can be found in: ## help("StockWatson2007") } \keyword{datasets} AER/man/USMacroSWQ.Rd0000644000176200001440000000167413615674673013661 0ustar liggesusers\name{USMacroSWQ} \alias{USMacroSWQ} \title{Quarterly US Macroeconomic Data (1947--2004, Stock \& Watson)} \description{ Time series data on 2 US macroeconomic variables for 1947--2004. } \usage{data("USMacroSWQ")} \format{ A quarterly multiple time series from 1947(1) to 2004(4) with 2 variables. \describe{ \item{gdp}{real GDP for the United States in billions of chained (2000) dollars seasonally adjusted, annual rate.} \item{tbill}{3-month treasury bill rate. Quarterly averages of daily dates in percentage points at an annual rate.} } } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{USMacroSW}}, \code{\link{USMacroSWM}}, \code{\link{USMacroB}}, \code{\link{USMacroG}}} \examples{ data("USMacroSWQ") plot(USMacroSWQ) } \keyword{datasets} AER/man/dispersiontest.Rd0000644000176200001440000000744313615674673015034 0ustar liggesusers\name{dispersiontest} \alias{dispersiontest} \title{Dispersion Test} \description{ Tests the null hypothesis of equidispersion in Poisson GLMs against the alternative of overdispersion and/or underdispersion. } \usage{ dispersiontest(object, trafo = NULL, alternative = c("greater", "two.sided", "less")) } \arguments{ \item{object}{a fitted Poisson GLM of class \code{"glm"} as fitted by \code{\link{glm}} with family \code{\link{poisson}}.} \item{trafo}{a specification of the alternative (see also details), can be numeric or a (positive) function or \code{NULL} (the default).} \item{alternative}{a character string specifying the alternative hypothesis: \code{"greater"} corresponds to overdispersion, \code{"less"} to underdispersion and \code{"two.sided"} to either one.} } \details{ The standard Poisson GLM models the (conditional) mean \eqn{\mathsf{E}[y] = \mu}{E[y] = mu} which is assumed to be equal to the variance \eqn{\mathsf{VAR}[y] = \mu}{VAR[y] = mu}. \code{dispersiontest} assesses the hypothesis that this assumption holds (equidispersion) against the alternative that the variance is of the form: \deqn{\mathsf{VAR}[y] \quad = \quad \mu \; + \; \alpha \cdot \mathrm{trafo}(\mu).}{VAR[y] = mu + alpha * trafo(mu).} Overdispersion corresponds to \eqn{\alpha > 0}{alpha > 0} and underdispersion to \eqn{\alpha < 0}{alpha < 0}. The coefficient \eqn{\alpha}{alpha} can be estimated by an auxiliary OLS regression and tested with the corresponding t (or z) statistic which is asymptotically standard normal under the null hypothesis. Common specifications of the transformation function \eqn{\mathrm{trafo}}{trafo} are \eqn{\mathrm{trafo}(\mu) = \mu^2}{trafo(mu) = mu^2} or \eqn{\mathrm{trafo}(\mu) = \mu}{trafo(mu) = mu}. The former corresponds to a negative binomial (NB) model with quadratic variance function (called NB2 by Cameron and Trivedi, 2005), the latter to a NB model with linear variance function (called NB1 by Cameron and Trivedi, 2005) or quasi-Poisson model with dispersion parameter, i.e., \deqn{\mathsf{VAR}[y] \quad = \quad (1 + \alpha) \cdot \mu = \mathrm{dispersion} \cdot \mu.}{VAR[y] = (1 + alpha) * mu = dispersion * mu.} By default, for \code{trafo = NULL}, the latter dispersion formulation is used in \code{dispersiontest}. Otherwise, if \code{trafo} is specified, the test is formulated in terms of the parameter \eqn{\alpha}{alpha}. The transformation \code{trafo} can either be specified as a function or an integer corresponding to the function \code{function(x) x^trafo}, such that \code{trafo = 1} and \code{trafo = 2} yield the linear and quadratic formulations respectively. } \value{An object of class \code{"htest"}.} \references{ Cameron, A.C. and Trivedi, P.K. (1990). Regression-based Tests for Overdispersion in the Poisson Model. \emph{Journal of Econometrics}, \bold{46}, 347--364. Cameron, A.C. and Trivedi, P.K. (1998). \emph{Regression Analysis of Count Data}. Cambridge: Cambridge University Press. Cameron, A.C. and Trivedi, P.K. (2005). \emph{Microeconometrics: Methods and Applications}. Cambridge: Cambridge University Press. } \seealso{\code{\link{glm}}, \code{\link{poisson}}, \code{\link[MASS]{glm.nb}}} \examples{ data("RecreationDemand") rd <- glm(trips ~ ., data = RecreationDemand, family = poisson) ## linear specification (in terms of dispersion) dispersiontest(rd) ## linear specification (in terms of alpha) dispersiontest(rd, trafo = 1) ## quadratic specification (in terms of alpha) dispersiontest(rd, trafo = 2) dispersiontest(rd, trafo = function(x) x^2) ## further examples data("DoctorVisits") dv <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = poisson) dispersiontest(dv) data("NMES1988") nmes <- glm(visits ~ health + age + gender + married + income + insurance, data = NMES1988, family = poisson) dispersiontest(nmes) } \keyword{htest} AER/man/DoctorVisits.Rd0000644000176200001440000000503313615674673014402 0ustar liggesusers\name{DoctorVisits} \alias{DoctorVisits} \title{Australian Health Service Utilization Data} \description{ Cross-section data originating from the 1977--1978 Australian Health Survey. } \usage{data("DoctorVisits")} \format{ A data frame containing 5,190 observations on 12 variables. \describe{ \item{visits}{Number of doctor visits in past 2 weeks.} \item{gender}{Factor indicating gender.} \item{age}{Age in years divided by 100.} \item{income}{Annual income in tens of thousands of dollars.} \item{illness}{Number of illnesses in past 2 weeks.} \item{reduced}{Number of days of reduced activity in past 2 weeks due to illness or injury.} \item{health}{General health questionnaire score using Goldberg's method.} \item{private}{Factor. Does the individual have private health insurance?} \item{freepoor}{Factor. Does the individual have free government health insurance due to low income?} \item{freerepat}{Factor. Does the individual have free government health insurance due to old age, disability or veteran status?} \item{nchronic}{Factor. Is there a chronic condition not limiting activity?} \item{lchronic}{Factor. Is there a chronic condition limiting activity?} } } \source{ Journal of Applied Econometrics Data Archive. \url{http://qed.econ.queensu.ca/jae/1997-v12.3/mullahy/} } \references{ Cameron, A.C. and Trivedi, P.K. (1986). Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests. \emph{Journal of Applied Econometrics}, \bold{1}, 29--53. Cameron, A.C. and Trivedi, P.K. (1998). \emph{Regression Analysis of Count Data}. Cambridge: Cambridge University Press. Mullahy, J. (1997). Heterogeneity, Excess Zeros, and the Structure of Count Data Models. \emph{Journal of Applied Econometrics}, \bold{12}, 337--350. } \seealso{\code{\link{CameronTrivedi1998}}} \examples{ data("DoctorVisits", package = "AER") library("MASS") ## Cameron and Trivedi (1986), Table III, col. (1) dv_lm <- lm(visits ~ . + I(age^2), data = DoctorVisits) summary(dv_lm) ## Cameron and Trivedi (1998), Table 3.3 dv_pois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = poisson) summary(dv_pois) ## MLH standard errors coeftest(dv_pois, vcov = vcovOPG) ## MLOP standard errors logLik(dv_pois) ## standard errors denoted RS ("unspecified omega robust sandwich estimate") coeftest(dv_pois, vcov = sandwich) ## Cameron and Trivedi (1986), Table III, col. (4) dv_nb <- glm.nb(visits ~ . + I(age^2), data = DoctorVisits) summary(dv_nb) logLik(dv_nb) } \keyword{datasets} AER/man/CameronTrivedi1998.Rd0000644000176200001440000001205213616354333015200 0ustar liggesusers\name{CameronTrivedi1998} \alias{CameronTrivedi1998} \title{Data and Examples from Cameron and Trivedi (1998)} \description{ This manual page collects a list of examples from the book. Some solutions might not be exact and the list is certainly not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. } \references{ Cameron, A.C. and Trivedi, P.K. (1998). \emph{Regression Analysis of Count Data}. Cambridge: Cambridge University Press. } \seealso{\code{\link{DoctorVisits}}, \code{\link{NMES1988}}, \code{\link{RecreationDemand}}} \examples{ \donttest{ library("MASS") library("pscl") ########################################### ## Australian health service utilization ## ########################################### ## data data("DoctorVisits", package = "AER") ## Poisson regression dv_pois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = poisson) dv_qpois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = quasipoisson) ## Table 3.3 round(cbind( Coef = coef(dv_pois), MLH = sqrt(diag(vcov(dv_pois))), MLOP = sqrt(diag(vcovOPG(dv_pois))), NB1 = sqrt(diag(vcov(dv_qpois))), RS = sqrt(diag(sandwich(dv_pois))) ), digits = 3) ## Table 3.4 ## NM2-ML dv_nb <- glm.nb(visits ~ . + I(age^2), data = DoctorVisits) summary(dv_nb) ## NB1-GLM = quasipoisson summary(dv_qpois) ## overdispersion tests (page 79) lrtest(dv_pois, dv_nb) ## p-value would need to be halved dispersiontest(dv_pois, trafo = 1) dispersiontest(dv_pois, trafo = 2) ########################################## ## Demand for medical care in NMES 1988 ## ########################################## ## select variables for analysis data("NMES1988", package = "AER") nmes <- NMES1988[,-(2:6)] ## dependent variable ## Table 6.1 table(cut(nmes$visits, c(0:13, 100)-0.5, labels = 0:13)) ## NegBin regression nmes_nb <- glm.nb(visits ~ ., data = nmes) ## NegBin hurdle nmes_h <- hurdle(visits ~ ., data = nmes, dist = "negbin") ## from Table 6.3 lrtest(nmes_nb, nmes_h) ## from Table 6.4 AIC(nmes_nb) AIC(nmes_nb, k = log(nrow(nmes))) AIC(nmes_h) AIC(nmes_h, k = log(nrow(nmes))) ## Table 6.8 coeftest(nmes_h, vcov = sandwich) logLik(nmes_h) 1/nmes_h$theta ################################################### ## Recreational boating trips to Lake Somerville ## ################################################### ## data data("RecreationDemand", package = "AER") ## Poisson model: ## Cameron and Trivedi (1998), Table 6.11 ## Ozuna and Gomez (1995), Table 2, col. 3 fm_pois <- glm(trips ~ ., data = RecreationDemand, family = poisson) summary(fm_pois) logLik(fm_pois) coeftest(fm_pois, vcov = sandwich) ## Negbin model: ## Cameron and Trivedi (1998), Table 6.11 ## Ozuna and Gomez (1995), Table 2, col. 5 library("MASS") fm_nb <- glm.nb(trips ~ ., data = RecreationDemand) coeftest(fm_nb, vcov = vcovOPG) logLik(fm_nb) ## ZIP model: ## Cameron and Trivedi (1998), Table 6.11 fm_zip <- zeroinfl(trips ~ . | quality + income, data = RecreationDemand) summary(fm_zip) logLik(fm_zip) ## Hurdle models ## Cameron and Trivedi (1998), Table 6.13 ## poisson-poisson sval <- list(count = c(2.15, 0.044, .467, -.097, .601, .002, -.036, .024), zero = c(-1.88, 0.815, .403, .01, 2.95, 0.006, -.052, .046)) fm_hp0 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson", zero = "poisson", start = sval, maxit = 0) fm_hp1 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson", zero = "poisson", start = sval) fm_hp2 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson", zero = "poisson") sapply(list(fm_hp0, fm_hp1, fm_hp2), logLik) ## negbin-negbin fm_hnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "negbin") summary(fm_hnb) logLik(fm_hnb) sval <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012), zero = c(-3.046, 4.638, -.025, .026, 16.203, 0.030, -.156, .117), theta = c(count = 1/1.7, zero = 1/5.609)) fm_hnb2 <- try(hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "negbin", start = sval)) if(!inherits(fm_hnb2, "try-error")) { summary(fm_hnb2) logLik(fm_hnb2) } ## geo-negbin sval98 <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012), zero = c(-2.88, 1.44, .4, .03, 9.43, 0.01, -.08, .071), theta = c(count = 1/1.7)) sval96 <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012), zero = c(-2.882, 1.437, .406, .026, 11.936, 0.008, -.081, .071), theta = c(count = 1/1.7)) fm_hgnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "geometric") summary(fm_hgnb) logLik(fm_hgnb) ## logLik with starting values from Gurmu + Trivedi 1996 fm_hgnb96 <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "geometric", start = sval96, maxit = 0) logLik(fm_hgnb96) ## logit-negbin fm_hgnb2 <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin") summary(fm_hgnb2) logLik(fm_hgnb2) ## Note: quasi-complete separation with(RecreationDemand, table(trips > 0, userfee)) } } \keyword{datasets} AER/man/USMacroG.Rd0000644000176200001440000000504113615674673013365 0ustar liggesusers\name{USMacroG} \alias{USMacroG} \title{US Macroeconomic Data (1950--2000, Greene)} \description{ Time series data on 12 US macroeconomic variables for 1950--2000. } \usage{data("USMacroG")} \format{ A quarterly multiple time series from 1950(1) to 2000(4) with 12 variables. \describe{ \item{gdp}{Real gross domestic product (in billion USD),} \item{consumption}{Real consumption expenditures,} \item{invest}{Real investment by private sector,} \item{government}{Real government expenditures,} \item{dpi}{Real disposable personal income,} \item{cpi}{Consumer price index,} \item{m1}{Nominal money stock,} \item{tbill}{Quarterly average of month end 90 day treasury bill rate,} \item{unemp}{Unemployment rate,} \item{population}{Population (in million), interpolation of year end figures using constant growth rate per quarter,} \item{inflation}{Inflation rate,} \item{interest}{Ex post real interest rate (essentially, \code{tbill - inflation}).} } } \source{ Online complements to Greene (2003). Table F5.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}, \code{\link{USMacroSW}}, \code{\link{USMacroSWQ}}, \code{\link{USMacroSWM}}, \code{\link{USMacroB}}} \examples{ ## data and trend as used by Greene (2003) data("USMacroG") ltrend <- 1:nrow(USMacroG) - 1 ## Example 6.1 ## Table 6.1 library("dynlm") fm6.1 <- dynlm(log(invest) ~ tbill + inflation + log(gdp) + ltrend, data = USMacroG) fm6.3 <- dynlm(log(invest) ~ I(tbill - inflation) + log(gdp) + ltrend, data = USMacroG) summary(fm6.1) summary(fm6.3) deviance(fm6.1) deviance(fm6.3) vcov(fm6.1)[2,3] ## F test linearHypothesis(fm6.1, "tbill + inflation = 0") ## alternatively anova(fm6.1, fm6.3) ## t statistic sqrt(anova(fm6.1, fm6.3)[2,5]) ## Example 8.2 ## Ct = b0 + b1*Yt + b2*Y(t-1) + v fm1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG) ## Ct = a0 + a1*Yt + a2*C(t-1) + u fm2 <- dynlm(consumption ~ dpi + L(consumption), data = USMacroG) ## Cox test in both directions: coxtest(fm1, fm2) ## ...and do the same for jtest() and encomptest(). ## Notice that in this particular case two of them are coincident. jtest(fm1, fm2) encomptest(fm1, fm2) ## encomptest could also be performed `by hand' via fmE <- dynlm(consumption ~ dpi + L(dpi) + L(consumption), data = USMacroG) waldtest(fm1, fmE, fm2) ## More examples can be found in: ## help("Greene2003") } \keyword{datasets} AER/man/USConsump1950.Rd0000644000176200001440000000325713615674673014167 0ustar liggesusers\name{USConsump1950} \alias{USConsump1950} \title{US Consumption Data (1940--1950)} \description{ Time series data on US income and consumption expenditure, 1940--1950. } \usage{data("USConsump1950")} \format{ An annual multiple time series from 1940 to 1950 with 3 variables. \describe{ \item{income}{Disposable income.} \item{expenditure}{Consumption expenditure.} \item{war}{Indicator variable: Was the year a year of war?} } } \source{ Online complements to Greene (2003). Table F2.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}, \code{\link{USConsump1979}}, \code{\link{USConsump1993}}} \examples{ ## Greene (2003) ## data data("USConsump1950") usc <- as.data.frame(USConsump1950) usc$war <- factor(usc$war, labels = c("no", "yes")) ## Example 2.1 plot(expenditure ~ income, data = usc, type = "n", xlim = c(225, 375), ylim = c(225, 350)) with(usc, text(income, expenditure, time(USConsump1950))) ## single model fm <- lm(expenditure ~ income, data = usc) summary(fm) ## different intercepts for war yes/no fm2 <- lm(expenditure ~ income + war, data = usc) summary(fm2) ## compare anova(fm, fm2) ## visualize abline(fm, lty = 3) abline(coef(fm2)[1:2]) abline(sum(coef(fm2)[c(1, 3)]), coef(fm2)[2], lty = 2) ## Example 3.2 summary(fm)$r.squared summary(lm(expenditure ~ income, data = usc, subset = war == "no"))$r.squared summary(fm2)$r.squared } \keyword{datasets} AER/man/CollegeDistance.Rd0000644000176200001440000000473713615674673015005 0ustar liggesusers\name{CollegeDistance} \alias{CollegeDistance} \title{College Distance Data} \description{ Cross-section data from the High School and Beyond survey conducted by the Department of Education in 1980, with a follow-up in 1986. The survey included students from approximately 1,100 high schools. } \usage{data("CollegeDistance")} \format{ A data frame containing 4,739 observations on 14 variables. \describe{ \item{gender}{factor indicating gender.} \item{ethnicity}{factor indicating ethnicity (African-American, Hispanic or other).} \item{score}{base year composite test score. These are achievement tests given to high school seniors in the sample.} \item{fcollege}{factor. Is the father a college graduate?} \item{mcollege}{factor. Is the mother a college graduate?} \item{home}{factor. Does the family own their home?} \item{urban}{factor. Is the school in an urban area?} \item{unemp}{county unemployment rate in 1980.} \item{wage}{state hourly wage in manufacturing in 1980.} \item{distance}{distance from 4-year college (in 10 miles).} \item{tuition}{average state 4-year college tuition (in 1000 USD).} \item{education}{number of years of education.} \item{income}{factor. Is the family income above USD 25,000 per year?} \item{region}{factor indicating region (West or other).} } } \details{ Rouse (1995) computed years of education by assigning 12 years to all members of the senior class. Each additional year of secondary education counted as a one year. Students with vocational degrees were assigned 13 years, AA degrees were assigned 14 years, BA degrees were assigned 16 years, those with some graduate education were assigned 17 years, and those with a graduate degree were assigned 18 years. Stock and Watson (2007) provide separate data files for the students from Western states and the remaining students. \code{CollegeDistance} includes both data sets, subsets are easily obtained (see also examples). } \source{ Online complements to Stock and Watson (2007). } \references{ Rouse, C.E. (1995). Democratization or Diversion? The Effect of Community Colleges on Educational Attainment. \emph{Journal of Business \& Economic Statistics}, \bold{12}, 217--224. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ ## exclude students from Western states data("CollegeDistance") cd <- subset(CollegeDistance, region != "west") summary(cd) } \keyword{datasets} AER/man/ConsumerGood.Rd0000644000176200001440000000131313615674673014347 0ustar liggesusers\name{ConsumerGood} \alias{ConsumerGood} \title{Properties of a Fast-Moving Consumer Good} \description{ Time series of distribution, market share and price of a fast-moving consumer good. } \usage{data("ConsumerGood")} \format{ A weekly multiple time series from 1989(11) to 1991(9) with 3 variables. \describe{ \item{distribution}{Distribution.} \item{share}{Market share.} \item{price}{Price.} } } \source{ Online complements to Franses (1998). } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}} \examples{ data("ConsumerGood") plot(ConsumerGood) } \keyword{datasets} AER/man/Journals.Rd0000644000176200001440000000637513615674673013555 0ustar liggesusers\name{Journals} \alias{Journals} \title{Economics Journal Subscription Data} \description{ Subscriptions to economics journals at US libraries, for the year 2000. } \usage{data("Journals")} \format{ A data frame containing 180 observations on 10 variables. \describe{ \item{title}{Journal title.} \item{publisher}{factor with publisher name.} \item{society}{factor. Is the journal published by a scholarly society?} \item{price}{Library subscription price.} \item{pages}{Number of pages.} \item{charpp}{Characters per page.} \item{citations}{Total number of citations.} \item{foundingyear}{Year journal was founded.} \item{subs}{Number of library subscriptions.} \item{field}{factor with field description.} } } \details{ Data on 180 economic journals, collected in particular for analyzing journal pricing. See also \url{http://www.econ.ucsb.edu/~tedb/Journals/jpricing.html} for general information on this topic as well as a more up-to-date version of the data set. This version is taken from Stock and Watson (2007). The data as obtained from the online complements for Stock and Watson (2007) contained two journals with title \dQuote{World Development}. One of these (observation 80) seemed to be an error and was changed to \dQuote{The World Economy}. } \source{ Online complements to Stock and Watson (2007). } \references{ Bergstrom, T. (2001). Free Labor for Costly Journals? \emph{Journal of Economic Perspectives}, 15, 183--198. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ ## data and transformed variables data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations journals$age <- 2000 - Journals$foundingyear journals$chars <- Journals$charpp*Journals$pages/10^6 ## Stock and Watson (2007) ## Figure 8.9 (a) and (b) plot(subs ~ citeprice, data = journals, pch = 19) plot(log(subs) ~ log(citeprice), data = journals, pch = 19) fm1 <- lm(log(subs) ~ log(citeprice), data = journals) abline(fm1) ## Table 8.2, use HC1 for comparability with Stata fm2 <- lm(subs ~ citeprice + age + chars, data = log(journals)) fm3 <- lm(subs ~ citeprice + I(citeprice^2) + I(citeprice^3) + age + I(age * citeprice) + chars, data = log(journals)) fm4 <- lm(subs ~ citeprice + age + I(age * citeprice) + chars, data = log(journals)) coeftest(fm1, vcov = vcovHC(fm1, type = "HC1")) coeftest(fm2, vcov = vcovHC(fm2, type = "HC1")) coeftest(fm3, vcov = vcovHC(fm3, type = "HC1")) coeftest(fm4, vcov = vcovHC(fm4, type = "HC1")) waldtest(fm3, fm4, vcov = vcovHC(fm3, type = "HC1")) ## changes with respect to age library("strucchange") ## Nyblom-Hansen test scus <- gefp(subs ~ citeprice, data = log(journals), fit = lm, order.by = ~ age) plot(scus, functional = meanL2BB) ## estimate breakpoint(s) journals <- journals[order(journals$age),] bp <- breakpoints(subs ~ citeprice, data = log(journals), h = 20) plot(bp) bp.age <- journals$age[bp$breakpoints] ## visualization plot(subs ~ citeprice, data = log(journals), pch = 19, col = (age > log(bp.age)) + 1) abline(coef(bp)[1,], col = 1) abline(coef(bp)[2,], col = 2) legend("bottomleft", legend = c("age > 18", "age < 18"), lty = 1, col = 2:1, bty = "n") } \keyword{datasets} AER/man/MarkPound.Rd0000644000176200001440000000371613615674673013654 0ustar liggesusers\name{MarkPound} \alias{MarkPound} \title{DEM/GBP Exchange Rate Returns} \description{ A daily time series of percentage returns of Deutsche mark/British pound (DEM/GBP) exchange rates from 1984-01-03 through 1991-12-31. } \usage{data("MarkPound")} \format{ A univariate time series of 1974 returns (exact dates unknown) for the DEM/GBP exchange rate. } \details{ Greene (2003, Table F11.1) rounded the series to six digits while eight digits are given in Bollerslev and Ghysels (1996). Here, we provide the original data. Using \code{\link{round}} a series can be produced that is virtually identical to that of Greene (2003) (except for eight observations where a slightly different rounding arithmetic was used). } \source{ Journal of Business \& Economic Statistics Data Archive. \verb{http://www.amstat.org/publications/jbes/upload/index.cfm?fuseaction=ViewArticles&pub=JBES&issue=96-2-APR} } \references{ Bollerslev, T., and Ghysels, E. (1996). Periodic Autoregressive Conditional Heteroskedasticity. \emph{Journal of Business \& Economic Statistics}, \bold{14}, 139--151. Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}, \code{\link{MarkDollar}}} \examples{ ## data as given by Greene (2003) data("MarkPound") mp <- round(MarkPound, digits = 6) ## Figure 11.3 in Greene (2003) plot(mp) ## Example 11.8 in Greene (2003), Table 11.5 library("tseries") mp_garch <- garch(mp, grad = "numerical") summary(mp_garch) logLik(mp_garch) ## Greene (2003) also includes a constant and uses different ## standard errors (presumably computed from Hessian), here ## OPG standard errors are used. garchFit() in "fGarch" ## implements the approach used by Greene (2003). ## compare Errata to Greene (2003) library("dynlm") res <- residuals(dynlm(mp ~ 1))^2 mp_ols <- dynlm(res ~ L(res, 1:10)) summary(mp_ols) logLik(mp_ols) summary(mp_ols)$r.squared * length(residuals(mp_ols)) } \keyword{datasets} AER/man/RecreationDemand.Rd0000644000176200001440000000653513615674673015162 0ustar liggesusers\name{RecreationDemand} \alias{RecreationDemand} \title{Recreation Demand Data} \description{ Cross-section data on the number of recreational boating trips to Lake Somerville, Texas, in 1980, based on a survey administered to 2,000 registered leisure boat owners in 23 counties in eastern Texas. } \usage{data("RecreationDemand")} \format{ A data frame containing 659 observations on 8 variables. \describe{ \item{trips}{Number of recreational boating trips.} \item{quality}{Facility's subjective quality ranking on a scale of 1 to 5.} \item{ski}{factor. Was the individual engaged in water-skiing at the lake?} \item{income}{Annual household income of the respondent (in 1,000 USD).} \item{userfee}{factor. Did the individual pay an annual user fee at Lake Somerville?} \item{costC}{Expenditure when visiting Lake Conroe (in USD).} \item{costS}{Expenditure when visiting Lake Somerville (in USD).} \item{costH}{Expenditure when visiting Lake Houston (in USD).} } } \details{ According to the original source (Seller, Stoll and Chavas, 1985, p. 168), the quality rating is on a scale from 1 to 5 and gives 0 for those who had not visited the lake. This explains the remarkably low mean for this variable, but also suggests that its treatment in various more recent publications is far from ideal. For consistency with other sources we handle the variable as a numerical variable, including the zeros. } \source{ Journal of Business \& Economic Statistics Data Archive. \verb{http://www.amstat.org/publications/jbes/upload/index.cfm?fuseaction=ViewArticles&pub=JBES&issue=96-4-OCT} } \references{ Cameron, A.C. and Trivedi, P.K. (1998). \emph{Regression Analysis of Count Data}. Cambridge: Cambridge University Press. Gurmu, S. and Trivedi, P.K. (1996). Excess Zeros in Count Models for Recreational Trips. \emph{Journal of Business \& Economic Statistics}, \bold{14}, 469--477. Ozuna, T. and Gomez, I.A. (1995). Specification and Testing of Count Data Recreation Demand Functions. \emph{Empirical Economics}, \bold{20}, 543--550. Seller, C., Stoll, J.R. and Chavas, J.-P. (1985). Validation of Empirical Measures of Welfare Change: A Comparison of Nonmarket Techniques. \emph{Land Economics}, \bold{61}, 156--175. } \seealso{\code{\link{CameronTrivedi1998}}} \examples{ data("RecreationDemand") ## Poisson model: ## Cameron and Trivedi (1998), Table 6.11 ## Ozuna and Gomez (1995), Table 2, col. 3 fm_pois <- glm(trips ~ ., data = RecreationDemand, family = poisson) summary(fm_pois) logLik(fm_pois) coeftest(fm_pois, vcov = sandwich) ## Negbin model: ## Cameron and Trivedi (1998), Table 6.11 ## Ozuna and Gomez (1995), Table 2, col. 5 library("MASS") fm_nb <- glm.nb(trips ~ ., data = RecreationDemand) coeftest(fm_nb, vcov = vcovOPG) ## ZIP model: ## Cameron and Trivedi (1998), Table 6.11 library("pscl") fm_zip <- zeroinfl(trips ~ . | quality + income, data = RecreationDemand) summary(fm_zip) ## Hurdle models ## Cameron and Trivedi (1998), Table 6.13 ## poisson-poisson fm_hp <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson", zero = "poisson") ## negbin-negbin fm_hnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "negbin") ## binom-negbin == geo-negbin fm_hgnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin") ## Note: quasi-complete separation with(RecreationDemand, table(trips > 0, userfee)) } \keyword{datasets} AER/man/ivreg.Rd0000644000176200001440000001250613615674673013065 0ustar liggesusers\name{ivreg} \alias{ivreg} \alias{print.ivreg} \title{Instrumental-Variable Regression} \description{ Fit instrumental-variable regression by two-stage least squares. This is equivalent to direct instrumental-variables estimation when the number of instruments is equal to the number of predictors. } \usage{ ivreg(formula, instruments, data, subset, na.action, weights, offset, contrasts = NULL, model = TRUE, y = TRUE, x = FALSE, \dots) } \arguments{ \item{formula, instruments}{formula specification(s) of the regression relationship and the instruments. Either \code{instruments} is missing and \code{formula} has three parts as in \code{y ~ x1 + x2 | z1 + z2 + z3} (recommended) or \code{formula} is \code{y ~ x1 + x2} and \code{instruments} is a one-sided formula \code{~ z1 + z2 + z3} (only for backward compatibility).} \item{data}{an optional data frame containing the variables in the model. By default the variables are taken from the environment of the \code{formula}.} \item{subset}{an optional vector specifying a subset of observations to be used in fitting the model.} \item{na.action}{a function that indicates what should happen when the data contain \code{NA}s. The default is set by the \code{na.action} option.} \item{weights}{an optional vector of weights to be used in the fitting process.} \item{offset}{an optional offset that can be used to specify an a priori known component to be included during fitting.} \item{contrasts}{an optional list. See the \code{contrasts.arg} of \code{\link[stats:model.matrix]{model.matrix.default}}.} \item{model, x, y}{logicals. If \code{TRUE} the corresponding components of the fit (the model frame, the model matrices , the response) are returned.} \item{\dots}{further arguments passed to \code{\link{ivreg.fit}}.} } \details{ \code{ivreg} is the high-level interface to the work-horse function \code{\link{ivreg.fit}}, a set of standard methods (including \code{print}, \code{summary}, \code{vcov}, \code{anova}, \code{hatvalues}, \code{predict}, \code{terms}, \code{model.matrix}, \code{bread}, \code{estfun}) is available and described on \code{\link{summary.ivreg}}. Regressors and instruments for \code{ivreg} are most easily specified in a formula with two parts on the right-hand side, e.g., \code{y ~ x1 + x2 | z1 + z2 + z3}, where \code{x1} and \code{x2} are the regressors and \code{z1}, \code{z2}, and \code{z3} are the instruments. Note that exogenous regressors have to be included as instruments for themselves. For example, if there is one exogenous regressor \code{ex} and one endogenous regressor \code{en} with instrument \code{in}, the appropriate formula would be \code{y ~ ex + en | ex + in}. Equivalently, this can be specified as \code{y ~ ex + en | . - en + in}, i.e., by providing an update formula with a \code{.} in the second part of the formula. The latter is typically more convenient, if there is a large number of exogenous regressors. } \value{ \code{ivreg} returns an object of class \code{"ivreg"}, with the following components: \item{coefficients}{parameter estimates.} \item{residuals}{a vector of residuals.} \item{fitted.values}{a vector of predicted means.} \item{weights}{either the vector of weights used (if any) or \code{NULL} (if none).} \item{offset}{either the offset used (if any) or \code{NULL} (if none).} \item{n}{number of observations.} \item{nobs}{number of observations with non-zero weights.} \item{rank}{the numeric rank of the fitted linear model.} \item{df.residual}{residual degrees of freedom for fitted model.} \item{cov.unscaled}{unscaled covariance matrix for the coefficients.} \item{sigma}{residual standard error.} \item{call}{the original function call.} \item{formula}{the model formula.} \item{terms}{a list with elements \code{"regressors"} and \code{"instruments"} containing the terms objects for the respective components.} \item{levels}{levels of the categorical regressors.} \item{contrasts}{the contrasts used for categorical regressors.} \item{model}{the full model frame (if \code{model = TRUE}).} \item{y}{the response vector (if \code{y = TRUE}).} \item{x}{a list with elements \code{"regressors"}, \code{"instruments"}, \code{"projected"}, containing the model matrices from the respective components (if \code{x = TRUE}). \code{"projected"} is the matrix of regressors projected on the image of the instruments.} } \references{ Greene, W. H. (1993) \emph{Econometric Analysis}, 2nd ed., Macmillan. } \seealso{\code{\link{ivreg.fit}}, \code{\link[stats]{lm}}, \code{\link[stats:lmfit]{lm.fit}}} \examples{ ## data data("CigarettesSW", package = "AER") CigarettesSW$rprice <- with(CigarettesSW, price/cpi) CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi) CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi) ## model fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi), data = CigarettesSW, subset = year == "1995") summary(fm) summary(fm, vcov = sandwich, df = Inf, diagnostics = TRUE) ## ANOVA fm2 <- ivreg(log(packs) ~ log(rprice) | tdiff, data = CigarettesSW, subset = year == "1995") anova(fm, fm2) } \keyword{regression} AER/man/Municipalities.Rd0000644000176200001440000000320413615674673014723 0ustar liggesusers\name{Municipalities} \alias{Municipalities} \title{Municipal Expenditure Data} \description{ Panel data set for 265 Swedish municipalities covering 9 years (1979-1987). } \usage{data("Municipalities")} \format{ A data frame containing 2,385 observations on 5 variables. \describe{ \item{municipality}{factor with ID number for municipality.} \item{year}{factor coding year.} \item{expenditures}{total expenditures.} \item{revenues}{total own-source revenues.} \item{grants}{intergovernmental grants received by the municipality.} } } \details{ Total expenditures contains both capital and current expenditures. Expenditures, revenues, and grants are expressed in million SEK. The series are deflated and in per capita form. The implicit deflator is a municipality-specific price index obtained by dividing total local consumption expenditures at current prices by total local consumption expenditures at fixed (1985) prices. The data are gathered by Statistics Sweden and obtained from Financial Accounts for the Municipalities (Kommunernas Finanser). } \source{ Journal of Applied Econometrics Data Archive. \url{http://qed.econ.queensu.ca/jae/2000-v15.4/dahlberg-johansson/} } \references{ Dahlberg, M., and Johansson, E. (2000). An Examination of the Dynamic Behavior of Local Governments Using GMM Bootstrapping Methods. \emph{Journal of Applied Econometrics}, \bold{15}, 401--416. Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ ## Greene (2003), Table 18.2 data("Municipalities") summary(Municipalities) } \keyword{datasets} AER/man/Medicaid1986.Rd0000644000176200001440000000720513615674673014000 0ustar liggesusers\name{Medicaid1986} \alias{Medicaid1986} \title{Medicaid Utilization Data} \description{ Cross-section data originating from the 1986 Medicaid Consumer Survey. The data comprise two groups of Medicaid eligibles at two sites in California (Santa Barbara and Ventura counties): a group enrolled in a managed care demonstration program and a fee-for-service comparison group of non-enrollees. } \usage{data("Medicaid1986")} \format{ A data frame containing 996 observations on 14 variables. \describe{ \item{visits}{Number of doctor visits.} \item{exposure}{Length of observation period for ambulatory care (days).} \item{children}{Total number of children in the household.} \item{age}{Age of the respondent.} \item{income}{Annual household income (average of income range in million USD).} \item{health1}{The first principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions.} \item{health2}{The second principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions.} \item{access}{Availability of health services (0 = low access, 1 = high access).} \item{married}{Factor. Is the individual married?} \item{gender}{Factor indicating gender.} \item{ethnicity}{Factor indicating ethnicity (\code{"cauc"} or \code{"other"}).} \item{school}{Number of years completed in school.} \item{enroll}{Factor. Is the individual enrolled in a demonstration program?} \item{program}{Factor indicating the managed care demonstration program: Aid to Families with Dependent Children (\code{"afdc"}) or non-institutionalized Supplementary Security Income (\code{"ssi"}).} } } \source{ Journal of Applied Econometrics Data Archive. \url{http://qed.econ.queensu.ca/jae/1997-v12.3/gurmu/} } \references{ Gurmu, S. (1997). Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization. \emph{Journal of Applied Econometrics}, \bold{12}, 225--242. } \examples{ ## data and packages data("Medicaid1986") library("MASS") library("pscl") ## scale regressors Medicaid1986$age2 <- Medicaid1986$age^2 / 100 Medicaid1986$school <- Medicaid1986$school / 10 Medicaid1986$income <- Medicaid1986$income / 10 ## subsets afdc <- subset(Medicaid1986, program == "afdc")[, c(1, 3:4, 15, 5:9, 11:13)] ssi <- subset(Medicaid1986, program == "ssi")[, c(1, 3:4, 15, 5:13)] ## Gurmu (1997): ## Table VI., Poisson and negbin models afdc_pois <- glm(visits ~ ., data = afdc, family = poisson) summary(afdc_pois) coeftest(afdc_pois, vcov = sandwich) afdc_nb <- glm.nb(visits ~ ., data = afdc) ssi_pois <- glm(visits ~ ., data = ssi, family = poisson) ssi_nb <- glm.nb(visits ~ ., data = ssi) ## Table VII., Hurdle models (without semi-parametric effects) afdc_hurdle <- hurdle(visits ~ . | . - access, data = afdc, dist = "negbin") ssi_hurdle <- hurdle(visits ~ . | . - access, data = ssi, dist = "negbin") ## Table VIII., Observed and expected frequencies round(cbind( Observed = table(afdc$visits)[1:8], Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(afdc_pois)))), Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(afdc_nb), size = afdc_nb$theta))), Hurdle = colSums(predict(afdc_hurdle, type = "prob")[,1:8]) )/nrow(afdc), digits = 3) * 100 round(cbind( Observed = table(ssi$visits)[1:8], Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(ssi_pois)))), Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(ssi_nb), size = ssi_nb$theta))), Hurdle = colSums(predict(ssi_hurdle, type = "prob")[,1:8]) )/nrow(ssi), digits = 3) * 100 } \keyword{datasets} AER/man/SIC33.Rd0000644000176200001440000000332113615674673012530 0ustar liggesusers\name{SIC33} \alias{SIC33} \title{SIC33 Production Data} \description{ Statewide production data for primary metals industry (SIC 33). } \usage{data("SIC33")} \format{ A data frame containing 27 observations on 3 variables. \describe{ \item{output}{Value added.} \item{labor}{Labor input.} \item{capital}{Capital stock.} } } \source{ Online complements to Greene (2003). Table F6.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("SIC33") ## Example 6.2 in Greene (2003) ## Translog model fm_tl <- lm(output ~ labor + capital + I(0.5 * labor^2) + I(0.5 * capital^2) + I(labor * capital), data = log(SIC33)) ## Cobb-Douglas model fm_cb <- lm(output ~ labor + capital, data = log(SIC33)) ## Table 6.2 in Greene (2003) deviance(fm_tl) deviance(fm_cb) summary(fm_tl) summary(fm_cb) vcov(fm_tl) vcov(fm_cb) ## Cobb-Douglas vs. Translog model anova(fm_cb, fm_tl) ## hypothesis of constant returns linearHypothesis(fm_cb, "labor + capital = 1") ## 3D Visualization if(require("scatterplot3d")) { s3d <- scatterplot3d(log(SIC33)[,c(2, 3, 1)], pch = 16) s3d$plane3d(fm_cb, lty.box = "solid", col = 4) } ## Interactive 3D Visualization \donttest{ if(require("rgl")) { x <- log(SIC33)[,2] y <- log(SIC33)[,3] z <- log(SIC33)[,1] rgl.open() rgl.bbox() rgl.spheres(x, y, z, radius = 0.15) x <- seq(4.5, 7.5, by = 0.5) y <- seq(5.5, 10, by = 0.5) z <- outer(x, y, function(x, y) predict(fm_cb, data.frame(labor = x, capital = y))) rgl.surface(x, y, z, color = "blue", alpha = 0.5, shininess = 128) } } } \keyword{datasets} AER/man/OECDGas.Rd0000644000176200001440000000217113615674673013113 0ustar liggesusers\name{OECDGas} \alias{OECDGas} \title{Gasoline Consumption Data} \description{ Panel data on gasoline consumption in 18 OECD countries over 19 years, 1960--1978. } \usage{data("OECDGas")} \format{ A data frame containing 342 observations on 6 variables. \describe{ \item{country}{Factor indicating country.} \item{year}{Year.} \item{gas}{Logarithm of motor gasoline consumption per car.} \item{income}{Logarithm of real per-capita income.} \item{price}{Logarithm of real motor gasoline price.} \item{cars}{Logarithm of the stock of cars per-capita.} } } \source{ The data is from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. Baltagi, B.H. and Griffin, J.M. (1983). Gasoline Demand in the OECD: An Application of Pooling and Testing Procedures. \emph{European Economic Review}, \bold{22}, 117--137. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("OECDGas") library("lattice") xyplot(exp(cars) ~ year | country, data = OECDGas, type = "l") xyplot(exp(gas) ~ year | country, data = OECDGas, type = "l") } \keyword{datasets} AER/man/WeakInstrument.Rd0000644000176200001440000000131713615674673014727 0ustar liggesusers\name{WeakInstrument} \alias{WeakInstrument} \title{Artificial Weak Instrument Data} \description{ Artificial data set to illustrate the problem of weak instruments. } \usage{data("WeakInstrument")} \format{ A data frame containing 200 observations on 3 variables. \describe{ \item{y}{dependent variable.} \item{x}{regressor variable.} \item{z}{instrument variable.} } } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("WeakInstrument") fm <- ivreg(y ~ x | z, data = WeakInstrument) summary(fm) } \keyword{datasets} AER/man/STAR.Rd0000644000176200001440000002056113615674673012522 0ustar liggesusers\name{STAR} \alias{STAR} \title{Project STAR: Student-Teacher Achievement Ratio} \description{ The Project STAR public access data set, assessing the effect of reducing class size on test scores in the early grades. } \usage{data("STAR")} \format{ A data frame containing 11,598 observations on 47 variables. \describe{ \item{gender}{factor indicating student's gender.} \item{ethnicity}{factor indicating student's ethnicity with levels \code{"cauc"} (Caucasian), \code{"afam"} (African-American), \code{"asian"} (Asian), \code{"hispanic"} (Hispanic), \code{"amindian"} (American-Indian) or \code{"other"}.} \item{birth}{student's birth quarter (of class \code{\link[zoo]{yearqtr}}).} \item{stark}{factor indicating the STAR class type in kindergarten: regular, small, or regular-with-aide. \code{NA} indicates that no STAR class was attended.} \item{star1}{factor indicating the STAR class type in 1st grade: regular, small, or regular-with-aide. \code{NA} indicates that no STAR class was attended.} \item{star2}{factor indicating the STAR class type in 2nd grade: regular, small, or regular-with-aide. \code{NA} indicates that no STAR class was attended.} \item{star3}{factor indicating the STAR class type in 3rd grade: regular, small, or regular-with-aide. \code{NA} indicates that no STAR class was attended.} \item{readk}{total reading scaled score in kindergarten.} \item{read1}{total reading scaled score in 1st grade.} \item{read2}{total reading scaled score in 2nd grade.} \item{read3}{total reading scaled score in 3rd grade.} \item{mathk}{total math scaled score in kindergarten.} \item{math1}{total math scaled score in 1st grade.} \item{math2}{total math scaled score in 2nd grade.} \item{math3}{total math scaled score in 3rd grade.} \item{lunchk}{factor indicating whether the student qualified for free lunch in kindergarten.} \item{lunch1}{factor indicating whether the student qualified for free lunch in 1st grade.} \item{lunch2}{factor indicating whether the student qualified for free lunch in 2nd grade.} \item{lunch3}{factor indicating whether the student qualified for free lunch in 3rd grade.} \item{schoolk}{factor indicating school type in kindergarten: \code{"inner-city"}, \code{"suburban"}, \code{"rural"} or \code{"urban"}.} \item{school1}{factor indicating school type in 1st grade: \code{"inner-city"}, \code{"suburban"}, \code{"rural"} or \code{"urban"}.} \item{school2}{factor indicating school type in 2nd grade: \code{"inner-city"}, \code{"suburban"}, \code{"rural"} or \code{"urban"}.} \item{school3}{factor indicating school type in 3rd grade: \code{"inner-city"}, \code{"suburban"}, \code{"rural"} or \code{"urban"}.} \item{degreek}{factor indicating highest degree of kindergarten teacher: \code{"bachelor"}, \code{"master"}, \code{"specialist"}, or \code{"master+"}.} \item{degree1}{factor indicating highest degree of 1st grade teacher: \code{"bachelor"}, \code{"master"}, \code{"specialist"}, or \code{"phd"}.} \item{degree2}{factor indicating highest degree of 2nd grade teacher: \code{"bachelor"}, \code{"master"}, \code{"specialist"}, or \code{"phd"}.} \item{degree3}{factor indicating highest degree of 3rd grade teacher: \code{"bachelor"}, \code{"master"}, \code{"specialist"}, or \code{"phd"}.} \item{ladderk}{factor indicating teacher's career ladder level in kindergarten: \code{"level1"}, \code{"level2"}, \code{"level3"}, \code{"apprentice"}, \code{"probation"} or \code{"pending"}.} \item{ladder1}{factor indicating teacher's career ladder level in 1st grade: \code{"level1"}, \code{"level2"}, \code{"level3"}, \code{"apprentice"}, \code{"probation"} or \code{"noladder"}.} \item{ladder2}{factor indicating teacher's career ladder level in 2nd grade: \code{"level1"}, \code{"level2"}, \code{"level3"}, \code{"apprentice"}, \code{"probation"} or \code{"noladder"}.} \item{ladder3}{factor indicating teacher's career ladder level in 3rd grade: \code{"level1"}, \code{"level2"}, \code{"level3"}, \code{"apprentice"}, \code{"probation"} or \code{"noladder"}.} \item{experiencek}{years of teacher's total teaching experience in kindergarten.} \item{experience1}{years of teacher's total teaching experience in 1st grade.} \item{experience2}{years of teacher's total teaching experience in 2nd grade.} \item{experience3}{years of teacher's total teaching experience in 3rd grade.} \item{tethnicityk}{factor indicating teacher's ethnicity in kindergarten with levels \code{"cauc"} (Caucasian) or \code{"afam"} (African-American).} \item{tethnicity1}{factor indicating teacher's ethnicity in 1st grade with levels \code{"cauc"} (Caucasian) or \code{"afam"} (African-American).} \item{tethnicity2}{factor indicating teacher's ethnicity in 2nd grade with levels \code{"cauc"} (Caucasian) or \code{"afam"} (African-American).} \item{tethnicity3}{factor indicating teacher's ethnicity in 3rd grade with levels \code{"cauc"} (Caucasian), \code{"afam"} (African-American), or \code{"asian"} (Asian).} \item{systemk}{factor indicating school system ID in kindergarten.} \item{system1}{factor indicating school system ID in 1st grade.} \item{system2}{factor indicating school system ID in 2nd grade.} \item{system3}{factor indicating school system ID in 3rd grade.} \item{schoolidk}{factor indicating school ID in kindergarten.} \item{schoolid1}{factor indicating school ID in 1st grade.} \item{schoolid2}{factor indicating school ID in 2nd grade.} \item{schoolid3}{factor indicating school ID in 3rd grade.} } } \details{ Project STAR (Student/Teacher Achievement Ratio) was a four-year longitudinal class-size study funded by the Tennessee General Assembly and conducted in the late 1980s by the State Department of Education. Over 7,000 students in 79 schools were randomly assigned into one of three interventions: small class (13 to 17 students per teacher), regular class (22 to 25 students per teacher), and regular-with-aide class (22 to 25 students with a full-time teacher's aide). Classroom teachers were also randomly assigned to the classes they would teach. The interventions were initiated as the students entered school in kindergarten and continued through third grade. The Project STAR public access data set contains data on test scores, treatment groups, and student and teacher characteristics for the four years of the experiment, from academic year 1985--1986 to academic year 1988--1989. The test score data analyzed in this chapter are the sum of the scores on the math and reading portion of the Stanford Achievement Test. Stock and Watson (2007) obtained the data set from the Project STAR Web site. The data is provided in wide format. Reshaping it into long format is illustrated below. Note that the levels of the \code{degree}, \code{ladder} and \code{tethnicity} variables differ slightly between kindergarten and higher grades. } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("STAR") ## Stock and Watson, p. 488 fmk <- lm(I(readk + mathk) ~ stark, data = STAR) fm1 <- lm(I(read1 + math1) ~ star1, data = STAR) fm2 <- lm(I(read2 + math2) ~ star2, data = STAR) fm3 <- lm(I(read3 + math3) ~ star3, data = STAR) coeftest(fm3, vcov = sandwich) plot(I(read3 + math3) ~ star3, data = STAR) ## Stock and Watson, p. 489 fmke <- lm(I(readk + mathk) ~ stark + experiencek, data = STAR) coeftest(fmke, vcov = sandwich) ## reshape data from wide into long format ## 1. variables and their levels nam <- c("star", "read", "math", "lunch", "school", "degree", "ladder", "experience", "tethnicity", "system", "schoolid") lev <- c("k", "1", "2", "3") ## 2. reshaping star <- reshape(STAR, idvar = "id", ids = row.names(STAR), times = lev, timevar = "grade", direction = "long", varying = lapply(nam, function(x) paste(x, lev, sep = ""))) ## 3. improve variable names and type names(star)[5:15] <- nam star$id <- factor(star$id) star$grade <- factor(star$grade, levels = lev, labels = c("kindergarten", "1st", "2nd", "3rd")) rm(nam, lev) ## fit a single model nested in grade (equivalent to fmk, fm1, fm2, fmk) fm <- lm(I(read + math) ~ 0 + grade/star, data = star) coeftest(fm, vcov = sandwich) ## visualization library("lattice") bwplot(I(read + math) ~ star | grade, data = star) } \keyword{datasets} AER/man/KleinI.Rd0000644000176200001440000000274513615674673013130 0ustar liggesusers\name{KleinI} \alias{KleinI} \title{Klein Model I} \description{ Klein's Model I for the US economy. } \usage{data("KleinI")} \format{ An annual multiple time series from 1920 to 1941 with 9 variables. \describe{ \item{consumption}{Consumption.} \item{cprofits}{Corporate profits.} \item{pwage}{Private wage bill.} \item{invest}{Investment.} \item{capital}{Previous year's capital stock.} \item{gnp}{Gross national product.} \item{gwage}{Government wage bill.} \item{gexpenditure}{Government spending.} \item{taxes}{Taxes.} } } \source{ Online complements to Greene (2003). Table F15.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Klein, L. (1950). \emph{Economic Fluctuations in the United States, 1921--1941}. New York: John Wiley. Maddala, G.S. (1977). \emph{Econometrics}. New York: McGraw-Hill. } \seealso{\code{\link{Greene2003}}} \examples{ data("KleinI", package = "AER") plot(KleinI) ## Greene (2003), Tab. 15.3, OLS library("dynlm") fm_cons <- dynlm(consumption ~ cprofits + L(cprofits) + I(pwage + gwage), data = KleinI) fm_inv <- dynlm(invest ~ cprofits + L(cprofits) + capital, data = KleinI) fm_pwage <- dynlm(pwage ~ gnp + L(gnp) + I(time(gnp) - 1931), data = KleinI) summary(fm_cons) summary(fm_inv) summary(fm_pwage) ## More examples can be found in: ## help("Greene2003") } \keyword{datasets} AER/man/USAirlines.Rd0000644000176200001440000000413413615674673013765 0ustar liggesusers\name{USAirlines} \alias{USAirlines} \title{Cost Data for US Airlines} \description{ Cost data for six US airlines in 1970--1984. } \usage{data("USAirlines")} \format{ A data frame containing 90 observations on 6 variables. \describe{ \item{firm}{factor indicating airline firm.} \item{year}{factor indicating year.} \item{output}{output revenue passenger miles index number.} \item{cost}{total cost (in USD 1000).} \item{price}{fuel price.} \item{load}{average capacity utilization of the fleet.} } } \source{ Online complements to Greene (2003). Table F7.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("USAirlines") ## Example 7.2 in Greene (2003) fm_full <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + year + firm, data = USAirlines) fm_time <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + year, data = USAirlines) fm_firm <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + firm, data = USAirlines) fm_no <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = USAirlines) ## Table 7.2 anova(fm_full, fm_time) anova(fm_full, fm_firm) anova(fm_full, fm_no) ## alternatively, use plm() library("plm") usair <- pdata.frame(USAirlines, c("firm", "year")) fm_full2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "within", effect = "twoways") fm_time2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "within", effect = "time") fm_firm2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "within", effect = "individual") fm_no2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "pooling") pFtest(fm_full2, fm_time2) pFtest(fm_full2, fm_firm2) pFtest(fm_full2, fm_no2) ## More examples can be found in: ## help("Greene2003") } \keyword{datasets} AER/man/HousePrices.Rd0000644000176200001440000000430213615674673014175 0ustar liggesusers\name{HousePrices} \alias{HousePrices} \title{House Prices in the City of Windsor, Canada} \description{ Sales prices of houses sold in the city of Windsor, Canada, during July, August and September, 1987. } \usage{data("HousePrices")} \format{ A data frame containing 546 observations on 12 variables. \describe{ \item{price}{Sale price of a house.} \item{lotsize}{Lot size of a property in square feet.} \item{bedrooms}{Number of bedrooms.} \item{bathrooms}{Number of full bathrooms.} \item{stories}{Number of stories excluding basement.} \item{driveway}{Factor. Does the house have a driveway?} \item{recreation}{Factor. Does the house have a recreational room?} \item{fullbase}{Factor. Does the house have a full finished basement?} \item{gasheat}{Factor. Does the house use gas for hot water heating?} \item{aircon}{Factor. Is there central air conditioning?} \item{garage}{Number of garage places.} \item{prefer}{Factor. Is the house located in the preferred neighborhood of the city?} } } \source{ Journal of Applied Econometrics Data Archive. \url{http://qed.econ.queensu.ca/jae/1996-v11.6/anglin-gencay/} } \references{ Anglin, P., and Gencay, R. (1996). Semiparametric Estimation of a Hedonic Price Function. \emph{Journal of Applied Econometrics}, \bold{11}, 633--648. Verbeek, M. (2004). \emph{A Guide to Modern Econometrics}, 2nd ed. Chichester, UK: John Wiley. } \examples{ data("HousePrices") ### Anglin + Gencay (1996), Table II fm_ag <- lm(log(price) ~ driveway + recreation + fullbase + gasheat + aircon + garage + prefer + log(lotsize) + log(bedrooms) + log(bathrooms) + log(stories), data = HousePrices) ### Anglin + Gencay (1996), Table III fm_ag2 <- lm(log(price) ~ driveway + recreation + fullbase + gasheat + aircon + garage + prefer + log(lotsize) + bedrooms + bathrooms + stories, data = HousePrices) ### Verbeek (2004), Table 3.1 fm <- lm(log(price) ~ log(lotsize) + bedrooms + bathrooms + aircon, data = HousePrices) summary(fm) ### Verbeek (2004), Table 3.2 fm_ext <- lm(log(price) ~ . - lotsize + log(lotsize), data = HousePrices) summary(fm_ext) ### Verbeek (2004), Table 3.3 fm_lin <- lm(price ~ . , data = HousePrices) summary(fm_lin) } \keyword{datasets} AER/man/Electricity1955.Rd0000644000176200001440000000360313615674673014553 0ustar liggesusers\name{Electricity1955} \alias{Electricity1955} \title{Cost Function of Electricity Producers (1955, Nerlove Data)} \description{ Cost function data for 145 (+14) US electricity producers in 1955. } \usage{data("Electricity1955")} \format{ A data frame containing 159 observations on 8 variables. \describe{ \item{cost}{total cost.} \item{output}{total output.} \item{labor}{wage rate.} \item{laborshare}{cost share for labor.} \item{capital}{capital price index.} \item{capitalshare}{cost share for capital.} \item{fuel}{fuel price.} \item{fuelshare}{cost share for fuel.} } } \details{ The data contains several extra observations that are aggregates of commonly owned firms. Only the first 145 observations should be used for analysis. } \source{ Online complements to Greene (2003). Table F14.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Nerlove, M. (1963) \dQuote{Returns to Scale in Electricity Supply.} In C. Christ (ed.), \emph{Measurement in Economics: Studies in Mathematical Economics and Econometrics in Memory of Yehuda Grunfeld}. Stanford University Press, 1963. } \seealso{\code{\link{Greene2003}}, \code{\link{Electricity1970}}} \examples{ data("Electricity1955") Electricity <- Electricity1955[1:145,] ## Greene (2003) ## Example 7.3 ## Cobb-Douglas cost function fm_all <- lm(log(cost/fuel) ~ log(output) + log(labor/fuel) + log(capital/fuel), data = Electricity) summary(fm_all) ## hypothesis of constant returns to scale linearHypothesis(fm_all, "log(output) = 1") ## Table 7.4 ## log quadratic cost function fm_all2 <- lm(log(cost/fuel) ~ log(output) + I(log(output)^2) + log(labor/fuel) + log(capital/fuel), data = Electricity) summary(fm_all2) ## More examples can be found in: ## help("Greene2003") } \keyword{datasets} AER/man/Parade2005.Rd0000644000176200001440000000320413615674673013447 0ustar liggesusers\name{Parade2005} \alias{Parade2005} \title{Parade Magazine 2005 Earnings Data} \description{ US earnings data, as provided in an annual survey of Parade (here from 2005), the Sunday newspaper magazine supplementing the Sunday (or Weekend) edition of many daily newspapers in the USA. } \usage{data("Parade2005")} \format{ A data frame containing 130 observations on 5 variables. \describe{ \item{earnings}{Annual personal earnings.} \item{age}{Age in years.} \item{gender}{Factor indicating gender.} \item{state}{Factor indicating state.} \item{celebrity}{Factor. Is the individual a celebrity?} } } \details{ In addition to the four variables provided by Parade (earnings, age, gender, and state), a fifth variable was introduced, the \dQuote{celebrity factor} (here actors, athletes, TV personalities, politicians, and CEOs are considered celebrities). The data are quite far from a simple random sample, there being substantial oversampling of celebrities. } \source{ Parade (2005). What People Earn. Issue March 13, 2005. } \examples{ ## data data("Parade2005") attach(Parade2005) summary(Parade2005) ## bivariate visualizations plot(density(log(earnings), bw = "SJ"), type = "l", main = "log(earnings)") rug(log(earnings)) plot(log(earnings) ~ gender, main = "log(earnings)") ## celebrity vs. non-celebrity earnings noncel <- subset(Parade2005, celebrity == "no") cel <- subset(Parade2005, celebrity == "yes") library("ineq") plot(Lc(noncel$earnings), main = "log(earnings)") lines(Lc(cel$earnings), lty = 2) lines(Lc(earnings), lty = 3) Gini(noncel$earnings) Gini(cel$earnings) Gini(earnings) ## detach data detach(Parade2005) } \keyword{datasets} AER/man/OrangeCounty.Rd0000644000176200001440000000122413615674673014361 0ustar liggesusers\name{OrangeCounty} \alias{OrangeCounty} \title{Orange County Employment} \description{ Quarterly time series data on employment in Orange county, 1965--1983. } \usage{data("OrangeCounty")} \format{ A quarterly multiple time series from 1965 to 1983 with 2 variables. \describe{ \item{employment}{Quarterly employment in Orange county.} \item{gnp}{Quarterly real GNP.} } } \source{ The data is from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("OrangeCounty") plot(OrangeCounty) } \keyword{datasets} AER/man/USMacroB.Rd0000644000176200001440000000155713615674673013370 0ustar liggesusers\name{USMacroB} \alias{USMacroB} \title{US Macroeconomic Data (1959--1995, Baltagi)} \description{ Time series data on 3 US macroeconomic variables for 1959--1995, extracted from the Citibank data base. } \usage{data("USMacroB")} \format{ A quarterly multiple time series from 1959(1) to 1995(2) with 3 variables. \describe{ \item{gnp}{Gross national product.} \item{mbase}{Average of the seasonally adjusted monetary base.} \item{tbill}{Average of 3 month treasury-bill rate (per annum).} } } \source{ The data is from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. } \seealso{\code{\link{Baltagi2002}}, \code{\link{USMacroSW}}, \code{\link{USMacroSWQ}}, \code{\link{USMacroSWM}}, \code{\link{USMacroG}}} \examples{ data("USMacroB") plot(USMacroB) } \keyword{datasets} AER/man/ManufactCosts.Rd0000644000176200001440000000223013615674673014514 0ustar liggesusers\name{ManufactCosts} \alias{ManufactCosts} \title{Manufacturing Costs Data} \description{ US time series data on prices and cost shares in manufacturing, 1947--1971. } \usage{data("ManufactCosts")} \format{ An annual multiple time series from 1947 to 1971 with 9 variables. \describe{ \item{cost}{Cost index.} \item{capitalcost}{Capital cost share.} \item{laborcost}{Labor cost share.} \item{energycost}{Energy cost share.} \item{materialscost}{Materials cost share.} \item{capitalprice}{Capital price.} \item{laborprice}{Labor price.} \item{energyprice}{Energy price.} \item{materialsprice}{Materials price.} } } \source{ Online complements to Greene (2003). \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Berndt, E. and Wood, D. (1975). Technology, Prices, and the Derived Demand for Energy. \emph{Review of Economics and Statistics}, \bold{57}, 376--384. Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("ManufactCosts") plot(ManufactCosts) } \keyword{datasets} AER/man/NYSESW.Rd0000644000176200001440000000136013615674673012775 0ustar liggesusers\name{NYSESW} \alias{NYSESW} \title{Daily NYSE Composite Index} \description{ A daily time series from 1990 to 2005 of the New York Stock Exchange composite index. } \usage{data("NYSESW")} \format{ A daily univariate time series from 1990-01-02 to 2005-11-11 (of class \code{"zoo"} with \code{"Date"} index). } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ ## returns data("NYSESW") ret <- 100 * diff(log(NYSESW)) plot(ret) ## Stock and Watson (2007), p. 667, GARCH(1,1) model library("tseries") fm <- garch(coredata(ret)) summary(fm) } \keyword{datasets} AER/man/GermanUnemployment.Rd0000644000176200001440000000134613615674673015577 0ustar liggesusers\name{GermanUnemployment} \alias{GermanUnemployment} \title{Unemployment in Germany Data} \description{ Time series of unemployment rate (in percent) in Germany. } \usage{data("GermanUnemployment")} \format{ A quarterly multiple time series from 1962(1) to 1991(4) with 2 variables. \describe{ \item{unadjusted}{Raw unemployment rate,} \item{adjusted}{Seasonally adjusted rate.} } } \source{ Online complements to Franses (1998). } \seealso{\code{\link{Franses1998}}} \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \examples{ data("GermanUnemployment") plot(GermanUnemployment, plot.type = "single", col = 1:2) } \keyword{datasets} AER/man/DutchAdvert.Rd0000644000176200001440000000337713615674673014174 0ustar liggesusers\name{DutchAdvert} \alias{DutchAdvert} \title{TV and Radio Advertising Expenditures Data} \description{ Time series of television and radio advertising expenditures (in real terms) in The Netherlands. } \usage{data("DutchAdvert")} \format{ A four-weekly multiple time series from 1978(1) to 1994(13) with 2 variables. \describe{ \item{tv}{Television advertising expenditures.} \item{radio}{Radio advertising expenditures.} } } \source{ Originally available as an online supplement to Franses (1998). Now available via online complements to Franses, van Dijk and Opschoor (2014). \url{http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition} } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. Franses, P.H., van Dijk, D. and Opschoor, A. (2014). \emph{Time Series Models for Business and Economic Forecasting}, 2nd ed. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}} \examples{ data("DutchAdvert") plot(DutchAdvert) ## EACF tables (Franses 1998, Sec. 5.1, p. 99) ctrafo <- function(x) residuals(lm(x ~ factor(cycle(x)))) ddiff <- function(x) diff(diff(x, frequency(x)), 1) eacf <- function(y, lag = 12) { stopifnot(all(lag > 0)) if(length(lag) < 2) lag <- 1:lag rval <- sapply( list(y = y, dy = diff(y), cdy = ctrafo(diff(y)), Dy = diff(y, frequency(y)), dDy = ddiff(y)), function(x) acf(x, plot = FALSE, lag.max = max(lag))$acf[lag + 1]) rownames(rval) <- lag return(rval) } ## Franses (1998, p. 103), Table 5.4 round(eacf(log(DutchAdvert[,"tv"]), lag = c(1:19, 26, 39)), digits = 3) } \keyword{datasets} AER/man/ArgentinaCPI.Rd0000644000176200001440000000200213615674673014203 0ustar liggesusers\name{ArgentinaCPI} \alias{ArgentinaCPI} \title{Consumer Price Index in Argentina} \description{ Time series of consumer price index (CPI) in Argentina (index with 1969(4) = 1). } \usage{data("ArgentinaCPI")} \format{ A quarterly univariate time series from 1970(1) to 1989(4). } \source{ Online complements to Franses (1998). } \references{ De Ruyter van Steveninck, M.A. (1996). \emph{The Impact of Capital Imports; Argentina 1970--1989}. Amsterdam: Thesis Publishers. Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}} \examples{ data("ArgentinaCPI") plot(ArgentinaCPI) plot(log(ArgentinaCPI)) library("dynlm") ## estimation sample 1970.3-1988.4 means acpi <- window(ArgentinaCPI, start = c(1970,1), end = c(1988,4)) ## eq. (3.90), p.54 acpi_ols <- dynlm(d(log(acpi)) ~ L(d(log(acpi)))) summary(acpi_ols) ## alternatively ar(diff(log(acpi)), order.max = 1, method = "ols") } \keyword{datasets} AER/man/EuroEnergy.Rd0000644000176200001440000000140613615674673014032 0ustar liggesusers\name{EuroEnergy} \alias{EuroEnergy} \title{European Energy Consumption Data} \description{ Cross-section data on energy consumption for 20 European countries, for the year 1980. } \usage{data("EuroEnergy")} \format{ A data frame containing 20 observations on 2 variables. \describe{ \item{gdp}{Real gross domestic product for the year 1980 (in million 1975 US dollars).} \item{energy}{Aggregate energy consumption (in million kilograms coal equivalence).} } } \source{ The data are from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("EuroEnergy") energy_lm <- lm(log(energy) ~ log(gdp), data = EuroEnergy) influence.measures(energy_lm) } \keyword{datasets} AER/man/MSCISwitzerland.Rd0000755000176200001440000000544713615674673014744 0ustar liggesusers\name{MSCISwitzerland} \alias{MSCISwitzerland} \title{MSCI Switzerland Index} \description{ Time series of the MSCI Switzerland index. } \usage{data("MSCISwitzerland")} \format{ A daily univariate time series from 1994-12-30 to 2012-12-31 (of class \code{"zoo"} with \code{"Date"} index). } \source{ Online complements to Franses, van Dijk and Opschoor (2014). \url{http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition} } \references{ Ding, Z., Granger, C. W. J. and Engle, R. F. (1993). A Long Memory Property of Stock Market Returns and a New Model. \emph{Journal of Empirical Finance}, 1(1), 83--106. Franses, P.H., van Dijk, D. and Opschoor, A. (2014). \emph{Time Series Models for Business and Economic Forecasting}, 2nd ed. Cambridge, UK: Cambridge University Press. } \examples{ data("MSCISwitzerland", package = "AER") ## p.190, Fig. 7.6 dlmsci <- 100 * diff(log(MSCISwitzerland)) plot(dlmsci) dlmsci9501 <- window(dlmsci, end = as.Date("2001-12-31")) ## Figure 7.7 plot(acf(dlmsci9501^2, lag.max = 200, na.action = na.exclude), ylim = c(-0.1, 0.3), type = "l") ## GARCH(1,1) model, p.190, eq. (7.60) ## standard errors using first derivatives (as apparently used by Franses et al.) library("tseries") msci9501_g11 <- garch(zooreg(dlmsci9501), trace = FALSE) summary(msci9501_g11) ## standard errors using second derivatives library("fGarch") msci9501_g11a <- garchFit( ~ garch(1,1), include.mean = FALSE, data = dlmsci9501, trace = FALSE) summary(msci9501_g11a) round(msci9501_g11a@fit$coef, 3) round(msci9501_g11a@fit$se.coef, 3) ## Fig. 7.8, p.192 plot(msci9501_g11a, which = 2) abline(h = sd(dlmsci9501)) ## TGARCH model (also known as GJR-GARCH model), p. 191, eq. (7.61) msci9501_tg11 <- garchFit( ~ aparch(1,1), include.mean = FALSE, include.delta = FALSE, delta = 2, data = dlmsci9501, trace = FALSE) summary(msci9501_tg11) ## GJR form using reparameterization as given by Ding et al. (1993, pp. 100-101) coef(msci9501_tg11)["alpha1"] * (1 - coef(msci9501_tg11)["gamma1"])^2 ## alpha* 4 * coef(msci9501_tg11)["alpha1"] * coef(msci9501_tg11)["gamma1"] ## gamma* ## GARCH and GJR-GARCH with rugarch library("rugarch") spec_g11 <- ugarchspec(variance.model = list(model = "sGARCH"), mean.model = list(armaOrder = c(0,0), include.mean = FALSE)) msci9501_g11b <- ugarchfit(spec_g11, data = dlmsci9501) msci9501_g11b spec_gjrg11 <- ugarchspec(variance.model = list(model = "gjrGARCH", garchOrder = c(1,1)), mean.model = list(armaOrder = c(0, 0), include.mean = FALSE)) msci9501_gjrg11 <- ugarchfit(spec_gjrg11, data = dlmsci9501) msci9501_gjrg11 round(coef(msci9501_gjrg11), 3) } \keyword{datasets} AER/man/CigarettesSW.Rd0000644000176200001440000000416713615674673014321 0ustar liggesusers\name{CigarettesSW} \alias{CigarettesSW} \title{Cigarette Consumption Panel Data} \description{ Panel data on cigarette consumption for the 48 continental US States from 1985--1995. } \usage{data("CigarettesSW")} \format{ A data frame containing 48 observations on 7 variables for 2 periods. \describe{ \item{state}{Factor indicating state.} \item{year}{Factor indicating year.} \item{cpi}{Consumer price index.} \item{population}{State population.} \item{packs}{Number of packs per capita.} \item{income}{State personal income (total, nominal).} \item{tax}{Average state, federal and average local excise taxes for fiscal year.} \item{price}{Average price during fiscal year, including sales tax.} \item{taxs}{Average excise taxes for fiscal year, including sales tax.} } } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{CigarettesB}}} \examples{ ## Stock and Watson (2007) ## data and transformations data("CigarettesSW") CigarettesSW$rprice <- with(CigarettesSW, price/cpi) CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi) CigarettesSW$rtax <- with(CigarettesSW, tax/cpi) CigarettesSW$rtdiff <- with(CigarettesSW, (taxs - tax)/cpi) c1985 <- subset(CigarettesSW, year == "1985") c1995 <- subset(CigarettesSW, year == "1995") ## convenience function: HC1 covariances hc1 <- function(x) vcovHC(x, type = "HC1") ## Equations 12.9--12.11 fm_s1 <- lm(log(rprice) ~ rtdiff, data = c1995) coeftest(fm_s1, vcov = hc1) fm_s2 <- lm(log(packs) ~ fitted(fm_s1), data = c1995) fm_ivreg <- ivreg(log(packs) ~ log(rprice) | rtdiff, data = c1995) coeftest(fm_ivreg, vcov = hc1) ## Equation 12.15 fm_ivreg2 <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + rtdiff, data = c1995) coeftest(fm_ivreg2, vcov = hc1) ## Equation 12.16 fm_ivreg3 <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + rtdiff + rtax, data = c1995) coeftest(fm_ivreg3, vcov = hc1) ## More examples can be found in: ## help("StockWatson2007") } \keyword{datasets} AER/man/ResumeNames.Rd0000644000176200001440000000725313615674673014200 0ustar liggesusers\name{ResumeNames} \alias{ResumeNames} \title{Are Emily and Greg More Employable Than Lakisha and Jamal?} \description{ Cross-section data about resume, call-back and employer information for 4,870 fictitious resumes. } \usage{data("ResumeNames")} \format{ A data frame containing 4,870 observations on 27 variables. \describe{ \item{name}{factor indicating applicant's first name.} \item{gender}{factor indicating gender.} \item{ethnicity}{factor indicating ethnicity (i.e., Caucasian-sounding vs. African-American sounding first name).} \item{quality}{factor indicating quality of resume.} \item{call}{factor. Was the applicant called back?} \item{city}{factor indicating city: Boston or Chicago.} \item{jobs}{number of jobs listed on resume.} \item{experience}{number of years of work experience on the resume.} \item{honors}{factor. Did the resume mention some honors?} \item{volunteer}{factor. Did the resume mention some volunteering experience?} \item{military}{factor. Does the applicant have military experience?} \item{holes}{factor. Does the resume have some employment holes?} \item{school}{factor. Does the resume mention some work experience while at school?} \item{email}{factor. Was the e-mail address on the applicant's resume?} \item{computer}{factor. Does the resume mention some computer skills?} \item{special}{factor. Does the resume mention some special skills?} \item{college}{factor. Does the applicant have a college degree or more?} \item{minimum}{factor indicating minimum experience requirement of the employer.} \item{equal}{factor. Is the employer EOE (equal opportunity employment)?} \item{wanted}{factor indicating type of position wanted by employer.} \item{requirements}{factor. Does the ad mention some requirement for the job?} \item{reqexp}{factor. Does the ad mention some experience requirement?} \item{reqcomm}{factor. Does the ad mention some communication skills requirement?} \item{reqeduc}{factor. Does the ad mention some educational requirement?} \item{reqcomp}{factor. Does the ad mention some computer skills requirement?} \item{reqorg}{factor. Does the ad mention some organizational skills requirement?} \item{industry}{factor indicating type of employer industry.} } } \details{ Cross-section data about resume, call-back and employer information for 4,870 fictitious resumes sent in response to employment advertisements in Chicago and Boston in 2001, in a randomized controlled experiment conducted by Bertrand and Mullainathan (2004). The resumes contained information concerning the ethnicity of the applicant. Because ethnicity is not typically included on a resume, resumes were differentiated on the basis of so-called \dQuote{Caucasian sounding names} (such as Emily Walsh or Gregory Baker) and \dQuote{African American sounding names} (such as Lakisha Washington or Jamal Jones). A large collection of fictitious resumes were created and the pre-supposed ethnicity (based on the sound of the name) was randomly assigned to each resume. These resumes were sent to prospective employers to see which resumes generated a phone call from the prospective employer. } \source{ Online complements to Stock and Watson (2007). } \references{ Bertrand, M. and Mullainathan, S. (2004). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. \emph{American Economic Review}, \bold{94}, 991--1013. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("ResumeNames") summary(ResumeNames) prop.table(xtabs(~ ethnicity + call, data = ResumeNames), 1) } \keyword{datasets} AER/man/Electricity1970.Rd0000644000176200001440000000314713615674673014553 0ustar liggesusers\name{Electricity1970} \alias{Electricity1970} \title{Cost Function of Electricity Producers 1970} \description{ Cross-section data, at the firm level, on electric power generation. } \usage{data("Electricity1970")} \format{ A data frame containing 158 cross-section observations on 9 variables. \describe{ \item{cost}{total cost.} \item{output}{total output.} \item{labor}{wage rate.} \item{laborshare}{cost share for labor.} \item{capital}{capital price index.} \item{capitalshare}{cost share for capital.} \item{fuel}{fuel price.} \item{fuelshare}{cost share for fuel.} } } \details{ The data are from Christensen and Greene (1976) and pertain to the year 1970. However, the file contains some extra observations, the holding companies. Only the first 123 observations are needed to replicate Christensen and Greene (1976). } \source{ Online complements to Greene (2003), Table F5.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Christensen, L. and Greene, W.H. (1976). Economies of Scale in U.S. Electric Power Generation. \emph{Journal of Political Economy}, \bold{84}, 655--676. Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}, \code{\link{Electricity1955}}} \examples{ data("Electricity1970") ## Greene (2003), Ex. 5.6: a generalized Cobb-Douglas cost function fm <- lm(log(cost/fuel) ~ log(output) + I(log(output)^2/2) + log(capital/fuel) + log(labor/fuel), data=Electricity1970[1:123,]) } \keyword{datasets} AER/man/USGasB.Rd0000644000176200001440000000143013615674673013027 0ustar liggesusers\name{USGasB} \alias{USGasB} \title{US Gasoline Market Data (1950--1987, Baltagi)} \description{ Time series data on the US gasoline market. } \usage{data("USGasB")} \format{ An annual multiple time series from 1950 to 1987 with 6 variables. \describe{ \item{cars}{Stock of cars.} \item{gas}{Consumption of motor gasoline (in 1000 gallons).} \item{price}{Retail price of motor gasoline.} \item{population}{Population.} \item{gnp}{Real gross national product (in 1982 dollars).} \item{deflator}{GNP deflator (1982 = 100).} } } \source{ The data are from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. } \seealso{\code{\link{Baltagi2002}}, \code{\link{USGasG}}} \examples{ data("USGasB") plot(USGasB) } \keyword{datasets} AER/man/Greene2003.Rd0000644000176200001440000010026313616354314013445 0ustar liggesusers\name{Greene2003} \alias{Greene2003} \title{Data and Examples from Greene (2003)} \description{ This manual page collects a list of examples from the book. Some solutions might not be exact and the list is certainly not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. URL \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm}. } \seealso{\code{\link{Affairs}}, \code{\link{BondYield}}, \code{\link{CreditCard}}, \code{\link{Electricity1955}}, \code{\link{Electricity1970}}, \code{\link{Equipment}}, \code{\link{Grunfeld}}, \code{\link{KleinI}}, \code{\link{Longley}}, \code{\link{ManufactCosts}}, \code{\link{MarkPound}}, \code{\link{Municipalities}}, \code{\link{ProgramEffectiveness}}, \code{\link{PSID1976}}, \code{\link{SIC33}}, \code{\link{ShipAccidents}}, \code{\link{StrikeDuration}}, \code{\link{TechChange}}, \code{\link{TravelMode}}, \code{\link{UKInflation}}, \code{\link{USConsump1950}}, \code{\link{USConsump1979}}, \code{\link{USGasG}}, \code{\link{USAirlines}}, \code{\link{USInvest}}, \code{\link{USMacroG}}, \code{\link{USMoney}}} \examples{ \donttest{ ##################################### ## US consumption data (1970-1979) ## ##################################### ## Example 1.1 data("USConsump1979", package = "AER") plot(expenditure ~ income, data = as.data.frame(USConsump1979), pch = 19) fm <- lm(expenditure ~ income, data = as.data.frame(USConsump1979)) summary(fm) abline(fm) ##################################### ## US consumption data (1940-1950) ## ##################################### ## data data("USConsump1950", package = "AER") usc <- as.data.frame(USConsump1950) usc$war <- factor(usc$war, labels = c("no", "yes")) ## Example 2.1 plot(expenditure ~ income, data = usc, type = "n", xlim = c(225, 375), ylim = c(225, 350)) with(usc, text(income, expenditure, time(USConsump1950))) ## single model fm <- lm(expenditure ~ income, data = usc) summary(fm) ## different intercepts for war yes/no fm2 <- lm(expenditure ~ income + war, data = usc) summary(fm2) ## compare anova(fm, fm2) ## visualize abline(fm, lty = 3) abline(coef(fm2)[1:2]) abline(sum(coef(fm2)[c(1, 3)]), coef(fm2)[2], lty = 2) ## Example 3.2 summary(fm)$r.squared summary(lm(expenditure ~ income, data = usc, subset = war == "no"))$r.squared summary(fm2)$r.squared ######################## ## US investment data ## ######################## data("USInvest", package = "AER") ## Chapter 3 in Greene (2003) ## transform (and round) data to match Table 3.1 us <- as.data.frame(USInvest) us$invest <- round(0.1 * us$invest/us$price, digits = 3) us$gnp <- round(0.1 * us$gnp/us$price, digits = 3) us$inflation <- c(4.4, round(100 * diff(us$price)/us$price[-15], digits = 2)) us$trend <- 1:15 us <- us[, c(2, 6, 1, 4, 5)] ## p. 22-24 coef(lm(invest ~ trend + gnp, data = us)) coef(lm(invest ~ gnp, data = us)) ## Example 3.1, Table 3.2 cor(us)[1,-1] pcor <- solve(cor(us)) dcor <- 1/sqrt(diag(pcor)) pcor <- (-pcor * (dcor \%o\% dcor))[1,-1] ## Table 3.4 fm <- lm(invest ~ trend + gnp + interest + inflation, data = us) fm1 <- lm(invest ~ 1, data = us) anova(fm1, fm) ## Example 4.1 set.seed(123) w <- rnorm(10000) x <- rnorm(10000) eps <- 0.5 * w y <- 0.5 + 0.5 * x + eps b <- rep(0, 500) for(i in 1:500) { ix <- sample(1:10000, 100) b[i] <- lm.fit(cbind(1, x[ix]), y[ix])$coef[2] } hist(b, breaks = 20, col = "lightgray") ############################### ## Longley's regression data ## ############################### ## package and data data("Longley", package = "AER") library("dynlm") ## Example 4.6 fm1 <- dynlm(employment ~ time(employment) + price + gnp + armedforces, data = Longley) fm2 <- update(fm1, end = 1961) cbind(coef(fm2), coef(fm1)) ## Figure 4.3 plot(rstandard(fm2), type = "b", ylim = c(-3, 3)) abline(h = c(-2, 2), lty = 2) ######################################### ## US gasoline market data (1960-1995) ## ######################################### ## data data("USGasG", package = "AER") ## Greene (2003) ## Example 2.3 fm <- lm(log(gas/population) ~ log(price) + log(income) + log(newcar) + log(usedcar), data = as.data.frame(USGasG)) summary(fm) ## Example 4.4 ## estimates and standard errors (note different offset for intercept) coef(fm) sqrt(diag(vcov(fm))) ## confidence interval confint(fm, parm = "log(income)") ## test linear hypothesis linearHypothesis(fm, "log(income) = 1") ## Figure 7.5 plot(price ~ gas, data = as.data.frame(USGasG), pch = 19, col = (time(USGasG) > 1973) + 1) legend("topleft", legend = c("after 1973", "up to 1973"), pch = 19, col = 2:1, bty = "n") ## Example 7.6 ## re-used in Example 8.3 ## linear time trend ltrend <- 1:nrow(USGasG) ## shock factor shock <- factor(time(USGasG) > 1973, levels = c(FALSE, TRUE), labels = c("before", "after")) ## 1960-1995 fm1 <- lm(log(gas/population) ~ log(income) + log(price) + log(newcar) + log(usedcar) + ltrend, data = as.data.frame(USGasG)) summary(fm1) ## pooled fm2 <- lm( log(gas/population) ~ shock + log(income) + log(price) + log(newcar) + log(usedcar) + ltrend, data = as.data.frame(USGasG)) summary(fm2) ## segmented fm3 <- lm( log(gas/population) ~ shock/(log(income) + log(price) + log(newcar) + log(usedcar) + ltrend), data = as.data.frame(USGasG)) summary(fm3) ## Chow test anova(fm3, fm1) library("strucchange") sctest(log(gas/population) ~ log(income) + log(price) + log(newcar) + log(usedcar) + ltrend, data = USGasG, point = c(1973, 1), type = "Chow") ## Recursive CUSUM test rcus <- efp(log(gas/population) ~ log(income) + log(price) + log(newcar) + log(usedcar) + ltrend, data = USGasG, type = "Rec-CUSUM") plot(rcus) sctest(rcus) ## Note: Greene's remark that the break is in 1984 (where the process crosses its boundary) ## is wrong. The break appears to be no later than 1976. ## Example 12.2 library("dynlm") resplot <- function(obj, bound = TRUE) { res <- residuals(obj) sigma <- summary(obj)$sigma plot(res, ylab = "Residuals", xlab = "Year") grid() abline(h = 0) if(bound) abline(h = c(-2, 2) * sigma, col = "red") lines(res) } resplot(dynlm(log(gas/population) ~ log(price), data = USGasG)) resplot(dynlm(log(gas/population) ~ log(price) + log(income), data = USGasG)) resplot(dynlm(log(gas/population) ~ log(price) + log(income) + log(newcar) + log(usedcar) + log(transport) + log(nondurable) + log(durable) +log(service) + ltrend, data = USGasG)) ## different shock variable than in 7.6 shock <- factor(time(USGasG) > 1974, levels = c(FALSE, TRUE), labels = c("before", "after")) resplot(dynlm(log(gas/population) ~ shock/(log(price) + log(income) + log(newcar) + log(usedcar) + log(transport) + log(nondurable) + log(durable) + log(service) + ltrend), data = USGasG)) ## NOTE: something seems to be wrong with the sigma estimates in the `full' models ## Table 12.4, OLS fm <- dynlm(log(gas/population) ~ log(price) + log(income) + log(newcar) + log(usedcar), data = USGasG) summary(fm) resplot(fm, bound = FALSE) dwtest(fm) ## ML g <- as.data.frame(USGasG) y <- log(g$gas/g$population) X <- as.matrix(cbind(log(g$price), log(g$income), log(g$newcar), log(g$usedcar))) arima(y, order = c(1, 0, 0), xreg = X) ####################################### ## US macroeconomic data (1950-2000) ## ####################################### ## data and trend data("USMacroG", package = "AER") ltrend <- 0:(nrow(USMacroG) - 1) ## Example 5.3 ## OLS and IV regression library("dynlm") fm_ols <- dynlm(consumption ~ gdp, data = USMacroG) fm_iv <- dynlm(consumption ~ gdp | L(consumption) + L(gdp), data = USMacroG) ## Hausman statistic library("MASS") b_diff <- coef(fm_iv) - coef(fm_ols) v_diff <- summary(fm_iv)$cov.unscaled - summary(fm_ols)$cov.unscaled (t(b_diff) \%*\% ginv(v_diff) \%*\% b_diff) / summary(fm_ols)$sigma^2 ## Wu statistic auxreg <- dynlm(gdp ~ L(consumption) + L(gdp), data = USMacroG) coeftest(dynlm(consumption ~ gdp + fitted(auxreg), data = USMacroG))[3,3] ## agrees with Greene (but not with errata) ## Example 6.1 ## Table 6.1 fm6.1 <- dynlm(log(invest) ~ tbill + inflation + log(gdp) + ltrend, data = USMacroG) fm6.3 <- dynlm(log(invest) ~ I(tbill - inflation) + log(gdp) + ltrend, data = USMacroG) summary(fm6.1) summary(fm6.3) deviance(fm6.1) deviance(fm6.3) vcov(fm6.1)[2,3] ## F test linearHypothesis(fm6.1, "tbill + inflation = 0") ## alternatively anova(fm6.1, fm6.3) ## t statistic sqrt(anova(fm6.1, fm6.3)[2,5]) ## Example 6.3 ## Distributed lag model: ## log(Ct) = b0 + b1 * log(Yt) + b2 * log(C(t-1)) + u us <- log(USMacroG[, c(2, 5)]) fm_distlag <- dynlm(log(consumption) ~ log(dpi) + L(log(consumption)), data = USMacroG) summary(fm_distlag) ## estimate and test long-run MPC coef(fm_distlag)[2]/(1-coef(fm_distlag)[3]) linearHypothesis(fm_distlag, "log(dpi) + L(log(consumption)) = 1") ## correct, see errata ## Example 6.4 ## predict investiment in 2001(1) predict(fm6.1, interval = "prediction", newdata = data.frame(tbill = 4.48, inflation = 5.262, gdp = 9316.8, ltrend = 204)) ## Example 7.7 ## no GMM available in "strucchange" ## using OLS instead yields fs <- Fstats(log(m1/cpi) ~ log(gdp) + tbill, data = USMacroG, vcov = NeweyWest, from = c(1957, 3), to = c(1991, 3)) plot(fs) ## which looks somewhat similar ... ## Example 8.2 ## Ct = b0 + b1*Yt + b2*Y(t-1) + v fm1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG) ## Ct = a0 + a1*Yt + a2*C(t-1) + u fm2 <- dynlm(consumption ~ dpi + L(consumption), data = USMacroG) ## Cox test in both directions: coxtest(fm1, fm2) ## ... and do the same for jtest() and encomptest(). ## Notice that in this particular case two of them are coincident. jtest(fm1, fm2) encomptest(fm1, fm2) ## encomptest could also be performed `by hand' via fmE <- dynlm(consumption ~ dpi + L(dpi) + L(consumption), data = USMacroG) waldtest(fm1, fmE, fm2) ## Table 9.1 fm_ols <- lm(consumption ~ dpi, data = as.data.frame(USMacroG)) fm_nls <- nls(consumption ~ alpha + beta * dpi^gamma, start = list(alpha = coef(fm_ols)[1], beta = coef(fm_ols)[2], gamma = 1), control = nls.control(maxiter = 100), data = as.data.frame(USMacroG)) summary(fm_ols) summary(fm_nls) deviance(fm_ols) deviance(fm_nls) vcov(fm_nls) ## Example 9.7 ## F test fm_nls2 <- nls(consumption ~ alpha + beta * dpi, start = list(alpha = coef(fm_ols)[1], beta = coef(fm_ols)[2]), control = nls.control(maxiter = 100), data = as.data.frame(USMacroG)) anova(fm_nls, fm_nls2) ## Wald test linearHypothesis(fm_nls, "gamma = 1") ## Example 9.8, Table 9.2 usm <- USMacroG[, c("m1", "tbill", "gdp")] fm_lin <- lm(m1 ~ tbill + gdp, data = usm) fm_log <- lm(m1 ~ tbill + gdp, data = log(usm)) ## PE auxiliary regressions aux_lin <- lm(m1 ~ tbill + gdp + I(fitted(fm_log) - log(fitted(fm_lin))), data = usm) aux_log <- lm(m1 ~ tbill + gdp + I(fitted(fm_lin) - exp(fitted(fm_log))), data = log(usm)) coeftest(aux_lin)[4,] coeftest(aux_log)[4,] ## matches results from errata ## With lmtest >= 0.9-24: ## petest(fm_lin, fm_log) ## Example 12.1 fm_m1 <- dynlm(log(m1) ~ log(gdp) + log(cpi), data = USMacroG) summary(fm_m1) ## Figure 12.1 par(las = 1) plot(0, 0, type = "n", axes = FALSE, xlim = c(1950, 2002), ylim = c(-0.3, 0.225), xaxs = "i", yaxs = "i", xlab = "Quarter", ylab = "", main = "Least Squares Residuals") box() axis(1, at = c(1950, 1963, 1976, 1989, 2002)) axis(2, seq(-0.3, 0.225, by = 0.075)) grid(4, 7, col = grey(0.6)) abline(0, 0) lines(residuals(fm_m1), lwd = 2) ## Example 12.3 fm_pc <- dynlm(d(inflation) ~ unemp, data = USMacroG) summary(fm_pc) ## Figure 12.3 plot(residuals(fm_pc)) ## natural unemployment rate coef(fm_pc)[1]/coef(fm_pc)[2] ## autocorrelation res <- residuals(fm_pc) summary(dynlm(res ~ L(res))) ## Example 12.4 coeftest(fm_m1) coeftest(fm_m1, vcov = NeweyWest(fm_m1, lag = 5)) summary(fm_m1)$r.squared dwtest(fm_m1) as.vector(acf(residuals(fm_m1), plot = FALSE)$acf)[2] ## matches Tab. 12.1 errata and Greene 6e, apart from Newey-West SE ################################################# ## Cost function of electricity producers 1870 ## ################################################# ## Example 5.6: a generalized Cobb-Douglas cost function data("Electricity1970", package = "AER") fm <- lm(log(cost/fuel) ~ log(output) + I(log(output)^2/2) + log(capital/fuel) + log(labor/fuel), data=Electricity1970[1:123,]) #################################################### ## SIC 33: Production for primary metals industry ## #################################################### ## data data("SIC33", package = "AER") ## Example 6.2 ## Translog model fm_tl <- lm( output ~ labor + capital + I(0.5 * labor^2) + I(0.5 * capital^2) + I(labor * capital), data = log(SIC33)) ## Cobb-Douglas model fm_cb <- lm(output ~ labor + capital, data = log(SIC33)) ## Table 6.2 in Greene (2003) deviance(fm_tl) deviance(fm_cb) summary(fm_tl) summary(fm_cb) vcov(fm_tl) vcov(fm_cb) ## Cobb-Douglas vs. Translog model anova(fm_cb, fm_tl) ## hypothesis of constant returns linearHypothesis(fm_cb, "labor + capital = 1") ############################### ## Cost data for US airlines ## ############################### ## data data("USAirlines", package = "AER") ## Example 7.2 fm_full <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + year + firm, data = USAirlines) fm_time <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + year, data = USAirlines) fm_firm <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + firm, data = USAirlines) fm_no <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = USAirlines) ## full fitted model coef(fm_full)[1:5] plot(1970:1984, c(coef(fm_full)[6:19], 0), type = "n", xlab = "Year", ylab = expression(delta(Year)), main = "Estimated Year Specific Effects") grid() points(1970:1984, c(coef(fm_full)[6:19], 0), pch = 19) ## Table 7.2 anova(fm_full, fm_time) anova(fm_full, fm_firm) anova(fm_full, fm_no) ## alternatively, use plm() library("plm") usair <- pdata.frame(USAirlines, c("firm", "year")) fm_full2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "within", effect = "twoways") fm_time2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "within", effect = "time") fm_firm2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "within", effect = "individual") fm_no2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = usair, model = "pooling") pFtest(fm_full2, fm_time2) pFtest(fm_full2, fm_firm2) pFtest(fm_full2, fm_no2) ## Example 13.1, Table 13.1 fm_no <- plm(log(cost) ~ log(output) + log(price) + load, data = usair, model = "pooling") fm_gm <- plm(log(cost) ~ log(output) + log(price) + load, data = usair, model = "between") fm_firm <- plm(log(cost) ~ log(output) + log(price) + load, data = usair, model = "within") fm_time <- plm(log(cost) ~ log(output) + log(price) + load, data = usair, model = "within", effect = "time") fm_ft <- plm(log(cost) ~ log(output) + log(price) + load, data = usair, model = "within", effect = "twoways") summary(fm_no) summary(fm_gm) summary(fm_firm) fixef(fm_firm) summary(fm_time) fixef(fm_time) summary(fm_ft) fixef(fm_ft, effect = "individual") fixef(fm_ft, effect = "time") ## Table 13.2 fm_rfirm <- plm(log(cost) ~ log(output) + log(price) + load, data = usair, model = "random") fm_rft <- plm(log(cost) ~ log(output) + log(price) + load, data = usair, model = "random", effect = "twoways") summary(fm_rfirm) summary(fm_rft) ################################################# ## Cost function of electricity producers 1955 ## ################################################# ## Nerlove data data("Electricity1955", package = "AER") Electricity <- Electricity1955[1:145,] ## Example 7.3 ## Cobb-Douglas cost function fm_all <- lm(log(cost/fuel) ~ log(output) + log(labor/fuel) + log(capital/fuel), data = Electricity) summary(fm_all) ## hypothesis of constant returns to scale linearHypothesis(fm_all, "log(output) = 1") ## Figure 7.4 plot(residuals(fm_all) ~ log(output), data = Electricity) ## scaling seems to be different in Greene (2003) with logQ > 10? ## grouped functions Electricity$group <- with(Electricity, cut(log(output), quantile(log(output), 0:5/5), include.lowest = TRUE, labels = 1:5)) fm_group <- lm( log(cost/fuel) ~ group/(log(output) + log(labor/fuel) + log(capital/fuel)) - 1, data = Electricity) ## Table 7.3 (close, but not quite) round(rbind(coef(fm_all)[-1], matrix(coef(fm_group), nrow = 5)[,-1]), digits = 3) ## Table 7.4 ## log quadratic cost function fm_all2 <- lm( log(cost/fuel) ~ log(output) + I(log(output)^2) + log(labor/fuel) + log(capital/fuel), data = Electricity) summary(fm_all2) ########################## ## Technological change ## ########################## ## Exercise 7.1 data("TechChange", package = "AER") fm1 <- lm(I(output/technology) ~ log(clr), data = TechChange) fm2 <- lm(I(output/technology) ~ I(1/clr), data = TechChange) fm3 <- lm(log(output/technology) ~ log(clr), data = TechChange) fm4 <- lm(log(output/technology) ~ I(1/clr), data = TechChange) ## Exercise 7.2 (a) and (c) plot(I(output/technology) ~ clr, data = TechChange) sctest(I(output/technology) ~ log(clr), data = TechChange, type = "Chow", point = c(1942, 1)) ################################## ## Expenditure and default data ## ################################## ## full data set (F21.4) data("CreditCard", package = "AER") ## extract data set F9.1 ccard <- CreditCard[1:100,] ccard$income <- round(ccard$income, digits = 2) ccard$expenditure <- round(ccard$expenditure, digits = 2) ccard$age <- round(ccard$age + .01) ## suspicious: CreditCard$age[CreditCard$age < 1] ## the first of these is also in TableF9.1 with 36 instead of 0.5: ccard$age[79] <- 36 ## Example 11.1 ccard <- ccard[order(ccard$income),] ccard0 <- subset(ccard, expenditure > 0) cc_ols <- lm(expenditure ~ age + owner + income + I(income^2), data = ccard0) ## Figure 11.1 plot(residuals(cc_ols) ~ income, data = ccard0, pch = 19) ## Table 11.1 mean(ccard$age) prop.table(table(ccard$owner)) mean(ccard$income) summary(cc_ols) sqrt(diag(vcovHC(cc_ols, type = "HC0"))) sqrt(diag(vcovHC(cc_ols, type = "HC2"))) sqrt(diag(vcovHC(cc_ols, type = "HC1"))) bptest(cc_ols, ~ (age + income + I(income^2) + owner)^2 + I(age^2) + I(income^4), data = ccard0) gqtest(cc_ols) bptest(cc_ols, ~ income + I(income^2), data = ccard0, studentize = FALSE) bptest(cc_ols, ~ income + I(income^2), data = ccard0) ## Table 11.2, WLS and FGLS cc_wls1 <- lm(expenditure ~ age + owner + income + I(income^2), weights = 1/income, data = ccard0) cc_wls2 <- lm(expenditure ~ age + owner + income + I(income^2), weights = 1/income^2, data = ccard0) auxreg1 <- lm(log(residuals(cc_ols)^2) ~ log(income), data = ccard0) cc_fgls1 <- lm(expenditure ~ age + owner + income + I(income^2), weights = 1/exp(fitted(auxreg1)), data = ccard0) auxreg2 <- lm(log(residuals(cc_ols)^2) ~ income + I(income^2), data = ccard0) cc_fgls2 <- lm(expenditure ~ age + owner + income + I(income^2), weights = 1/exp(fitted(auxreg2)), data = ccard0) alphai <- coef(lm(log(residuals(cc_ols)^2) ~ log(income), data = ccard0))[2] alpha <- 0 while(abs((alphai - alpha)/alpha) > 1e-7) { alpha <- alphai cc_fgls3 <- lm(expenditure ~ age + owner + income + I(income^2), weights = 1/income^alpha, data = ccard0) alphai <- coef(lm(log(residuals(cc_fgls3)^2) ~ log(income), data = ccard0))[2] } alpha ## 1.7623 for Greene cc_fgls3 <- lm(expenditure ~ age + owner + income + I(income^2), weights = 1/income^alpha, data = ccard0) llik <- function(alpha) -logLik(lm(expenditure ~ age + owner + income + I(income^2), weights = 1/income^alpha, data = ccard0)) plot(0:100/20, -sapply(0:100/20, llik), type = "l", xlab = "alpha", ylab = "logLik") alpha <- optimize(llik, interval = c(0, 5))$minimum cc_fgls4 <- lm(expenditure ~ age + owner + income + I(income^2), weights = 1/income^alpha, data = ccard0) ## Table 11.2 cc_fit <- list(cc_ols, cc_wls1, cc_wls2, cc_fgls2, cc_fgls1, cc_fgls3, cc_fgls4) t(sapply(cc_fit, coef)) t(sapply(cc_fit, function(obj) sqrt(diag(vcov(obj))))) ## Table 21.21, Poisson and logit models cc_pois <- glm(reports ~ age + income + expenditure, data = CreditCard, family = poisson) summary(cc_pois) logLik(cc_pois) xhat <- colMeans(CreditCard[, c("age", "income", "expenditure")]) xhat <- as.data.frame(t(xhat)) lambda <- predict(cc_pois, newdata = xhat, type = "response") ppois(0, lambda) * nrow(CreditCard) cc_logit <- glm(factor(reports > 0) ~ age + income + owner, data = CreditCard, family = binomial) summary(cc_logit) logLik(cc_logit) ## Table 21.21, "split population model" library("pscl") cc_zip <- zeroinfl(reports ~ age + income + expenditure | age + income + owner, data = CreditCard) summary(cc_zip) sum(predict(cc_zip, type = "prob")[,1]) ################################### ## DEM/GBP exchange rate returns ## ################################### ## data as given by Greene (2003) data("MarkPound") mp <- round(MarkPound, digits = 6) ## Figure 11.3 in Greene (2003) plot(mp) ## Example 11.8 in Greene (2003), Table 11.5 library("tseries") mp_garch <- garch(mp, grad = "numerical") summary(mp_garch) logLik(mp_garch) ## Greene (2003) also includes a constant and uses different ## standard errors (presumably computed from Hessian), here ## OPG standard errors are used. garchFit() in "fGarch" ## implements the approach used by Greene (2003). ## compare Errata to Greene (2003) library("dynlm") res <- residuals(dynlm(mp ~ 1))^2 mp_ols <- dynlm(res ~ L(res, 1:10)) summary(mp_ols) logLik(mp_ols) summary(mp_ols)$r.squared * length(residuals(mp_ols)) ################################ ## Grunfeld's investment data ## ################################ ## subset of data with mistakes data("Grunfeld", package = "AER") ggr <- subset(Grunfeld, firm \%in\% c("General Motors", "US Steel", "General Electric", "Chrysler", "Westinghouse")) ggr[c(26, 38), 1] <- c(261.6, 645.2) ggr[32, 3] <- 232.6 ## Tab. 13.4 fm_pool <- lm(invest ~ value + capital, data = ggr) summary(fm_pool) logLik(fm_pool) ## White correction sqrt(diag(vcovHC(fm_pool, type = "HC0"))) ## heteroskedastic FGLS auxreg1 <- lm(residuals(fm_pool)^2 ~ firm - 1, data = ggr) fm_pfgls <- lm(invest ~ value + capital, data = ggr, weights = 1/fitted(auxreg1)) summary(fm_pfgls) ## ML, computed as iterated FGLS sigmasi <- fitted(lm(residuals(fm_pfgls)^2 ~ firm - 1 , data = ggr)) sigmas <- 0 while(any(abs((sigmasi - sigmas)/sigmas) > 1e-7)) { sigmas <- sigmasi fm_pfgls_i <- lm(invest ~ value + capital, data = ggr, weights = 1/sigmas) sigmasi <- fitted(lm(residuals(fm_pfgls_i)^2 ~ firm - 1 , data = ggr)) } fm_pmlh <- lm(invest ~ value + capital, data = ggr, weights = 1/sigmas) summary(fm_pmlh) logLik(fm_pmlh) ## Tab. 13.5 auxreg2 <- lm(residuals(fm_pfgls)^2 ~ firm - 1, data = ggr) auxreg3 <- lm(residuals(fm_pmlh)^2 ~ firm - 1, data = ggr) rbind( "OLS" = coef(auxreg1), "Het. FGLS" = coef(auxreg2), "Het. ML" = coef(auxreg3)) ## Chapter 14: explicitly treat as panel data library("plm") pggr <- pdata.frame(ggr, c("firm", "year")) ## Tab. 14.1 library("systemfit") fm_sur <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", methodResidCov = "noDfCor") fm_psur <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", pooled = TRUE, methodResidCov = "noDfCor", residCovWeighted = TRUE) ## Tab 14.2 fm_ols <- systemfit(invest ~ value + capital, data = pggr, method = "OLS") fm_pols <- systemfit(invest ~ value + capital, data = pggr, method = "OLS", pooled = TRUE) ## or "by hand" fm_gm <- lm(invest ~ value + capital, data = ggr, subset = firm == "General Motors") mean(residuals(fm_gm)^2) ## Greene uses MLE ## etc. fm_pool <- lm(invest ~ value + capital, data = ggr) ## Tab. 14.3 (and Tab 13.4, cross-section ML) ## (not run due to long computation time) \dontrun{ fm_ml <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", methodResidCov = "noDfCor", maxiter = 1000, tol = 1e-10) fm_pml <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", pooled = TRUE, methodResidCov = "noDfCor", residCovWeighted = TRUE, maxiter = 1000, tol = 1e-10) } ## Fig. 14.2 plot(unlist(residuals(fm_sur)[, c(3, 1, 2, 5, 4)]), type = "l", ylab = "SUR residuals", ylim = c(-400, 400), xaxs = "i", yaxs = "i") abline(v = c(20,40,60,80), h = 0, lty = 2) ################### ## Klein model I ## ################### ## data data("KleinI", package = "AER") ## Tab. 15.3, OLS library("dynlm") fm_cons <- dynlm(consumption ~ cprofits + L(cprofits) + I(pwage + gwage), data = KleinI) fm_inv <- dynlm(invest ~ cprofits + L(cprofits) + capital, data = KleinI) fm_pwage <- dynlm(pwage ~ gnp + L(gnp) + I(time(gnp) - 1931), data = KleinI) summary(fm_cons) summary(fm_inv) summary(fm_pwage) ## Notes: ## - capital refers to previous year's capital stock -> no lag needed! ## - trend used by Greene (p. 381, "time trend measured as years from 1931") ## Maddala uses years since 1919 ## preparation of data frame for systemfit KI <- ts.intersect(KleinI, lag(KleinI, k = -1), dframe = TRUE) names(KI) <- c(colnames(KleinI), paste("L", colnames(KleinI), sep = "")) KI$trend <- (1921:1941) - 1931 library("systemfit") system <- list( consumption = consumption ~ cprofits + Lcprofits + I(pwage + gwage), invest = invest ~ cprofits + Lcprofits + capital, pwage = pwage ~ gnp + Lgnp + trend) ## Tab. 15.3 OLS again fm_ols <- systemfit(system, method = "OLS", data = KI) summary(fm_ols) ## Tab. 15.3 2SLS, 3SLS, I3SLS inst <- ~ Lcprofits + capital + Lgnp + gexpenditure + taxes + trend + gwage fm_2sls <- systemfit(system, method = "2SLS", inst = inst, methodResidCov = "noDfCor", data = KI) fm_3sls <- systemfit(system, method = "3SLS", inst = inst, methodResidCov = "noDfCor", data = KI) fm_i3sls <- systemfit(system, method = "3SLS", inst = inst, methodResidCov = "noDfCor", maxiter = 100, data = KI) ############################################ ## Transportation equipment manufacturing ## ############################################ ## data data("Equipment", package = "AER") ## Example 17.5 ## Cobb-Douglas fm_cd <- lm(log(valueadded/firms) ~ log(capital/firms) + log(labor/firms), data = Equipment) ## generalized Cobb-Douglas with Zellner-Revankar trafo GCobbDouglas <- function(theta) lm(I(log(valueadded/firms) + theta * valueadded/firms) ~ log(capital/firms) + log(labor/firms), data = Equipment) ## yields classical Cobb-Douglas for theta = 0 fm_cd0 <- GCobbDouglas(0) ## ML estimation of generalized model ## choose starting values from classical model par0 <- as.vector(c(coef(fm_cd0), 0, mean(residuals(fm_cd0)^2))) ## set up likelihood function nlogL <- function(par) { beta <- par[1:3] theta <- par[4] sigma2 <- par[5] Y <- with(Equipment, valueadded/firms) K <- with(Equipment, capital/firms) L <- with(Equipment, labor/firms) rhs <- beta[1] + beta[2] * log(K) + beta[3] * log(L) lhs <- log(Y) + theta * Y rval <- sum(log(1 + theta * Y) - log(Y) + dnorm(lhs, mean = rhs, sd = sqrt(sigma2), log = TRUE)) return(-rval) } ## optimization opt <- optim(par0, nlogL, hessian = TRUE) ## Table 17.2 opt$par sqrt(diag(solve(opt$hessian)))[1:4] -opt$value ## re-fit ML model fm_ml <- GCobbDouglas(opt$par[4]) deviance(fm_ml) sqrt(diag(vcov(fm_ml))) ## fit NLS model rss <- function(theta) deviance(GCobbDouglas(theta)) optim(0, rss) opt2 <- optimize(rss, c(-1, 1)) fm_nls <- GCobbDouglas(opt2$minimum) -nlogL(c(coef(fm_nls), opt2$minimum, mean(residuals(fm_nls)^2))) ############################ ## Municipal expenditures ## ############################ ## Table 18.2 data("Municipalities", package = "AER") summary(Municipalities) ########################### ## Program effectiveness ## ########################### ## Table 21.1, col. "Probit" data("ProgramEffectiveness", package = "AER") fm_probit <- glm(grade ~ average + testscore + participation, data = ProgramEffectiveness, family = binomial(link = "probit")) summary(fm_probit) #################################### ## Labor force participation data ## #################################### ## data and transformations data("PSID1976", package = "AER") PSID1976$kids <- with(PSID1976, factor((youngkids + oldkids) > 0, levels = c(FALSE, TRUE), labels = c("no", "yes"))) PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000) ## Example 4.1, Table 4.2 ## (reproduced in Example 7.1, Table 7.1) gr_lm <- lm(log(hours * wage) ~ age + I(age^2) + education + kids, data = PSID1976, subset = participation == "yes") summary(gr_lm) vcov(gr_lm) ## Example 4.5 summary(gr_lm) ## or equivalently gr_lm1 <- lm(log(hours * wage) ~ 1, data = PSID1976, subset = participation == "yes") anova(gr_lm1, gr_lm) ## Example 21.4, p. 681, and Tab. 21.3, p. 682 gr_probit1 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education + kids, data = PSID1976, family = binomial(link = "probit") ) gr_probit2 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education, data = PSID1976, family = binomial(link = "probit")) gr_probit3 <- glm(participation ~ kids/(age + I(age^2) + I(fincome/10000) + education), data = PSID1976, family = binomial(link = "probit")) ## LR test of all coefficients lrtest(gr_probit1) ## Chow-type test lrtest(gr_probit2, gr_probit3) ## equivalently: anova(gr_probit2, gr_probit3, test = "Chisq") ## Table 21.3 summary(gr_probit1) ## Example 22.8, Table 22.7, p. 786 library("sampleSelection") gr_2step <- selection(participation ~ age + I(age^2) + fincome + education + kids, wage ~ experience + I(experience^2) + education + city, data = PSID1976, method = "2step") gr_ml <- selection(participation ~ age + I(age^2) + fincome + education + kids, wage ~ experience + I(experience^2) + education + city, data = PSID1976, method = "ml") gr_ols <- lm(wage ~ experience + I(experience^2) + education + city, data = PSID1976, subset = participation == "yes") ## NOTE: ML estimates agree with Greene, 5e errata. ## Standard errors are based on the Hessian (here), while Greene has BHHH/OPG. #################### ## Ship accidents ## #################### ## subset data data("ShipAccidents", package = "AER") sa <- subset(ShipAccidents, service > 0) ## Table 21.20 sa_full <- glm(incidents ~ type + construction + operation, family = poisson, data = sa, offset = log(service)) summary(sa_full) sa_notype <- glm(incidents ~ construction + operation, family = poisson, data = sa, offset = log(service)) summary(sa_notype) sa_noperiod <- glm(incidents ~ type + operation, family = poisson, data = sa, offset = log(service)) summary(sa_noperiod) ## model comparison anova(sa_full, sa_notype, test = "Chisq") anova(sa_full, sa_noperiod, test = "Chisq") ## test for overdispersion dispersiontest(sa_full) dispersiontest(sa_full, trafo = 2) ###################################### ## Fair's extramarital affairs data ## ###################################### ## data data("Affairs", package = "AER") ## Tab. 22.3 and 22.4 fm_ols <- lm(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) fm_probit <- glm(I(affairs > 0) ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs, family = binomial(link = "probit")) fm_tobit <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) fm_tobit2 <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, right = 4, data = Affairs) fm_pois <- glm(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs, family = poisson) library("MASS") fm_nb <- glm.nb(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) ## Tab. 22.6 library("pscl") fm_zip <- zeroinfl(affairs ~ age + yearsmarried + religiousness + occupation + rating | age + yearsmarried + religiousness + occupation + rating, data = Affairs) ###################### ## Strike durations ## ###################### ## data and package data("StrikeDuration", package = "AER") library("MASS") ## Table 22.10 fit_exp <- fitdistr(StrikeDuration$duration, "exponential") fit_wei <- fitdistr(StrikeDuration$duration, "weibull") fit_wei$estimate[2]^(-1) fit_lnorm <- fitdistr(StrikeDuration$duration, "lognormal") 1/fit_lnorm$estimate[2] exp(-fit_lnorm$estimate[1]) ## Weibull and lognormal distribution have ## different parameterizations, see Greene p. 794 ## Example 22.10 library("survival") fm_wei <- survreg(Surv(duration) ~ uoutput, dist = "weibull", data = StrikeDuration) summary(fm_wei) } } \keyword{datasets} AER/man/ShipAccidents.Rd0000644000176200001440000000475113615674673014475 0ustar liggesusers\name{ShipAccidents} \alias{ShipAccidents} \title{Ship Accidents} \description{ Data on ship accidents. } \usage{data("ShipAccidents")} \format{ A data frame containing 40 observations on 5 ship types in 4 vintages and 2 service periods. \describe{ \item{type}{factor with levels \code{"A"} to \code{"E"} for the different ship types,} \item{construction}{factor with levels \code{"1960-64"}, \code{"1965-69"}, \code{"1970-74"}, \code{"1975-79"} for the periods of construction,} \item{operation}{factor with levels \code{"1960-74"}, \code{"1975-79"} for the periods of operation,} \item{service}{aggregate months of service,} \item{incidents}{number of damage incidents.} } } \details{ The data are from McCullagh and Nelder (1989, p. 205, Table 6.2) and were also used by Greene (2003, Ch. 21), see below. There are five ships (observations 7, 15, 23, 31, 39) with an operation period \emph{before} the construction period, hence the variables \code{service} and \code{incidents} are necessarily 0. An additional observation (34) has entries representing \emph{accidentally empty cells} (see McCullagh and Nelder, 1989, p. 205). It is a bit unclear what exactly the above means. In any case, the models are fit only to those observations with \code{service > 0}. } \source{ Online complements to Greene (2003). \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. McCullagh, P. and Nelder, J.A. (1989). \emph{Generalized Linear Models}, 2nd edition. London: Chapman \& Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("ShipAccidents") sa <- subset(ShipAccidents, service > 0) ## Greene (2003), Table 21.20 ## (see also McCullagh and Nelder, 1989, Table 6.3) sa_full <- glm(incidents ~ type + construction + operation, family = poisson, data = sa, offset = log(service)) summary(sa_full) sa_notype <- glm(incidents ~ construction + operation, family = poisson, data = sa, offset = log(service)) summary(sa_notype) sa_noperiod <- glm(incidents ~ type + operation, family = poisson, data = sa, offset = log(service)) summary(sa_noperiod) ## model comparison anova(sa_full, sa_notype, test = "Chisq") anova(sa_full, sa_noperiod, test = "Chisq") ## test for overdispersion dispersiontest(sa_full) dispersiontest(sa_full, trafo = 2) } \keyword{datasets} AER/man/BankWages.Rd0000644000176200001440000000326413615674673013614 0ustar liggesusers\name{BankWages} \alias{BankWages} \title{Bank Wages} \description{Wages of employees of a US bank. } \usage{data("BankWages")} \format{ A data frame containing 474 observations on 4 variables. \describe{ \item{job}{Ordered factor indicating job category, with levels \code{"custodial"}, \code{"admin"} and \code{"manage"}.} \item{education}{Education in years.} \item{gender}{Factor indicating gender.} \item{minority}{Factor. Is the employee member of a minority?} } } \source{ Online complements to Heij, de Boer, Franses, Kloek, and van Dijk (2004). \url{http://www.oup.com/uk/booksites/content/0199268010/datasets/ch6/xr614bwa.asc} } \references{ Heij, C., de Boer, P.M.C., Franses, P.H., Kloek, T. and van Dijk, H.K. (2004). \emph{Econometric Methods with Applications in Business and Economics}. Oxford: Oxford University Press. } \examples{ data("BankWages") ## exploratory analysis of job ~ education ## (tables and spine plots, some education levels merged) xtabs(~ education + job, data = BankWages) edcat <- factor(BankWages$education) levels(edcat)[3:10] <- rep(c("14-15", "16-18", "19-21"), c(2, 3, 3)) tab <- xtabs(~ edcat + job, data = BankWages) prop.table(tab, 1) spineplot(tab, off = 0) plot(job ~ edcat, data = BankWages, off = 0) ## fit multinomial model for male employees library("nnet") fm_mnl <- multinom(job ~ education + minority, data = BankWages, subset = gender == "male", trace = FALSE) summary(fm_mnl) confint(fm_mnl) ## same with mlogit package if(require("mlogit")) { fm_mlogit <- mlogit(job ~ 1 | education + minority, data = BankWages, subset = gender == "male", shape = "wide", reflevel = "custodial") summary(fm_mlogit) } } \keyword{datasets} AER/man/TravelMode.Rd0000644000176200001440000000330313615674673014006 0ustar liggesusers\name{TravelMode} \alias{TravelMode} \title{Travel Mode Choice Data} \description{ Data on travel mode choice for travel between Sydney and Melbourne, Australia. } \usage{data("TravelMode")} \format{ A data frame containing 840 observations on 4 modes for 210 individuals. \describe{ \item{individual}{Factor indicating individual with levels \code{1} to \code{200}.} \item{mode}{Factor indicating travel mode with levels \code{"car"}, \code{"air"}, \code{"train"}, or \code{"bus"}.} \item{choice}{Factor indicating choice with levels \code{"no"} and \code{"yes"}.} \item{wait}{Terminal waiting time, 0 for car.} \item{vcost}{Vehicle cost component.} \item{travel}{Travel time in the vehicle.} \item{gcost}{Generalized cost measure.} \item{income}{Household income.} \item{size}{Party size.} } } \source{ Online complements to Greene (2003). \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("TravelMode") ## overall proportions for chosen mode with(TravelMode, prop.table(table(mode[choice == "yes"]))) ## travel vs. waiting time for different travel modes library("lattice") xyplot(travel ~ wait | mode, data = TravelMode) ## Greene (2003), Table 21.11, conditional logit model if(require("mlogit")) { TravelMode$incair <- with(TravelMode, income * (mode == "air")) tm_cl <- mlogit(choice ~ gcost + wait + incair, data = TravelMode, shape = "long", alt.var = "mode", reflevel = "car") summary(tm_cl) } } \keyword{datasets} AER/man/PepperPrice.Rd0000644000176200001440000000342213616353016014146 0ustar liggesusers\name{PepperPrice} \alias{PepperPrice} \title{Black and White Pepper Prices} \description{ Time series of average monthly European spot prices for black and white pepper (fair average quality) in US dollars per ton. } \usage{data("PepperPrice")} \format{ A monthly multiple time series from 1973(10) to 1996(4) with 2 variables. \describe{ \item{black}{spot price for black pepper,} \item{white}{spot price for white pepper.} } } \source{ Originally available as an online supplement to Franses (1998). Now available via online complements to Franses, van Dijk and Opschoor (2014). \url{http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition} } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. Franses, P.H., van Dijk, D. and Opschoor, A. (2014). \emph{Time Series Models for Business and Economic Forecasting}, 2nd ed. Cambridge, UK: Cambridge University Press. } \examples{ ## data data("PepperPrice") plot(PepperPrice, plot.type = "single", col = 1:2) ## package library("tseries") library("urca") ## unit root tests adf.test(log(PepperPrice[, "white"])) adf.test(diff(log(PepperPrice[, "white"]))) pp.test(log(PepperPrice[, "white"]), type = "Z(t_alpha)") pepper_ers <- ur.ers(log(PepperPrice[, "white"]), type = "DF-GLS", model = "const", lag.max = 4) summary(pepper_ers) ## stationarity tests kpss.test(log(PepperPrice[, "white"])) ## cointegration po.test(log(PepperPrice)) pepper_jo <- ca.jo(log(PepperPrice), ecdet = "const", type = "trace") summary(pepper_jo) pepper_jo2 <- ca.jo(log(PepperPrice), ecdet = "const", type = "eigen") summary(pepper_jo2) } \keyword{datasets} AER/man/MurderRates.Rd0000644000176200001440000000436313615674673014210 0ustar liggesusers\name{MurderRates} \alias{MurderRates} \title{Determinants of Murder Rates in the United States} \description{ Cross-section data on states in 1950. } \usage{data("MurderRates")} \format{ A data frame containing 44 observations on 8 variables. \describe{ \item{rate}{Murder rate per 100,000 (FBI estimate, 1950).} \item{convictions}{Number of convictions divided by number of murders in 1950.} \item{executions}{Average number of executions during 1946--1950 divided by convictions in 1950.} \item{time}{Median time served (in months) of convicted murderers released in 1951.} \item{income}{Median family income in 1949 (in 1,000 USD).} \item{lfp}{Labor force participation rate in 1950 (in percent).} \item{noncauc}{Proportion of population that is non-Caucasian in 1950.} \item{southern}{Factor indicating region.} } } \source{ Maddala (2001), Table 8.4, p. 330 } \references{ Maddala, G.S. (2001). \emph{Introduction to Econometrics}, 3rd ed. New York: John Wiley. McManus, W.S. (1985). Estimates of the Deterrent Effect of Capital Punishment: The Importance of the Researcher's Prior Beliefs. \emph{Journal of Political Economy}, \bold{93}, 417--425. Stokes, H. (2004). On the Advantage of Using Two or More Econometric Software Systems to Solve the Same Problem. \emph{Journal of Economic and Social Measurement}, \bold{29}, 307--320. } \examples{ data("MurderRates") ## Maddala (2001, pp. 331) fm_lm <- lm(rate ~ . + I(executions > 0), data = MurderRates) summary(fm_lm) model <- I(executions > 0) ~ time + income + noncauc + lfp + southern fm_lpm <- lm(model, data = MurderRates) summary(fm_lpm) ## Binomial models. Note: southern coefficient fm_logit <- glm(model, data = MurderRates, family = binomial) summary(fm_logit) fm_logit2 <- glm(model, data = MurderRates, family = binomial, control = list(epsilon = 1e-15, maxit = 50, trace = FALSE)) summary(fm_logit2) fm_probit <- glm(model, data = MurderRates, family = binomial(link = "probit")) summary(fm_probit) fm_probit2 <- glm(model, data = MurderRates , family = binomial(link = "probit"), control = list(epsilon = 1e-15, maxit = 50, trace = FALSE)) summary(fm_probit2) ## Explanation: quasi-complete separation with(MurderRates, table(executions > 0, southern)) } \keyword{datasets} AER/man/HealthInsurance.Rd0000644000176200001440000000324113615674673015022 0ustar liggesusers\name{HealthInsurance} \alias{HealthInsurance} \title{Medical Expenditure Panel Survey Data} \description{ Cross-section data originating from the Medical Expenditure Panel Survey survey conducted in 1996. } \usage{data("HealthInsurance")} \format{ A data frame containing 8,802 observations on 11 variables. \describe{ \item{health}{factor. Is the self-reported health status \dQuote{healthy}?.} \item{age}{age in years.} \item{limit}{factor. Is there any limitation?} \item{gender}{factor indicating gender.} \item{insurance}{factor. Does the individual have a health insurance?} \item{married}{factor. Is the individual married?} \item{selfemp}{factor. Is the individual self-employed?} \item{family}{family size.} \item{region}{factor indicating region.} \item{ethnicity}{factor indicating ethnicity: African-American, Caucasian, other.} \item{education}{factor indicating highest degree attained: no degree, GED (high school equivalent), high school, bachelor, master, PhD, other.} } } \details{ This is a subset of the data used in Perry and Rosen (2004). } \source{ Online complements to Stock and Watson (2007). } \references{ Perry, C. and Rosen, H.S. (2004). \dQuote{The Self-Employed are Less Likely than Wage-Earners to Have Health Insurance. So What?} in Holtz-Eakin, D. and Rosen, H.S. (eds.), \emph{Entrepeneurship and Public Policy}, MIT Press. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("HealthInsurance") summary(HealthInsurance) prop.table(xtabs(~ selfemp + insurance, data = HealthInsurance), 1) } \keyword{datasets} AER/man/USConsump1993.Rd0000644000176200001440000000402513615674673014170 0ustar liggesusers\name{USConsump1993} \alias{USConsump1993} \title{US Consumption Data (1950--1993)} \description{ Time series data on US income and consumption expenditure, 1950--1993. } \usage{data("USConsump1993")} \format{ An annual multiple time series from 1950 to 1993 with 2 variables. \describe{ \item{income}{Disposable personal income (in 1987 USD).} \item{expenditure}{Personal consumption expenditures (in 1987 USD).} } } \source{ The data is from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. } \seealso{\code{\link{Baltagi2002}}, \code{\link{USConsump1950}}, \code{\link{USConsump1979}}} \examples{ ## data from Baltagi (2002) data("USConsump1993", package = "AER") plot(USConsump1993, plot.type = "single", col = 1:2) ## Chapter 5 (p. 122-125) fm <- lm(expenditure ~ income, data = USConsump1993) summary(fm) ## Durbin-Watson test (p. 122) dwtest(fm) ## Breusch-Godfrey test (Table 5.4, p. 124) bgtest(fm) ## Newey-West standard errors (Table 5.5, p. 125) coeftest(fm, vcov = NeweyWest(fm, lag = 3, prewhite = FALSE, adjust = TRUE)) ## Chapter 8 library("strucchange") ## Recursive residuals rr <- recresid(fm) rr ## Recursive CUSUM test rcus <- efp(expenditure ~ income, data = USConsump1993) plot(rcus) sctest(rcus) ## Harvey-Collier test harvtest(fm) ## NOTE" Mistake in Baltagi (2002) who computes ## the t-statistic incorrectly as 0.0733 via mean(rr)/sd(rr)/sqrt(length(rr)) ## whereas it should be (as in harvtest) mean(rr)/sd(rr) * sqrt(length(rr)) ## Rainbow test raintest(fm, center = 23) ## J test for non-nested models library("dynlm") fm1 <- dynlm(expenditure ~ income + L(income), data = USConsump1993) fm2 <- dynlm(expenditure ~ income + L(expenditure), data = USConsump1993) jtest(fm1, fm2) ## Chapter 14 ## ACF and PACF for expenditures and first differences exps <- USConsump1993[, "expenditure"] (acf(exps)) (pacf(exps)) (acf(diff(exps))) (pacf(diff(exps))) ## dynamic regressions, eq. (14.8) fm <- dynlm(d(exps) ~ I(time(exps) - 1949) + L(exps)) summary(fm) } \keyword{datasets} AER/man/ProgramEffectiveness.Rd0000644000176200001440000000265013615674673016071 0ustar liggesusers\name{ProgramEffectiveness} \alias{ProgramEffectiveness} \title{Program Effectiveness Data} \description{ Data used to study the effectiveness of a program. } \usage{data("ProgramEffectiveness")} \format{ A data frame containing 32 cross-section observations on 4 variables. \describe{ \item{grade}{Factor with levels \code{"increase"} and \code{"decrease"}.} \item{average}{Grade-point average.} \item{testscore}{Test score on economics test.} \item{participation}{Factor. Did the individual participate in the program?} } } \details{ The data are taken form Spencer and Mazzeo (1980) who examined whether a new method of teaching economics significantly influenced performance in later economics courses. } \source{ Online complements to Greene (2003). \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Spector, L. and Mazzeo, M. (1980). Probit Analysis and Economic Education. \emph{Journal of Economic Education}, \bold{11}, 37--44. } \seealso{\code{\link{Greene2003}}} \examples{ data("ProgramEffectiveness") ## Greene (2003), Table 21.1, col. "Probit" fm_probit <- glm(grade ~ average + testscore + participation, data = ProgramEffectiveness, family = binomial(link = "probit")) summary(fm_probit) } \keyword{datasets} AER/man/USGasG.Rd0000644000176200001440000000563213615674673013044 0ustar liggesusers\name{USGasG} \alias{USGasG} \title{US Gasoline Market Data (1960--1995, Greene)} \description{ Time series data on the US gasoline market. } \usage{data("USGasG")} \format{ An annual multiple time series from 1960 to 1995 with 10 variables. \describe{ \item{gas}{Total US gasoline consumption (computed as total expenditure divided by price index).} \item{price}{Price index for gasoline.} \item{income}{Per capita disposable income.} \item{newcar}{Price index for new cars.} \item{usedcar}{Price index for used cars.} \item{transport}{Price index for public transportation.} \item{durable}{Aggregate price index for consumer durables.} \item{nondurable}{Aggregate price index for consumer nondurables.} \item{service}{Aggregate price index for consumer services.} \item{population}{US total population in millions.} } } \source{ Online complements to Greene (2003). Table F2.2. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}, \code{\link{USGasB}}} \examples{ data("USGasG", package = "AER") plot(USGasG) ## Greene (2003) ## Example 2.3 fm <- lm(log(gas/population) ~ log(price) + log(income) + log(newcar) + log(usedcar), data = as.data.frame(USGasG)) summary(fm) ## Example 4.4 ## estimates and standard errors (note different offset for intercept) coef(fm) sqrt(diag(vcov(fm))) ## confidence interval confint(fm, parm = "log(income)") ## test linear hypothesis linearHypothesis(fm, "log(income) = 1") ## Example 7.6 ## re-used in Example 8.3 trend <- 1:nrow(USGasG) shock <- factor(time(USGasG) > 1973, levels = c(FALSE, TRUE), labels = c("before", "after")) ## 1960-1995 fm1 <- lm(log(gas/population) ~ log(income) + log(price) + log(newcar) + log(usedcar) + trend, data = as.data.frame(USGasG)) summary(fm1) ## pooled fm2 <- lm(log(gas/population) ~ shock + log(income) + log(price) + log(newcar) + log(usedcar) + trend, data = as.data.frame(USGasG)) summary(fm2) ## segmented fm3 <- lm(log(gas/population) ~ shock/(log(income) + log(price) + log(newcar) + log(usedcar) + trend), data = as.data.frame(USGasG)) summary(fm3) ## Chow test anova(fm3, fm1) library("strucchange") sctest(log(gas/population) ~ log(income) + log(price) + log(newcar) + log(usedcar) + trend, data = USGasG, point = c(1973, 1), type = "Chow") ## Recursive CUSUM test rcus <- efp(log(gas/population) ~ log(income) + log(price) + log(newcar) + log(usedcar) + trend, data = USGasG, type = "Rec-CUSUM") plot(rcus) sctest(rcus) ## Note: Greene's remark that the break is in 1984 (where the process crosses its ## boundary) is wrong. The break appears to be no later than 1976. ## More examples can be found in: ## help("Greene2003") } \keyword{datasets} AER/man/WinkelmannBoes2009.Rd0000644000176200001440000003100113616354265015160 0ustar liggesusers\name{WinkelmannBoes2009} \alias{WinkelmannBoes2009} \title{Data and Examples from Winkelmann and Boes (2009)} \description{ This manual page collects a list of examples from the book. Some solutions might not be exact and the list is not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. } \references{ Winkelmann, R., and Boes, S. (2009). \emph{Analysis of Microdata}, 2nd ed. Berlin and Heidelberg: Springer-Verlag. } \seealso{\code{\link{GSS7402}}, \code{\link{GSOEP9402}}, \code{\link{PSID1976}}} \examples{ \donttest{ ######################################### ## US General Social Survey 1974--2002 ## ######################################### ## data data("GSS7402", package = "AER") ## completed fertility subset gss40 <- subset(GSS7402, age >= 40) ## Chapter 1 ## Table 1.1 gss_kids <- table(gss40$kids) cbind(absolute = gss_kids, relative = round(prop.table(gss_kids) * 100, digits = 2)) ## Table 1.2 sd1 <- function(x) sd(x) / sqrt(length(x)) with(gss40, round(cbind( "obs" = tapply(kids, year, length), "av kids" = tapply(kids, year, mean), " " = tapply(kids, year, sd1), "prop childless" = tapply(kids, year, function(x) mean(x <= 0)), " " = tapply(kids, year, function(x) sd1(x <= 0)), "av schooling" = tapply(education, year, mean), " " = tapply(education, year, sd1) ), digits = 2)) ## Table 1.3 gss40$trend <- gss40$year - 1974 kids_lm1 <- lm(kids ~ factor(year), data = gss40) kids_lm2 <- lm(kids ~ trend, data = gss40) kids_lm3 <- lm(kids ~ trend + education, data = gss40) ## Chapter 2 ## Table 2.1 kids_tab <- prop.table(xtabs(~ kids + year, data = gss40), 2) * 100 round(kids_tab[,c(4, 8)], digits = 2) ## Figure 2.1 barplot(t(kids_tab[, c(4, 8)]), beside = TRUE, legend = TRUE) ## Chapter 3, Example 3.14 ## Table 3.1 gss40$nokids <- factor(gss40$kids <= 0, levels = c(FALSE, TRUE), labels = c("no", "yes")) nokids_p1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "probit")) nokids_p2 <- glm(nokids ~ trend, data = gss40, family = binomial(link = "probit")) nokids_p3 <- glm(nokids ~ trend + education + ethnicity + siblings, data = gss40, family = binomial(link = "probit")) ## p. 87 lrtest(nokids_p1, nokids_p2, nokids_p3) ## Chapter 4, Example 4.1 gss40$nokids01 <- as.numeric(gss40$nokids) - 1 nokids_lm3 <- lm(nokids01 ~ trend + education + ethnicity + siblings, data = gss40) coeftest(nokids_lm3, vcov = sandwich) ## Example 4.3 ## Table 4.1 nokids_l1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "logit")) nokids_l3 <- glm(nokids ~ trend + education + ethnicity + siblings, data = gss40, family = binomial(link = "logit")) lrtest(nokids_p3) lrtest(nokids_l3) ## Table 4.2 nokids_xbar <- colMeans(model.matrix(nokids_l3)) sum(coef(nokids_p3) * nokids_xbar) sum(coef(nokids_l3) * nokids_xbar) dnorm(sum(coef(nokids_p3) * nokids_xbar)) dlogis(sum(coef(nokids_l3) * nokids_xbar)) dnorm(sum(coef(nokids_p3) * nokids_xbar)) * coef(nokids_p3)[3] dlogis(sum(coef(nokids_l3) * nokids_xbar)) * coef(nokids_l3)[3] exp(coef(nokids_l3)[3]) ## Figure 4.4 ## everything by hand (for ethnicity = "cauc" group) nokids_xbar <- as.vector(nokids_xbar) nokids_nd <- data.frame(education = seq(0, 20, by = 0.5), trend = nokids_xbar[2], ethnicity = "cauc", siblings = nokids_xbar[4]) nokids_p3_fit <- predict(nokids_p3, newdata = nokids_nd, type = "response", se.fit = TRUE) plot(nokids_nd$education, nokids_p3_fit$fit, type = "l", xlab = "education", ylab = "predicted probability", ylim = c(0, 0.3)) polygon(c(nokids_nd$education, rev(nokids_nd$education)), c(nokids_p3_fit$fit + 1.96 * nokids_p3_fit$se.fit, rev(nokids_p3_fit$fit - 1.96 * nokids_p3_fit$se.fit)), col = "lightgray", border = "lightgray") lines(nokids_nd$education, nokids_p3_fit$fit) ## using "effects" package (for average "ethnicity" variable) library("effects") nokids_p3_ef <- effect("education", nokids_p3, xlevels = list(education = 0:20)) plot(nokids_p3_ef, rescale.axis = FALSE, ylim = c(0, 0.3)) ## using "effects" plus modification by hand nokids_p3_ef1 <- as.data.frame(nokids_p3_ef) plot(pnorm(fit) ~ education, data = nokids_p3_ef1, type = "n", ylim = c(0, 0.3)) polygon(c(0:20, 20:0), pnorm(c(nokids_p3_ef1$upper, rev(nokids_p3_ef1$lower))), col = "lightgray", border = "lightgray") lines(pnorm(fit) ~ education, data = nokids_p3_ef1) ## Table 4.6 ## McFadden's R^2 1 - as.numeric( logLik(nokids_p3) / logLik(nokids_p1) ) 1 - as.numeric( logLik(nokids_l3) / logLik(nokids_l1) ) ## McKelvey and Zavoina R^2 r2mz <- function(obj) { ystar <- predict(obj) sse <- sum((ystar - mean(ystar))^2) s2 <- switch(obj$family$link, "probit" = 1, "logit" = pi^2/3, NA) n <- length(residuals(obj)) sse / (n * s2 + sse) } r2mz(nokids_p3) r2mz(nokids_l3) ## AUC library("ROCR") nokids_p3_pred <- prediction(fitted(nokids_p3), gss40$nokids) nokids_l3_pred <- prediction(fitted(nokids_l3), gss40$nokids) plot(performance(nokids_p3_pred, "tpr", "fpr")) abline(0, 1, lty = 2) performance(nokids_p3_pred, "auc") plot(performance(nokids_l3_pred, "tpr", "fpr")) abline(0, 1, lty = 2) performance(nokids_l3_pred, "auc")@y.values ## Chapter 7 ## Table 7.3 ## subset selection gss02 <- subset(GSS7402, year == 2002 & (age < 40 | !is.na(agefirstbirth))) #Z# This selection conforms with top of page 229. However, there #Z# are too many observations: 1374. Furthermore, there are six #Z# observations with agefirstbirth <= 14 which will cause problems in #Z# taking logs! ## computing time to first birth gss02$tfb <- with(gss02, ifelse(is.na(agefirstbirth), age - 14, agefirstbirth - 14)) #Z# currently this is still needed before taking logs gss02$tfb <- pmax(gss02$tfb, 1) tfb_tobit <- tobit(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant, data = gss02, left = -Inf, right = log(gss02$age - 14)) tfb_ols <- lm(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant, data = gss02, subset = !is.na(agefirstbirth)) ## Chapter 8 ## Example 8.3 gss2002 <- subset(GSS7402, year == 2002 & (agefirstbirth < 40 | age < 40)) gss2002$afb <- with(gss2002, Surv(ifelse(kids > 0, agefirstbirth, age), kids > 0)) afb_km <- survfit(afb ~ 1, data = gss2002) afb_skm <- summary(afb_km) print(afb_skm) with(afb_skm, plot(n.event/n.risk ~ time, type = "s")) plot(afb_km, xlim = c(10, 40), conf.int = FALSE) ## Example 8.9 library("survival") afb_ex <- survreg( afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16, data = gss2002, dist = "exponential") afb_wb <- survreg( afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16, data = gss2002, dist = "weibull") afb_ln <- survreg( afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16, data = gss2002, dist = "lognormal") ## Example 8.11 kids_pois <- glm(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16, data = gss40, family = poisson) library("MASS") kids_nb <- glm.nb(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16, data = gss40) lrtest(kids_pois, kids_nb) ############################################ ## German Socio-Economic Panel 1994--2002 ## ############################################ ## data data("GSOEP9402", package = "AER") ## some convenience data transformations gsoep <- GSOEP9402 gsoep$meducation2 <- cut(gsoep$meducation, breaks = c(6, 10.25, 12.25, 18), labels = c("7-10", "10.5-12", "12.5-18")) gsoep$year2 <- factor(gsoep$year) ## Chapter 1 ## Table 1.4 plus visualizations gsoep_tab <- xtabs(~ meducation2 + school, data = gsoep) round(prop.table(gsoep_tab, 1) * 100, digits = 2) spineplot(gsoep_tab) plot(school ~ meducation, data = gsoep, breaks = c(7, 10.25, 12.25, 18)) plot(school ~ meducation, data = gsoep, breaks = c(7, 9, 10.5, 11.5, 12.5, 15, 18)) ## Chapter 5 ## Table 5.1 library("nnet") gsoep_mnl <- multinom( school ~ meducation + memployment + log(income) + log(size) + parity + year2, data = gsoep) coeftest(gsoep_mnl)[c(1:6, 1:6 + 14),] ## alternatively if(require("mlogit")) { gsoep_mnl2 <- mlogit(school ~ 0 | meducation + memployment + log(income) + log(size) + parity + year2, data = gsoep, shape = "wide", reflevel = "Hauptschule") coeftest(gsoep_mnl2)[1:12,] } ## Table 5.2 library("effects") gsoep_eff <- effect("meducation", gsoep_mnl, xlevels = list(meducation = sort(unique(gsoep$meducation)))) gsoep_eff$prob plot(gsoep_eff, confint = FALSE) ## Table 5.3, odds exp(coef(gsoep_mnl)[, "meducation"]) ## all effects eff_mnl <- allEffects(gsoep_mnl) plot(eff_mnl, ask = FALSE, confint = FALSE) plot(eff_mnl, ask = FALSE, style = "stacked", colors = gray.colors(3)) ## omit year gsoep_mnl1 <- multinom( school ~ meducation + memployment + log(income) + log(size) + parity, data = gsoep) lrtest(gsoep_mnl, gsoep_mnl1) eff_mnl1 <- allEffects(gsoep_mnl1) plot(eff_mnl1, ask = FALSE, confint = FALSE) plot(eff_mnl1, ask = FALSE, style = "stacked", colors = gray.colors(3)) ## Chapter 6 ## Table 6.1 library("MASS") gsoep$munemp <- factor(gsoep$memployment != "none", levels = c(FALSE, TRUE), labels = c("no", "yes")) gsoep_pop <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2, data = gsoep, method = "probit", Hess = TRUE) gsoep_pol <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2, data = gsoep, Hess = TRUE) lrtest(gsoep_pop) lrtest(gsoep_pol) ## Table 6.2 ## todo eff_pol <- allEffects(gsoep_pol) plot(eff_pol, ask = FALSE, confint = FALSE) plot(eff_pol, ask = FALSE, style = "stacked", colors = gray.colors(3)) #################################### ## Labor Force Participation Data ## #################################### ## Mroz data data("PSID1976", package = "AER") PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000) ## visualizations plot(hours ~ nwincome, data = PSID1976, xlab = "Non-wife income (in USD 1000)", ylab = "Hours of work in 1975") plot(jitter(hours, 200) ~ jitter(wage, 50), data = PSID1976, xlab = "Wife's average hourly wage (jittered)", ylab = "Hours of work in 1975 (jittered)") ## Chapter 1, p. 18 hours_lm <- lm(hours ~ wage + nwincome + youngkids + oldkids, data = PSID1976, subset = participation == "yes") ## Chapter 7 ## Example 7.2, Table 7.1 hours_tobit <- tobit(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976) hours_ols1 <- lm(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976) hours_ols2 <- lm(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976, subset = participation == "yes") ## Example 7.10, Table 7.4 wage_ols <- lm(log(wage) ~ education + experience + I(experience^2), data = PSID1976, subset = participation == "yes") library("sampleSelection") wage_ghr <- selection(participation ~ nwincome + age + youngkids + oldkids + education + experience + I(experience^2), log(wage) ~ education + experience + I(experience^2), data = PSID1976) ## Exercise 7.13 hours_cragg1 <- glm(participation ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976, family = binomial(link = "probit")) library("truncreg") hours_cragg2 <- truncreg(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976, subset = participation == "yes") ## Exercise 7.15 wage_olscoef <- sapply(c(-Inf, 0.5, 1, 1.5, 2), function(censpoint) coef(lm(log(wage) ~ education + experience + I(experience^2), data = PSID1976[log(PSID1976$wage) > censpoint,]))) wage_mlcoef <- sapply(c(0.5, 1, 1.5, 2), function(censpoint) coef(tobit(log(wage) ~ education + experience + I(experience^2), data = PSID1976, left = censpoint))) ################################## ## Choice of Brand for Crackers ## ################################## ## data if(require("mlogit")) { data("Cracker", package = "mlogit") head(Cracker, 3) crack <- mlogit.data(Cracker, varying = 2:13, shape = "wide", choice = "choice") head(crack, 12) ## Table 5.6 (model 3 probably not fully converged in W&B) crack$price <- crack$price/100 crack_mlogit1 <- mlogit(choice ~ price | 0, data = crack, reflevel = "private") crack_mlogit2 <- mlogit(choice ~ price | 1, data = crack, reflevel = "private") crack_mlogit3 <- mlogit(choice ~ price + feat + disp | 1, data = crack, reflevel = "private") lrtest(crack_mlogit1, crack_mlogit2, crack_mlogit3) ## IIA test crack_mlogit_all <- update(crack_mlogit2, reflevel = "nabisco") crack_mlogit_res <- update(crack_mlogit_all, alt.subset = c("keebler", "nabisco", "sunshine")) hmftest(crack_mlogit_all, crack_mlogit_res) } } } \keyword{datasets} AER/man/Guns.Rd0000644000176200001440000000516513615674673012670 0ustar liggesusers\name{Guns} \alias{Guns} \title{More Guns, Less Crime?} \description{ Guns is a balanced panel of data on 50 US states, plus the District of Columbia (for a total of 51 states), by year for 1977--1999. } \usage{data("Guns")} \format{ A data frame containing 1,173 observations on 13 variables. \describe{ \item{state}{factor indicating state.} \item{year}{factor indicating year.} \item{violent}{violent crime rate (incidents per 100,000 members of the population).} \item{murder}{murder rate (incidents per 100,000).} \item{robbery}{robbery rate (incidents per 100,000).} \item{prisoners}{incarceration rate in the state in the previous year (sentenced prisoners per 100,000 residents; value for the previous year).} \item{afam}{percent of state population that is African-American, ages 10 to 64.} \item{cauc}{percent of state population that is Caucasian, ages 10 to 64.} \item{male}{percent of state population that is male, ages 10 to 29.} \item{population}{state population, in millions of people.} \item{income}{real per capita personal income in the state (US dollars).} \item{density}{population per square mile of land area, divided by 1,000.} \item{law}{factor. Does the state have a shall carry law in effect in that year?} } } \details{ Each observation is a given state in a given year. There are a total of 51 states times 23 years = 1,173 observations. } \source{ Online complements to Stock and Watson (2007). } \references{ Ayres, I., and Donohue, J.J. (2003). Shooting Down the \sQuote{More Guns Less Crime} Hypothesis. \emph{Stanford Law Review}, \bold{55}, 1193--1312. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ ## data data("Guns") ## visualization library("lattice") xyplot(log(violent) ~ as.numeric(as.character(year)) | state, data = Guns, type = "l") ## Stock & Watson (2007), Empirical Exercise 10.1, pp. 376--377 fm1 <- lm(log(violent) ~ law, data = Guns) coeftest(fm1, vcov = sandwich) fm2 <- lm(log(violent) ~ law + prisoners + density + income + population + afam + cauc + male, data = Guns) coeftest(fm2, vcov = sandwich) fm3 <- lm(log(violent) ~ law + prisoners + density + income + population + afam + cauc + male + state, data = Guns) printCoefmat(coeftest(fm3, vcov = sandwich)[1:9,]) fm4 <- lm(log(violent) ~ law + prisoners + density + income + population + afam + cauc + male + state + year, data = Guns) printCoefmat(coeftest(fm4, vcov = sandwich)[1:9,]) } \keyword{datasets} AER/man/MotorCycles2.Rd0000644000176200001440000000214013615674673014267 0ustar liggesusers\name{MotorCycles2} \alias{MotorCycles2} \title{Motor Cycles in The Netherlands} \description{ Time series of stock of motor cycles (two wheels) in The Netherlands (in thousands). } \usage{data("MotorCycles2")} \format{ An annual univariate time series from 1946 to 2012. } \details{This is an update of the series that was available with Franses (1998). However, the values for the years 1992 and 1993 differ.} \source{ Online complements to Franses, van Dijk and Opschoor (2014). \url{http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition} } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. Franses, P.H., van Dijk, D. and Opschoor, A. (2014). \emph{Time Series Models for Business and Economic Forecasting}, 2nd ed. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}, \code{\link{MotorCycles}}} \examples{ data("MotorCycles2") plot(MotorCycles2) } \keyword{datasets} AER/man/CPS1988.Rd0000644000176200001440000000520313615674673012724 0ustar liggesusers\name{CPS1988} \alias{CPS1988} \title{Determinants of Wages Data (CPS 1988)} \description{ Cross-section data originating from the March 1988 Current Population Survey by the US Census Bureau. } \usage{data("CPS1988")} \format{ A data frame containing 28,155 observations on 7 variables. \describe{ \item{wage}{Wage (in dollars per week).} \item{education}{Number of years of education.} \item{experience}{Number of years of potential work experience.} \item{ethnicity}{Factor with levels \code{"cauc"} and \code{"afam"} (African-American).} \item{smsa}{Factor. Does the individual reside in a Standard Metropolitan Statistical Area (SMSA)?} \item{region}{Factor with levels \code{"northeast"}, \code{"midwest"}, \code{"south"}, \code{"west"}.} \item{parttime}{Factor. Does the individual work part-time?} } } \details{ A sample of men aged 18 to 70 with positive annual income greater than USD 50 in 1992, who are not self-employed nor working without pay. Wages are deflated by the deflator of Personal Consumption Expenditure for 1992. A problem with CPS data is that it does not provide actual work experience. It is therefore customary to compute experience as \code{age - education - 6} (as was done by Bierens and Ginther, 2001), this may be considered potential experience. As a result, some respondents have negative experience. } \source{ \url{http://www.personal.psu.edu/hxb11/MEDIAN.HTM} } \references{ Bierens, H.J., and Ginther, D. (2001). Integrated Conditional Moment Testing of Quantile Regression Models. \emph{Empirical Economics}, \bold{26}, 307--324. Buchinsky, M. (1998). Recent Advances in Quantile Regression Models: A Practical Guide for Empirical Research. \emph{Journal of Human Resources}, \bold{33}, 88--126. } \seealso{\code{\link{CPS1985}}, \code{\link{CPSSW}}} \examples{ ## data and packages library("quantreg") data("CPS1988") CPS1988$region <- relevel(CPS1988$region, ref = "south") ## Model equations: Mincer-type, quartic, Buchinsky-type mincer <- log(wage) ~ ethnicity + education + experience + I(experience^2) quart <- log(wage) ~ ethnicity + education + experience + I(experience^2) + I(experience^3) + I(experience^4) buchinsky <- log(wage) ~ ethnicity * (education + experience + parttime) + region*smsa + I(experience^2) + I(education^2) + I(education*experience) ## OLS and LAD fits (for LAD see Bierens and Ginter, Tables 1-3.A.) mincer_ols <- lm(mincer, data = CPS1988) mincer_lad <- rq(mincer, data = CPS1988) quart_ols <- lm(quart, data = CPS1988) quart_lad <- rq(quart, data = CPS1988) buchinsky_ols <- lm(buchinsky, data = CPS1988) buchinsky_lad <- rq(buchinsky, data = CPS1988) } \keyword{datasets} AER/man/TradeCredit.Rd0000644000176200001440000000176013615674673014143 0ustar liggesusers\name{TradeCredit} \alias{TradeCredit} \title{Trade Credit and the Money Market} \description{ Macroeconomic time series data from 1946 to 1966 on trade credit and the money market. } \usage{data("TradeCredit")} \format{ An annual multiple time series from 1946 to 1966 on 7 variables. \describe{ \item{trade}{Nominal total trade money.} \item{reserve}{Nominal effective reserve money.} \item{gnp}{GNP in current dollars.} \item{utilization}{Degree of market utilization.} \item{interest}{Short-term rate of interest.} \item{size}{Mean real size of the representative economic unit (1939 = 100).} \item{price}{GNP price deflator (1958 = 100).} } } \source{ The data are from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. Laffer, A.B. (1970). Trade Credit and the Money Market. \emph{Journal of Political Economy}, \bold{78}, 239--267. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("TradeCredit") plot(TradeCredit) } \keyword{datasets} AER/man/EquationCitations.Rd0000644000176200001440000000746713615674673015426 0ustar liggesusers\name{EquationCitations} \alias{EquationCitations} \title{Number of Equations and Citations for Evolutionary Biology Publications} \description{ Analysis of citations of evolutionary biology papers published in 1998 in the top three journals (as judged by their 5-year impact factors in the Thomson Reuters Journal Citation Reports 2010). } \usage{data("EquationCitations")} \format{ A data frame containing 649 observations on 13 variables. \describe{ \item{journal}{Factor. Journal in which the paper was published (The American Naturalist, Evolution, Proceedings of the Royal Society of London B: Biological Sciences).} \item{authors}{Character. Names of authors.} \item{volume}{Volume in which the paper was published.} \item{startpage}{Starting page of publication.} \item{pages}{Number of pages.} \item{equations}{Number of equations in total.} \item{mainequations}{Number of equations in main text.} \item{appequations}{Number of equations in appendix.} \item{cites}{Number of citations in total.} \item{selfcites}{Number of citations by the authors themselves.} \item{othercites}{Number of citations by other authors.} \item{theocites}{Number of citations by theoretical papers.} \item{nontheocites}{Number of citations by nontheoretical papers.} } } \details{ Fawcett and Higginson (2012) investigate the relationship between the number of citations evolutionary biology papers receive, depending on the number of equations per page in the cited paper. Overall it can be shown that papers with many mathematical equations significantly lower the number of citations they receive, in particular from nontheoretical papers. } \source{ Online supplements to Fawcett and Higginson (2012). \url{http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1205259109/-/DCSupplemental} } \references{ Fawcett, T.W. and Higginson, A.D. (2012). Heavy Use of Equations Impedes Communication among Biologists. \emph{PNAS -- Proceedings of the National Academy of Sciences of the United States of America}, \bold{109}, 11735--11739. \url{http://dx.doi.org/10.1073/pnas.1205259109} } \seealso{\code{\link{PhDPublications}}} \examples{ ## load data and MASS package data("EquationCitations", package = "AER") library("MASS") ## convenience function for summarizing NB models nbtable <- function(obj, digits = 3) round(cbind( "OR" = exp(coef(obj)), "CI" = exp(confint.default(obj)), "Wald z" = coeftest(obj)[,3], "p" = coeftest(obj)[, 4]), digits = digits) ################# ## Replication ## ################# ## Table 1 m1a <- glm.nb(othercites ~ I(equations/pages) * pages + journal, data = EquationCitations) m1b <- update(m1a, nontheocites ~ .) m1c <- update(m1a, theocites ~ .) nbtable(m1a) nbtable(m1b) nbtable(m1c) ## Table 2 m2a <- glm.nb( othercites ~ (I(mainequations/pages) + I(appequations/pages)) * pages + journal, data = EquationCitations) m2b <- update(m2a, nontheocites ~ .) m2c <- update(m2a, theocites ~ .) nbtable(m2a) nbtable(m2b) nbtable(m2c) ############### ## Extension ## ############### ## nonlinear page effect: use log(pages) instead of pages+interaction m3a <- glm.nb(othercites ~ I(equations/pages) + log(pages) + journal, data = EquationCitations) m3b <- update(m3a, nontheocites ~ .) m3c <- update(m3a, theocites ~ .) ## nested models: allow different equation effects over journals m4a <- glm.nb(othercites ~ journal / I(equations/pages) + log(pages), data = EquationCitations) m4b <- update(m4a, nontheocites ~ .) m4c <- update(m4a, theocites ~ .) ## nested model best (wrt AIC) for all responses AIC(m1a, m2a, m3a, m4a) nbtable(m4a) AIC(m1b, m2b, m3b, m4b) nbtable(m4b) AIC(m1c, m2c, m3c, m4c) nbtable(m4c) ## equation effect by journal/response ## comb nontheo theo ## AmNat =/- - + ## Evolution =/+ = + ## ProcB - - =/+ } \keyword{datasets} AER/man/MotorCycles.Rd0000644000176200001440000000135213615674673014211 0ustar liggesusers\name{MotorCycles} \alias{MotorCycles} \title{Motor Cycles in The Netherlands} \description{ Time series of stock of motor cycles (two wheels) in The Netherlands (in thousands). } \usage{data("MotorCycles")} \format{ An annual univariate time series from 1946 to 1993. } \details{An updated version is available under the name \code{MotorCycles2}. However, the values for the years 1992 and 1993 differ there.} \source{ Online complements to Franses (1998). } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}, \code{\link{MotorCycles2}}} \examples{ data("MotorCycles") plot(MotorCycles) } \keyword{datasets} AER/man/StrikeDuration.Rd0000644000176200001440000000372413615674673014722 0ustar liggesusers\name{StrikeDuration} \alias{StrikeDuration} \title{Strike Durations} \description{ Data on the duration of strikes in US manufacturing industries, 1968--1976. } \usage{data("StrikeDuration")} \format{ A data frame containing 62 observations on 2 variables for the period 1968--1976. \describe{ \item{duration}{strike duration in days.} \item{uoutput}{unanticipated output (a measure of unanticipated aggregate industrial production net of seasonal and trend components).} } } \details{ The original data provided by Kennan (1985) are on a monthly basis, for the period 1968(1) through 1976(12). Greene (2003) only provides the June data for each year. Also, the duration for observation 36 is given as 3 by Greene while Kennan has 2. Here we use Greene's version. \code{uoutput} is the residual from a regression of the logarithm of industrial production in manufacturing on time, time squared, and monthly dummy variables. } \source{ Online complements to Greene (2003). \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Kennan, J. (1985). The Duration of Contract Strikes in US Manufacturing. \emph{Journal of Econometrics}, \bold{28}, 5--28. } \seealso{\code{\link{Greene2003}}} \examples{ data("StrikeDuration") library("MASS") ## Greene (2003), Table 22.10 fit_exp <- fitdistr(StrikeDuration$duration, "exponential") fit_wei <- fitdistr(StrikeDuration$duration, "weibull") fit_wei$estimate[2]^(-1) fit_lnorm <- fitdistr(StrikeDuration$duration, "lognormal") 1/fit_lnorm$estimate[2] exp(-fit_lnorm$estimate[1]) ## Weibull and lognormal distribution have ## different parameterizations, see Greene p. 794 ## Greene (2003), Example 22.10 library("survival") fm_wei <- survreg(Surv(duration) ~ uoutput, dist = "weibull", data = StrikeDuration) summary(fm_wei) } \keyword{datasets} AER/man/BondYield.Rd0000644000176200001440000000120713615674673013616 0ustar liggesusers\name{BondYield} \alias{BondYield} \title{Bond Yield Data} \description{ Monthly averages of the yield on a Moody's Aaa rated corporate bond (in percent/year). } \usage{data("BondYield")} \format{ A monthly univariate time series from 1990(1) to 1994(12). } \source{ Online complements to Greene (2003), Table F20.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("BondYield") plot(BondYield) } \keyword{datasets} AER/man/GrowthDJ.Rd0000644000176200001440000000562313615674673013443 0ustar liggesusers\name{GrowthDJ} \alias{GrowthDJ} \encoding{UTF-8} \title{Determinants of Economic Growth} \description{Growth regression data as provided by Durlauf & Johnson (1995).} \usage{data("GrowthDJ")} \format{ A data frame containing 121 observations on 10 variables. \describe{ \item{oil}{factor. Is the country an oil-producing country?} \item{inter}{factor. Does the country have better quality data?} \item{oecd}{factor. Is the country a member of the OECD?} \item{gdp60}{Per capita GDP in 1960.} \item{gdp85}{Per capita GDP in 1985.} \item{gdpgrowth}{Average growth rate of per capita GDP from 1960 to 1985 (in percent).} \item{popgrowth}{Average growth rate of working-age population 1960 to 1985 (in percent).} \item{invest}{Average ratio of investment (including Government Investment) to GDP from 1960 to 1985 (in percent).} \item{school}{Average fraction of working-age population enrolled in secondary school from 1960 to 1985 (in percent).} \item{literacy60}{Fraction of the population over 15 years old that is able to read and write in 1960 (in percent).} } } \details{ The data are derived from the Penn World Table 4.0 and are given in Mankiw, Romer and Weil (1992), except \code{literacy60} that is from the World Bank's World Development Report. } \source{ Journal of Applied Econometrics Data Archive. \url{http://qed.econ.queensu.ca/jae/1995-v10.4/durlauf-johnson/} } \references{ Durlauf, S.N., and Johnson, P.A. (1995). Multiple Regimes and Cross-Country Growth Behavior. \emph{Journal of Applied Econometrics}, \bold{10}, 365--384. Koenker, R., and Zeileis, A. (2009). On Reproducible Econometric Research. \emph{Journal of Applied Econometrics}, \bold{24}(5), 833--847. Mankiw, N.G, Romer, D., and Weil, D.N. (1992). A Contribution to the Empirics of Economic Growth. \emph{Quarterly Journal of Economics}, \bold{107}, 407--437. Masanjala, W.H., and Papageorgiou, C. (2004). The Solow Model with CES Technology: Nonlinearities and Parameter Heterogeneity. \emph{Journal of Applied Econometrics}, \bold{19}, 171--201. } \seealso{\code{\link{OECDGrowth}}, \code{\link{GrowthSW}}} \examples{ ## data for non-oil-producing countries data("GrowthDJ") dj <- subset(GrowthDJ, oil == "no") ## Different scalings have been used by different authors, ## different types of standard errors, etc., ## see Koenker & Zeileis (2009) for an overview ## Durlauf & Johnson (1995), Table II mrw_model <- I(log(gdp85) - log(gdp60)) ~ log(gdp60) + log(invest/100) + log(popgrowth/100 + 0.05) + log(school/100) dj_mrw <- lm(mrw_model, data = dj) coeftest(dj_mrw) dj_model <- I(log(gdp85) - log(gdp60)) ~ log(gdp60) + log(invest) + log(popgrowth/100 + 0.05) + log(school) dj_sub1 <- lm(dj_model, data = dj, subset = gdp60 < 1800 & literacy60 < 50) coeftest(dj_sub1, vcov = sandwich) dj_sub2 <- lm(dj_model, data = dj, subset = gdp60 >= 1800 & literacy60 >= 50) coeftest(dj_sub2, vcov = sandwich) } \keyword{datasets} AER/man/PSID1982.Rd0000644000176200001440000000454213615674673013035 0ustar liggesusers\name{PSID1982} \alias{PSID1982} \title{PSID Earnings Data 1982} \description{ Cross-section data originating from the Panel Study on Income Dynamics, 1982. } \usage{data("PSID1982")} \format{ A data frame containing 595 observations on 12 variables. \describe{ \item{experience}{Years of full-time work experience.} \item{weeks}{Weeks worked.} \item{occupation}{factor. Is the individual a white-collar (\code{"white"}) or blue-collar (\code{"blue"}) worker?} \item{industry}{factor. Does the individual work in a manufacturing industry?} \item{south}{factor. Does the individual reside in the South?} \item{smsa}{factor. Does the individual reside in a SMSA (standard metropolitan statistical area)?} \item{married}{factor. Is the individual married?} \item{gender}{factor indicating gender.} \item{union}{factor. Is the individual's wage set by a union contract?} \item{education}{Years of education.} \item{ethnicity}{factor indicating ethnicity. Is the individual African-American (\code{"afam"}) or not (\code{"other"})?} \item{wage}{Wage.} } } \details{ \code{PSID1982} is the cross-section for the year 1982 taken from a larger panel data set \code{\link{PSID7682}} for the years 1976--1982, originating from Cornwell and Rupert (1988). Baltagi (2002) just uses the 1982 cross-section; hence \code{PSID1982} is available as a standalone data set because it was included in \pkg{AER} prior to the availability of the full \code{PSID7682} panel version. } \source{ The data is from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. Cornwell, C., and Rupert, P. (1988). Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variables Estimators. \emph{Journal of Applied Econometrics}, \bold{3}, 149--155. } \seealso{\code{\link{PSID7682}}, \code{\link{Baltagi2002}}} \examples{ data("PSID1982") plot(density(PSID1982$wage, bw = "SJ")) ## Baltagi (2002), Table 4.1 earn_lm <- lm(log(wage) ~ . + I(experience^2), data = PSID1982) summary(earn_lm) ## Baltagi (2002), Table 13.1 union_lpm <- lm(I(as.numeric(union) - 1) ~ . - wage, data = PSID1982) union_probit <- glm(union ~ . - wage, data = PSID1982, family = binomial(link = "probit")) union_logit <- glm(union ~ . - wage, data = PSID1982, family = binomial) ## probit OK, logit and LPM rather different. } \keyword{datasets} AER/man/DJFranses.Rd0000644000176200001440000000113413615674673013563 0ustar liggesusers\name{DJFranses} \alias{DJFranses} \title{Dow Jones Index Data (Franses)} \description{ Dow Jones index time series computed at the end of the week where week is assumed to run from Thursday to Wednesday. } \usage{data("DJFranses")} \format{ A weekly univariate time series from 1980(1) to 1994(42). } \source{ Online complements to Franses (1998). } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}} \examples{ data("DJFranses") plot(DJFranses) } \keyword{datasets} AER/man/Baltagi2002.Rd0000644000176200001440000001337713615674673013627 0ustar liggesusers\name{Baltagi2002} \alias{Baltagi2002} \title{Data and Examples from Baltagi (2002)} \description{ This manual page collects a list of examples from the book. Some solutions might not be exact and the list is certainly not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed., Berlin: Springer-Verlag. } \seealso{\code{\link{BenderlyZwick}}, \code{\link{CigarettesB}}, \code{\link{EuroEnergy}}, \code{\link{Grunfeld}}, \code{\link{Mortgage}}, \code{\link{NaturalGas}}, \code{\link{OECDGas}}, \code{\link{OrangeCounty}}, \code{\link{PSID1982}}, \code{\link{TradeCredit}}, \code{\link{USConsump1993}}, \code{\link{USCrudes}}, \code{\link{USGasB}}, \code{\link{USMacroB}}} \examples{ ################################ ## Cigarette consumption data ## ################################ ## data data("CigarettesB", package = "AER") ## Table 3.3 cig_lm <- lm(packs ~ price, data = CigarettesB) summary(cig_lm) ## Figure 3.9 plot(residuals(cig_lm) ~ price, data = CigarettesB) abline(h = 0, lty = 2) ## Figure 3.10 cig_pred <- with(CigarettesB, data.frame(price = seq(from = min(price), to = max(price), length = 30))) cig_pred <- cbind(cig_pred, predict(cig_lm, newdata = cig_pred, interval = "confidence")) plot(packs ~ price, data = CigarettesB) lines(fit ~ price, data = cig_pred) lines(lwr ~ price, data = cig_pred, lty = 2) lines(upr ~ price, data = cig_pred, lty = 2) ## Chapter 5: diagnostic tests (p. 111-115) cig_lm2 <- lm(packs ~ price + income, data = CigarettesB) summary(cig_lm2) ## Glejser tests (p. 112) ares <- abs(residuals(cig_lm2)) summary(lm(ares ~ income, data = CigarettesB)) summary(lm(ares ~ I(1/income), data = CigarettesB)) summary(lm(ares ~ I(1/sqrt(income)), data = CigarettesB)) summary(lm(ares ~ sqrt(income), data = CigarettesB)) ## Goldfeld-Quandt test (p. 112) gqtest(cig_lm2, order.by = ~ income, data = CigarettesB, fraction = 12, alternative = "less") ## NOTE: Baltagi computes the test statistic as mss1/mss2, ## i.e., tries to find decreasing variances. gqtest() always uses ## mss2/mss1 and has an "alternative" argument. ## Spearman rank correlation test (p. 113) cor.test(~ ares + income, data = CigarettesB, method = "spearman") ## Breusch-Pagan test (p. 113) bptest(cig_lm2, varformula = ~ income, data = CigarettesB, student = FALSE) ## White test (Table 5.1, p. 113) bptest(cig_lm2, ~ income * price + I(income^2) + I(price^2), data = CigarettesB) ## White HC standard errors (Table 5.2, p. 114) coeftest(cig_lm2, vcov = vcovHC(cig_lm2, type = "HC1")) ## Jarque-Bera test (Figure 5.2, p. 115) hist(residuals(cig_lm2), breaks = 16, ylim = c(0, 10), col = "lightgray") library("tseries") jarque.bera.test(residuals(cig_lm2)) ## Tables 8.1 and 8.2 influence.measures(cig_lm2) ##################################### ## US consumption data (1950-1993) ## ##################################### ## data data("USConsump1993", package = "AER") plot(USConsump1993, plot.type = "single", col = 1:2) ## Chapter 5 (p. 122-125) fm <- lm(expenditure ~ income, data = USConsump1993) summary(fm) ## Durbin-Watson test (p. 122) dwtest(fm) ## Breusch-Godfrey test (Table 5.4, p. 124) bgtest(fm) ## Newey-West standard errors (Table 5.5, p. 125) coeftest(fm, vcov = NeweyWest(fm, lag = 3, prewhite = FALSE, adjust = TRUE)) ## Chapter 8 library("strucchange") ## Recursive residuals rr <- recresid(fm) rr ## Recursive CUSUM test rcus <- efp(expenditure ~ income, data = USConsump1993) plot(rcus) sctest(rcus) ## Harvey-Collier test harvtest(fm) ## NOTE" Mistake in Baltagi (2002) who computes ## the t-statistic incorrectly as 0.0733 via mean(rr)/sd(rr)/sqrt(length(rr)) ## whereas it should be (as in harvtest) mean(rr)/sd(rr) * sqrt(length(rr)) ## Rainbow test raintest(fm, center = 23) ## J test for non-nested models library("dynlm") fm1 <- dynlm(expenditure ~ income + L(income), data = USConsump1993) fm2 <- dynlm(expenditure ~ income + L(expenditure), data = USConsump1993) jtest(fm1, fm2) ## Chapter 11 ## Table 11.1 Instrumental-variables regression usc <- as.data.frame(USConsump1993) usc$investment <- usc$income - usc$expenditure fm_ols <- lm(expenditure ~ income, data = usc) fm_iv <- ivreg(expenditure ~ income | investment, data = usc) ## Hausman test cf_diff <- coef(fm_iv) - coef(fm_ols) vc_diff <- vcov(fm_iv) - vcov(fm_ols) x2_diff <- as.vector(t(cf_diff) \%*\% solve(vc_diff) \%*\% cf_diff) pchisq(x2_diff, df = 2, lower.tail = FALSE) ## Chapter 14 ## ACF and PACF for expenditures and first differences exps <- USConsump1993[, "expenditure"] (acf(exps)) (pacf(exps)) (acf(diff(exps))) (pacf(diff(exps))) ## dynamic regressions, eq. (14.8) fm <- dynlm(d(exps) ~ I(time(exps) - 1949) + L(exps)) summary(fm) ################################ ## Grunfeld's investment data ## ################################ ## select the first three companies (as panel data) data("Grunfeld", package = "AER") pgr <- subset(Grunfeld, firm \%in\% levels(Grunfeld$firm)[1:3]) library("plm") pgr <- pdata.frame(pgr, c("firm", "year")) ## Ex. 10.9 library("systemfit") gr_ols <- systemfit(invest ~ value + capital, method = "OLS", data = pgr) gr_sur <- systemfit(invest ~ value + capital, method = "SUR", data = pgr) ######################################### ## Panel study on income dynamics 1982 ## ######################################### ## data data("PSID1982", package = "AER") ## Table 4.1 earn_lm <- lm(log(wage) ~ . + I(experience^2), data = PSID1982) summary(earn_lm) ## Table 13.1 union_lpm <- lm(I(as.numeric(union) - 1) ~ . - wage, data = PSID1982) union_probit <- glm(union ~ . - wage, data = PSID1982, family = binomial(link = "probit")) union_logit <- glm(union ~ . - wage, data = PSID1982, family = binomial) ## probit OK, logit and LPM rather different. } \keyword{datasets} AER/man/tobit.Rd0000644000176200001440000000552513615674673013075 0ustar liggesusers\name{tobit} \alias{tobit} \alias{print.tobit} \alias{summary.tobit} \alias{print.summary.tobit} \alias{formula.tobit} \alias{model.frame.tobit} \alias{update.tobit} \alias{waldtest.tobit} \alias{lrtest.tobit} \alias{linearHypothesis.tobit} \alias{deviance.survreg} \title{Tobit Regression} \description{ Fitting and testing tobit regression models for censored data. } \usage{ tobit(formula, left = 0, right = Inf, dist = "gaussian", subset = NULL, data = list(), \dots) } \arguments{ \item{formula}{a symbolic description of a regression model of type \code{y ~ x1 + x2 + \dots}.} \item{left}{left limit for the censored dependent variable \code{y}. If set to \code{-Inf}, \code{y} is assumed not to be left-censored.} \item{right}{right limit for the censored dependent variable \code{y}. If set to \code{Inf}, the default, \code{y} is assumed not to be right-censored.} \item{dist}{assumed distribution for the dependent variable \code{y}. This is passed to \code{\link[survival]{survreg}}, see the respective man page for more details.} \item{subset}{a specification of the rows to be used.} \item{data}{a data frame containing the variables in the model.} \item{\dots}{further arguments passed to \code{\link[survival]{survreg}}.} } \details{ The function \code{tobit} is a convenience interface to \code{\link[survival]{survreg}} (for survival regression, including censored regression) setting different defaults and providing a more convenient interface for specification of the censoring information. The default is the classical tobit model (Tobin 1958, Greene 2003) assuming a normal distribution for the dependent variable with left-censoring at 0. Technically, the formula of type \code{y ~ x1 + x2 + \dots} passed to \code{tobit} is simply transformed into a formula suitable for \code{\link[survival]{survreg}}: This means the dependent variable is first censored and then wrapped into a \code{\link[survival]{Surv}} object containing the censoring information which is subsequently passed to \code{\link[survival]{survreg}}, e.g., \code{Surv(ifelse(y <= 0, 0, y), y > 0, type = "left") ~ x1 + x2 + \dots} for the default settings. } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. \emph{Econometrica}, \bold{26}, 24--36. } \value{ An object of class \code{"tobit"} inheriting from class \code{"survreg"}. } \examples{ data("Affairs") ## from Table 22.4 in Greene (2003) fm.tobit <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) fm.tobit2 <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, right = 4, data = Affairs) summary(fm.tobit) summary(fm.tobit2) } \keyword{regression} AER/man/ChinaIncome.Rd0000644000176200001440000000203113615674673014116 0ustar liggesusers\name{ChinaIncome} \alias{ChinaIncome} \title{Chinese Real National Income Data} \description{ Time series of real national income in China per section (index with 1952 = 100). } \usage{data("ChinaIncome")} \format{ An annual multiple time series from 1952 to 1988 with 5 variables. \describe{ \item{agriculture}{Real national income in agriculture sector.} \item{industry}{Real national income in industry sector.} \item{construction}{Real national income in construction sector.} \item{transport}{Real national income in transport sector.} \item{commerce}{Real national income in commerce sector.} } } \source{ Online complements to Franses (1998). } \seealso{\code{\link{Franses1998}}} \references{ Chow, G.C. (1993). Capital Formation and Economic Growth in China. \emph{Quarterly Journal of Economics}, \bold{103}, 809--842. Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \examples{ data("ChinaIncome") plot(ChinaIncome) } \keyword{datasets} AER/man/USInvest.Rd0000644000176200001440000000314013615674673013463 0ustar liggesusers\name{USInvest} \alias{USInvest} \title{US Investment Data} \description{ Time series data on investments in the US, 1968--1982. } \usage{data("USInvest")} \format{ An annual multiple time series from 1968 to 1982 with 4 variables. \describe{ \item{gnp}{Nominal gross national product,} \item{invest}{Nominal investment,} \item{price}{Consumer price index,} \item{interest}{Interest rate (average yearly discount rate at the New York Federal Reserve Bank).} } } \source{ Online complements to Greene (2003). Table F3.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("USInvest") ## Chapter 3 in Greene (2003) ## transform (and round) data to match Table 3.1 us <- as.data.frame(USInvest) us$invest <- round(0.1 * us$invest/us$price, digits = 3) us$gnp <- round(0.1 * us$gnp/us$price, digits = 3) us$inflation <- c(4.4, round(100 * diff(us$price)/us$price[-15], digits = 2)) us$trend <- 1:15 us <- us[, c(2, 6, 1, 4, 5)] ## p. 22-24 coef(lm(invest ~ trend + gnp, data = us)) coef(lm(invest ~ gnp, data = us)) ## Example 3.1, Table 3.2 cor(us)[1,-1] pcor <- solve(cor(us)) dcor <- 1/sqrt(diag(pcor)) pcor <- (-pcor * (dcor \%o\% dcor))[1,-1] ## Table 3.4 fm <- lm(invest ~ trend + gnp + interest + inflation, data = us) fm1 <- lm(invest ~ 1, data = us) anova(fm1, fm) ## More examples can be found in: ## help("Greene2003") } \keyword{datasets} AER/man/GSOEP9402.Rd0000644000176200001440000001011213615674673013134 0ustar liggesusers\name{GSOEP9402} \alias{GSOEP9402} \title{German Socio-Economic Panel 1994--2002} \description{ Cross-section data for 675 14-year old children born between 1980 and 1988. The sample is taken from the German Socio-Economic Panel (GSOEP) for the years 1994 to 2002 to investigate the determinants of secondary school choice. } \usage{data("GSOEP9402")} \format{ A data frame containing 675 observations on 12 variables. \describe{ \item{school}{factor. Child's secondary school level.} \item{birthyear}{Year of child's birth.} \item{gender}{factor indicating child's gender.} \item{kids}{Total number of kids living in household.} \item{parity}{Birth order.} \item{income}{Household income.} \item{size}{Household size} \item{state}{factor indicating German federal state.} \item{marital}{factor indicating mother's marital status.} \item{meducation}{Mother's educational level in years.} \item{memployment}{factor indicating mother's employment level: full-time, part-time, or not working.} \item{year}{Year of GSOEP wave.} } } \details{ This sample from the German Socio-Economic Panel (GSOEP) for the years between 1994 and 2002 has been selected by Winkelmann and Boes (2009) to investigate the determinants of secondary school choice. In the German schooling system, students are separated relatively early into different school types, depending on their ability as perceived by the teachers after four years of primary school. After that, around the age of ten, students are placed into one of three types of secondary school: \code{"Hauptschule"} (lower secondary school), \code{"Realschule"} (middle secondary school), or \code{"Gymnasium"} (upper secondary school). Only a degree from the latter type of school (called Abitur) provides direct access to universities. A frequent criticism of this system is that the tracking takes place too early, and that it cements inequalities in education across generations. Although the secondary school choice is based on the teachers' recommendations, it is typically also influenced by the parents; both indirectly through their own educational level and directly through influence on the teachers. } \source{ Online complements to Winkelmann and Boes (2009). } \references{ Winkelmann, R., and Boes, S. (2009). \emph{Analysis of Microdata}, 2nd ed. Berlin and Heidelberg: Springer-Verlag. } \seealso{\code{\link{WinkelmannBoes2009}}} \examples{ ## data data("GSOEP9402", package = "AER") ## some convenience data transformations gsoep <- GSOEP9402 gsoep$year2 <- factor(gsoep$year) ## visualization plot(school ~ meducation, data = gsoep, breaks = c(7, 9, 10.5, 11.5, 12.5, 15, 18)) ## Chapter 5, Table 5.1 library("nnet") gsoep_mnl <- multinom( school ~ meducation + memployment + log(income) + log(size) + parity + year2, data = gsoep) coeftest(gsoep_mnl)[c(1:6, 1:6 + 14),] ## alternatively if(require("mlogit")) { gsoep_mnl2 <- mlogit( school ~ 0 | meducation + memployment + log(income) + log(size) + parity + year2, data = gsoep, shape = "wide", reflevel = "Hauptschule") coeftest(gsoep_mnl2)[1:12,] } ## Table 5.2 library("effects") gsoep_eff <- effect("meducation", gsoep_mnl, xlevels = list(meducation = sort(unique(gsoep$meducation)))) gsoep_eff$prob plot(gsoep_eff, confint = FALSE) ## omit year gsoep_mnl1 <- multinom( school ~ meducation + memployment + log(income) + log(size) + parity, data = gsoep) lrtest(gsoep_mnl, gsoep_mnl1) ## Chapter 6 ## Table 6.1 library("MASS") gsoep_pop <- polr( school ~ meducation + I(memployment != "none") + log(income) + log(size) + parity + year2, data = gsoep, method = "probit", Hess = TRUE) gsoep_pol <- polr( school ~ meducation + I(memployment != "none") + log(income) + log(size) + parity + year2, data = gsoep, Hess = TRUE) ## compare polr and multinom via AIC gsoep_pol1 <- polr( school ~ meducation + memployment + log(income) + log(size) + parity, data = gsoep, Hess = TRUE) AIC(gsoep_pol1, gsoep_mnl) ## effects eff_pol1 <- allEffects(gsoep_pol1) plot(eff_pol1, ask = FALSE, confint = FALSE) ## More examples can be found in: ## help("WinkelmannBoes2009") } \keyword{datasets} AER/man/NaturalGas.Rd0000644000176200001440000000167713615674673014021 0ustar liggesusers\name{NaturalGas} \alias{NaturalGas} \title{Natural Gas Data} \description{ Panel data originating from 6 US states over the period 1967--1989. } \usage{data("NaturalGas")} \format{ A data frame containing 138 observations on 10 variables. \describe{ \item{state}{factor. State abbreviation.} \item{statecode}{factor. State Code.} \item{year}{factor coding year.} \item{consumption}{Consumption of natural gas by the residential sector.} \item{price}{Price of natural gas} \item{eprice}{Price of electricity.} \item{oprice}{Price of distillate fuel oil.} \item{lprice}{Price of liquefied petroleum gas.} \item{heating}{Heating degree days.} \item{income}{Real per-capita personal income.} } } \source{ The data are from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("NaturalGas") summary(NaturalGas) } \keyword{datasets} AER/man/USConsump1979.Rd0000644000176200001440000000201013615674673014164 0ustar liggesusers\name{USConsump1979} \alias{USConsump1979} \title{US Consumption Data (1970--1979)} \description{ Time series data on US income and consumption expenditure, 1970--1979. } \usage{data("USConsump1979")} \format{ An annual multiple time series from 1970 to 1979 with 2 variables. \describe{ \item{income}{Disposable income.} \item{expenditure}{Consumption expenditure.} } } \source{ Online complements to Greene (2003). Table F1.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}, \code{\link{USConsump1950}}, \code{\link{USConsump1993}}} \examples{ data("USConsump1979") plot(USConsump1979) ## Example 1.1 in Greene (2003) plot(expenditure ~ income, data = as.data.frame(USConsump1979), pch = 19) fm <- lm(expenditure ~ income, data = as.data.frame(USConsump1979)) summary(fm) abline(fm) } \keyword{datasets} AER/man/FrozenJuice.Rd0000644000176200001440000000630113615674673014170 0ustar liggesusers\name{FrozenJuice} \alias{FrozenJuice} \title{Price of Frozen Orange Juice} \description{ Monthly data on the price of frozen orange juice concentrate and temperature in the orange-growing region of Florida. } \usage{data("FrozenJuice")} \format{ A monthly multiple time series from 1950(1) to 2000(12) with 3 variables. \describe{ \item{price}{Average producer price for frozen orange juice.} \item{ppi}{Producer price index for finished goods. Used to deflate the overall producer price index for finished goods to eliminate the effects of overall price inflation.} \item{fdd}{Number of freezing degree days at the Orlando, Florida, airport. Calculated as the sum of the number of degrees Fahrenheit that the minimum temperature falls below freezing (32 degrees Fahrenheit = about 0 degrees Celsius) in a given day over all days in the month: \code{fdd} = sum(max(0, 32 - minimum daily temperature)), e.g. for February \code{fdd} is the number of freezing degree days from January 11 to February 10.} } } \details{ The orange juice price data are the frozen orange juice component of processed foods and feeds group of the Producer Price Index (PPI), collected by the US Bureau of Labor Statistics (BLS series wpu02420301). The orange juice price series was divided by the overall PPI for finished goods to adjust for general price inflation. The freezing degree days series was constructed from daily minimum temperatures recorded at Orlando area airports, obtained from the National Oceanic and Atmospheric Administration (NOAA) of the US Department of Commerce. } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ ## load data data("FrozenJuice") ## Stock and Watson, p. 594 library("dynlm") fm_dyn <- dynlm(d(100 * log(price/ppi)) ~ fdd, data = FrozenJuice) coeftest(fm_dyn, vcov = vcovHC(fm_dyn, type = "HC1")) ## equivalently, returns can be computed 'by hand' ## (reducing the complexity of the formula notation) fj <- ts.union(fdd = FrozenJuice[, "fdd"], ret = 100 * diff(log(FrozenJuice[,"price"]/FrozenJuice[,"ppi"]))) fm_dyn <- dynlm(ret ~ fdd, data = fj) ## Stock and Watson, p. 595 fm_dl <- dynlm(ret ~ L(fdd, 0:6), data = fj) coeftest(fm_dl, vcov = vcovHC(fm_dl, type = "HC1")) ## Stock and Watson, Table 15.1, p. 620, numbers refer to columns ## (1) Dynamic Multipliers fm1 <- dynlm(ret ~ L(fdd, 0:18), data = fj) coeftest(fm1, vcov = NeweyWest(fm1, lag = 7, prewhite = FALSE)) ## (2) Cumulative Multipliers fm2 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18), data = fj) coeftest(fm2, vcov = NeweyWest(fm2, lag = 7, prewhite = FALSE)) ## (3) Cumulative Multipliers, more lags in NW coeftest(fm2, vcov = NeweyWest(fm2, lag = 14, prewhite = FALSE)) ## (4) Cumulative Multipliers with monthly indicators fm4 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18) + season(fdd), data = fj) coeftest(fm4, vcov = NeweyWest(fm4, lag = 7, prewhite = FALSE)) ## monthly indicators needed? fm4r <- update(fm4, . ~ . - season(fdd)) waldtest(fm4, fm4r, vcov= NeweyWest(fm4, lag = 7, prewhite = FALSE)) ## close ... } \keyword{datasets} AER/man/Fatalities.Rd0000644000176200001440000001556613615674673014047 0ustar liggesusers\name{Fatalities} \alias{Fatalities} \title{US Traffic Fatalities} \description{ US traffic fatalities panel data for the \dQuote{lower 48} US states (i.e., excluding Alaska and Hawaii), annually for 1982 through 1988. } \usage{data("Fatalities")} \format{ A data frame containing 336 observations on 34 variables. \describe{ \item{state}{factor indicating state.} \item{year}{factor indicating year.} \item{spirits}{numeric. Spirits consumption.} \item{unemp}{numeric. Unemployment rate.} \item{income}{numeric. Per capita personal income in 1987 dollars.} \item{emppop}{numeric. Employment/population ratio.} \item{beertax}{numeric. Tax on case of beer.} \item{baptist}{numeric. Percent of southern baptist.} \item{mormon}{numeric. Percent of mormon.} \item{drinkage}{numeric. Minimum legal drinking age.} \item{dry}{numeric. Percent residing in \dQuote{dry} countries.} \item{youngdrivers}{numeric. Percent of drivers aged 15--24.} \item{miles}{numeric. Average miles per driver.} \item{breath}{factor. Preliminary breath test law?} \item{jail}{factor. Mandatory jail sentence?} \item{service}{factor. Mandatory community service?} \item{fatal}{numeric. Number of vehicle fatalities.} \item{nfatal}{numeric. Number of night-time vehicle fatalities.} \item{sfatal}{numeric. Number of single vehicle fatalities.} \item{fatal1517}{numeric. Number of vehicle fatalities, 15--17 year olds.} \item{nfatal1517}{numeric. Number of night-time vehicle fatalities, 15--17 year olds.} \item{fatal1820}{numeric. Number of vehicle fatalities, 18--20 year olds.} \item{nfatal1820}{numeric. Number of night-time vehicle fatalities, 18--20 year olds.} \item{fatal2124}{numeric. Number of vehicle fatalities, 21--24 year olds.} \item{nfatal2124}{numeric. Number of night-time vehicle fatalities, 21--24 year olds.} \item{afatal}{numeric. Number of alcohol-involved vehicle fatalities.} \item{pop}{numeric. Population.} \item{pop1517}{numeric. Population, 15--17 year olds.} \item{pop1820}{numeric. Population, 18--20 year olds.} \item{pop2124}{numeric. Population, 21--24 year olds.} \item{milestot}{numeric. Total vehicle miles (millions).} \item{unempus}{numeric. US unemployment rate.} \item{emppopus}{numeric. US employment/population ratio.} \item{gsp}{numeric. GSP rate of change.} } } \details{ Traffic fatalities are from the US Department of Transportation Fatal Accident Reporting System. The beer tax is the tax on a case of beer, which is an available measure of state alcohol taxes more generally. The drinking age variable is a factor indicating whether the legal drinking age is 18, 19, or 20. The two binary punishment variables describe the state's minimum sentencing requirements for an initial drunk driving conviction. Total vehicle miles traveled annually by state was obtained from the Department of Transportation. Personal income was obtained from the US Bureau of Economic Analysis, and the unemployment rate was obtained from the US Bureau of Labor Statistics. } \source{ Online complements to Stock and Watson (2007). } \references{ Ruhm, C. J. (1996). Alcohol Policies and Highway Vehicle Fatalities. \emph{Journal of Health Economics}, \bold{15}, 435--454. Stock, J. H. and Watson, M. W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ ## data from Stock and Watson (2007) data("Fatalities", package = "AER") ## add fatality rate (number of traffic deaths ## per 10,000 people living in that state in that year) Fatalities$frate <- with(Fatalities, fatal/pop * 10000) ## add discretized version of minimum legal drinking age Fatalities$drinkagec <- cut(Fatalities$drinkage, breaks = 18:22, include.lowest = TRUE, right = FALSE) Fatalities$drinkagec <- relevel(Fatalities$drinkagec, ref = 4) ## any punishment? Fatalities$punish <- with(Fatalities, factor(jail == "yes" | service == "yes", labels = c("no", "yes"))) ## plm package library("plm") ## for comparability with Stata we use HC1 below ## p. 351, Eq. (10.2) f1982 <- subset(Fatalities, year == "1982") fm_1982 <- lm(frate ~ beertax, data = f1982) coeftest(fm_1982, vcov = vcovHC(fm_1982, type = "HC1")) ## p. 353, Eq. (10.3) f1988 <- subset(Fatalities, year == "1988") fm_1988 <- lm(frate ~ beertax, data = f1988) coeftest(fm_1988, vcov = vcovHC(fm_1988, type = "HC1")) ## pp. 355, Eq. (10.8) fm_diff <- lm(I(f1988$frate - f1982$frate) ~ I(f1988$beertax - f1982$beertax)) coeftest(fm_diff, vcov = vcovHC(fm_diff, type = "HC1")) ## pp. 360, Eq. (10.15) ## (1) via formula fm_sfe <- lm(frate ~ beertax + state - 1, data = Fatalities) ## (2) by hand fat <- with(Fatalities, data.frame(frates = frate - ave(frate, state), beertaxs = beertax - ave(beertax, state))) fm_sfe2 <- lm(frates ~ beertaxs - 1, data = fat) ## (3) via plm() fm_sfe3 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within") coeftest(fm_sfe, vcov = vcovHC(fm_sfe, type = "HC1"))[1,] ## uses different df in sd and p-value coeftest(fm_sfe2, vcov = vcovHC(fm_sfe2, type = "HC1"))[1,] ## uses different df in p-value coeftest(fm_sfe3, vcov = vcovHC(fm_sfe3, type = "HC1", method = "white1"))[1,] ## pp. 363, Eq. (10.21) ## via lm() fm_stfe <- lm(frate ~ beertax + state + year - 1, data = Fatalities) coeftest(fm_stfe, vcov = vcovHC(fm_stfe, type = "HC1"))[1,] ## via plm() fm_stfe2 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") coeftest(fm_stfe2, vcov = vcovHC) ## different ## p. 368, Table 10.1, numbers refer to cols. fm1 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "pooling") fm2 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within") fm3 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm4 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income), data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm5 <- plm(frate ~ beertax + drinkagec + jail + service + miles, data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm6 <- plm(frate ~ beertax + drinkage + punish + miles + unemp + log(income), data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm7 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income), data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") ## summaries not too close, s.e.s generally too small coeftest(fm1, vcov = vcovHC) coeftest(fm2, vcov = vcovHC) coeftest(fm3, vcov = vcovHC) coeftest(fm4, vcov = vcovHC) coeftest(fm5, vcov = vcovHC) coeftest(fm6, vcov = vcovHC) coeftest(fm7, vcov = vcovHC) ## TODO: Testing exclusion restrictions } \keyword{datasets} AER/man/DJIA8012.Rd0000644000176200001440000000260613615674673012773 0ustar liggesusers\name{DJIA8012} \alias{DJIA8012} \title{Dow Jones Industrial Average (DJIA) index} \description{ Time series of the Dow Jones Industrial Average (DJIA) index. } \usage{data("DJIA8012")} \format{ A daily univariate time series from 1980-01-01 to 2012-12-31 (of class \code{"zoo"} with \code{"Date"} index). } \source{ Online complements to Franses, van Dijk and Opschoor (2014). \url{http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition} } \references{ Franses, P.H., van Dijk, D. and Opschoor, A. (2014). \emph{Time Series Models for Business and Economic Forecasting}, 2nd ed. Cambridge, UK: Cambridge University Press. } \examples{ data("DJIA8012") plot(DJIA8012) # p.26, Figure 2.18 dldjia <- diff(log(DJIA8012)) plot(dldjia) # p.141, Figure 6.4 plot(window(dldjia, start = "1987-09-01", end = "1987-12-31")) # p.167, Figure 7.1 dldjia9005 <- window(dldjia, start = "1990-01-01", end = "2005-12-31") qqnorm(dldjia9005) qqline(dldjia9005, lty = 2) # p.170, Figure 7.4 acf(dldjia9005, na.action = na.exclude, lag.max = 250, ylim = c(-0.1, 0.25)) acf(dldjia9005^2, na.action = na.exclude, lag.max = 250, ylim = c(-0.1, 0.25)) acf(abs(dldjia9005), na.action = na.exclude, lag.max = 250, ylim = c(-0.1, 0.25)) } \keyword{datasets} AER/man/SportsCards.Rd0000644000176200001440000000425513615674673014222 0ustar liggesusers\name{SportsCards} \alias{SportsCards} \title{Endowment Effect for Sports Cards} \description{ Trading sports cards: Does ownership increase the value of goods to consumers? } \usage{data("SportsCards")} \format{ A data frame containing 148 observations on 9 variables. \describe{ \item{good}{factor. Was the individual given good A or B (see below)?} \item{dealer}{factor. Was the individual a dealer?} \item{permonth}{number of trades per month reported by the individual.} \item{years}{number of years that the individual has been trading.} \item{income}{factor indicating income group (in 1000 USD).} \item{gender}{factor indicating gender.} \item{education}{factor indicating highest level of education (8th grade or less, high school, 2-year college, other post-high school, 4-year college or graduate school).} \item{age}{age in years.} \item{trade}{factor. Did the individual trade the good he was given for the other good?} } } \details{ \code{SportsCards} contains data from 148 randomly selected traders who attended a trading card show in Orlando, Florida, in 1998. Traders were randomly given one of two sports collectables, say good A or good B, that had approximately equal market value. Those receiving good A were then given the option of trading good A for good B with the experimenter; those receiving good B were given the option of trading good B for good A with the experimenter. Good A was a ticket stub from the game that Cal Ripken Jr. set the record for consecutive games played, and Good B was a souvenir from the game that Nolan Ryan won his 300th game. } \source{ Online complements to Stock and Watson (2007). } \references{ List, J.A. (2003). Does Market Experience Eliminate Market Anomalies? \emph{Quarterly Journal of Economcis}, \bold{118}, 41--71. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("SportsCards") summary(SportsCards) plot(trade ~ permonth, data = SportsCards, ylevels = 2:1, breaks = c(0, 5, 10, 20, 30, 70)) plot(trade ~ years, data = SportsCards, ylevels = 2:1, breaks = c(0, 5, 10, 20, 60)) } \keyword{datasets} AER/man/USMacroSW.Rd0000644000176200001440000000571413615674673013537 0ustar liggesusers\name{USMacroSW} \alias{USMacroSW} \title{US Macroeconomic Data (1957--2005, Stock \& Watson)} \description{ Time series data on 7 (mostly) US macroeconomic variables for 1957--2005. } \usage{data("USMacroSW") } \format{ A quarterly multiple time series from 1957(1) to 2005(1) with 7 variables. \describe{ \item{unemp}{Unemployment rate.} \item{cpi}{Consumer price index.} \item{ffrate}{Federal funds interest rate.} \item{tbill}{3-month treasury bill interest rate.} \item{tbond}{1-year treasury bond interest rate.} \item{gbpusd}{GBP/USD exchange rate (US dollar in cents per British pound).} \item{gdpjp}{GDP for Japan.} } } \details{ The US Consumer Price Index is measured using monthly surveys and is compiled by the Bureau of Labor Statistics (BLS). The unemployment rate is computed from the BLS's Current Population. The quarterly data used here were computed by averaging the monthly values. The interest data are the monthly average of daily rates as reported by the Federal Reserve and the dollar-pound exchange rate data are the monthly average of daily rates; both are for the final month in the quarter. Japanese real GDP data were obtained from the OECD. } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{USMacroSWM}}, \code{\link{USMacroSWQ}}, \code{\link{USMacroB}}, \code{\link{USMacroG}}} \examples{ ## Stock and Watson (2007) data("USMacroSW", package = "AER") library("dynlm") library("strucchange") usm <- ts.intersect(USMacroSW, 4 * 100 * diff(log(USMacroSW[, "cpi"]))) colnames(usm) <- c(colnames(USMacroSW), "infl") ## Equations 14.7, 14.13, 14.16, 14.17, pp. 536 fm_ar1 <- dynlm(d(infl) ~ L(d(infl)), data = usm, start = c(1962,1), end = c(2004,4)) fm_ar4 <- dynlm(d(infl) ~ L(d(infl), 1:4), data = usm, start = c(1962,1), end = c(2004,4)) fm_adl41 <- dynlm(d(infl) ~ L(d(infl), 1:4) + L(unemp), data = usm, start = c(1962,1), end = c(2004,4)) fm_adl44 <- dynlm(d(infl) ~ L(d(infl), 1:4) + L(unemp, 1:4), data = usm, start = c(1962,1), end = c(2004,4)) coeftest(fm_ar1, vcov = sandwich) coeftest(fm_ar4, vcov = sandwich) coeftest(fm_adl41, vcov = sandwich) coeftest(fm_adl44, vcov = sandwich) ## Granger causality test mentioned on p. 547 waldtest(fm_ar4, fm_adl44, vcov = sandwich) ## Figure 14.5, p. 570 ## SW perform partial break test of unemp coefs ## here full model is used mf <- model.frame(fm_adl44) ## re-use fm_adl44 mf <- ts(as.matrix(mf), start = c(1962, 1), freq = 4) colnames(mf) <- c("y", paste("x", 1:8, sep = "")) ff <- as.formula(paste("y", "~", paste("x", 1:8, sep = "", collapse = " + "))) fs <- Fstats(ff, data = mf, from = 0.1) plot(fs) lines(boundary(fs, alpha = 0.01), lty = 2, col = 2) lines(boundary(fs, alpha = 0.1), lty = 3, col = 2) ## More examples can be found in: ## help("StockWatson2007") } \keyword{datasets} AER/man/Franses1998.Rd0000644000176200001440000000546113615674673013707 0ustar liggesusers\name{Franses1998} \alias{Franses1998} \title{Data and Examples from Franses (1998)} \description{ This manual page collects a list of examples from the book. Some solutions might not be exact and the list is certainly not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{ArgentinaCPI}}, \code{\link{ChinaIncome}}, \code{\link{ConsumerGood}}, \code{\link{DJFranses}}, \code{\link{DutchAdvert}}, \code{\link{DutchSales}}, \code{\link{GermanUnemployment}}, \code{\link{MotorCycles}}, \code{\link{OlympicTV}}, \code{\link{PepperPrice}}, \code{\link{UKNonDurables}}, \code{\link{USProdIndex}}} \examples{ ########################### ## Convenience functions ## ########################### ## EACF tables (Franses 1998, p. 99) ctrafo <- function(x) residuals(lm(x ~ factor(cycle(x)))) ddiff <- function(x) diff(diff(x, frequency(x)), 1) eacf <- function(y, lag = 12) { stopifnot(all(lag > 0)) if(length(lag) < 2) lag <- 1:lag rval <- sapply( list(y = y, dy = diff(y), cdy = ctrafo(diff(y)), Dy = diff(y, frequency(y)), dDy = ddiff(y)), function(x) acf(x, plot = FALSE, lag.max = max(lag))$acf[lag + 1]) rownames(rval) <- lag return(rval) } ####################################### ## Index of US industrial production ## ####################################### data("USProdIndex", package = "AER") plot(USProdIndex, plot.type = "single", col = 1:2) ## Franses (1998), Table 5.1 round(eacf(log(USProdIndex[,1])), digits = 3) ## Franses (1998), Equation 5.6: Unrestricted airline model ## (Franses: ma1 = 0.388 (0.063), ma4 = -0.739 (0.060), ma5 = -0.452 (0.069)) arima(log(USProdIndex[,1]), c(0, 1, 5), c(0, 1, 0), fixed = c(NA, 0, 0, NA, NA)) ########################################### ## Consumption of non-durables in the UK ## ########################################### data("UKNonDurables", package = "AER") plot(UKNonDurables) ## Franses (1998), Table 5.2 round(eacf(log(UKNonDurables)), digits = 3) ## Franses (1998), Equation 5.51 ## (Franses: sma1 = -0.632 (0.069)) arima(log(UKNonDurables), c(0, 1, 0), c(0, 1, 1)) ############################## ## Dutch retail sales index ## ############################## data("DutchSales", package = "AER") plot(DutchSales) ## Franses (1998), Table 5.3 round(eacf(log(DutchSales), lag = c(1:18, 24, 36)), digits = 3) ########################################### ## TV and radio advertising expenditures ## ########################################### data("DutchAdvert", package = "AER") plot(DutchAdvert) ## Franses (1998), Table 5.4 round(eacf(log(DutchAdvert[,"tv"]), lag = c(1:19, 26, 39)), digits = 3) } \keyword{datasets} AER/man/Grunfeld.Rd0000644000176200001440000001265313615674673013522 0ustar liggesusers\name{Grunfeld} \alias{Grunfeld} \title{Grunfeld's Investment Data} \description{ Panel data on 11 large US manufacturing firms over 20 years, for the years 1935--1954. } \usage{data("Grunfeld")} \format{ A data frame containing 20 annual observations on 3 variables for 11 firms. \describe{ \item{invest}{Gross investment, defined as additions to plant and equipment plus maintenance and repairs in millions of dollars deflated by the implicit price deflator of producers' durable equipment (base 1947).} \item{value}{Market value of the firm, defined as the price of common shares at December 31 (or, for WH, IBM and CH, the average price of December 31 and January 31 of the following year) times the number of common shares outstanding plus price of preferred shares at December 31 (or average price of December 31 and January 31 of the following year) times number of preferred shares plus total book value of debt at December 31 in millions of dollars deflated by the implicit GNP price deflator (base 1947).} \item{capital}{Stock of plant and equipment, defined as the accumulated sum of net additions to plant and equipment deflated by the implicit price deflator for producers' durable equipment (base 1947) minus depreciation allowance deflated by depreciation expense deflator (10 years moving average of wholesale price index of metals and metal products, base 1947).} \item{firm}{factor with 11 levels: \code{"General Motors"}, \code{"US Steel"}, \code{"General Electric"}, \code{"Chrysler"}, \code{"Atlantic Refining"}, \code{"IBM"}, \code{"Union Oil"}, \code{"Westinghouse"}, \code{"Goodyear"}, \code{"Diamond Match"}, \code{"American Steel"}.} \item{year}{Year.} } } \details{ This is a popular data set for teaching purposes. Unfortunately, there exist several different versions (see Kleiber and Zeileis, 2010, for a detailed discussion). In particular, the version provided by Greene (2003) has a couple of errors for \code{"US Steel"} (firm 2): investment in 1940 is 261.6 (instead of the correct 361.6), investment in 1952 is 645.2 (instead of the correct 645.5), capital in 1946 is 132.6 (instead of the correct 232.6). Here, we provide the original data from Grunfeld (1958). The data for the first 10 firms are identical to those of Baltagi (2002) or Baltagi (2005), now also used by Greene (2008). } \source{ The data are taken from Grunfeld (1958, Appendix, Tables 2--9 and 11--13). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed., Berlin: Springer-Verlag. Baltagi, B.H. (2005). \emph{Econometric Analysis of Panel Data}, 3rd ed. Chichester, UK: John Wiley. Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Greene, W.H. (2008). \emph{Econometric Analysis}, 6th edition. Upper Saddle River, NJ: Prentice Hall. Grunfeld, Y. (1958). \emph{The Determinants of Corporate Investment}. Unpublished Ph.D. Dissertation, University of Chicago. Kleiber, C., and Zeileis, A. (2010). \dQuote{The Grunfeld Data at 50.} \emph{German Economic Review}, \bold{11}(4), 404--417. \url{http://dx.doi.org/10.1111/j.1468-0475.2010.00513.x} } \seealso{\code{\link{Baltagi2002}}, \code{\link{Greene2003}}} \examples{ data("Grunfeld", package = "AER") ## Greene (2003) ## subset of data with mistakes ggr <- subset(Grunfeld, firm \%in\% c("General Motors", "US Steel", "General Electric", "Chrysler", "Westinghouse")) ggr[c(26, 38), 1] <- c(261.6, 645.2) ggr[32, 3] <- 232.6 ## Tab. 14.2, col. "GM" fm_gm <- lm(invest ~ value + capital, data = ggr, subset = firm == "General Motors") mean(residuals(fm_gm)^2) ## Greene uses MLE ## Tab. 14.2, col. "Pooled" fm_pool <- lm(invest ~ value + capital, data = ggr) ## equivalently library("plm") pggr <- pdata.frame(ggr, c("firm", "year")) library("systemfit") fm_ols <- systemfit(invest ~ value + capital, data = pggr, method = "OLS") fm_pols <- systemfit(invest ~ value + capital, data = pggr, method = "OLS", pooled = TRUE) ## Tab. 14.1 fm_sur <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", methodResidCov = "noDfCor") fm_psur <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", pooled = TRUE, methodResidCov = "noDfCor", residCovWeighted = TRUE) ## Further examples: ## help("Greene2003") ## Panel models library("plm") pg <- pdata.frame(subset(Grunfeld, firm != "American Steel"), c("firm", "year")) fm_fe <- plm(invest ~ value + capital, model = "within", data = pg) summary(fm_fe) coeftest(fm_fe, vcov = vcovHC) fm_reswar <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "swar") summary(fm_reswar) ## testing for random effects fm_ols <- plm(invest ~ value + capital, data = pg, model = "pooling") plmtest(fm_ols, type = "bp") plmtest(fm_ols, type = "honda") ## Random effects models fm_ream <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "amemiya") fm_rewh <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "walhus") fm_rener <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "nerlove") ## Baltagi (2005), Tab. 2.1 rbind( "OLS(pooled)" = coef(fm_ols), "FE" = c(NA, coef(fm_fe)), "RE-SwAr" = coef(fm_reswar), "RE-Amemiya" = coef(fm_ream), "RE-WalHus" = coef(fm_rewh), "RE-Nerlove" = coef(fm_rener)) ## Hausman test phtest(fm_fe, fm_reswar) ## Further examples: ## help("Baltagi2002") ## help("Greene2003") } \keyword{datasets} AER/man/CigarettesB.Rd0000644000176200001440000000461713615674673014151 0ustar liggesusers\name{CigarettesB} \alias{CigarettesB} \title{Cigarette Consumption Data} \description{ Cross-section data on cigarette consumption for 46 US States, for the year 1992. } \usage{data("CigarettesB")} \format{ A data frame containing 46 observations on 3 variables. \describe{ \item{packs}{Logarithm of cigarette consumption (in packs) per person of smoking age (> 16 years).} \item{price}{Logarithm of real price of cigarette in each state.} \item{income}{Logarithm of real disposable income (per capita) in each state.} } } \source{ The data are from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. Baltagi, B.H. and Levin, D. (1992). Cigarette Taxation: Raising Revenues and Reducing Consumption. \emph{Structural Change and Economic Dynamics}, \bold{3}, 321--335. } \seealso{\code{\link{Baltagi2002}}, \code{\link{CigarettesSW}}} \examples{ data("CigarettesB") ## Baltagi (2002) ## Table 3.3 cig_lm <- lm(packs ~ price, data = CigarettesB) summary(cig_lm) ## Chapter 5: diagnostic tests (p. 111-115) cig_lm2 <- lm(packs ~ price + income, data = CigarettesB) summary(cig_lm2) ## Glejser tests (p. 112) ares <- abs(residuals(cig_lm2)) summary(lm(ares ~ income, data = CigarettesB)) summary(lm(ares ~ I(1/income), data = CigarettesB)) summary(lm(ares ~ I(1/sqrt(income)), data = CigarettesB)) summary(lm(ares ~ sqrt(income), data = CigarettesB)) ## Goldfeld-Quandt test (p. 112) gqtest(cig_lm2, order.by = ~ income, data = CigarettesB, fraction = 12, alternative = "less") ## NOTE: Baltagi computes the test statistic as mss1/mss2, ## i.e., tries to find decreasing variances. gqtest() always uses ## mss2/mss1 and has an "alternative" argument. ## Spearman rank correlation test (p. 113) cor.test(~ ares + income, data = CigarettesB, method = "spearman") ## Breusch-Pagan test (p. 113) bptest(cig_lm2, varformula = ~ income, data = CigarettesB, student = FALSE) ## White test (Table 5.1, p. 113) bptest(cig_lm2, ~ income * price + I(income^2) + I(price^2), data = CigarettesB) ## White HC standard errors (Table 5.2, p. 114) coeftest(cig_lm2, vcov = vcovHC(cig_lm2, type = "HC1")) ## Jarque-Bera test (Figure 5.2, p. 115) hist(residuals(cig_lm2), breaks = 16, ylim = c(0, 10), col = "lightgray") library("tseries") jarque.bera.test(residuals(cig_lm2)) ## Tables 8.1 and 8.2 influence.measures(cig_lm2) ## More examples can be found in: ## help("Baltagi2002") } \keyword{datasets} AER/man/USSeatBelts.Rd0000644000176200001440000000365513615674673014114 0ustar liggesusers\name{USSeatBelts} \alias{USSeatBelts} \title{Effects of Mandatory Seat Belt Laws in the US} \description{ Balanced panel data for the years 1983--1997 from 50 US States, plus the District of Columbia, for assessing traffic fatalities and seat belt usage. } \usage{data("USSeatBelts")} \format{ A data frame containing 765 observations on 12 variables. \describe{ \item{state}{factor indicating US state (abbreviation).} \item{year}{factor indicating year.} \item{miles}{millions of traffic miles per year.} \item{fatalities}{number of fatalities per million of traffic miles (absolute frequencies of fatalities = \code{fatalities} times \code{miles}).} \item{seatbelt}{seat belt usage rate, as self-reported by state population surveyed.} \item{speed65}{factor. Is there a 65 mile per hour speed limit?} \item{speed70}{factor. Is there a 70 (or higher) mile per hour speed limit?} \item{drinkage}{factor. Is there a minimum drinking age of 21 years?} \item{alcohol}{factor. Is there a maximum of 0.08 blood alcohol content?} \item{income}{median per capita income (in current US dollar).} \item{age}{mean age.} \item{enforce}{factor indicating seat belt law enforcement (\code{"no"}, \code{"primary"}, \code{"secondary"}).} } } \details{ Some data series from Cohen and Einav (2003) have not been included in the data frame. } \source{ Online complements to Stock and Watson (2007). } \references{ Cohen, A., and Einav, L. (2003). The Effects of Mandatory Seat Belt Laws on Driving Behavior and Traffic Fatalities. \emph{The Review of Economics and Statistics}, \bold{85}, 828--843 Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("USSeatBelts") summary(USSeatBelts) library("lattice") xyplot(fatalities ~ as.numeric(as.character(year)) | state, data = USSeatBelts, type = "l") } \keyword{datasets} AER/man/UKNonDurables.Rd0000644000176200001440000000257113615674673014426 0ustar liggesusers\name{UKNonDurables} \alias{UKNonDurables} \title{Consumption of Non-Durables in the UK} \description{ Time series of consumption of non-durables in the UK (in 1985 prices). } \usage{data("UKNonDurables")} \format{ A quarterly univariate time series from 1955(1) to 1988(4). } \source{ Online complements to Franses (1998). } \references{ Osborn, D.R. (1988). A Survey of Seasonality in UK Macroeconomic Variables. \emph{International Journal of Forecasting}, \bold{6}, 327--336. Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}} \examples{ data("UKNonDurables") plot(UKNonDurables) ## EACF tables (Franses 1998, p. 99) ctrafo <- function(x) residuals(lm(x ~ factor(cycle(x)))) ddiff <- function(x) diff(diff(x, frequency(x)), 1) eacf <- function(y, lag = 12) { stopifnot(all(lag > 0)) if(length(lag) < 2) lag <- 1:lag rval <- sapply( list(y = y, dy = diff(y), cdy = ctrafo(diff(y)), Dy = diff(y, frequency(y)), dDy = ddiff(y)), function(x) acf(x, plot = FALSE, lag.max = max(lag))$acf[lag + 1]) rownames(rval) <- lag return(rval) } ## Franses (1998), Table 5.2 round(eacf(log(UKNonDurables)), digits = 3) ## Franses (1998), Equation 5.51 ## (Franses: sma1 = -0.632 (0.069)) arima(log(UKNonDurables), c(0, 1, 0), c(0, 1, 1)) } \keyword{datasets} AER/man/UKInflation.Rd0000644000176200001440000000163513615674673014135 0ustar liggesusers\name{UKInflation} \alias{UKInflation} \title{UK Manufacturing Inflation Data} \description{ Time series of observed and expected price changes in British manufacturing. } \usage{data("UKInflation")} \format{ A quarterly multiple time series from 1972(1) to 1985(2) with 2 variables. \describe{ \item{actual}{Actual inflation.} \item{expected}{Expected inflation.} } } \source{ Online complements to Greene (2003), Table F8.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. Pesaran, M.H., and Hall, A.D. (1988). Tests of Non-nested Linear Regression Models Subject To Linear Restrictions. \emph{Economics Letters}, \bold{27}, 341--348. } \seealso{\code{\link{Greene2003}}} \examples{ data("UKInflation") plot(UKInflation) } \keyword{datasets} AER/man/StockWatson2007.Rd0000644000176200001440000005550513615674673014547 0ustar liggesusers\name{StockWatson2007} \alias{StockWatson2007} \title{Data and Examples from Stock and Watson (2007)} \description{ This manual page collects a list of examples from the book. Some solutions might not be exact and the list is certainly not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. } \references{ Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{CartelStability}}, \code{\link{CASchools}}, \code{\link{CigarettesSW}}, \code{\link{CollegeDistance}}, \code{\link{CPSSW04}}, \code{\link{CPSSW3}}, \code{\link{CPSSW8}}, \code{\link{CPSSW9298}}, \code{\link{CPSSW9204}}, \code{\link{CPSSWEducation}}, \code{\link{Fatalities}}, \code{\link{Fertility}}, \code{\link{Fertility2}}, \code{\link{FrozenJuice}}, \code{\link{GrowthSW}}, \code{\link{Guns}}, \code{\link{HealthInsurance}}, \code{\link{HMDA}}, \code{\link{Journals}}, \code{\link{MASchools}}, \code{\link{NYSESW}}, \code{\link{ResumeNames}}, \code{\link{SmokeBan}}, \code{\link{SportsCards}}, \code{\link{STAR}}, \code{\link{TeachingRatings}}, \code{\link{USMacroSW}}, \code{\link{USMacroSWM}}, \code{\link{USMacroSWQ}}, \code{\link{USSeatBelts}}, \code{\link{USStocksSW}}, \code{\link{WeakInstrument}}} \examples{ ############################### ## Current Population Survey ## ############################### ## p. 165 data("CPSSWEducation", package = "AER") plot(earnings ~ education, data = CPSSWEducation) fm <- lm(earnings ~ education, data = CPSSWEducation) coeftest(fm, vcov = sandwich) abline(fm) ############################ ## California test scores ## ############################ ## data and transformations data("CASchools", package = "AER") CASchools$stratio <- with(CASchools, students/teachers) CASchools$score <- with(CASchools, (math + read)/2) ## p. 152 fm1 <- lm(score ~ stratio, data = CASchools) coeftest(fm1, vcov = sandwich) ## p. 159 fm2 <- lm(score ~ I(stratio < 20), data = CASchools) ## p. 199 fm3 <- lm(score ~ stratio + english, data = CASchools) ## p. 224 fm4 <- lm(score ~ stratio + expenditure + english, data = CASchools) ## Table 7.1, p. 242 (numbers refer to columns) fmc3 <- lm(score ~ stratio + english + lunch, data = CASchools) fmc4 <- lm(score ~ stratio + english + calworks, data = CASchools) fmc5 <- lm(score ~ stratio + english + lunch + calworks, data = CASchools) ## Equation 8.2, p. 258 fmquad <- lm(score ~ income + I(income^2), data = CASchools) ## Equation 8.11, p. 266 fmcub <- lm(score ~ income + I(income^2) + I(income^3), data = CASchools) ## Equation 8.23, p. 272 fmloglog <- lm(log(score) ~ log(income), data = CASchools) ## Equation 8.24, p. 274 fmloglin <- lm(log(score) ~ income, data = CASchools) ## Equation 8.26, p. 275 fmlinlogcub <- lm(score ~ log(income) + I(log(income)^2) + I(log(income)^3), data = CASchools) ## Table 8.3, p. 292 (numbers refer to columns) fmc2 <- lm(score ~ stratio + english + lunch + log(income), data = CASchools) fmc7 <- lm(score ~ stratio + I(stratio^2) + I(stratio^3) + english + lunch + log(income), data = CASchools) ##################################### ## Economics journal Subscriptions ## ##################################### ## data and transformed variables data("Journals", package = "AER") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations journals$age <- 2000 - Journals$foundingyear journals$chars <- Journals$charpp*Journals$pages/10^6 ## Figure 8.9 (a) and (b) plot(subs ~ citeprice, data = journals, pch = 19) plot(log(subs) ~ log(citeprice), data = journals, pch = 19) fm1 <- lm(log(subs) ~ log(citeprice), data = journals) abline(fm1) ## Table 8.2, use HC1 for comparability with Stata fm1 <- lm(subs ~ citeprice, data = log(journals)) fm2 <- lm(subs ~ citeprice + age + chars, data = log(journals)) fm3 <- lm(subs ~ citeprice + I(citeprice^2) + I(citeprice^3) + age + I(age * citeprice) + chars, data = log(journals)) fm4 <- lm(subs ~ citeprice + age + I(age * citeprice) + chars, data = log(journals)) coeftest(fm1, vcov = vcovHC(fm1, type = "HC1")) coeftest(fm2, vcov = vcovHC(fm2, type = "HC1")) coeftest(fm3, vcov = vcovHC(fm3, type = "HC1")) coeftest(fm4, vcov = vcovHC(fm4, type = "HC1")) waldtest(fm3, fm4, vcov = vcovHC(fm3, type = "HC1")) ############################### ## Massachusetts test scores ## ############################### ## compare Massachusetts with California data("MASchools", package = "AER") data("CASchools", package = "AER") CASchools$stratio <- with(CASchools, students/teachers) CASchools$score4 <- with(CASchools, (math + read)/2) ## parts of Table 9.1, p. 330 vars <- c("score4", "stratio", "english", "lunch", "income") cbind( CA_mean = sapply(CASchools[, vars], mean), CA_sd = sapply(CASchools[, vars], sd), MA_mean = sapply(MASchools[, vars], mean), MA_sd = sapply(MASchools[, vars], sd)) ## Table 9.2, pp. 332--333, numbers refer to columns MASchools$higheng <- with(MASchools, english > median(english)) fm1 <- lm(score4 ~ stratio, data = MASchools) fm2 <- lm(score4 ~ stratio + english + lunch + log(income), data = MASchools) fm3 <- lm(score4 ~ stratio + english + lunch + income + I(income^2) + I(income^3), data = MASchools) fm4 <- lm(score4 ~ stratio + I(stratio^2) + I(stratio^3) + english + lunch + income + I(income^2) + I(income^3), data = MASchools) fm5 <- lm(score4 ~ stratio + higheng + I(higheng * stratio) + lunch + income + I(income^2) + I(income^3), data = MASchools) fm6 <- lm(score4 ~ stratio + lunch + income + I(income^2) + I(income^3), data = MASchools) ## for comparability with Stata use HC1 below coeftest(fm1, vcov = vcovHC(fm1, type = "HC1")) coeftest(fm2, vcov = vcovHC(fm2, type = "HC1")) coeftest(fm3, vcov = vcovHC(fm3, type = "HC1")) coeftest(fm4, vcov = vcovHC(fm4, type = "HC1")) coeftest(fm5, vcov = vcovHC(fm5, type = "HC1")) coeftest(fm6, vcov = vcovHC(fm6, type = "HC1")) ## Testing exclusion of groups of variables fm3r <- update(fm3, . ~ . - I(income^2) - I(income^3)) waldtest(fm3, fm3r, vcov = vcovHC(fm3, type = "HC1")) fm4r_str1 <- update(fm4, . ~ . - stratio - I(stratio^2) - I(stratio^3)) waldtest(fm4, fm4r_str1, vcov = vcovHC(fm4, type = "HC1")) fm4r_str2 <- update(fm4, . ~ . - I(stratio^2) - I(stratio^3)) waldtest(fm4, fm4r_str2, vcov = vcovHC(fm4, type = "HC1")) fm4r_inc <- update(fm4, . ~ . - I(income^2) - I(income^3)) waldtest(fm4, fm4r_inc, vcov = vcovHC(fm4, type = "HC1")) fm5r_str <- update(fm5, . ~ . - stratio - I(higheng * stratio)) waldtest(fm5, fm5r_str, vcov = vcovHC(fm5, type = "HC1")) fm5r_inc <- update(fm5, . ~ . - I(income^2) - I(income^3)) waldtest(fm5, fm5r_inc, vcov = vcovHC(fm5, type = "HC1")) fm5r_high <- update(fm5, . ~ . - higheng - I(higheng * stratio)) waldtest(fm5, fm5r_high, vcov = vcovHC(fm5, type = "HC1")) fm6r_inc <- update(fm6, . ~ . - I(income^2) - I(income^3)) waldtest(fm6, fm6r_inc, vcov = vcovHC(fm6, type = "HC1")) ################################## ## Home mortgage disclosure act ## ################################## ## data data("HMDA", package = "AER") ## 11.1, 11.3, 11.7, 11.8 and 11.10, pp. 387--395 fm1 <- lm(I(as.numeric(deny) - 1) ~ pirat, data = HMDA) fm2 <- lm(I(as.numeric(deny) - 1) ~ pirat + afam, data = HMDA) fm3 <- glm(deny ~ pirat, family = binomial(link = "probit"), data = HMDA) fm4 <- glm(deny ~ pirat + afam, family = binomial(link = "probit"), data = HMDA) fm5 <- glm(deny ~ pirat + afam, family = binomial(link = "logit"), data = HMDA) ## Table 11.1, p. 401 mean(HMDA$pirat) mean(HMDA$hirat) mean(HMDA$lvrat) mean(as.numeric(HMDA$chist)) mean(as.numeric(HMDA$mhist)) mean(as.numeric(HMDA$phist)-1) prop.table(table(HMDA$insurance)) prop.table(table(HMDA$selfemp)) prop.table(table(HMDA$single)) prop.table(table(HMDA$hschool)) mean(HMDA$unemp) prop.table(table(HMDA$condomin)) prop.table(table(HMDA$afam)) prop.table(table(HMDA$deny)) ## Table 11.2, pp. 403--404, numbers refer to columns HMDA$lvrat <- factor(ifelse(HMDA$lvrat < 0.8, "low", ifelse(HMDA$lvrat >= 0.8 & HMDA$lvrat <= 0.95, "medium", "high")), levels = c("low", "medium", "high")) HMDA$mhist <- as.numeric(HMDA$mhist) HMDA$chist <- as.numeric(HMDA$chist) fm1 <- lm(I(as.numeric(deny) - 1) ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance + selfemp, data = HMDA) fm2 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance + selfemp, family = binomial, data = HMDA) fm3 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance + selfemp, family = binomial(link = "probit"), data = HMDA) fm4 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance + selfemp + single + hschool + unemp, family = binomial(link = "probit"), data = HMDA) fm5 <- glm(deny ~ afam + pirat + hirat + lvrat + chist + mhist + phist + insurance + selfemp + single + hschool + unemp + condomin + I(mhist==3) + I(mhist==4) + I(chist==3) + I(chist==4) + I(chist==5) + I(chist==6), family = binomial(link = "probit"), data = HMDA) fm6 <- glm(deny ~ afam * (pirat + hirat) + lvrat + chist + mhist + phist + insurance + selfemp + single + hschool + unemp, family = binomial(link = "probit"), data = HMDA) coeftest(fm1, vcov = sandwich) fm4r <- update(fm4, . ~ . - single - hschool - unemp) waldtest(fm4, fm4r, vcov = sandwich) fm5r <- update(fm5, . ~ . - single - hschool - unemp) waldtest(fm5, fm5r, vcov = sandwich) fm6r <- update(fm6, . ~ . - single - hschool - unemp) waldtest(fm6, fm6r, vcov = sandwich) fm5r2 <- update(fm5, . ~ . - I(mhist==3) - I(mhist==4) - I(chist==3) - I(chist==4) - I(chist==5) - I(chist==6)) waldtest(fm5, fm5r2, vcov = sandwich) fm6r2 <- update(fm6, . ~ . - afam * (pirat + hirat) + pirat + hirat) waldtest(fm6, fm6r2, vcov = sandwich) fm6r3 <- update(fm6, . ~ . - afam * (pirat + hirat) + pirat + hirat + afam) waldtest(fm6, fm6r3, vcov = sandwich) ######################################################### ## Shooting down the "More Guns Less Crime" hypothesis ## ######################################################### ## data data("Guns", package = "AER") ## Empirical Exercise 10.1 fm1 <- lm(log(violent) ~ law, data = Guns) fm2 <- lm(log(violent) ~ law + prisoners + density + income + population + afam + cauc + male, data = Guns) fm3 <- lm(log(violent) ~ law + prisoners + density + income + population + afam + cauc + male + state, data = Guns) fm4 <- lm(log(violent) ~ law + prisoners + density + income + population + afam + cauc + male + state + year, data = Guns) coeftest(fm1, vcov = sandwich) coeftest(fm2, vcov = sandwich) printCoefmat(coeftest(fm3, vcov = sandwich)[1:9,]) printCoefmat(coeftest(fm4, vcov = sandwich)[1:9,]) ########################### ## US traffic fatalities ## ########################### ## data from Stock and Watson (2007) data("Fatalities") ## add fatality rate (number of traffic deaths ## per 10,000 people living in that state in that year) Fatalities$frate <- with(Fatalities, fatal/pop * 10000) ## add discretized version of minimum legal drinking age Fatalities$drinkagec <- cut(Fatalities$drinkage, breaks = 18:22, include.lowest = TRUE, right = FALSE) Fatalities$drinkagec <- relevel(Fatalities$drinkagec, ref = 4) ## any punishment? Fatalities$punish <- with(Fatalities, factor(jail == "yes" | service == "yes", labels = c("no", "yes"))) ## plm package library("plm") ## for comparability with Stata we use HC1 below ## p. 351, Eq. (10.2) f1982 <- subset(Fatalities, year == "1982") fm_1982 <- lm(frate ~ beertax, data = f1982) coeftest(fm_1982, vcov = vcovHC(fm_1982, type = "HC1")) ## p. 353, Eq. (10.3) f1988 <- subset(Fatalities, year == "1988") fm_1988 <- lm(frate ~ beertax, data = f1988) coeftest(fm_1988, vcov = vcovHC(fm_1988, type = "HC1")) ## pp. 355, Eq. (10.8) fm_diff <- lm(I(f1988$frate - f1982$frate) ~ I(f1988$beertax - f1982$beertax)) coeftest(fm_diff, vcov = vcovHC(fm_diff, type = "HC1")) ## pp. 360, Eq. (10.15) ## (1) via formula fm_sfe <- lm(frate ~ beertax + state - 1, data = Fatalities) ## (2) by hand fat <- with(Fatalities, data.frame(frates = frate - ave(frate, state), beertaxs = beertax - ave(beertax, state))) fm_sfe2 <- lm(frates ~ beertaxs - 1, data = fat) ## (3) via plm() fm_sfe3 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within") coeftest(fm_sfe, vcov = vcovHC(fm_sfe, type = "HC1"))[1,] ## uses different df in sd and p-value coeftest(fm_sfe2, vcov = vcovHC(fm_sfe2, type = "HC1"))[1,] ## uses different df in p-value coeftest(fm_sfe3, vcov = vcovHC(fm_sfe3, type = "HC1", method = "white1"))[1,] ## pp. 363, Eq. (10.21) ## via lm() fm_stfe <- lm(frate ~ beertax + state + year - 1, data = Fatalities) coeftest(fm_stfe, vcov = vcovHC(fm_stfe, type = "HC1"))[1,] ## via plm() fm_stfe2 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") coeftest(fm_stfe2, vcov = vcovHC) ## different ## p. 368, Table 10.1, numbers refer to cols. fm1 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "pooling") fm2 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within") fm3 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm4 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income), data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm5 <- plm(frate ~ beertax + drinkagec + jail + service + miles, data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm6 <- plm(frate ~ beertax + drinkage + punish + miles + unemp + log(income), data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") fm7 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income), data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways") ## summaries not too close, s.e.s generally too small coeftest(fm1, vcov = vcovHC) coeftest(fm2, vcov = vcovHC) coeftest(fm3, vcov = vcovHC) coeftest(fm4, vcov = vcovHC) coeftest(fm5, vcov = vcovHC) coeftest(fm6, vcov = vcovHC) coeftest(fm7, vcov = vcovHC) ###################################### ## Cigarette consumption panel data ## ###################################### ## data and transformations data("CigarettesSW") CigarettesSW$rprice <- with(CigarettesSW, price/cpi) CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi) CigarettesSW$rtax <- with(CigarettesSW, tax/cpi) CigarettesSW$rtdiff <- with(CigarettesSW, (taxs - tax)/cpi) c1985 <- subset(CigarettesSW, year == "1985") c1995 <- subset(CigarettesSW, year == "1995") ## convenience function: HC1 covariances hc1 <- function(x) vcovHC(x, type = "HC1") ## Equations 12.9--12.11 fm_s1 <- lm(log(rprice) ~ rtdiff, data = c1995) coeftest(fm_s1, vcov = hc1) fm_s2 <- lm(log(packs) ~ fitted(fm_s1), data = c1995) fm_ivreg <- ivreg(log(packs) ~ log(rprice) | rtdiff, data = c1995) coeftest(fm_ivreg, vcov = hc1) ## Equation 12.15 fm_ivreg2 <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + rtdiff, data = c1995) coeftest(fm_ivreg2, vcov = hc1) ## Equation 12.16 fm_ivreg3 <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + rtdiff + rtax, data = c1995) coeftest(fm_ivreg3, vcov = hc1) ## Table 12.1, p. 448 ydiff <- log(c1995$packs) - log(c1985$packs) pricediff <- log(c1995$price/c1995$cpi) - log(c1985$price/c1985$cpi) incdiff <- log(c1995$income/c1995$population/c1995$cpi) - log(c1985$income/c1985$population/c1985$cpi) taxsdiff <- (c1995$taxs - c1995$tax)/c1995$cpi - (c1985$taxs - c1985$tax)/c1985$cpi taxdiff <- c1995$tax/c1995$cpi - c1985$tax/c1985$cpi fm_diff1 <- ivreg(ydiff ~ pricediff + incdiff | incdiff + taxsdiff) fm_diff2 <- ivreg(ydiff ~ pricediff + incdiff | incdiff + taxdiff) fm_diff3 <- ivreg(ydiff ~ pricediff + incdiff | incdiff + taxsdiff + taxdiff) coeftest(fm_diff1, vcov = hc1) coeftest(fm_diff2, vcov = hc1) coeftest(fm_diff3, vcov = hc1) ## checking instrument relevance fm_rel1 <- lm(pricediff ~ taxsdiff + incdiff) fm_rel2 <- lm(pricediff ~ taxdiff + incdiff) fm_rel3 <- lm(pricediff ~ incdiff + taxsdiff + taxdiff) linearHypothesis(fm_rel1, "taxsdiff = 0", vcov = hc1) linearHypothesis(fm_rel2, "taxdiff = 0", vcov = hc1) linearHypothesis(fm_rel3, c("taxsdiff = 0", "taxdiff = 0"), vcov = hc1) ## testing overidentifying restrictions (J test) fm_or <- lm(residuals(fm_diff3) ~ incdiff + taxsdiff + taxdiff) (fm_or_test <- linearHypothesis(fm_or, c("taxsdiff = 0", "taxdiff = 0"), test = "Chisq")) ## warning: df (and hence p-value) invalid above. ## correct df: # instruments - # endogenous variables pchisq(fm_or_test[2,5], df.residual(fm_diff3) - df.residual(fm_or), lower.tail = FALSE) ##################################################### ## Project STAR: Student-teacher achievement ratio ## ##################################################### ## data data("STAR", package = "AER") ## p. 488 fmk <- lm(I(readk + mathk) ~ stark, data = STAR) fm1 <- lm(I(read1 + math1) ~ star1, data = STAR) fm2 <- lm(I(read2 + math2) ~ star2, data = STAR) fm3 <- lm(I(read3 + math3) ~ star3, data = STAR) coeftest(fm3, vcov = sandwich) ## p. 489 fmke <- lm(I(readk + mathk) ~ stark + experiencek, data = STAR) coeftest(fmke, vcov = sandwich) ## equivalently: ## - reshape data from wide into long format ## - fit a single model nested in grade ## (a) variables and their levels nam <- c("star", "read", "math", "lunch", "school", "degree", "ladder", "experience", "tethnicity", "system", "schoolid") lev <- c("k", "1", "2", "3") ## (b) reshaping star <- reshape(STAR, idvar = "id", ids = row.names(STAR), times = lev, timevar = "grade", direction = "long", varying = lapply(nam, function(x) paste(x, lev, sep = ""))) ## (c) improve variable names and type names(star)[5:15] <- nam star$id <- factor(star$id) star$grade <- factor(star$grade, levels = lev, labels = c("kindergarten", "1st", "2nd", "3rd")) rm(nam, lev) ## (d) model fitting fm <- lm(I(read + math) ~ 0 + grade/star, data = star) ################################################# ## Quarterly US macroeconomic data (1957-2005) ## ################################################# ## data data("USMacroSW", package = "AER") library("dynlm") usm <- ts.intersect(USMacroSW, 4 * 100 * diff(log(USMacroSW[, "cpi"]))) colnames(usm) <- c(colnames(USMacroSW), "infl") ## Equation 14.7, p. 536 fm_ar1 <- dynlm(d(infl) ~ L(d(infl)), data = usm, start = c(1962,1), end = c(2004,4)) coeftest(fm_ar1, vcov = sandwich) ## Equation 14.13, p. 538 fm_ar4 <- dynlm(d(infl) ~ L(d(infl), 1:4), data = usm, start = c(1962,1), end = c(2004,4)) coeftest(fm_ar4, vcov = sandwich) ## Equation 14.16, p. 542 fm_adl41 <- dynlm(d(infl) ~ L(d(infl), 1:4) + L(unemp), data = usm, start = c(1962,1), end = c(2004,4)) coeftest(fm_adl41, vcov = sandwich) ## Equation 14.17, p. 542 fm_adl44 <- dynlm(d(infl) ~ L(d(infl), 1:4) + L(unemp, 1:4), data = usm, start = c(1962,1), end = c(2004,4)) coeftest(fm_adl44, vcov = sandwich) ## Granger causality test mentioned on p. 547 waldtest(fm_ar4, fm_adl44, vcov = sandwich) ## Equation 14.28, p. 559 fm_sp1 <- dynlm(infl ~ log(gdpjp), start = c(1965,1), end = c(1981,4), data = usm) coeftest(fm_sp1, vcov = sandwich) ## Equation 14.29, p. 559 fm_sp2 <- dynlm(infl ~ log(gdpjp), start = c(1982,1), end = c(2004,4), data = usm) coeftest(fm_sp2, vcov = sandwich) ## Equation 14.34, p. 563: ADF by hand fm_adf <- dynlm(d(infl) ~ L(infl) + L(d(infl), 1:4), data = usm, start = c(1962,1), end = c(2004,4)) coeftest(fm_adf) ## Figure 14.5, p. 570 ## SW perform partial break test of unemp coefs ## here full model is used library("strucchange") infl <- usm[, "infl"] unemp <- usm[, "unemp"] usm <- ts.intersect(diff(infl), lag(diff(infl), k = -1), lag(diff(infl), k = -2), lag(diff(infl), k = -3), lag(diff(infl), k = -4), lag(unemp, k = -1), lag(unemp, k = -2), lag(unemp, k = -3), lag(unemp, k = -4)) colnames(usm) <- c("dinfl", paste("dinfl", 1:4, sep = ""), paste("unemp", 1:4, sep = "")) usm <- window(usm, start = c(1962, 1), end = c(2004, 4)) fs <- Fstats(dinfl ~ ., data = usm) sctest(fs, type = "supF") plot(fs) ## alternatively: re-use fm_adl44 mf <- model.frame(fm_adl44) mf <- ts(as.matrix(mf), start = c(1962, 1), freq = 4) colnames(mf) <- c("y", paste("x", 1:8, sep = "")) ff <- as.formula(paste("y", "~", paste("x", 1:8, sep = "", collapse = " + "))) fs <- Fstats(ff, data = mf, from = 0.1) plot(fs) lines(boundary(fs, alpha = 0.01), lty = 2, col = 2) lines(boundary(fs, alpha = 0.1), lty = 3, col = 2) ########################################## ## Monthly US stock returns (1931-2002) ## ########################################## ## package and data library("dynlm") data("USStocksSW", package = "AER") ## Table 14.3, p. 540 fm1 <- dynlm(returns ~ L(returns), data = USStocksSW, start = c(1960,1)) coeftest(fm1, vcov = sandwich) fm2 <- dynlm(returns ~ L(returns, 1:2), data = USStocksSW, start = c(1960,1)) waldtest(fm2, vcov = sandwich) fm3 <- dynlm(returns ~ L(returns, 1:4), data = USStocksSW, start = c(1960,1)) waldtest(fm3, vcov = sandwich) ## Table 14.7, p. 574 fm4 <- dynlm(returns ~ L(returns) + L(d(dividend)), data = USStocksSW, start = c(1960, 1)) fm5 <- dynlm(returns ~ L(returns, 1:2) + L(d(dividend), 1:2), data = USStocksSW, start = c(1960, 1)) fm6 <- dynlm(returns ~ L(returns) + L(dividend), data = USStocksSW, start = c(1960, 1)) ################################## ## Price of frozen orange juice ## ################################## ## load data data("FrozenJuice") ## Stock and Watson, p. 594 library("dynlm") fm_dyn <- dynlm(d(100 * log(price/ppi)) ~ fdd, data = FrozenJuice) coeftest(fm_dyn, vcov = vcovHC(fm_dyn, type = "HC1")) ## equivalently, returns can be computed 'by hand' ## (reducing the complexity of the formula notation) fj <- ts.union(fdd = FrozenJuice[, "fdd"], ret = 100 * diff(log(FrozenJuice[,"price"]/FrozenJuice[,"ppi"]))) fm_dyn <- dynlm(ret ~ fdd, data = fj) ## Stock and Watson, p. 595 fm_dl <- dynlm(ret ~ L(fdd, 0:6), data = fj) coeftest(fm_dl, vcov = vcovHC(fm_dl, type = "HC1")) ## Stock and Watson, Table 15.1, p. 620, numbers refer to columns ## (1) Dynamic Multipliers fm1 <- dynlm(ret ~ L(fdd, 0:18), data = fj) coeftest(fm1, vcov = NeweyWest(fm1, lag = 7, prewhite = FALSE)) ## (2) Cumulative Multipliers fm2 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18), data = fj) coeftest(fm2, vcov = NeweyWest(fm2, lag = 7, prewhite = FALSE)) ## (3) Cumulative Multipliers, more lags in NW coeftest(fm2, vcov = NeweyWest(fm2, lag = 14, prewhite = FALSE)) ## (4) Cumulative Multipliers with monthly indicators fm4 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18) + season(fdd), data = fj) coeftest(fm4, vcov = NeweyWest(fm4, lag = 7, prewhite = FALSE)) ## monthly indicators needed? fm4r <- update(fm4, . ~ . - season(fdd)) waldtest(fm4, fm4r, vcov= NeweyWest(fm4, lag = 7, prewhite = FALSE)) ## close ... ############################################# ## New York Stock Exchange composite index ## ############################################# ## returns data("NYSESW", package = "AER") ret <- 100 * diff(log(NYSESW)) plot(ret) ## fit GARCH(1,1) library("tseries") fm <- garch(coredata(ret)) } \keyword{datasets} AER/man/CASchools.Rd0000644000176200001440000000551013615674673013564 0ustar liggesusers\name{CASchools} \alias{CASchools} \title{California Test Score Data} \description{The dataset contains data on test performance, school characteristics and student demographic backgrounds for school districts in California.} \usage{data("CASchools")} \format{ A data frame containing 420 observations on 14 variables. \describe{ \item{district}{character. District code.} \item{school}{character. School name.} \item{county}{factor indicating county.} \item{grades}{factor indicating grade span of district.} \item{students}{Total enrollment.} \item{teachers}{Number of teachers.} \item{calworks}{Percent qualifying for CalWorks (income assistance).} \item{lunch}{Percent qualifying for reduced-price lunch.} \item{computer}{Number of computers.} \item{expenditure}{Expenditure per student.} \item{income}{District average income (in USD 1,000).} \item{english}{Percent of English learners.} \item{read}{Average reading score.} \item{math}{Average math score.} } } \details{ The data used here are from all 420 K-6 and K-8 districts in California with data available for 1998 and 1999. Test scores are on the Stanford 9 standardized test administered to 5th grade students. School characteristics (averaged across the district) include enrollment, number of teachers (measured as \dQuote{full-time equivalents}, number of computers per classroom, and expenditures per student. Demographic variables for the students are averaged across the district. The demographic variables include the percentage of students in the public assistance program CalWorks (formerly AFDC), the percentage of students that qualify for a reduced price lunch, and the percentage of students that are English learners (that is, students for whom English is a second language). } \source{ Online complements to Stock and Watson (2007). } \references{ Stock, J. H. and Watson, M. W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}, \code{\link{MASchools}}} \examples{ ## data and transformations data("CASchools") CASchools$stratio <- with(CASchools, students/teachers) CASchools$score <- with(CASchools, (math + read)/2) ## Stock and Watson (2007) ## p. 152 fm1 <- lm(score ~ stratio, data = CASchools) coeftest(fm1, vcov = sandwich) ## p. 159 fm2 <- lm(score ~ I(stratio < 20), data = CASchools) ## p. 199 fm3 <- lm(score ~ stratio + english, data = CASchools) ## p. 224 fm4 <- lm(score ~ stratio + expenditure + english, data = CASchools) ## Table 7.1, p. 242 (numbers refer to columns) fmc3 <- lm(score ~ stratio + english + lunch, data = CASchools) fmc4 <- lm(score ~ stratio + english + calworks, data = CASchools) fmc5 <- lm(score ~ stratio + english + lunch + calworks, data = CASchools) ## More examples can be found in: ## help("StockWatson2007") } \keyword{datasets} AER/man/CreditCard.Rd0000644000176200001440000000565213615674673013761 0ustar liggesusers\name{CreditCard} \alias{CreditCard} \title{Expenditure and Default Data} \description{ Cross-section data on the credit history for a sample of applicants for a type of credit card. } \usage{data("CreditCard")} \format{ A data frame containing 1,319 observations on 12 variables. \describe{ \item{card}{Factor. Was the application for a credit card accepted?} \item{reports}{Number of major derogatory reports.} \item{age}{Age in years plus twelfths of a year.} \item{income}{Yearly income (in USD 10,000).} \item{share}{Ratio of monthly credit card expenditure to yearly income.} \item{expenditure}{Average monthly credit card expenditure.} \item{owner}{Factor. Does the individual own their home?} \item{selfemp}{Factor. Is the individual self-employed?} \item{dependents}{Number of dependents.} \item{months}{Months living at current address.} \item{majorcards}{Number of major credit cards held.} \item{active}{Number of active credit accounts.} } } \details{ According to Greene (2003, p. 952) \code{dependents} equals \code{1 + number of dependents}, our calculations suggest that it equals \code{number of dependents}. Greene (2003) provides this data set twice in Table F21.4 and F9.1, respectively. Table F9.1 has just the observations, rounded to two digits. Here, we give the F21.4 version, see the examples for the F9.1 version. Note that \code{age} has some suspiciously low values (below one year) for some applicants. One of these differs between the F9.1 and F21.4 version. } \source{ Online complements to Greene (2003). Table F21.4. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. } \seealso{\code{\link{Greene2003}}} \examples{ data("CreditCard") ## Greene (2003) ## extract data set F9.1 ccard <- CreditCard[1:100,] ccard$income <- round(ccard$income, digits = 2) ccard$expenditure <- round(ccard$expenditure, digits = 2) ccard$age <- round(ccard$age + .01) ## suspicious: CreditCard$age[CreditCard$age < 1] ## the first of these is also in TableF9.1 with 36 instead of 0.5: ccard$age[79] <- 36 ## Example 11.1 ccard <- ccard[order(ccard$income),] ccard0 <- subset(ccard, expenditure > 0) cc_ols <- lm(expenditure ~ age + owner + income + I(income^2), data = ccard0) ## Figure 11.1 plot(residuals(cc_ols) ~ income, data = ccard0, pch = 19) ## Table 11.1 mean(ccard$age) prop.table(table(ccard$owner)) mean(ccard$income) summary(cc_ols) sqrt(diag(vcovHC(cc_ols, type = "HC0"))) sqrt(diag(vcovHC(cc_ols, type = "HC2"))) sqrt(diag(vcovHC(cc_ols, type = "HC1"))) bptest(cc_ols, ~ (age + income + I(income^2) + owner)^2 + I(age^2) + I(income^4), data = ccard0) gqtest(cc_ols) bptest(cc_ols, ~ income + I(income^2), data = ccard0, studentize = FALSE) bptest(cc_ols, ~ income + I(income^2), data = ccard0) ## More examples can be found in: ## help("Greene2003") } \keyword{datasets} AER/man/Fertility.Rd0000644000176200001440000000465313615674673013730 0ustar liggesusers\name{Fertility} \alias{Fertility} \alias{Fertility2} \title{Fertility and Women's Labor Supply} \description{ Cross-section data from the 1980 US Census on married women aged 21--35 with two or more children. } \usage{ data("Fertility") data("Fertility2") } \format{ A data frame containing 254,654 (and 30,000, respectively) observations on 8 variables. \describe{ \item{morekids}{factor. Does the mother have more than 2 children?} \item{gender1}{factor indicating gender of first child.} \item{gender2}{factor indicating gender of second child.} \item{age}{age of mother at census.} \item{afam}{factor. Is the mother African-American?} \item{hispanic}{factor. Is the mother Hispanic?} \item{other}{factor. Is the mother's ethnicity neither African-American nor Hispanic, nor Caucasian? (see below)} \item{work}{number of weeks in which the mother worked in 1979.} } } \details{ \code{Fertility2} is a random subset of \code{Fertility} with 30,000 observations. There are conflicts in the ethnicity coding (see also examples). Hence, it was not possible to create a single factor and the original three indicator variables have been retained. Not all variables from Angrist and Evans (1998) have been included. } \source{ Online complements to Stock and Watson (2007). } \references{ Angrist, J.D., and Evans, W.N. (1998). Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size \emph{American Economic Review}, \bold{88}, 450--477. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("Fertility2") ## conflicts in ethnicity coding ftable(xtabs(~ afam + hispanic + other, data = Fertility2)) ## create convenience variables Fertility2$mkids <- with(Fertility2, as.numeric(morekids) - 1) Fertility2$samegender <- with(Fertility2, factor(gender1 == gender2)) Fertility2$twoboys <- with(Fertility2, factor(gender1 == "male" & gender2 == "male")) Fertility2$twogirls <- with(Fertility2, factor(gender1 == "female" & gender2 == "female")) ## similar to Angrist and Evans, p. 462 fm1 <- lm(mkids ~ samegender, data = Fertility2) summary(fm1) fm2 <- lm(mkids ~ gender1 + gender2 + samegender + age + afam + hispanic + other, data = Fertility2) summary(fm2) fm3 <- lm(mkids ~ gender1 + twoboys + twogirls + age + afam + hispanic + other, data = Fertility2) summary(fm3) } \keyword{datasets} AER/man/PSID1976.Rd0000644000176200001440000001615113615674673013037 0ustar liggesusers\name{PSID1976} \alias{PSID1976} \title{Labor Force Participation Data} \description{ Cross-section data originating from the 1976 Panel Study of Income Dynamics (PSID), based on data for the previous year, 1975. } \usage{data("PSID1976")} \format{ A data frame containing 753 observations on 21 variables. \describe{ \item{participation}{Factor. Did the individual participate in the labor force in 1975? (This is essentially \code{wage > 0} or \code{hours > 0}.)} \item{hours}{Wife's hours of work in 1975.} \item{youngkids}{Number of children less than 6 years old in household.} \item{oldkids}{Number of children between ages 6 and 18 in household.} \item{age}{Wife's age in years.} \item{education}{Wife's education in years.} \item{wage}{Wife's average hourly wage, in 1975 dollars.} \item{repwage}{Wife's wage reported at the time of the 1976 interview (not the same as the 1975 estimated wage). To use the subsample with this wage, one needs to select 1975 workers with \code{participation == "yes"}, then select only those women with non-zero wage. Only 325 women work in 1975 and have a non-zero wage in 1976.} \item{hhours}{Husband's hours worked in 1975.} \item{hage}{Husband's age in years.} \item{heducation}{Husband's education in years.} \item{hwage}{Husband's wage, in 1975 dollars.} \item{fincome}{Family income, in 1975 dollars. (This variable is used to construct the property income variable.)} \item{tax}{Marginal tax rate facing the wife, and is taken from published federal tax tables (state and local income taxes are excluded). The taxable income on which this tax rate is calculated includes Social Security, if applicable to wife.} \item{meducation}{Wife's mother's educational attainment, in years.} \item{feducation}{Wife's father's educational attainment, in years.} \item{unemp}{Unemployment rate in county of residence, in percentage points. (This is taken from bracketed ranges.)} \item{city}{Factor. Does the individual live in a large city?} \item{experience}{Actual years of wife's previous labor market experience.} \item{college}{Factor. Did the individual attend college?} \item{hcollege}{Factor. Did the individual's husband attend college?} } } \details{ This data set is also known as the Mroz (1987) data. Warning: Typical applications using these data employ the variable \code{wage} (aka \code{earnings} in previous versions of the data) as the dependent variable. The variable \code{repwage} is the reported wage in a 1976 interview, named RPWG by Greene (2003). } \source{ Online complements to Greene (2003). Table F4.1. \url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} } \references{ Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. McCullough, B.D. (2004). Some Details of Nonlinear Estimation. In: Altman, M., Gill, J., and McDonald, M.P.: \emph{Numerical Issues in Statistical Computing for the Social Scientist}. Hoboken, NJ: John Wiley, Ch. 8, 199--218. Mroz, T.A. (1987). The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions. \emph{Econometrica}, \bold{55}, 765--799. Winkelmann, R., and Boes, S. (2009). \emph{Analysis of Microdata}, 2nd ed. Berlin and Heidelberg: Springer-Verlag. Wooldridge, J.M. (2002). \emph{Econometric Analysis of Cross-Section and Panel Data}. Cambridge, MA: MIT Press. } \seealso{\code{\link{Greene2003}}, \code{\link{WinkelmannBoes2009}}} \examples{ ## data and transformations data("PSID1976") PSID1976$kids <- with(PSID1976, factor((youngkids + oldkids) > 0, levels = c(FALSE, TRUE), labels = c("no", "yes"))) PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000) PSID1976$partnum <- as.numeric(PSID1976$participation) - 1 ################### ## Greene (2003) ## ################### ## Example 4.1, Table 4.2 ## (reproduced in Example 7.1, Table 7.1) gr_lm <- lm(log(hours * wage) ~ age + I(age^2) + education + kids, data = PSID1976, subset = participation == "yes") summary(gr_lm) vcov(gr_lm) ## Example 4.5 summary(gr_lm) ## or equivalently gr_lm1 <- lm(log(hours * wage) ~ 1, data = PSID1976, subset = participation == "yes") anova(gr_lm1, gr_lm) ## Example 21.4, p. 681, and Tab. 21.3, p. 682 gr_probit1 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education + kids, data = PSID1976, family = binomial(link = "probit") ) gr_probit2 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education, data = PSID1976, family = binomial(link = "probit")) gr_probit3 <- glm(participation ~ kids/(age + I(age^2) + I(fincome/10000) + education), data = PSID1976, family = binomial(link = "probit")) ## LR test of all coefficients lrtest(gr_probit1) ## Chow-type test lrtest(gr_probit2, gr_probit3) ## equivalently: anova(gr_probit2, gr_probit3, test = "Chisq") ## Table 21.3 summary(gr_probit1) ## Example 22.8, Table 22.7, p. 786 library("sampleSelection") gr_2step <- selection(participation ~ age + I(age^2) + fincome + education + kids, wage ~ experience + I(experience^2) + education + city, data = PSID1976, method = "2step") gr_ml <- selection(participation ~ age + I(age^2) + fincome + education + kids, wage ~ experience + I(experience^2) + education + city, data = PSID1976, method = "ml") gr_ols <- lm(wage ~ experience + I(experience^2) + education + city, data = PSID1976, subset = participation == "yes") ## NOTE: ML estimates agree with Greene, 5e errata. ## Standard errors are based on the Hessian (here), while Greene has BHHH/OPG. ####################### ## Wooldridge (2002) ## ####################### ## Table 15.1, p. 468 wl_lpm <- lm(partnum ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976) wl_logit <- glm(participation ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, family = binomial, data = PSID1976) wl_probit <- glm(participation ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, family = binomial(link = "probit"), data = PSID1976) ## (same as Altman et al.) ## convenience functions pseudoR2 <- function(obj) 1 - as.vector(logLik(obj)/logLik(update(obj, . ~ 1))) misclass <- function(obj) 1 - sum(diag(prop.table(table( model.response(model.frame(obj)), round(fitted(obj)))))) coeftest(wl_logit) logLik(wl_logit) misclass(wl_logit) pseudoR2(wl_logit) coeftest(wl_probit) logLik(wl_probit) misclass(wl_probit) pseudoR2(wl_probit) ## Table 16.2, p. 528 form <- hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids wl_ols <- lm(form, data = PSID1976) wl_tobit <- tobit(form, data = PSID1976) summary(wl_ols) summary(wl_tobit) ####################### ## McCullough (2004) ## ####################### ## p. 203 mc_probit <- glm(participation ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, family = binomial(link = "probit"), data = PSID1976) mc_tobit <- tobit(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976) coeftest(mc_probit) coeftest(mc_tobit) coeftest(mc_tobit, vcov = vcovOPG) } \keyword{datasets} AER/man/Mortgage.Rd0000644000176200001440000000340213615674673013511 0ustar liggesusers\name{Mortgage} \alias{Mortgage} \title{Fixed versus Adjustable Mortgages} \description{ Cross-section data about fixed versus adjustable mortgages for 78 households. } \usage{data("Mortgage")} \format{ A data frame containing 78 observations on 16 variables. \describe{ \item{rate}{Factor with levels \code{"fixed"} and \code{"adjustable"}.} \item{age}{Age of the borrower.} \item{school}{Years of schooling for the borrower.} \item{networth}{Net worth of the borrower.} \item{interest}{Fixed interest rate.} \item{points}{Ratio of points paid on adjustable to fixed rate mortgages.} \item{maturities}{Ratio of maturities on adjustable to fixed rate mortgages.} \item{years}{Years at the present address.} \item{married}{Factor. Is the borrower married?} \item{first}{Factor. Is the borrower a first-time home buyer?} \item{selfemp}{Factor. Is the borrower self-employed?} \item{tdiff}{The difference between the 10-year treasury rate less the 1-year treasury rate.} \item{margin}{The margin on the adjustable rate mortgage.} \item{coborrower}{Factor. Is there a co-borrower?} \item{liability}{Short-term liabilities.} \item{liquid}{Liquid assets.} } } \source{ The data is from Baltagi (2002). } \references{ Baltagi, B.H. (2002). \emph{Econometrics}, 3rd ed. Berlin, Springer. Dhillon, U.S., Shilling, J.D. and Sirmans, C.F. (1987). Choosing Between Fixed and Adjustable Rate Mortgages. \emph{Journal of Money, Credit and Banking}, \bold{19}, 260--267. } \seealso{\code{\link{Baltagi2002}}} \examples{ data("Mortgage") plot(rate ~ interest, data = Mortgage, breaks = fivenum(Mortgage$interest)) plot(rate ~ margin, data = Mortgage, breaks = fivenum(Mortgage$margin)) plot(rate ~ coborrower, data = Mortgage) } \keyword{datasets} AER/man/CartelStability.Rd0000644000176200001440000000223413615674673015045 0ustar liggesusers\name{CartelStability} \alias{CartelStability} \title{CartelStability} \description{ Weekly observations on prices and other factors from 1880--1886, for a total of 326 weeks. } \usage{data("CartelStability")} \format{ A data frame containing 328 observations on 5 variables. \describe{ \item{price}{weekly index of price of shipping a ton of grain by rail.} \item{cartel}{factor. Is a railroad cartel operative?} \item{quantity}{total tonnage of grain shipped in the week.} \item{season}{factor indicating season of year. To match the weekly data, the calendar has been divided into 13 periods, each approximately 4 weeks long.} \item{ice}{factor. Are the Great Lakes innavigable because of ice?} } } \source{ Online complements to Stock and Watson (2007). } \references{ Porter, R. H. (1983). A Study of Cartel Stability: The Joint Executive Committee, 1880--1886. \emph{The Bell Journal of Economics}, \bold{14}, 301--314. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("CartelStability") summary(CartelStability) } \keyword{datasets} AER/man/NMES1988.Rd0000644000176200001440000001260413615674673013044 0ustar liggesusers\name{NMES1988} \alias{NMES1988} \title{Demand for Medical Care in NMES 1988} \description{ Cross-section data originating from the US National Medical Expenditure Survey (NMES) conducted in 1987 and 1988. The NMES is based upon a representative, national probability sample of the civilian non-institutionalized population and individuals admitted to long-term care facilities during 1987. The data are a subsample of individuals ages 66 and over all of whom are covered by Medicare (a public insurance program providing substantial protection against health-care costs). } \usage{data("NMES1988")} \format{ A data frame containing 4,406 observations on 19 variables. \describe{ \item{visits}{Number of physician office visits.} \item{nvisits}{Number of non-physician office visits.} \item{ovisits}{Number of physician hospital outpatient visits.} \item{novisits}{Number of non-physician hospital outpatient visits.} \item{emergency}{Emergency room visits.} \item{hospital}{Number of hospital stays.} \item{health}{Factor indicating self-perceived health status, levels are \code{"poor"}, \code{"average"} (reference category), \code{"excellent"}.} \item{chronic}{Number of chronic conditions.} \item{adl}{Factor indicating whether the individual has a condition that limits activities of daily living (\code{"limited"}) or not (\code{"normal"}).} \item{region}{Factor indicating region, levels are \code{northeast}, \code{midwest}, \code{west}, \code{other} (reference category).} \item{age}{Age in years (divided by 10).} \item{afam}{Factor. Is the individual African-American?} \item{gender}{Factor indicating gender.} \item{married}{Factor. is the individual married?} \item{school}{Number of years of education.} \item{income}{Family income in USD 10,000.} \item{employed}{Factor. Is the individual employed?} \item{insurance}{Factor. Is the individual covered by private insurance?} \item{medicaid}{Factor. Is the individual covered by Medicaid?} } } \source{ Journal of Applied Econometrics Data Archive for Deb and Trivedi (1997). \url{http://qed.econ.queensu.ca/jae/1997-v12.3/deb-trivedi/} } \references{ Cameron, A.C. and Trivedi, P.K. (1998). \emph{Regression Analysis of Count Data}. Cambridge: Cambridge University Press. Deb, P., and Trivedi, P.K. (1997). Demand for Medical Care by the Elderly: A Finite Mixture Approach. \emph{Journal of Applied Econometrics}, \bold{12}, 313--336. Zeileis, A., Kleiber, C., and Jackman, S. (2008). Regression Models for Count Data in R. \emph{Journal of Statistical Software}, \bold{27}(8). URL \url{http://www.jstatsoft.org/v27/i08/}. } \seealso{\code{\link{CameronTrivedi1998}}} \examples{ ## packages library("MASS") library("pscl") ## select variables for analysis data("NMES1988") nmes <- NMES1988[, c(1, 7:8, 13, 15, 18)] ## dependent variable hist(nmes$visits, breaks = 0:(max(nmes$visits)+1) - 0.5) plot(table(nmes$visits)) ## convenience transformations for exploratory graphics clog <- function(x) log(x + 0.5) cfac <- function(x, breaks = NULL) { if(is.null(breaks)) breaks <- unique(quantile(x, 0:10/10)) x <- cut(x, breaks, include.lowest = TRUE, right = FALSE) levels(x) <- paste(breaks[-length(breaks)], ifelse(diff(breaks) > 1, c(paste("-", breaks[-c(1, length(breaks))] - 1, sep = ""), "+"), ""), sep = "") return(x) } ## bivariate visualization par(mfrow = c(3, 2)) plot(clog(visits) ~ health, data = nmes, varwidth = TRUE) plot(clog(visits) ~ cfac(chronic), data = nmes) plot(clog(visits) ~ insurance, data = nmes, varwidth = TRUE) plot(clog(visits) ~ gender, data = nmes, varwidth = TRUE) plot(cfac(visits, c(0:2, 4, 6, 10, 100)) ~ school, data = nmes, breaks = 9) par(mfrow = c(1, 1)) ## Poisson regression nmes_pois <- glm(visits ~ ., data = nmes, family = poisson) summary(nmes_pois) ## LM test for overdispersion dispersiontest(nmes_pois) dispersiontest(nmes_pois, trafo = 2) ## sandwich covariance matrix coeftest(nmes_pois, vcov = sandwich) ## quasipoisson model nmes_qpois <- glm(visits ~ ., data = nmes, family = quasipoisson) ## NegBin regression nmes_nb <- glm.nb(visits ~ ., data = nmes) ## hurdle regression nmes_hurdle <- hurdle(visits ~ . | chronic + insurance + school + gender, data = nmes, dist = "negbin") ## zero-inflated regression model nmes_zinb <- zeroinfl(visits ~ . | chronic + insurance + school + gender, data = nmes, dist = "negbin") ## compare estimated coefficients fm <- list("ML-Pois" = nmes_pois, "Quasi-Pois" = nmes_qpois, "NB" = nmes_nb, "Hurdle-NB" = nmes_hurdle, "ZINB" = nmes_zinb) round(sapply(fm, function(x) coef(x)[1:7]), digits = 3) ## associated standard errors round(cbind("ML-Pois" = sqrt(diag(vcov(nmes_pois))), "Adj-Pois" = sqrt(diag(sandwich(nmes_pois))), sapply(fm[-1], function(x) sqrt(diag(vcov(x)))[1:7])), digits = 3) ## log-likelihoods and number of estimated parameters rbind(logLik = sapply(fm, function(x) round(logLik(x), digits = 0)), Df = sapply(fm, function(x) attr(logLik(x), "df"))) ## predicted number of zeros round(c("Obs" = sum(nmes$visits < 1), "ML-Pois" = sum(dpois(0, fitted(nmes_pois))), "Adj-Pois" = NA, "Quasi-Pois" = NA, "NB" = sum(dnbinom(0, mu = fitted(nmes_nb), size = nmes_nb$theta)), "NB-Hurdle" = sum(predict(nmes_hurdle, type = "prob")[,1]), "ZINB" = sum(predict(nmes_zinb, type = "prob")[,1]))) ## coefficients of zero-augmentation models t(sapply(fm[4:5], function(x) round(x$coefficients$zero, digits = 3))) } \keyword{datasets} AER/man/DutchSales.Rd0000644000176200001440000000213713615674673014007 0ustar liggesusers\name{DutchSales} \alias{DutchSales} \title{Dutch Retail Sales Index Data} \description{ Time series of retail sales index in The Netherlands. } \usage{data("DutchSales")} \format{ A monthly univariate time series from 1960(5) to 1995(9). } \source{ Online complements to Franses (1998). } \references{ Franses, P.H. (1998). \emph{Time Series Models for Business and Economic Forecasting}. Cambridge, UK: Cambridge University Press. } \seealso{\code{\link{Franses1998}}} \examples{ data("DutchSales") plot(DutchSales) ## EACF tables (Franses 1998, p. 99) ctrafo <- function(x) residuals(lm(x ~ factor(cycle(x)))) ddiff <- function(x) diff(diff(x, frequency(x)), 1) eacf <- function(y, lag = 12) { stopifnot(all(lag > 0)) if(length(lag) < 2) lag <- 1:lag rval <- sapply( list(y = y, dy = diff(y), cdy = ctrafo(diff(y)), Dy = diff(y, frequency(y)), dDy = ddiff(y)), function(x) acf(x, plot = FALSE, lag.max = max(lag))$acf[lag + 1]) rownames(rval) <- lag return(rval) } ## Franses (1998), Table 5.3 round(eacf(log(DutchSales), lag = c(1:18, 24, 36)), digits = 3) } \keyword{datasets} AER/man/PhDPublications.Rd0000644000176200001440000000352613615674673015003 0ustar liggesusers\name{PhDPublications} \alias{PhDPublications} \title{Doctoral Publications} \description{ Cross-section data on the scientific productivity of PhD students in biochemistry. } \usage{data("PhDPublications")} \format{ A data frame containing 915 observations on 6 variables. \describe{ \item{articles}{Number of articles published during last 3 years of PhD.} \item{gender}{factor indicating gender.} \item{married}{factor. Is the PhD student married?} \item{kids}{Number of children less than 6 years old.} \item{prestige}{Prestige of the graduate program.} \item{mentor}{Number of articles published by student's mentor.} } } \source{ Online complements to Long (1997). \url{http://www.indiana.edu/~jslsoc/research_rm4cldvs.htm} } \references{ Long, J.S. (1990). \emph{Regression Models for Categorical and Limited Dependent Variables}. Thousand Oaks: Sage Publications. Long, J.S. (1997). The Origin of Sex Differences in Science. \emph{Social Forces}, \bold{68}, 1297--1315. } \examples{ ## from Long (1997) data("PhDPublications") ## Table 8.1, p. 227 summary(PhDPublications) ## Figure 8.2, p. 220 plot(0:10, dpois(0:10, mean(PhDPublications$articles)), type = "b", col = 2, xlab = "Number of articles", ylab = "Probability") lines(0:10, prop.table(table(PhDPublications$articles))[1:11], type = "b") legend("topright", c("observed", "predicted"), col = 1:2, lty = rep(1, 2), bty = "n") ## Table 8.2, p. 228 fm_lrm <- lm(log(articles + 0.5) ~ ., data = PhDPublications) summary(fm_lrm) -2 * logLik(fm_lrm) fm_prm <- glm(articles ~ ., data = PhDPublications, family = poisson) library("MASS") fm_nbrm <- glm.nb(articles ~ ., data = PhDPublications) ## Table 8.3, p. 246 library("pscl") fm_zip <- zeroinfl(articles ~ . | ., data = PhDPublications) fm_zinb <- zeroinfl(articles ~ . | ., data = PhDPublications, dist = "negbin") } \keyword{datasets} AER/man/SmokeBan.Rd0000644000176200001440000000357513615674673013456 0ustar liggesusers\name{SmokeBan} \alias{SmokeBan} \title{Do Workplace Smoking Bans Reduce Smoking?} \description{ Estimation of the effect of workplace smoking bans on smoking of indoor workers. } \usage{data("SmokeBan")} \format{ A data frame containing 10,000 observations on 7 variables. \describe{ \item{smoker}{factor. Is the individual a current smoker?} \item{ban}{factor. Is there a work area smoking ban?} \item{age}{age in years.} \item{education}{factor indicating highest education level attained: high school (hs) drop out, high school graduate, some college, college graduate, master's degree (or higher).} \item{afam}{factor. Is the individual African-American?} \item{hispanic}{factor. Is the individual Hispanic?} \item{gender}{factor indicating gender.} } } \details{ \code{SmokeBank} is a cross-sectional data set with observations on 10,000 indoor workers, which is a subset of a 18,090-observation data set collected as part of the National Health Interview Survey in 1991 and then again (with different respondents) in 1993. The data set contains information on whether individuals were, or were not, subject to a workplace smoking ban, whether or not the individuals smoked and other individual characteristics. } \source{ Online complements to Stock and Watson (2007). } \references{ Evans, W. N., Farrelly, M.C., and Montgomery, E. (1999). Do Workplace Smoking Bans Reduce Smoking? \emph{American Economic Review}, \bold{89}, 728--747. Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley. } \seealso{\code{\link{StockWatson2007}}} \examples{ data("SmokeBan") ## proportion of non-smokers increases with education plot(smoker ~ education, data = SmokeBan) ## proportion of non-smokers constant over age plot(smoker ~ age, data = SmokeBan) } \keyword{datasets} AER/man/SwissLabor.Rd0000644000176200001440000000254413615674673014042 0ustar liggesusers\name{SwissLabor} \alias{SwissLabor} \title{Swiss Labor Market Participation Data} \description{ Cross-section data originating from the health survey SOMIPOPS for Switzerland in 1981. } \usage{data("SwissLabor")} \format{ A data frame containing 872 observations on 7 variables. \describe{ \item{participation}{Factor. Did the individual participate in the labor force?} \item{income}{Logarithm of nonlabor income.} \item{age}{Age in decades (years divided by 10).} \item{education}{Years of formal education.} \item{youngkids}{Number of young children (under 7 years of age).} \item{oldkids}{Number of older children (over 7 years of age).} \item{foreign}{Factor. Is the individual a foreigner (i.e., not Swiss)?} } } \source{ Journal of Applied Econometrics Data Archive. \url{http://qed.econ.queensu.ca/jae/1996-v11.3/gerfin/} } \references{ Gerfin, M. (1996). Parametric and Semi-Parametric Estimation of the Binary Response Model of Labour Market Participation. \emph{Journal of Applied Econometrics}, \bold{11}, 321--339. } \examples{ data("SwissLabor") ### Gerfin (1996), Table I. fm_probit <- glm(participation ~ . + I(age^2), data = SwissLabor, family = binomial(link = "probit")) summary(fm_probit) ### alternatively fm_logit <- glm(participation ~ . + I(age^2), data = SwissLabor, family = binomial) summary(fm_logit) } \keyword{datasets} AER/DESCRIPTION0000644000176200001440000000273713616730104012401 0ustar liggesusersPackage: AER Version: 1.2-9 Date: 2020-02-04 Title: Applied Econometrics with R Authors@R: c(person(given = "Christian", family = "Kleiber", role = "aut", email = "Christian.Kleiber@unibas.ch", comment = c(ORCID = "0000-0002-6781-4733")), person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org", comment = c(ORCID = "0000-0003-0918-3766"))) Description: Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.) LazyLoad: yes Depends: R (>= 3.0.0), car (>= 2.0-19), lmtest, sandwich (>= 2.4-0), survival (>= 2.37-5), zoo Suggests: boot, dynlm, effects, fGarch, forecast, foreign, ineq, KernSmooth, lattice, longmemo, MASS, mlogit, nlme, nnet, np, plm, pscl, quantreg, rgl, ROCR, rugarch, sampleSelection, scatterplot3d, strucchange, systemfit (>= 1.1-20), truncreg, tseries, urca, vars Imports: stats, Formula (>= 0.2-0) License: GPL-2 | GPL-3 NeedsCompilation: no Packaged: 2020-02-04 22:03:51 UTC; zeileis Author: Christian Kleiber [aut] (), Achim Zeileis [aut, cre] () Maintainer: Achim Zeileis Repository: CRAN Date/Publication: 2020-02-06 06:20:52 UTC AER/build/0000755000176200001440000000000013616365107011770 5ustar liggesusersAER/build/vignette.rds0000644000176200001440000000050313616365107014325 0ustar liggesusersuQN@Eh?`Ϧ&mꡡxPWa쮢7Aꁅ7ޛ711?O=gM=`#>/i$KMUKL+E L2VRh /}ь!~-h-r5X ~_C^d8wB"hV1BI*M eEnfa֭M^5 TTb('ع] V+bȸQ-A#JP3bY bHeҦ8ŏRiFZviҧ;&30m8N}znAER/tests/0000755000176200001440000000000013616353614012033 5ustar liggesusersAER/tests/Ch-LinearRegression.R0000644000176200001440000004657413463421741015776 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: data-journals ################################################### data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations summary(journals) ################################################### ### chunk number 3: linreg-plot eval=FALSE ################################################### ## plot(log(subs) ~ log(citeprice), data = journals) ## jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) ## abline(jour_lm) ################################################### ### chunk number 4: linreg-plot1 ################################################### plot(log(subs) ~ log(citeprice), data = journals) jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) abline(jour_lm) ################################################### ### chunk number 5: linreg-class ################################################### class(jour_lm) ################################################### ### chunk number 6: linreg-names ################################################### names(jour_lm) ################################################### ### chunk number 7: linreg-summary ################################################### summary(jour_lm) ################################################### ### chunk number 8: linreg-summary ################################################### jour_slm <- summary(jour_lm) class(jour_slm) names(jour_slm) ################################################### ### chunk number 9: linreg-coef ################################################### jour_slm$coefficients ################################################### ### chunk number 10: linreg-anova ################################################### anova(jour_lm) ################################################### ### chunk number 11: journals-coef ################################################### coef(jour_lm) ################################################### ### chunk number 12: journals-confint ################################################### confint(jour_lm, level = 0.95) ################################################### ### chunk number 13: journals-predict ################################################### predict(jour_lm, newdata = data.frame(citeprice = 2.11), interval = "confidence") predict(jour_lm, newdata = data.frame(citeprice = 2.11), interval = "prediction") ################################################### ### chunk number 14: predict-plot eval=FALSE ################################################### ## lciteprice <- seq(from = -6, to = 4, by = 0.25) ## jour_pred <- predict(jour_lm, interval = "prediction", ## newdata = data.frame(citeprice = exp(lciteprice))) ## plot(log(subs) ~ log(citeprice), data = journals) ## lines(jour_pred[, 1] ~ lciteprice, col = 1) ## lines(jour_pred[, 2] ~ lciteprice, col = 1, lty = 2) ## lines(jour_pred[, 3] ~ lciteprice, col = 1, lty = 2) ################################################### ### chunk number 15: predict-plot1 ################################################### lciteprice <- seq(from = -6, to = 4, by = 0.25) jour_pred <- predict(jour_lm, interval = "prediction", newdata = data.frame(citeprice = exp(lciteprice))) plot(log(subs) ~ log(citeprice), data = journals) lines(jour_pred[, 1] ~ lciteprice, col = 1) lines(jour_pred[, 2] ~ lciteprice, col = 1, lty = 2) lines(jour_pred[, 3] ~ lciteprice, col = 1, lty = 2) ################################################### ### chunk number 16: journals-plot eval=FALSE ################################################### ## par(mfrow = c(2, 2)) ## plot(jour_lm) ## par(mfrow = c(1, 1)) ################################################### ### chunk number 17: journals-plot1 ################################################### par(mfrow = c(2, 2)) plot(jour_lm) par(mfrow = c(1, 1)) ################################################### ### chunk number 18: journal-lht ################################################### linearHypothesis(jour_lm, "log(citeprice) = -0.5") ################################################### ### chunk number 19: CPS-data ################################################### data("CPS1988") summary(CPS1988) ################################################### ### chunk number 20: CPS-base ################################################### cps_lm <- lm(log(wage) ~ experience + I(experience^2) + education + ethnicity, data = CPS1988) ################################################### ### chunk number 21: CPS-visualization-unused eval=FALSE ################################################### ## ex <- 0:56 ## ed <- with(CPS1988, tapply(education, ## list(ethnicity, experience), mean))[, as.character(ex)] ## fm <- cps_lm ## wago <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) ## wagb <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "afam", education = as.numeric(ed["afam",]))) ## plot(log(wage) ~ experience, data = CPS1988, pch = ".", ## col = as.numeric(ethnicity)) ## lines(ex, wago) ## lines(ex, wagb, col = 2) ################################################### ### chunk number 22: CPS-summary ################################################### summary(cps_lm) ################################################### ### chunk number 23: CPS-noeth ################################################### cps_noeth <- lm(log(wage) ~ experience + I(experience^2) + education, data = CPS1988) anova(cps_noeth, cps_lm) ################################################### ### chunk number 24: CPS-anova ################################################### anova(cps_lm) ################################################### ### chunk number 25: CPS-noeth2 eval=FALSE ################################################### ## cps_noeth <- update(cps_lm, formula = . ~ . - ethnicity) ################################################### ### chunk number 26: CPS-waldtest ################################################### waldtest(cps_lm, . ~ . - ethnicity) ################################################### ### chunk number 27: CPS-spline ################################################### library("splines") cps_plm <- lm(log(wage) ~ bs(experience, df = 5) + education + ethnicity, data = CPS1988) ################################################### ### chunk number 28: CPS-spline-summary eval=FALSE ################################################### ## summary(cps_plm) ################################################### ### chunk number 29: CPS-BIC ################################################### cps_bs <- lapply(3:10, function(i) lm(log(wage) ~ bs(experience, df = i) + education + ethnicity, data = CPS1988)) structure(sapply(cps_bs, AIC, k = log(nrow(CPS1988))), .Names = 3:10) ################################################### ### chunk number 30: plm-plot eval=FALSE ################################################### ## cps <- data.frame(experience = -2:60, education = ## with(CPS1988, mean(education[ethnicity == "cauc"])), ## ethnicity = "cauc") ## cps$yhat1 <- predict(cps_lm, newdata = cps) ## cps$yhat2 <- predict(cps_plm, newdata = cps) ## ## plot(log(wage) ~ jitter(experience, factor = 3), pch = 19, ## col = rgb(0.5, 0.5, 0.5, alpha = 0.02), data = CPS1988) ## lines(yhat1 ~ experience, data = cps, lty = 2) ## lines(yhat2 ~ experience, data = cps) ## legend("topleft", c("quadratic", "spline"), lty = c(2,1), ## bty = "n") ################################################### ### chunk number 31: plm-plot1 ################################################### cps <- data.frame(experience = -2:60, education = with(CPS1988, mean(education[ethnicity == "cauc"])), ethnicity = "cauc") cps$yhat1 <- predict(cps_lm, newdata = cps) cps$yhat2 <- predict(cps_plm, newdata = cps) plot(log(wage) ~ jitter(experience, factor = 3), pch = 19, col = rgb(0.5, 0.5, 0.5, alpha = 0.02), data = CPS1988) lines(yhat1 ~ experience, data = cps, lty = 2) lines(yhat2 ~ experience, data = cps) legend("topleft", c("quadratic", "spline"), lty = c(2,1), bty = "n") ################################################### ### chunk number 32: CPS-int ################################################### cps_int <- lm(log(wage) ~ experience + I(experience^2) + education * ethnicity, data = CPS1988) coeftest(cps_int) ################################################### ### chunk number 33: CPS-int2 eval=FALSE ################################################### ## cps_int <- lm(log(wage) ~ experience + I(experience^2) + ## education + ethnicity + education:ethnicity, ## data = CPS1988) ################################################### ### chunk number 34: CPS-sep ################################################### cps_sep <- lm(log(wage) ~ ethnicity / (experience + I(experience^2) + education) - 1, data = CPS1988) ################################################### ### chunk number 35: CPS-sep-coef ################################################### cps_sep_cf <- matrix(coef(cps_sep), nrow = 2) rownames(cps_sep_cf) <- levels(CPS1988$ethnicity) colnames(cps_sep_cf) <- names(coef(cps_lm))[1:4] cps_sep_cf ################################################### ### chunk number 36: CPS-sep-anova ################################################### anova(cps_sep, cps_lm) ################################################### ### chunk number 37: CPS-sep-visualization-unused eval=FALSE ################################################### ## ex <- 0:56 ## ed <- with(CPS1988, tapply(education, list(ethnicity, ## experience), mean))[, as.character(ex)] ## fm <- cps_lm ## wago <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) ## wagb <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "afam", education = as.numeric(ed["afam",]))) ## plot(log(wage) ~ jitter(experience, factor = 2), ## data = CPS1988, pch = ".", col = as.numeric(ethnicity)) ## ## ## plot(log(wage) ~ as.factor(experience), data = CPS1988, ## pch = ".") ## lines(ex, wago, lwd = 2) ## lines(ex, wagb, col = 2, lwd = 2) ## fm <- cps_sep ## wago <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) ## wagb <- predict(fm, newdata = data.frame(experience = ex, ## ethnicity = "afam", education = as.numeric(ed["afam",]))) ## lines(ex, wago, lty = 2, lwd = 2) ## lines(ex, wagb, col = 2, lty = 2, lwd = 2) ################################################### ### chunk number 38: CPS-region ################################################### CPS1988$region <- relevel(CPS1988$region, ref = "south") cps_region <- lm(log(wage) ~ ethnicity + education + experience + I(experience^2) + region, data = CPS1988) coef(cps_region) ################################################### ### chunk number 39: wls1 ################################################### jour_wls1 <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice^2) ################################################### ### chunk number 40: wls2 ################################################### jour_wls2 <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice) ################################################### ### chunk number 41: journals-wls1 eval=FALSE ################################################### ## plot(log(subs) ~ log(citeprice), data = journals) ## abline(jour_lm) ## abline(jour_wls1, lwd = 2, lty = 2) ## abline(jour_wls2, lwd = 2, lty = 3) ## legend("bottomleft", c("OLS", "WLS1", "WLS2"), ## lty = 1:3, lwd = 2, bty = "n") ################################################### ### chunk number 42: journals-wls11 ################################################### plot(log(subs) ~ log(citeprice), data = journals) abline(jour_lm) abline(jour_wls1, lwd = 2, lty = 2) abline(jour_wls2, lwd = 2, lty = 3) legend("bottomleft", c("OLS", "WLS1", "WLS2"), lty = 1:3, lwd = 2, bty = "n") ################################################### ### chunk number 43: fgls1 ################################################### auxreg <- lm(log(residuals(jour_lm)^2) ~ log(citeprice), data = journals) jour_fgls1 <- lm(log(subs) ~ log(citeprice), weights = 1/exp(fitted(auxreg)), data = journals) ################################################### ### chunk number 44: fgls2 ################################################### gamma2i <- coef(auxreg)[2] gamma2 <- 0 while(abs((gamma2i - gamma2)/gamma2) > 1e-7) { gamma2 <- gamma2i fglsi <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice^gamma2) gamma2i <- coef(lm(log(residuals(fglsi)^2) ~ log(citeprice), data = journals))[2] } jour_fgls2 <- lm(log(subs) ~ log(citeprice), data = journals, weights = 1/citeprice^gamma2) ################################################### ### chunk number 45: fgls2-coef ################################################### coef(jour_fgls2) ################################################### ### chunk number 46: journals-fgls ################################################### plot(log(subs) ~ log(citeprice), data = journals) abline(jour_lm) abline(jour_fgls2, lty = 2, lwd = 2) ################################################### ### chunk number 47: usmacro-plot eval=FALSE ################################################### ## data("USMacroG") ## plot(USMacroG[, c("dpi", "consumption")], lty = c(3, 1), ## plot.type = "single", ylab = "") ## legend("topleft", legend = c("income", "consumption"), ## lty = c(3, 1), bty = "n") ################################################### ### chunk number 48: usmacro-plot1 ################################################### data("USMacroG") plot(USMacroG[, c("dpi", "consumption")], lty = c(3, 1), plot.type = "single", ylab = "") legend("topleft", legend = c("income", "consumption"), lty = c(3, 1), bty = "n") ################################################### ### chunk number 49: usmacro-fit ################################################### library("dynlm") cons_lm1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG) cons_lm2 <- dynlm(consumption ~ dpi + L(consumption), data = USMacroG) ################################################### ### chunk number 50: usmacro-summary1 ################################################### summary(cons_lm1) ################################################### ### chunk number 51: usmacro-summary2 ################################################### summary(cons_lm2) ################################################### ### chunk number 52: dynlm-plot eval=FALSE ################################################### ## plot(merge(as.zoo(USMacroG[,"consumption"]), fitted(cons_lm1), ## fitted(cons_lm2), 0, residuals(cons_lm1), ## residuals(cons_lm2)), screens = rep(1:2, c(3, 3)), ## lty = rep(1:3, 2), ylab = c("Fitted values", "Residuals"), ## xlab = "Time", main = "") ## legend(0.05, 0.95, c("observed", "cons_lm1", "cons_lm2"), ## lty = 1:3, bty = "n") ################################################### ### chunk number 53: dynlm-plot1 ################################################### plot(merge(as.zoo(USMacroG[,"consumption"]), fitted(cons_lm1), fitted(cons_lm2), 0, residuals(cons_lm1), residuals(cons_lm2)), screens = rep(1:2, c(3, 3)), lty = rep(1:3, 2), ylab = c("Fitted values", "Residuals"), xlab = "Time", main = "") legend(0.05, 0.95, c("observed", "cons_lm1", "cons_lm2"), lty = 1:3, bty = "n") ################################################### ### chunk number 54: encompassing1 ################################################### cons_lmE <- dynlm(consumption ~ dpi + L(dpi) + L(consumption), data = USMacroG) ################################################### ### chunk number 55: encompassing2 ################################################### anova(cons_lm1, cons_lmE, cons_lm2) ################################################### ### chunk number 56: encompassing3 ################################################### encomptest(cons_lm1, cons_lm2) ################################################### ### chunk number 57: pdata.frame ################################################### data("Grunfeld", package = "AER") library("plm") gr <- subset(Grunfeld, firm %in% c("General Electric", "General Motors", "IBM")) pgr <- pdata.frame(gr, index = c("firm", "year")) ################################################### ### chunk number 58: plm-pool ################################################### gr_pool <- plm(invest ~ value + capital, data = pgr, model = "pooling") ################################################### ### chunk number 59: plm-FE ################################################### gr_fe <- plm(invest ~ value + capital, data = pgr, model = "within") summary(gr_fe) ################################################### ### chunk number 60: plm-pFtest ################################################### pFtest(gr_fe, gr_pool) ################################################### ### chunk number 61: plm-RE ################################################### gr_re <- plm(invest ~ value + capital, data = pgr, model = "random", random.method = "walhus") summary(gr_re) ################################################### ### chunk number 62: plm-plmtest ################################################### plmtest(gr_pool) ################################################### ### chunk number 63: plm-phtest ################################################### phtest(gr_re, gr_fe) ################################################### ### chunk number 64: EmplUK-data ################################################### data("EmplUK", package = "plm") ################################################### ### chunk number 65: plm-AB ################################################### empl_ab <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + lag(log(output), 0:1) | lag(log(emp), 2:99), data = EmplUK, index = c("firm", "year"), effect = "twoways", model = "twosteps") ################################################### ### chunk number 66: plm-AB-summary ################################################### summary(empl_ab, robust = FALSE) ################################################### ### chunk number 67: systemfit ################################################### library("systemfit") gr2 <- subset(Grunfeld, firm %in% c("Chrysler", "IBM")) pgr2 <- pdata.frame(gr2, c("firm", "year")) ################################################### ### chunk number 68: SUR ################################################### gr_sur <- systemfit(invest ~ value + capital, method = "SUR", data = pgr2) summary(gr_sur, residCov = FALSE, equations = FALSE) ################################################### ### chunk number 69: nlme eval=FALSE ################################################### ## library("nlme") ## g1 <- subset(Grunfeld, firm == "Westinghouse") ## gls(invest ~ value + capital, data = g1, correlation = corAR1()) AER/tests/Ch-Microeconometrics.R0000644000176200001440000003104313616353614016173 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: swisslabor-data ################################################### data("SwissLabor") swiss_probit <- glm(participation ~ . + I(age^2), data = SwissLabor, family = binomial(link = "probit")) summary(swiss_probit) ################################################### ### chunk number 3: swisslabor-plot eval=FALSE ################################################### ## plot(participation ~ age, data = SwissLabor, ylevels = 2:1) ################################################### ### chunk number 4: swisslabor-plot-refined ################################################### plot(participation ~ education, data = SwissLabor, ylevels = 2:1) fm <- glm(participation ~ education + I(education^2), data = SwissLabor, family = binomial) edu <- sort(unique(SwissLabor$education)) prop <- sapply(edu, function(x) mean(SwissLabor$education <= x)) lines(predict(fm, newdata = data.frame(education = edu), type = "response") ~ prop, col = 2) plot(participation ~ age, data = SwissLabor, ylevels = 2:1) fm <- glm(participation ~ age + I(age^2), data = SwissLabor, family = binomial) ag <- sort(unique(SwissLabor$age)) prop <- sapply(ag, function(x) mean(SwissLabor$age <= x)) lines(predict(fm, newdata = data.frame(age = ag), type = "response") ~ prop, col = 2) ################################################### ### chunk number 5: effects1 ################################################### fav <- mean(dnorm(predict(swiss_probit, type = "link"))) fav * coef(swiss_probit) ################################################### ### chunk number 6: effects2 ################################################### av <- colMeans(SwissLabor[, -c(1, 7)]) av <- data.frame(rbind(swiss = av, foreign = av), foreign = factor(c("no", "yes"))) av <- predict(swiss_probit, newdata = av, type = "link") av <- dnorm(av) av["swiss"] * coef(swiss_probit)[-7] ################################################### ### chunk number 7: effects3 ################################################### av["foreign"] * coef(swiss_probit)[-7] ################################################### ### chunk number 8: mcfadden ################################################### swiss_probit0 <- update(swiss_probit, formula = . ~ 1) 1 - as.vector(logLik(swiss_probit)/logLik(swiss_probit0)) ################################################### ### chunk number 9: confusion-matrix ################################################### table(true = SwissLabor$participation, pred = round(fitted(swiss_probit))) ################################################### ### chunk number 10: confusion-matrix1 ################################################### tab <- table(true = SwissLabor$participation, pred = round(fitted(swiss_probit))) tabp <- round(100 * c(tab[1,1] + tab[2,2], tab[2,1] + tab[1,2])/sum(tab), digits = 2) ################################################### ### chunk number 11: roc-plot eval=FALSE ################################################### ## library("ROCR") ## pred <- prediction(fitted(swiss_probit), ## SwissLabor$participation) ## plot(performance(pred, "acc")) ## plot(performance(pred, "tpr", "fpr")) ## abline(0, 1, lty = 2) ################################################### ### chunk number 12: roc-plot1 ################################################### library("ROCR") pred <- prediction(fitted(swiss_probit), SwissLabor$participation) plot(performance(pred, "acc")) plot(performance(pred, "tpr", "fpr")) abline(0, 1, lty = 2) ################################################### ### chunk number 13: rss ################################################### deviance(swiss_probit) sum(residuals(swiss_probit, type = "deviance")^2) sum(residuals(swiss_probit, type = "pearson")^2) ################################################### ### chunk number 14: coeftest eval=FALSE ################################################### ## coeftest(swiss_probit, vcov = sandwich) ################################################### ### chunk number 15: murder ################################################### data("MurderRates") ## murder_logit <- glm(I(executions > 0) ~ time + income + ## IGNORE_RDIFF, excluded due to small numeric deviations on different platforms ## noncauc + lfp + southern, data = MurderRates, ## family = binomial) ## ## ## ################################################### ## ### chunk number 16: murder-coeftest ## ################################################### ## coeftest(murder_logit) ## ## ## ################################################### ## ### chunk number 17: murder2 ## ################################################### ## murder_logit2 <- glm(I(executions > 0) ~ time + income + ## noncauc + lfp + southern, data = MurderRates, ## family = binomial, control = list(epsilon = 1e-15, ## maxit = 50, trace = FALSE)) ## ## ## ################################################### ## ### chunk number 18: murder2-coeftest ## ################################################### ## coeftest(murder_logit2) ################################################### ### chunk number 19: separation ################################################### table(I(MurderRates$executions > 0), MurderRates$southern) ################################################### ### chunk number 20: countreg-pois ################################################### data("RecreationDemand") rd_pois <- glm(trips ~ ., data = RecreationDemand, family = poisson) ################################################### ### chunk number 21: countreg-pois-coeftest ################################################### coeftest(rd_pois) ################################################### ### chunk number 22: countreg-pois-logLik ################################################### logLik(rd_pois) ################################################### ### chunk number 23: countreg-odtest1 ################################################### dispersiontest(rd_pois) ################################################### ### chunk number 24: countreg-odtest2 ################################################### dispersiontest(rd_pois, trafo = 2) ################################################### ### chunk number 25: countreg-nbin ################################################### library("MASS") rd_nb <- glm.nb(trips ~ ., data = RecreationDemand) coeftest(rd_nb) logLik(rd_nb) ################################################### ### chunk number 26: countreg-se ################################################### round(sqrt(rbind(diag(vcov(rd_pois)), diag(sandwich(rd_pois)))), digits = 3) ################################################### ### chunk number 27: countreg-sandwich ################################################### coeftest(rd_pois, vcov = sandwich) ################################################### ### chunk number 28: countreg-OPG ################################################### round(sqrt(diag(vcovOPG(rd_pois))), 3) ################################################### ### chunk number 29: countreg-plot ################################################### plot(table(RecreationDemand$trips), ylab = "") ################################################### ### chunk number 30: countreg-zeros ################################################### rbind(obs = table(RecreationDemand$trips)[1:10], exp = round( sapply(0:9, function(x) sum(dpois(x, fitted(rd_pois)))))) ################################################### ### chunk number 31: countreg-pscl ################################################### library("pscl") ################################################### ### chunk number 32: countreg-zinb ################################################### rd_zinb <- zeroinfl(trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") ################################################### ### chunk number 33: countreg-zinb-summary ################################################### summary(rd_zinb) ################################################### ### chunk number 34: countreg-zinb-expected ################################################### round(colSums(predict(rd_zinb, type = "prob")[,1:10])) ################################################### ### chunk number 35: countreg-hurdle ################################################### rd_hurdle <- hurdle(trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") summary(rd_hurdle) ################################################### ### chunk number 36: countreg-hurdle-expected ################################################### round(colSums(predict(rd_hurdle, type = "prob")[,1:10])) ################################################### ### chunk number 37: tobit1 ################################################### data("Affairs") aff_tob <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) summary(aff_tob) ################################################### ### chunk number 38: tobit2 ################################################### aff_tob2 <- update(aff_tob, right = 4) summary(aff_tob2) ################################################### ### chunk number 39: tobit3 ################################################### linearHypothesis(aff_tob, c("age = 0", "occupation = 0"), vcov = sandwich) ################################################### ### chunk number 40: numeric-response ################################################### SwissLabor$partnum <- as.numeric(SwissLabor$participation) - 1 ################################################### ### chunk number 41: kleinspady eval=FALSE ################################################### ## library("np") ## swiss_bw <- npindexbw(partnum ~ income + age + education + ## youngkids + oldkids + foreign + I(age^2), data = SwissLabor, ## method = "kleinspady", nmulti = 5) ################################################### ### chunk number 42: kleinspady-bw eval=FALSE ################################################### ## summary(swiss_bw) ################################################### ### chunk number 43: kleinspady-summary eval=FALSE ################################################### ## swiss_ks <- npindex(bws = swiss_bw, gradients = TRUE) ## summary(swiss_ks) ################################################### ### chunk number 44: probit-confusion ################################################### table(Actual = SwissLabor$participation, Predicted = round(predict(swiss_probit, type = "response"))) ################################################### ### chunk number 45: bw-tab ################################################### data("BankWages") edcat <- factor(BankWages$education) levels(edcat)[3:10] <- rep(c("14-15", "16-18", "19-21"), c(2, 3, 3)) tab <- xtabs(~ edcat + job, data = BankWages) prop.table(tab, 1) ################################################### ### chunk number 46: bw-plot eval=FALSE ################################################### ## plot(job ~ edcat, data = BankWages, off = 0) ################################################### ### chunk number 47: bw-plot1 ################################################### plot(job ~ edcat, data = BankWages, off = 0) box() ################################################### ### chunk number 48: bw-multinom ################################################### library("nnet") bank_mnl <- multinom(job ~ education + minority, data = BankWages, subset = gender == "male", trace = FALSE) ################################################### ### chunk number 49: bw-multinom-coeftest ################################################### coeftest(bank_mnl) ################################################### ### chunk number 50: bw-polr ################################################### library("MASS") bank_polr <- polr(job ~ education + minority, data = BankWages, subset = gender == "male", Hess = TRUE) coeftest(bank_polr) ################################################### ### chunk number 51: bw-AIC ################################################### AIC(bank_mnl) AIC(bank_polr) AER/tests/Ch-Programming.Rout.save0000644000176200001440000002601513457447233016465 0ustar liggesusers R version 3.5.2 (2018-12-20) -- "Eggshell Igloo" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: DGP R> ################################################### R> dgp <- function(nobs = 15, model = c("trend", "dynamic"), + corr = 0, coef = c(0.25, -0.75), sd = 1) + { + model <- match.arg(model) + coef <- rep(coef, length.out = 2) + + err <- as.vector(filter(rnorm(nobs, sd = sd), corr, + method = "recursive")) + if(model == "trend") { + x <- 1:nobs + y <- coef[1] + coef[2] * x + err + } else { + y <- rep(NA, nobs) + y[1] <- coef[1] + err[1] + for(i in 2:nobs) + y[i] <- coef[1] + coef[2] * y[i-1] + err[i] + x <- c(0, y[1:(nobs-1)]) + } + return(data.frame(y = y, x = x)) + } R> R> R> ################################################### R> ### chunk number 3: simpower R> ################################################### R> simpower <- function(nrep = 100, size = 0.05, ...) + { + pval <- matrix(rep(NA, 2 * nrep), ncol = 2) + colnames(pval) <- c("dwtest", "bgtest") + for(i in 1:nrep) { + dat <- dgp(...) + pval[i,1] <- dwtest(y ~ x, data = dat, + alternative = "two.sided")$p.value + pval[i,2] <- bgtest(y ~ x, data = dat)$p.value + } + return(colMeans(pval < size)) + } R> R> R> ################################################### R> ### chunk number 4: simulation-function R> ################################################### R> simulation <- function(corr = c(0, 0.2, 0.4, 0.6, 0.8, + 0.9, 0.95, 0.99), nobs = c(15, 30, 50), + model = c("trend", "dynamic"), ...) + { + prs <- expand.grid(corr = corr, nobs = nobs, model = model) + nprs <- nrow(prs) + + pow <- matrix(rep(NA, 2 * nprs), ncol = 2) + for(i in 1:nprs) pow[i,] <- simpower(corr = prs[i,1], + nobs = prs[i,2], model = as.character(prs[i,3]), ...) + + rval <- rbind(prs, prs) + rval$test <- factor(rep(1:2, c(nprs, nprs)), + labels = c("dwtest", "bgtest")) + rval$power <- c(pow[,1], pow[,2]) + rval$nobs <- factor(rval$nobs) + return(rval) + } R> R> R> ################################################### R> ### chunk number 5: simulation R> ################################################### R> set.seed(123) R> psim <- simulation() R> R> R> ################################################### R> ### chunk number 6: simulation-table R> ################################################### R> tab <- xtabs(power ~ corr + test + model + nobs, data = psim) R> ftable(tab, row.vars = c("model", "nobs", "test"), + col.vars = "corr") corr 0 0.2 0.4 0.6 0.8 0.9 0.95 0.99 model nobs test trend 15 dwtest 0.05 0.10 0.21 0.36 0.55 0.65 0.66 0.62 bgtest 0.07 0.05 0.05 0.10 0.30 0.40 0.41 0.31 30 dwtest 0.09 0.20 0.57 0.80 0.96 1.00 0.96 0.98 bgtest 0.09 0.09 0.37 0.69 0.93 0.99 0.94 0.93 50 dwtest 0.03 0.31 0.76 0.99 1.00 1.00 1.00 1.00 bgtest 0.05 0.23 0.63 0.95 1.00 1.00 1.00 1.00 dynamic 15 dwtest 0.02 0.01 0.00 0.00 0.01 0.03 0.01 0.00 bgtest 0.07 0.04 0.01 0.09 0.14 0.21 0.17 0.26 30 dwtest 0.00 0.01 0.01 0.06 0.00 0.03 0.03 0.19 bgtest 0.05 0.05 0.18 0.39 0.52 0.63 0.64 0.74 50 dwtest 0.02 0.02 0.01 0.03 0.03 0.15 0.39 0.56 bgtest 0.05 0.10 0.36 0.72 0.91 0.90 0.93 0.91 R> R> R> ################################################### R> ### chunk number 7: simulation-visualization R> ################################################### R> library("lattice") R> xyplot(power ~ corr | model + nobs, groups = ~ test, + data = psim, type = "b") R> R> R> ################################################### R> ### chunk number 8: simulation-visualization1 R> ################################################### R> library("lattice") R> trellis.par.set(theme = canonical.theme(color = FALSE)) R> print(xyplot(power ~ corr | model + nobs, groups = ~ test, data = psim, type = "b")) R> R> R> ################################################### R> ### chunk number 9: journals-lm R> ################################################### R> data("Journals") R> journals <- Journals[, c("subs", "price")] R> journals$citeprice <- Journals$price/Journals$citations R> jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) R> R> R> ################################################### R> ### chunk number 10: journals-residuals-based-resampling-unused eval=FALSE R> ################################################### R> ## refit <- function(data, i) { R> ## d <- data R> ## d$subs <- exp(d$fitted + d$res[i]) R> ## coef(lm(log(subs) ~ log(citeprice), data = d)) R> ## } R> R> R> ################################################### R> ### chunk number 11: journals-case-based-resampling R> ################################################### R> refit <- function(data, i) + coef(lm(log(subs) ~ log(citeprice), data = data[i,])) R> R> R> ################################################### R> ### chunk number 12: journals-boot R> ################################################### R> library("boot") Attaching package: 'boot' The following object is masked from 'package:lattice': melanoma The following object is masked from 'package:survival': aml The following object is masked from 'package:car': logit R> set.seed(123) R> jour_boot <- boot(journals, refit, R = 999) R> R> R> ################################################### R> ### chunk number 13: journals-boot-print R> ################################################### R> jour_boot ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = journals, statistic = refit, R = 999) Bootstrap Statistics : original bias std. error t1* 4.7662 -0.0010560 0.05545 t2* -0.5331 -0.0001606 0.03304 R> R> R> ################################################### R> ### chunk number 14: journals-lm-coeftest R> ################################################### R> coeftest(jour_lm) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.7662 0.0559 85.2 <2e-16 log(citeprice) -0.5331 0.0356 -15.0 <2e-16 R> R> R> ################################################### R> ### chunk number 15: journals-boot-ci R> ################################################### R> boot.ci(jour_boot, index = 2, type = "basic") BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 999 bootstrap replicates CALL : boot.ci(boot.out = jour_boot, type = "basic", index = 2) Intervals : Level Basic 95% (-0.5952, -0.4665 ) Calculations and Intervals on Original Scale R> R> R> ################################################### R> ### chunk number 16: journals-lm-ci R> ################################################### R> confint(jour_lm, parm = 2) 2.5 % 97.5 % log(citeprice) -0.6033 -0.4628 R> R> R> ################################################### R> ### chunk number 17: ml-loglik R> ################################################### R> data("Equipment", package = "AER") R> R> nlogL <- function(par) { + beta <- par[1:3] + theta <- par[4] + sigma2 <- par[5] + + Y <- with(Equipment, valueadded/firms) + K <- with(Equipment, capital/firms) + L <- with(Equipment, labor/firms) + + rhs <- beta[1] + beta[2] * log(K) + beta[3] * log(L) + lhs <- log(Y) + theta * Y + + rval <- sum(log(1 + theta * Y) - log(Y) + + dnorm(lhs, mean = rhs, sd = sqrt(sigma2), log = TRUE)) + return(-rval) + } R> R> R> ################################################### R> ### chunk number 18: ml-0 R> ################################################### R> fm0 <- lm(log(valueadded/firms) ~ log(capital/firms) + + log(labor/firms), data = Equipment) R> R> R> ################################################### R> ### chunk number 19: ml-0-coef R> ################################################### R> par0 <- as.vector(c(coef(fm0), 0, mean(residuals(fm0)^2))) R> R> R> ################################################### R> ### chunk number 20: ml-optim R> ################################################### R> opt <- optim(par0, nlogL, hessian = TRUE) Warning messages: 1: In log(1 + theta * Y) : NaNs produced 2: In sqrt(sigma2) : NaNs produced R> R> R> ################################################### R> ### chunk number 21: ml-optim-output R> ################################################### R> opt$par [1] 2.91469 0.34998 1.09232 0.10666 0.04275 R> sqrt(diag(solve(opt$hessian)))[1:4] [1] 0.36055 0.09671 0.14079 0.05850 R> -opt$value [1] -8.939 R> R> R> ################################################### R> ### chunk number 22: Sweave eval=FALSE R> ################################################### R> ## Sweave("Sweave-journals.Rnw") R> R> R> ################################################### R> ### chunk number 23: Stangle eval=FALSE R> ################################################### R> ## Stangle("Sweave-journals.Rnw") R> R> R> ################################################### R> ### chunk number 24: texi2dvi eval=FALSE R> ################################################### R> ## texi2dvi("Sweave-journals.tex", pdf = TRUE) R> R> R> ################################################### R> ### chunk number 25: vignette eval=FALSE R> ################################################### R> ## vignette("Sweave-journals", package = "AER") R> R> R> > proc.time() user system elapsed 14.829 0.073 14.896 AER/tests/Ch-TimeSeries.Rout.save0000644000176200001440000004414613616365055016257 0ustar liggesusers R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: options R> ################################################### R> options(digits = 6) R> R> R> ################################################### R> ### chunk number 3: ts-plot eval=FALSE R> ################################################### R> ## data("UKNonDurables") R> ## plot(UKNonDurables) R> R> R> ################################################### R> ### chunk number 4: UKNonDurables-data R> ################################################### R> data("UKNonDurables") R> R> R> ################################################### R> ### chunk number 5: tsp R> ################################################### R> tsp(UKNonDurables) [1] 1955.00 1988.75 4.00 R> R> R> ################################################### R> ### chunk number 6: window R> ################################################### R> window(UKNonDurables, end = c(1956, 4)) Qtr1 Qtr2 Qtr3 Qtr4 1955 24030 25620 26209 27167 1956 24620 25972 26285 27659 R> R> R> ################################################### R> ### chunk number 7: filter eval=FALSE R> ################################################### R> ## data("UKDriverDeaths") R> ## plot(UKDriverDeaths) R> ## lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), R> ## col = 2) R> R> R> ################################################### R> ### chunk number 8: ts-plot1 R> ################################################### R> data("UKNonDurables") R> plot(UKNonDurables) R> data("UKDriverDeaths") R> plot(UKDriverDeaths) R> lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), + col = 2) R> R> R> ################################################### R> ### chunk number 9: filter1 eval=FALSE R> ################################################### R> ## data("UKDriverDeaths") R> ## plot(UKDriverDeaths) R> ## lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), R> ## col = 2) R> R> R> ################################################### R> ### chunk number 10: rollapply R> ################################################### R> plot(rollapply(UKDriverDeaths, 12, sd)) R> R> R> ################################################### R> ### chunk number 11: ar-sim R> ################################################### R> set.seed(1234) R> x <- filter(rnorm(100), 0.9, method = "recursive") R> R> R> ################################################### R> ### chunk number 12: decompose R> ################################################### R> dd_dec <- decompose(log(UKDriverDeaths)) R> dd_stl <- stl(log(UKDriverDeaths), s.window = 13) R> R> R> ################################################### R> ### chunk number 13: decompose-components R> ################################################### R> plot(dd_dec$trend, ylab = "trend") R> lines(dd_stl$time.series[,"trend"], lty = 2, lwd = 2) R> R> R> ################################################### R> ### chunk number 14: seat-mean-sd R> ################################################### R> plot(dd_dec$trend, ylab = "trend") R> lines(dd_stl$time.series[,"trend"], lty = 2, lwd = 2) R> plot(rollapply(UKDriverDeaths, 12, sd)) R> R> R> ################################################### R> ### chunk number 15: stl R> ################################################### R> plot(dd_stl) R> R> R> ################################################### R> ### chunk number 16: Holt-Winters R> ################################################### R> dd_past <- window(UKDriverDeaths, end = c(1982, 12)) R> ## dd_hw <- try(HoltWinters(dd_past)) ## IGNORE_RDIFF, excluded due to small numeric deviations on different platforms R> ## if(!inherits(dd_hw, "try-error")) { R> ## dd_pred <- predict(dd_hw, n.ahead = 24) R> ## R> ## R> ## ################################################### R> ## ### chunk number 17: Holt-Winters-plot R> ## ################################################### R> ## plot(dd_hw, dd_pred, ylim = range(UKDriverDeaths)) R> ## lines(UKDriverDeaths) R> ## R> ## R> ## ################################################### R> ## ### chunk number 18: Holt-Winters-plot1 R> ## ################################################### R> ## plot(dd_hw, dd_pred, ylim = range(UKDriverDeaths)) R> ## lines(UKDriverDeaths) R> ## } R> R> ################################################### R> ### chunk number 19: acf eval=FALSE R> ################################################### R> ## acf(x) R> ## pacf(x) R> R> R> ################################################### R> ### chunk number 20: acf1 R> ################################################### R> acf(x, ylim = c(-0.2, 1)) R> pacf(x, ylim = c(-0.2, 1)) R> R> R> ################################################### R> ### chunk number 21: ar R> ################################################### R> ar(x) Call: ar(x = x) Coefficients: 1 0.928 Order selected 1 sigma^2 estimated as 1.29 R> R> R> ################################################### R> ### chunk number 22: window-non-durab R> ################################################### R> nd <- window(log(UKNonDurables), end = c(1970, 4)) R> R> R> ################################################### R> ### chunk number 23: non-durab-acf R> ################################################### R> acf(diff(nd), ylim = c(-1, 1)) R> pacf(diff(nd), ylim = c(-1, 1)) R> acf(diff(diff(nd, 4)), ylim = c(-1, 1)) R> pacf(diff(diff(nd, 4)), ylim = c(-1, 1)) R> R> R> ################################################### R> ### chunk number 24: non-durab-acf1 R> ################################################### R> acf(diff(nd), ylim = c(-1, 1)) R> pacf(diff(nd), ylim = c(-1, 1)) R> acf(diff(diff(nd, 4)), ylim = c(-1, 1)) R> pacf(diff(diff(nd, 4)), ylim = c(-1, 1)) R> R> R> ################################################### R> ### chunk number 25: arima-setup R> ################################################### R> nd_pars <- expand.grid(ar = 0:2, diff = 1, ma = 0:2, + sar = 0:1, sdiff = 1, sma = 0:1) R> nd_aic <- rep(0, nrow(nd_pars)) R> for(i in seq(along = nd_aic)) nd_aic[i] <- AIC(arima(nd, + unlist(nd_pars[i, 1:3]), unlist(nd_pars[i, 4:6])), + k = log(length(nd))) R> nd_pars[which.min(nd_aic),] ar diff ma sar sdiff sma 22 0 1 1 0 1 1 R> R> R> ################################################### R> ### chunk number 26: arima R> ################################################### R> nd_arima <- arima(nd, order = c(0,1,1), seasonal = c(0,1,1)) R> nd_arima Call: arima(x = nd, order = c(0, 1, 1), seasonal = c(0, 1, 1)) Coefficients: ma1 sma1 -0.353 -0.583 s.e. 0.143 0.138 sigma^2 estimated as 9.65e-05: log likelihood = 188.14, aic = -370.27 R> R> R> ################################################### R> ### chunk number 27: tsdiag R> ################################################### R> tsdiag(nd_arima) R> R> R> ################################################### R> ### chunk number 28: tsdiag1 R> ################################################### R> tsdiag(nd_arima) R> R> R> ################################################### R> ### chunk number 29: arima-predict R> ################################################### R> nd_pred <- predict(nd_arima, n.ahead = 18 * 4) R> R> R> ################################################### R> ### chunk number 30: arima-compare R> ################################################### R> plot(log(UKNonDurables)) R> lines(nd_pred$pred, col = 2) R> R> R> ################################################### R> ### chunk number 31: arima-compare1 R> ################################################### R> plot(log(UKNonDurables)) R> lines(nd_pred$pred, col = 2) R> R> R> ################################################### R> ### chunk number 32: pepper R> ################################################### R> data("PepperPrice") R> plot(PepperPrice, plot.type = "single", col = 1:2) R> legend("topleft", c("black", "white"), bty = "n", + col = 1:2, lty = rep(1,2)) R> R> R> ################################################### R> ### chunk number 33: pepper1 R> ################################################### R> data("PepperPrice") R> plot(PepperPrice, plot.type = "single", col = 1:2) R> legend("topleft", c("black", "white"), bty = "n", + col = 1:2, lty = rep(1,2)) R> R> R> ################################################### R> ### chunk number 34: adf1 R> ################################################### R> library("tseries") R> adf.test(log(PepperPrice[, "white"])) Augmented Dickey-Fuller Test data: log(PepperPrice[, "white"]) Dickey-Fuller = -1.744, Lag order = 6, p-value = 0.684 alternative hypothesis: stationary R> R> R> ################################################### R> ### chunk number 35: adf1 R> ################################################### R> adf.test(diff(log(PepperPrice[, "white"]))) Augmented Dickey-Fuller Test data: diff(log(PepperPrice[, "white"])) Dickey-Fuller = -5.336, Lag order = 6, p-value = 0.01 alternative hypothesis: stationary Warning message: In adf.test(diff(log(PepperPrice[, "white"]))) : p-value smaller than printed p-value R> R> R> ################################################### R> ### chunk number 36: pp R> ################################################### R> pp.test(log(PepperPrice[, "white"]), type = "Z(t_alpha)") Phillips-Perron Unit Root Test data: log(PepperPrice[, "white"]) Dickey-Fuller Z(t_alpha) = -1.644, Truncation lag parameter = 5, p-value = 0.726 alternative hypothesis: stationary R> R> R> ################################################### R> ### chunk number 37: urca eval=FALSE R> ################################################### R> ## library("urca") R> ## pepper_ers <- ur.ers(log(PepperPrice[, "white"]), R> ## type = "DF-GLS", model = "const", lag.max = 4) R> ## summary(pepper_ers) R> R> R> ################################################### R> ### chunk number 38: kpss R> ################################################### R> kpss.test(log(PepperPrice[, "white"])) KPSS Test for Level Stationarity data: log(PepperPrice[, "white"]) KPSS Level = 0.6173, Truncation lag parameter = 5, p-value = 0.0211 R> R> R> ################################################### R> ### chunk number 39: po R> ################################################### R> po.test(log(PepperPrice)) Phillips-Ouliaris Cointegration Test data: log(PepperPrice) Phillips-Ouliaris demeaned = -24.1, Truncation lag parameter = 2, p-value = 0.024 R> R> R> ################################################### R> ### chunk number 40: joh-trace R> ################################################### R> library("urca") R> pepper_jo <- ca.jo(log(PepperPrice), ecdet = "const", + type = "trace") R> ## summary(pepper_jo) ## IGNORE_RDIFF, excluded due to small numeric deviations on different platforms R> R> R> ################################################### R> ### chunk number 41: joh-lmax eval=FALSE R> ################################################### R> ## pepper_jo2 <- ca.jo(log(PepperPrice), ecdet = "const", type = "eigen") R> ## summary(pepper_jo2) R> R> R> ################################################### R> ### chunk number 42: dynlm-by-hand R> ################################################### R> dd <- log(UKDriverDeaths) R> dd_dat <- ts.intersect(dd, dd1 = lag(dd, k = -1), + dd12 = lag(dd, k = -12)) R> lm(dd ~ dd1 + dd12, data = dd_dat) Call: lm(formula = dd ~ dd1 + dd12, data = dd_dat) Coefficients: (Intercept) dd1 dd12 0.421 0.431 0.511 R> R> R> ################################################### R> ### chunk number 43: dynlm R> ################################################### R> library("dynlm") R> dynlm(dd ~ L(dd) + L(dd, 12)) Time series regression with "ts" data: Start = 1970(1), End = 1984(12) Call: dynlm(formula = dd ~ L(dd) + L(dd, 12)) Coefficients: (Intercept) L(dd) L(dd, 12) 0.421 0.431 0.511 R> R> R> ################################################### R> ### chunk number 44: efp R> ################################################### R> library("strucchange") R> dd_ocus <- efp(dd ~ dd1 + dd12, data = dd_dat, + type = "OLS-CUSUM") R> R> R> ################################################### R> ### chunk number 45: efp-test R> ################################################### R> sctest(dd_ocus) OLS-based CUSUM test data: dd_ocus S0 = 1.487, p-value = 0.0241 R> R> R> ################################################### R> ### chunk number 46: efp-plot eval=FALSE R> ################################################### R> ## plot(dd_ocus) R> R> R> ################################################### R> ### chunk number 47: Fstats R> ################################################### R> dd_fs <- Fstats(dd ~ dd1 + dd12, data = dd_dat, from = 0.1) R> plot(dd_fs) R> sctest(dd_fs) supF test data: dd_fs sup.F = 19.33, p-value = 0.00672 R> R> R> ################################################### R> ### chunk number 48: ocus-supF R> ################################################### R> plot(dd_ocus) R> plot(dd_fs, main = "supF test") R> R> R> ################################################### R> ### chunk number 49: GermanM1 R> ################################################### R> data("GermanM1") R> LTW <- dm ~ dy2 + dR + dR1 + dp + m1 + y1 + R1 + season R> R> R> ################################################### R> ### chunk number 50: re eval=FALSE R> ################################################### R> ## m1_re <- efp(LTW, data = GermanM1, type = "RE") R> ## plot(m1_re) R> R> R> ################################################### R> ### chunk number 51: re1 R> ################################################### R> m1_re <- efp(LTW, data = GermanM1, type = "RE") R> plot(m1_re) R> R> R> ################################################### R> ### chunk number 52: dating R> ################################################### R> dd_bp <- breakpoints(dd ~ dd1 + dd12, data = dd_dat, h = 0.1) R> R> R> ################################################### R> ### chunk number 53: dating-coef R> ################################################### R> coef(dd_bp, breaks = 2) (Intercept) dd1 dd12 1970(1) - 1973(10) 1.45776 0.117323 0.694480 1973(11) - 1983(1) 1.53421 0.218214 0.572330 1983(2) - 1984(12) 1.68690 0.548609 0.214166 R> R> R> ################################################### R> ### chunk number 54: dating-plot eval=FALSE R> ################################################### R> ## plot(dd) R> ## lines(fitted(dd_bp, breaks = 2), col = 4) R> ## lines(confint(dd_bp, breaks = 2)) R> R> R> ################################################### R> ### chunk number 55: dating-plot1 R> ################################################### R> plot(dd_bp, legend = FALSE, main = "") R> plot(dd) R> lines(fitted(dd_bp, breaks = 2), col = 4) R> lines(confint(dd_bp, breaks = 2)) R> R> R> ################################################### R> ### chunk number 56: StructTS R> ################################################### R> dd_struct <- StructTS(log(UKDriverDeaths)) R> R> R> ################################################### R> ### chunk number 57: StructTS-plot eval=FALSE R> ################################################### R> ## plot(cbind(fitted(dd_struct), residuals(dd_struct))) R> R> R> ################################################### R> ### chunk number 58: StructTS-plot1 R> ################################################### R> dd_struct_plot <- cbind(fitted(dd_struct), residuals = residuals(dd_struct)) R> colnames(dd_struct_plot) <- c("level", "slope", "season", "residuals") R> plot(dd_struct_plot, main = "") R> R> R> ################################################### R> ### chunk number 59: garch-plot R> ################################################### R> data("MarkPound") R> plot(MarkPound, main = "") R> R> R> ################################################### R> ### chunk number 60: garch R> ################################################### R> data("MarkPound") R> mp <- garch(MarkPound, grad = "numerical", trace = FALSE) R> summary(mp) Call: garch(x = MarkPound, grad = "numerical", trace = FALSE) Model: GARCH(1,1) Residuals: Min 1Q Median 3Q Max -6.79739 -0.53703 -0.00264 0.55233 5.24867 Coefficient(s): Estimate Std. Error t value Pr(>|t|) a0 0.0109 0.0013 8.38 <2e-16 a1 0.1546 0.0139 11.14 <2e-16 b1 0.8044 0.0160 50.13 <2e-16 Diagnostic Tests: Jarque Bera Test data: Residuals X-squared = 1060, df = 2, p-value <2e-16 Box-Ljung test data: Squared.Residuals X-squared = 2.478, df = 1, p-value = 0.115 R> R> R> > proc.time() user system elapsed 4.372 2.273 3.386 AER/tests/Ch-Basics.R0000644000176200001440000005175513457447330013731 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: calc1 ################################################### 1 + 1 2^3 ################################################### ### chunk number 3: calc2 ################################################### log(exp(sin(pi/4)^2) * exp(cos(pi/4)^2)) ################################################### ### chunk number 4: vec1 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) ################################################### ### chunk number 5: length ################################################### length(x) ################################################### ### chunk number 6: vec2 ################################################### 2 * x + 3 5:1 * x + 1:5 ################################################### ### chunk number 7: vec3 ################################################### log(x) ################################################### ### chunk number 8: subset1 ################################################### x[c(1, 4)] ################################################### ### chunk number 9: subset2 ################################################### x[-c(2, 3, 5)] ################################################### ### chunk number 10: pattern1 ################################################### ones <- rep(1, 10) even <- seq(from = 2, to = 20, by = 2) trend <- 1981:2005 ################################################### ### chunk number 11: pattern2 ################################################### c(ones, even) ################################################### ### chunk number 12: matrix1 ################################################### A <- matrix(1:6, nrow = 2) ################################################### ### chunk number 13: matrix2 ################################################### t(A) ################################################### ### chunk number 14: matrix3 ################################################### dim(A) nrow(A) ncol(A) ################################################### ### chunk number 15: matrix-subset ################################################### A1 <- A[1:2, c(1, 3)] ################################################### ### chunk number 16: matrix4 ################################################### solve(A1) ################################################### ### chunk number 17: matrix-solve ################################################### A1 %*% solve(A1) ################################################### ### chunk number 18: diag ################################################### diag(4) ################################################### ### chunk number 19: matrix-combine1 ################################################### cbind(1, A1) ################################################### ### chunk number 20: matrix-combine2 ################################################### rbind(A1, diag(4, 2)) ################################################### ### chunk number 21: vector-mode ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) ################################################### ### chunk number 22: logical ################################################### x > 3.5 ################################################### ### chunk number 23: names ################################################### names(x) <- c("a", "b", "c", "d", "e") x ################################################### ### chunk number 24: subset-more ################################################### x[3:5] x[c("c", "d", "e")] x[x > 3.5] ################################################### ### chunk number 25: list1 ################################################### mylist <- list(sample = rnorm(5), family = "normal distribution", parameters = list(mean = 0, sd = 1)) mylist ################################################### ### chunk number 26: list2 ################################################### mylist[[1]] mylist[["sample"]] mylist$sample ################################################### ### chunk number 27: list3 ################################################### mylist[[3]]$sd ################################################### ### chunk number 28: logical2 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) x > 3 & x <= 4 ################################################### ### chunk number 29: logical3 ################################################### which(x > 3 & x <= 4) ################################################### ### chunk number 30: logical4 ################################################### all(x > 3) any(x > 3) ################################################### ### chunk number 31: logical5 ################################################### (1.5 - 0.5) == 1 (1.9 - 0.9) == 1 ################################################### ### chunk number 32: logical6 ################################################### all.equal(1.9 - 0.9, 1) ################################################### ### chunk number 33: logical7 ################################################### 7 + TRUE ################################################### ### chunk number 34: coercion1 ################################################### is.numeric(x) is.character(x) as.character(x) ################################################### ### chunk number 35: coercion2 ################################################### c(1, "a") ################################################### ### chunk number 36: rng1 ################################################### set.seed(123) rnorm(2) rnorm(2) set.seed(123) rnorm(2) ################################################### ### chunk number 37: rng2 ################################################### sample(1:5) sample(c("male", "female"), size = 5, replace = TRUE, prob = c(0.2, 0.8)) ################################################### ### chunk number 38: flow1 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) if(rnorm(1) > 0) sum(x) else mean(x) ################################################### ### chunk number 39: flow2 ################################################### ifelse(x > 4, sqrt(x), x^2) ################################################### ### chunk number 40: flow3 ################################################### for(i in 2:5) { x[i] <- x[i] - x[i-1] } x[-1] ################################################### ### chunk number 41: flow4 ################################################### while(sum(x) < 100) { x <- 2 * x } x ################################################### ### chunk number 42: cmeans ################################################### cmeans <- function(X) { rval <- rep(0, ncol(X)) for(j in 1:ncol(X)) { mysum <- 0 for(i in 1:nrow(X)) mysum <- mysum + X[i,j] rval[j] <- mysum/nrow(X) } return(rval) } ################################################### ### chunk number 43: colmeans1 ################################################### X <- matrix(1:20, ncol = 2) cmeans(X) ################################################### ### chunk number 44: colmeans2 ################################################### colMeans(X) ################################################### ### chunk number 45: colmeans3 eval=FALSE ################################################### ## X <- matrix(rnorm(2*10^6), ncol = 2) ## system.time(colMeans(X)) ## system.time(cmeans(X)) ################################################### ### chunk number 46: colmeans4 ################################################### cmeans2 <- function(X) { rval <- rep(0, ncol(X)) for(j in 1:ncol(X)) rval[j] <- mean(X[,j]) return(rval) } ################################################### ### chunk number 47: colmeans5 eval=FALSE ################################################### ## system.time(cmeans2(X)) ################################################### ### chunk number 48: colmeans6 eval=FALSE ################################################### ## apply(X, 2, mean) ################################################### ### chunk number 49: colmeans7 eval=FALSE ################################################### ## system.time(apply(X, 2, mean)) ################################################### ### chunk number 50: formula1 ################################################### f <- y ~ x class(f) ################################################### ### chunk number 51: formula2 ################################################### x <- seq(from = 0, to = 10, by = 0.5) y <- 2 + 3 * x + rnorm(21) ################################################### ### chunk number 52: formula3 eval=FALSE ################################################### ## plot(y ~ x) ## lm(y ~ x) ################################################### ### chunk number 53: formula3a ################################################### print(lm(y ~ x)) ################################################### ### chunk number 54: formula3b ################################################### plot(y ~ x) ################################################### ### chunk number 55: formula3c ################################################### fm <- lm(y ~ x) ################################################### ### chunk number 56: mydata1 ################################################### mydata <- data.frame(one = 1:10, two = 11:20, three = 21:30) ################################################### ### chunk number 57: mydata1a ################################################### mydata <- as.data.frame(matrix(1:30, ncol = 3)) names(mydata) <- c("one", "two", "three") ################################################### ### chunk number 58: mydata2 ################################################### mydata$two mydata[, "two"] mydata[, 2] ################################################### ### chunk number 59: attach ################################################### attach(mydata) mean(two) detach(mydata) ################################################### ### chunk number 60: with ################################################### with(mydata, mean(two)) ################################################### ### chunk number 61: mydata-subset ################################################### mydata.sub <- subset(mydata, two <= 16, select = -two) ################################################### ### chunk number 62: write-table ################################################### write.table(mydata, file = "mydata.txt", col.names = TRUE) ################################################### ### chunk number 63: read-table ################################################### newdata <- read.table("mydata.txt", header = TRUE) ################################################### ### chunk number 64: save ################################################### save(mydata, file = "mydata.rda") ################################################### ### chunk number 65: load ################################################### load("mydata.rda") ################################################### ### chunk number 66: file-remove ################################################### file.remove("mydata.rda") ################################################### ### chunk number 67: data ################################################### data("Journals", package = "AER") ################################################### ### chunk number 68: foreign ################################################### library("foreign") write.dta(mydata, file = "mydata.dta") ################################################### ### chunk number 69: read-dta ################################################### mydata <- read.dta("mydata.dta") ################################################### ### chunk number 70: cleanup ################################################### file.remove("mydata.dta") ################################################### ### chunk number 71: factor ################################################### g <- rep(0:1, c(2, 4)) g <- factor(g, levels = 0:1, labels = c("male", "female")) g ################################################### ### chunk number 72: na1 ################################################### newdata <- read.table("mydata.txt", na.strings = "-999") ################################################### ### chunk number 73: na2 ################################################### file.remove("mydata.txt") ################################################### ### chunk number 74: oop1 ################################################### x <- c(1.8, 3.14, 4, 88.169, 13) g <- factor(rep(c(0, 1), c(2, 4)), levels = c(0, 1), labels = c("male", "female")) ################################################### ### chunk number 75: oop2 ################################################### summary(x) summary(g) ################################################### ### chunk number 76: oop3 ################################################### class(x) class(g) ################################################### ### chunk number 77: oop4 ################################################### summary ################################################### ### chunk number 78: oop5 ################################################### normsample <- function(n, ...) { rval <- rnorm(n, ...) class(rval) <- "normsample" return(rval) } ################################################### ### chunk number 79: oop6 ################################################### set.seed(123) x <- normsample(10, mean = 5) class(x) ################################################### ### chunk number 80: oop7 ################################################### summary.normsample <- function(object, ...) { rval <- c(length(object), mean(object), sd(object)) names(rval) <- c("sample size","mean","standard deviation") return(rval) } ################################################### ### chunk number 81: oop8 ################################################### summary(x) ################################################### ### chunk number 82: journals-data eval=FALSE ################################################### ## data("Journals") ## Journals$citeprice <- Journals$price/Journals$citations ## attach(Journals) ## plot(log(subs), log(citeprice)) ## rug(log(subs)) ## rug(log(citeprice), side = 2) ## detach(Journals) ################################################### ### chunk number 83: journals-data1 ################################################### data("Journals") Journals$citeprice <- Journals$price/Journals$citations attach(Journals) plot(log(subs), log(citeprice)) rug(log(subs)) rug(log(citeprice), side = 2) detach(Journals) ################################################### ### chunk number 84: plot-formula ################################################### plot(log(subs) ~ log(citeprice), data = Journals) ################################################### ### chunk number 85: graphics1 ################################################### plot(log(subs) ~ log(citeprice), data = Journals, pch = 20, col = "blue", ylim = c(0, 8), xlim = c(-7, 4), main = "Library subscriptions") ################################################### ### chunk number 86: graphics2 ################################################### pdf("myfile.pdf", height = 5, width = 6) plot(1:20, pch = 1:20, col = 1:20, cex = 2) dev.off() ################################################### ### chunk number 87: dnorm-annotate eval=FALSE ################################################### ## curve(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, ## main = "Density of the standard normal distribution") ## text(-5, 0.3, expression(f(x) == frac(1, sigma ~~ ## sqrt(2*pi)) ~~ e^{-frac((x - mu)^2, 2*sigma^2)}), adj = 0) ################################################### ### chunk number 88: dnorm-annotate1 ################################################### curve(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, main = "Density of the standard normal distribution") text(-5, 0.3, expression(f(x) == frac(1, sigma ~~ sqrt(2*pi)) ~~ e^{-frac((x - mu)^2, 2*sigma^2)}), adj = 0) ################################################### ### chunk number 89: eda1 ################################################### data("CPS1985") str(CPS1985) ################################################### ### chunk number 90: eda2 ################################################### head(CPS1985) ################################################### ### chunk number 91: eda3 ################################################### levels(CPS1985$occupation)[c(2, 6)] <- c("techn", "mgmt") attach(CPS1985) ################################################### ### chunk number 92: eda4 ################################################### summary(wage) ################################################### ### chunk number 93: eda5 ################################################### mean(wage) median(wage) ################################################### ### chunk number 94: eda6 ################################################### var(wage) sd(wage) ################################################### ### chunk number 95: wage-hist ################################################### hist(wage, freq = FALSE) hist(log(wage), freq = FALSE) lines(density(log(wage)), col = 4) ################################################### ### chunk number 96: wage-hist1 ################################################### hist(wage, freq = FALSE) hist(log(wage), freq = FALSE) lines(density(log(wage)), col = 4) ################################################### ### chunk number 97: occ-table ################################################### summary(occupation) ################################################### ### chunk number 98: occ-table ################################################### tab <- table(occupation) prop.table(tab) ################################################### ### chunk number 99: occ-barpie ################################################### barplot(tab) pie(tab) ################################################### ### chunk number 100: occ-barpie ################################################### par(mar = c(4, 3, 1, 1)) barplot(tab, las = 3) par(mar = c(2, 3, 1, 3)) pie(tab, radius = 1) ################################################### ### chunk number 101: xtabs ################################################### xtabs(~ gender + occupation, data = CPS1985) ################################################### ### chunk number 102: spine eval=FALSE ################################################### ## plot(gender ~ occupation, data = CPS1985) ################################################### ### chunk number 103: spine1 ################################################### plot(gender ~ occupation, data = CPS1985) ################################################### ### chunk number 104: wageeduc-cor ################################################### cor(log(wage), education) cor(log(wage), education, method = "spearman") ################################################### ### chunk number 105: wageeduc-scatter eval=FALSE ################################################### ## plot(log(wage) ~ education) ################################################### ### chunk number 106: wageeduc-scatter1 ################################################### plot(log(wage) ~ education) ################################################### ### chunk number 107: tapply ################################################### tapply(log(wage), gender, mean) ################################################### ### chunk number 108: boxqq1 eval=FALSE ################################################### ## plot(log(wage) ~ gender) ################################################### ### chunk number 109: boxqq2 eval=FALSE ################################################### ## mwage <- subset(CPS1985, gender == "male")$wage ## fwage <- subset(CPS1985, gender == "female")$wage ## qqplot(mwage, fwage, xlim = range(wage), ylim = range(wage), ## xaxs = "i", yaxs = "i", xlab = "male", ylab = "female") ## abline(0, 1) ################################################### ### chunk number 110: qq ################################################### plot(log(wage) ~ gender) mwage <- subset(CPS1985, gender == "male")$wage fwage <- subset(CPS1985, gender == "female")$wage qqplot(mwage, fwage, xlim = range(wage), ylim = range(wage), xaxs = "i", yaxs = "i", xlab = "male", ylab = "female") abline(0, 1) ################################################### ### chunk number 111: detach ################################################### detach(CPS1985) AER/tests/Ch-Intro.R0000644000176200001440000001446413517437356013620 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: journals-data ################################################### data("Journals", package = "AER") ################################################### ### chunk number 3: journals-dim ################################################### dim(Journals) names(Journals) ################################################### ### chunk number 4: journals-plot eval=FALSE ################################################### ## plot(log(subs) ~ log(price/citations), data = Journals) ################################################### ### chunk number 5: journals-lm eval=FALSE ################################################### ## j_lm <- lm(log(subs) ~ log(price/citations), data = Journals) ## abline(j_lm) ################################################### ### chunk number 6: journals-lmplot ################################################### plot(log(subs) ~ log(price/citations), data = Journals) j_lm <- lm(log(subs) ~ log(price/citations), data = Journals) abline(j_lm) ################################################### ### chunk number 7: journals-lm-summary ################################################### summary(j_lm) ################################################### ### chunk number 8: cps-data ################################################### data("CPS1985", package = "AER") cps <- CPS1985 ################################################### ### chunk number 9: cps-data1 eval=FALSE ################################################### ## data("CPS1985", package = "AER") ## cps <- CPS1985 ################################################### ### chunk number 10: cps-reg ################################################### library("quantreg") cps_lm <- lm(log(wage) ~ experience + I(experience^2) + education, data = cps) cps_rq <- rq(log(wage) ~ experience + I(experience^2) + education, data = cps, tau = seq(0.2, 0.8, by = 0.15)) ################################################### ### chunk number 11: cps-predict ################################################### cps2 <- data.frame(education = mean(cps$education), experience = min(cps$experience):max(cps$experience)) cps2 <- cbind(cps2, predict(cps_lm, newdata = cps2, interval = "prediction")) cps2 <- cbind(cps2, predict(cps_rq, newdata = cps2, type = "")) ################################################### ### chunk number 12: rq-plot eval=FALSE ################################################### ## plot(log(wage) ~ experience, data = cps) ## for(i in 6:10) lines(cps2[,i] ~ experience, ## data = cps2, col = "red") ################################################### ### chunk number 13: rq-plot1 ################################################### plot(log(wage) ~ experience, data = cps) for(i in 6:10) lines(cps2[,i] ~ experience, data = cps2, col = "red") ################################################### ### chunk number 14: srq-plot eval=FALSE ################################################### ## plot(summary(cps_rq)) ################################################### ### chunk number 15: srq-plot1 ################################################### try(plot(summary(cps_rq))) ################################################### ### chunk number 16: bkde-fit ################################################### library("KernSmooth") cps_bkde <- bkde2D(cbind(cps$experience, log(cps$wage)), bandwidth = c(3.5, 0.5), gridsize = c(200, 200)) ################################################### ### chunk number 17: bkde-plot eval=FALSE ################################################### ## image(cps_bkde$x1, cps_bkde$x2, cps_bkde$fhat, ## col = rev(gray.colors(10, gamma = 1)), ## xlab = "experience", ylab = "log(wage)") ## box() ## lines(fit ~ experience, data = cps2) ## lines(lwr ~ experience, data = cps2, lty = 2) ## lines(upr ~ experience, data = cps2, lty = 2) ################################################### ### chunk number 18: bkde-plot1 ################################################### image(cps_bkde$x1, cps_bkde$x2, cps_bkde$fhat, col = rev(gray.colors(10, gamma = 1)), xlab = "experience", ylab = "log(wage)") box() lines(fit ~ experience, data = cps2) lines(lwr ~ experience, data = cps2, lty = 2) lines(upr ~ experience, data = cps2, lty = 2) ################################################### ### chunk number 19: install eval=FALSE ################################################### ## install.packages("AER") ################################################### ### chunk number 20: library ################################################### library("AER") ################################################### ### chunk number 21: objects ################################################### objects() ################################################### ### chunk number 22: search ################################################### search() ################################################### ### chunk number 23: assignment ################################################### x <- 2 objects() ################################################### ### chunk number 24: remove ################################################### remove(x) objects() ################################################### ### chunk number 25: log eval=FALSE ################################################### ## log(16, 2) ## log(x = 16, 2) ## log(16, base = 2) ## log(base = 2, x = 16) ################################################### ### chunk number 26: q eval=FALSE ################################################### ## q() ################################################### ### chunk number 27: apropos ################################################### apropos("help") AER/tests/Ch-TimeSeries.R0000644000176200001440000003236713616353543014573 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: options ################################################### options(digits = 6) ################################################### ### chunk number 3: ts-plot eval=FALSE ################################################### ## data("UKNonDurables") ## plot(UKNonDurables) ################################################### ### chunk number 4: UKNonDurables-data ################################################### data("UKNonDurables") ################################################### ### chunk number 5: tsp ################################################### tsp(UKNonDurables) ################################################### ### chunk number 6: window ################################################### window(UKNonDurables, end = c(1956, 4)) ################################################### ### chunk number 7: filter eval=FALSE ################################################### ## data("UKDriverDeaths") ## plot(UKDriverDeaths) ## lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), ## col = 2) ################################################### ### chunk number 8: ts-plot1 ################################################### data("UKNonDurables") plot(UKNonDurables) data("UKDriverDeaths") plot(UKDriverDeaths) lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), col = 2) ################################################### ### chunk number 9: filter1 eval=FALSE ################################################### ## data("UKDriverDeaths") ## plot(UKDriverDeaths) ## lines(filter(UKDriverDeaths, c(1/2, rep(1, 11), 1/2)/12), ## col = 2) ################################################### ### chunk number 10: rollapply ################################################### plot(rollapply(UKDriverDeaths, 12, sd)) ################################################### ### chunk number 11: ar-sim ################################################### set.seed(1234) x <- filter(rnorm(100), 0.9, method = "recursive") ################################################### ### chunk number 12: decompose ################################################### dd_dec <- decompose(log(UKDriverDeaths)) dd_stl <- stl(log(UKDriverDeaths), s.window = 13) ################################################### ### chunk number 13: decompose-components ################################################### plot(dd_dec$trend, ylab = "trend") lines(dd_stl$time.series[,"trend"], lty = 2, lwd = 2) ################################################### ### chunk number 14: seat-mean-sd ################################################### plot(dd_dec$trend, ylab = "trend") lines(dd_stl$time.series[,"trend"], lty = 2, lwd = 2) plot(rollapply(UKDriverDeaths, 12, sd)) ################################################### ### chunk number 15: stl ################################################### plot(dd_stl) ################################################### ### chunk number 16: Holt-Winters ################################################### dd_past <- window(UKDriverDeaths, end = c(1982, 12)) ## dd_hw <- try(HoltWinters(dd_past)) ## IGNORE_RDIFF, excluded due to small numeric deviations on different platforms ## if(!inherits(dd_hw, "try-error")) { ## dd_pred <- predict(dd_hw, n.ahead = 24) ## ## ## ################################################### ## ### chunk number 17: Holt-Winters-plot ## ################################################### ## plot(dd_hw, dd_pred, ylim = range(UKDriverDeaths)) ## lines(UKDriverDeaths) ## ## ## ################################################### ## ### chunk number 18: Holt-Winters-plot1 ## ################################################### ## plot(dd_hw, dd_pred, ylim = range(UKDriverDeaths)) ## lines(UKDriverDeaths) ## } ################################################### ### chunk number 19: acf eval=FALSE ################################################### ## acf(x) ## pacf(x) ################################################### ### chunk number 20: acf1 ################################################### acf(x, ylim = c(-0.2, 1)) pacf(x, ylim = c(-0.2, 1)) ################################################### ### chunk number 21: ar ################################################### ar(x) ################################################### ### chunk number 22: window-non-durab ################################################### nd <- window(log(UKNonDurables), end = c(1970, 4)) ################################################### ### chunk number 23: non-durab-acf ################################################### acf(diff(nd), ylim = c(-1, 1)) pacf(diff(nd), ylim = c(-1, 1)) acf(diff(diff(nd, 4)), ylim = c(-1, 1)) pacf(diff(diff(nd, 4)), ylim = c(-1, 1)) ################################################### ### chunk number 24: non-durab-acf1 ################################################### acf(diff(nd), ylim = c(-1, 1)) pacf(diff(nd), ylim = c(-1, 1)) acf(diff(diff(nd, 4)), ylim = c(-1, 1)) pacf(diff(diff(nd, 4)), ylim = c(-1, 1)) ################################################### ### chunk number 25: arima-setup ################################################### nd_pars <- expand.grid(ar = 0:2, diff = 1, ma = 0:2, sar = 0:1, sdiff = 1, sma = 0:1) nd_aic <- rep(0, nrow(nd_pars)) for(i in seq(along = nd_aic)) nd_aic[i] <- AIC(arima(nd, unlist(nd_pars[i, 1:3]), unlist(nd_pars[i, 4:6])), k = log(length(nd))) nd_pars[which.min(nd_aic),] ################################################### ### chunk number 26: arima ################################################### nd_arima <- arima(nd, order = c(0,1,1), seasonal = c(0,1,1)) nd_arima ################################################### ### chunk number 27: tsdiag ################################################### tsdiag(nd_arima) ################################################### ### chunk number 28: tsdiag1 ################################################### tsdiag(nd_arima) ################################################### ### chunk number 29: arima-predict ################################################### nd_pred <- predict(nd_arima, n.ahead = 18 * 4) ################################################### ### chunk number 30: arima-compare ################################################### plot(log(UKNonDurables)) lines(nd_pred$pred, col = 2) ################################################### ### chunk number 31: arima-compare1 ################################################### plot(log(UKNonDurables)) lines(nd_pred$pred, col = 2) ################################################### ### chunk number 32: pepper ################################################### data("PepperPrice") plot(PepperPrice, plot.type = "single", col = 1:2) legend("topleft", c("black", "white"), bty = "n", col = 1:2, lty = rep(1,2)) ################################################### ### chunk number 33: pepper1 ################################################### data("PepperPrice") plot(PepperPrice, plot.type = "single", col = 1:2) legend("topleft", c("black", "white"), bty = "n", col = 1:2, lty = rep(1,2)) ################################################### ### chunk number 34: adf1 ################################################### library("tseries") adf.test(log(PepperPrice[, "white"])) ################################################### ### chunk number 35: adf1 ################################################### adf.test(diff(log(PepperPrice[, "white"]))) ################################################### ### chunk number 36: pp ################################################### pp.test(log(PepperPrice[, "white"]), type = "Z(t_alpha)") ################################################### ### chunk number 37: urca eval=FALSE ################################################### ## library("urca") ## pepper_ers <- ur.ers(log(PepperPrice[, "white"]), ## type = "DF-GLS", model = "const", lag.max = 4) ## summary(pepper_ers) ################################################### ### chunk number 38: kpss ################################################### kpss.test(log(PepperPrice[, "white"])) ################################################### ### chunk number 39: po ################################################### po.test(log(PepperPrice)) ################################################### ### chunk number 40: joh-trace ################################################### library("urca") pepper_jo <- ca.jo(log(PepperPrice), ecdet = "const", type = "trace") ## summary(pepper_jo) ## IGNORE_RDIFF, excluded due to small numeric deviations on different platforms ################################################### ### chunk number 41: joh-lmax eval=FALSE ################################################### ## pepper_jo2 <- ca.jo(log(PepperPrice), ecdet = "const", type = "eigen") ## summary(pepper_jo2) ################################################### ### chunk number 42: dynlm-by-hand ################################################### dd <- log(UKDriverDeaths) dd_dat <- ts.intersect(dd, dd1 = lag(dd, k = -1), dd12 = lag(dd, k = -12)) lm(dd ~ dd1 + dd12, data = dd_dat) ################################################### ### chunk number 43: dynlm ################################################### library("dynlm") dynlm(dd ~ L(dd) + L(dd, 12)) ################################################### ### chunk number 44: efp ################################################### library("strucchange") dd_ocus <- efp(dd ~ dd1 + dd12, data = dd_dat, type = "OLS-CUSUM") ################################################### ### chunk number 45: efp-test ################################################### sctest(dd_ocus) ################################################### ### chunk number 46: efp-plot eval=FALSE ################################################### ## plot(dd_ocus) ################################################### ### chunk number 47: Fstats ################################################### dd_fs <- Fstats(dd ~ dd1 + dd12, data = dd_dat, from = 0.1) plot(dd_fs) sctest(dd_fs) ################################################### ### chunk number 48: ocus-supF ################################################### plot(dd_ocus) plot(dd_fs, main = "supF test") ################################################### ### chunk number 49: GermanM1 ################################################### data("GermanM1") LTW <- dm ~ dy2 + dR + dR1 + dp + m1 + y1 + R1 + season ################################################### ### chunk number 50: re eval=FALSE ################################################### ## m1_re <- efp(LTW, data = GermanM1, type = "RE") ## plot(m1_re) ################################################### ### chunk number 51: re1 ################################################### m1_re <- efp(LTW, data = GermanM1, type = "RE") plot(m1_re) ################################################### ### chunk number 52: dating ################################################### dd_bp <- breakpoints(dd ~ dd1 + dd12, data = dd_dat, h = 0.1) ################################################### ### chunk number 53: dating-coef ################################################### coef(dd_bp, breaks = 2) ################################################### ### chunk number 54: dating-plot eval=FALSE ################################################### ## plot(dd) ## lines(fitted(dd_bp, breaks = 2), col = 4) ## lines(confint(dd_bp, breaks = 2)) ################################################### ### chunk number 55: dating-plot1 ################################################### plot(dd_bp, legend = FALSE, main = "") plot(dd) lines(fitted(dd_bp, breaks = 2), col = 4) lines(confint(dd_bp, breaks = 2)) ################################################### ### chunk number 56: StructTS ################################################### dd_struct <- StructTS(log(UKDriverDeaths)) ################################################### ### chunk number 57: StructTS-plot eval=FALSE ################################################### ## plot(cbind(fitted(dd_struct), residuals(dd_struct))) ################################################### ### chunk number 58: StructTS-plot1 ################################################### dd_struct_plot <- cbind(fitted(dd_struct), residuals = residuals(dd_struct)) colnames(dd_struct_plot) <- c("level", "slope", "season", "residuals") plot(dd_struct_plot, main = "") ################################################### ### chunk number 59: garch-plot ################################################### data("MarkPound") plot(MarkPound, main = "") ################################################### ### chunk number 60: garch ################################################### data("MarkPound") mp <- garch(MarkPound, grad = "numerical", trace = FALSE) summary(mp) AER/tests/Ch-Programming.R0000644000176200001440000001650213457447567015012 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: DGP ################################################### dgp <- function(nobs = 15, model = c("trend", "dynamic"), corr = 0, coef = c(0.25, -0.75), sd = 1) { model <- match.arg(model) coef <- rep(coef, length.out = 2) err <- as.vector(filter(rnorm(nobs, sd = sd), corr, method = "recursive")) if(model == "trend") { x <- 1:nobs y <- coef[1] + coef[2] * x + err } else { y <- rep(NA, nobs) y[1] <- coef[1] + err[1] for(i in 2:nobs) y[i] <- coef[1] + coef[2] * y[i-1] + err[i] x <- c(0, y[1:(nobs-1)]) } return(data.frame(y = y, x = x)) } ################################################### ### chunk number 3: simpower ################################################### simpower <- function(nrep = 100, size = 0.05, ...) { pval <- matrix(rep(NA, 2 * nrep), ncol = 2) colnames(pval) <- c("dwtest", "bgtest") for(i in 1:nrep) { dat <- dgp(...) pval[i,1] <- dwtest(y ~ x, data = dat, alternative = "two.sided")$p.value pval[i,2] <- bgtest(y ~ x, data = dat)$p.value } return(colMeans(pval < size)) } ################################################### ### chunk number 4: simulation-function ################################################### simulation <- function(corr = c(0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99), nobs = c(15, 30, 50), model = c("trend", "dynamic"), ...) { prs <- expand.grid(corr = corr, nobs = nobs, model = model) nprs <- nrow(prs) pow <- matrix(rep(NA, 2 * nprs), ncol = 2) for(i in 1:nprs) pow[i,] <- simpower(corr = prs[i,1], nobs = prs[i,2], model = as.character(prs[i,3]), ...) rval <- rbind(prs, prs) rval$test <- factor(rep(1:2, c(nprs, nprs)), labels = c("dwtest", "bgtest")) rval$power <- c(pow[,1], pow[,2]) rval$nobs <- factor(rval$nobs) return(rval) } ################################################### ### chunk number 5: simulation ################################################### set.seed(123) psim <- simulation() ################################################### ### chunk number 6: simulation-table ################################################### tab <- xtabs(power ~ corr + test + model + nobs, data = psim) ftable(tab, row.vars = c("model", "nobs", "test"), col.vars = "corr") ################################################### ### chunk number 7: simulation-visualization ################################################### library("lattice") xyplot(power ~ corr | model + nobs, groups = ~ test, data = psim, type = "b") ################################################### ### chunk number 8: simulation-visualization1 ################################################### library("lattice") trellis.par.set(theme = canonical.theme(color = FALSE)) print(xyplot(power ~ corr | model + nobs, groups = ~ test, data = psim, type = "b")) ################################################### ### chunk number 9: journals-lm ################################################### data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) ################################################### ### chunk number 10: journals-residuals-based-resampling-unused eval=FALSE ################################################### ## refit <- function(data, i) { ## d <- data ## d$subs <- exp(d$fitted + d$res[i]) ## coef(lm(log(subs) ~ log(citeprice), data = d)) ## } ################################################### ### chunk number 11: journals-case-based-resampling ################################################### refit <- function(data, i) coef(lm(log(subs) ~ log(citeprice), data = data[i,])) ################################################### ### chunk number 12: journals-boot ################################################### library("boot") set.seed(123) jour_boot <- boot(journals, refit, R = 999) ################################################### ### chunk number 13: journals-boot-print ################################################### jour_boot ################################################### ### chunk number 14: journals-lm-coeftest ################################################### coeftest(jour_lm) ################################################### ### chunk number 15: journals-boot-ci ################################################### boot.ci(jour_boot, index = 2, type = "basic") ################################################### ### chunk number 16: journals-lm-ci ################################################### confint(jour_lm, parm = 2) ################################################### ### chunk number 17: ml-loglik ################################################### data("Equipment", package = "AER") nlogL <- function(par) { beta <- par[1:3] theta <- par[4] sigma2 <- par[5] Y <- with(Equipment, valueadded/firms) K <- with(Equipment, capital/firms) L <- with(Equipment, labor/firms) rhs <- beta[1] + beta[2] * log(K) + beta[3] * log(L) lhs <- log(Y) + theta * Y rval <- sum(log(1 + theta * Y) - log(Y) + dnorm(lhs, mean = rhs, sd = sqrt(sigma2), log = TRUE)) return(-rval) } ################################################### ### chunk number 18: ml-0 ################################################### fm0 <- lm(log(valueadded/firms) ~ log(capital/firms) + log(labor/firms), data = Equipment) ################################################### ### chunk number 19: ml-0-coef ################################################### par0 <- as.vector(c(coef(fm0), 0, mean(residuals(fm0)^2))) ################################################### ### chunk number 20: ml-optim ################################################### opt <- optim(par0, nlogL, hessian = TRUE) ################################################### ### chunk number 21: ml-optim-output ################################################### opt$par sqrt(diag(solve(opt$hessian)))[1:4] -opt$value ################################################### ### chunk number 22: Sweave eval=FALSE ################################################### ## Sweave("Sweave-journals.Rnw") ################################################### ### chunk number 23: Stangle eval=FALSE ################################################### ## Stangle("Sweave-journals.Rnw") ################################################### ### chunk number 24: texi2dvi eval=FALSE ################################################### ## texi2dvi("Sweave-journals.tex", pdf = TRUE) ################################################### ### chunk number 25: vignette eval=FALSE ################################################### ## vignette("Sweave-journals", package = "AER") AER/tests/Ch-LinearRegression.Rout.save0000644000176200001440000010322413463423530017443 0ustar liggesusers R version 3.6.0 (2019-04-26) -- "Planting of a Tree" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: data-journals R> ################################################### R> data("Journals") R> journals <- Journals[, c("subs", "price")] R> journals$citeprice <- Journals$price/Journals$citations R> summary(journals) subs price citeprice Min. : 2 Min. : 20 Min. : 0.005 1st Qu.: 52 1st Qu.: 134 1st Qu.: 0.464 Median : 122 Median : 282 Median : 1.321 Mean : 197 Mean : 418 Mean : 2.548 3rd Qu.: 268 3rd Qu.: 541 3rd Qu.: 3.440 Max. :1098 Max. :2120 Max. :24.459 R> R> R> ################################################### R> ### chunk number 3: linreg-plot eval=FALSE R> ################################################### R> ## plot(log(subs) ~ log(citeprice), data = journals) R> ## jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) R> ## abline(jour_lm) R> R> R> ################################################### R> ### chunk number 4: linreg-plot1 R> ################################################### R> plot(log(subs) ~ log(citeprice), data = journals) R> jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) R> abline(jour_lm) R> R> R> ################################################### R> ### chunk number 5: linreg-class R> ################################################### R> class(jour_lm) [1] "lm" R> R> R> ################################################### R> ### chunk number 6: linreg-names R> ################################################### R> names(jour_lm) [1] "coefficients" "residuals" "effects" [4] "rank" "fitted.values" "assign" [7] "qr" "df.residual" "xlevels" [10] "call" "terms" "model" R> R> R> ################################################### R> ### chunk number 7: linreg-summary R> ################################################### R> summary(jour_lm) Call: lm(formula = log(subs) ~ log(citeprice), data = journals) Residuals: Min 1Q Median 3Q Max -2.7248 -0.5361 0.0372 0.4662 1.8481 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.7662 0.0559 85.2 <2e-16 log(citeprice) -0.5331 0.0356 -15.0 <2e-16 Residual standard error: 0.75 on 178 degrees of freedom Multiple R-squared: 0.557, Adjusted R-squared: 0.555 F-statistic: 224 on 1 and 178 DF, p-value: <2e-16 R> R> R> ################################################### R> ### chunk number 8: linreg-summary R> ################################################### R> jour_slm <- summary(jour_lm) R> class(jour_slm) [1] "summary.lm" R> names(jour_slm) [1] "call" "terms" "residuals" [4] "coefficients" "aliased" "sigma" [7] "df" "r.squared" "adj.r.squared" [10] "fstatistic" "cov.unscaled" R> R> R> ################################################### R> ### chunk number 9: linreg-coef R> ################################################### R> jour_slm$coefficients Estimate Std. Error t value Pr(>|t|) (Intercept) 4.7662 0.05591 85.25 2.954e-146 log(citeprice) -0.5331 0.03561 -14.97 2.564e-33 R> R> R> ################################################### R> ### chunk number 10: linreg-anova R> ################################################### R> anova(jour_lm) Analysis of Variance Table Response: log(subs) Df Sum Sq Mean Sq F value Pr(>F) log(citeprice) 1 126 125.9 224 <2e-16 Residuals 178 100 0.6 R> R> R> ################################################### R> ### chunk number 11: journals-coef R> ################################################### R> coef(jour_lm) (Intercept) log(citeprice) 4.7662 -0.5331 R> R> R> ################################################### R> ### chunk number 12: journals-confint R> ################################################### R> confint(jour_lm, level = 0.95) 2.5 % 97.5 % (Intercept) 4.6559 4.8765 log(citeprice) -0.6033 -0.4628 R> R> R> ################################################### R> ### chunk number 13: journals-predict R> ################################################### R> predict(jour_lm, newdata = data.frame(citeprice = 2.11), + interval = "confidence") fit lwr upr 1 4.368 4.247 4.489 R> predict(jour_lm, newdata = data.frame(citeprice = 2.11), + interval = "prediction") fit lwr upr 1 4.368 2.884 5.853 R> R> R> ################################################### R> ### chunk number 14: predict-plot eval=FALSE R> ################################################### R> ## lciteprice <- seq(from = -6, to = 4, by = 0.25) R> ## jour_pred <- predict(jour_lm, interval = "prediction", R> ## newdata = data.frame(citeprice = exp(lciteprice))) R> ## plot(log(subs) ~ log(citeprice), data = journals) R> ## lines(jour_pred[, 1] ~ lciteprice, col = 1) R> ## lines(jour_pred[, 2] ~ lciteprice, col = 1, lty = 2) R> ## lines(jour_pred[, 3] ~ lciteprice, col = 1, lty = 2) R> R> R> ################################################### R> ### chunk number 15: predict-plot1 R> ################################################### R> lciteprice <- seq(from = -6, to = 4, by = 0.25) R> jour_pred <- predict(jour_lm, interval = "prediction", + newdata = data.frame(citeprice = exp(lciteprice))) R> plot(log(subs) ~ log(citeprice), data = journals) R> lines(jour_pred[, 1] ~ lciteprice, col = 1) R> lines(jour_pred[, 2] ~ lciteprice, col = 1, lty = 2) R> lines(jour_pred[, 3] ~ lciteprice, col = 1, lty = 2) R> R> R> ################################################### R> ### chunk number 16: journals-plot eval=FALSE R> ################################################### R> ## par(mfrow = c(2, 2)) R> ## plot(jour_lm) R> ## par(mfrow = c(1, 1)) R> R> R> ################################################### R> ### chunk number 17: journals-plot1 R> ################################################### R> par(mfrow = c(2, 2)) R> plot(jour_lm) R> par(mfrow = c(1, 1)) R> R> R> ################################################### R> ### chunk number 18: journal-lht R> ################################################### R> linearHypothesis(jour_lm, "log(citeprice) = -0.5") Linear hypothesis test Hypothesis: log(citeprice) = - 0.5 Model 1: restricted model Model 2: log(subs) ~ log(citeprice) Res.Df RSS Df Sum of Sq F Pr(>F) 1 179 100 2 178 100 1 0.484 0.86 0.35 R> R> R> ################################################### R> ### chunk number 19: CPS-data R> ################################################### R> data("CPS1988") R> summary(CPS1988) wage education experience ethnicity Min. : 50 Min. : 0.0 Min. :-4.0 cauc:25923 1st Qu.: 309 1st Qu.:12.0 1st Qu.: 8.0 afam: 2232 Median : 522 Median :12.0 Median :16.0 Mean : 604 Mean :13.1 Mean :18.2 3rd Qu.: 783 3rd Qu.:15.0 3rd Qu.:27.0 Max. :18777 Max. :18.0 Max. :63.0 smsa region parttime no : 7223 northeast:6441 no :25631 yes:20932 midwest :6863 yes: 2524 south :8760 west :6091 R> R> R> ################################################### R> ### chunk number 20: CPS-base R> ################################################### R> cps_lm <- lm(log(wage) ~ experience + I(experience^2) + + education + ethnicity, data = CPS1988) R> R> R> ################################################### R> ### chunk number 21: CPS-visualization-unused eval=FALSE R> ################################################### R> ## ex <- 0:56 R> ## ed <- with(CPS1988, tapply(education, R> ## list(ethnicity, experience), mean))[, as.character(ex)] R> ## fm <- cps_lm R> ## wago <- predict(fm, newdata = data.frame(experience = ex, R> ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) R> ## wagb <- predict(fm, newdata = data.frame(experience = ex, R> ## ethnicity = "afam", education = as.numeric(ed["afam",]))) R> ## plot(log(wage) ~ experience, data = CPS1988, pch = ".", R> ## col = as.numeric(ethnicity)) R> ## lines(ex, wago) R> ## lines(ex, wagb, col = 2) R> R> R> ################################################### R> ### chunk number 22: CPS-summary R> ################################################### R> summary(cps_lm) Call: lm(formula = log(wage) ~ experience + I(experience^2) + education + ethnicity, data = CPS1988) Residuals: Min 1Q Median 3Q Max -2.943 -0.316 0.058 0.376 4.383 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.321395 0.019174 225.4 <2e-16 experience 0.077473 0.000880 88.0 <2e-16 I(experience^2) -0.001316 0.000019 -69.3 <2e-16 education 0.085673 0.001272 67.3 <2e-16 ethnicityafam -0.243364 0.012918 -18.8 <2e-16 Residual standard error: 0.584 on 28150 degrees of freedom Multiple R-squared: 0.335, Adjusted R-squared: 0.335 F-statistic: 3.54e+03 on 4 and 28150 DF, p-value: <2e-16 R> R> R> ################################################### R> ### chunk number 23: CPS-noeth R> ################################################### R> cps_noeth <- lm(log(wage) ~ experience + I(experience^2) + + education, data = CPS1988) R> anova(cps_noeth, cps_lm) Analysis of Variance Table Model 1: log(wage) ~ experience + I(experience^2) + education Model 2: log(wage) ~ experience + I(experience^2) + education + ethnicity Res.Df RSS Df Sum of Sq F Pr(>F) 1 28151 9720 2 28150 9599 1 121 355 <2e-16 R> R> R> ################################################### R> ### chunk number 24: CPS-anova R> ################################################### R> anova(cps_lm) Analysis of Variance Table Response: log(wage) Df Sum Sq Mean Sq F value Pr(>F) experience 1 840 840 2462 <2e-16 I(experience^2) 1 2249 2249 6597 <2e-16 education 1 1620 1620 4750 <2e-16 ethnicity 1 121 121 355 <2e-16 Residuals 28150 9599 0 R> R> R> ################################################### R> ### chunk number 25: CPS-noeth2 eval=FALSE R> ################################################### R> ## cps_noeth <- update(cps_lm, formula = . ~ . - ethnicity) R> R> R> ################################################### R> ### chunk number 26: CPS-waldtest R> ################################################### R> waldtest(cps_lm, . ~ . - ethnicity) Wald test Model 1: log(wage) ~ experience + I(experience^2) + education + ethnicity Model 2: log(wage) ~ experience + I(experience^2) + education Res.Df Df F Pr(>F) 1 28150 2 28151 -1 355 <2e-16 R> R> R> ################################################### R> ### chunk number 27: CPS-spline R> ################################################### R> library("splines") R> cps_plm <- lm(log(wage) ~ bs(experience, df = 5) + + education + ethnicity, data = CPS1988) R> R> R> ################################################### R> ### chunk number 28: CPS-spline-summary eval=FALSE R> ################################################### R> ## summary(cps_plm) R> R> R> ################################################### R> ### chunk number 29: CPS-BIC R> ################################################### R> cps_bs <- lapply(3:10, function(i) lm(log(wage) ~ + bs(experience, df = i) + education + ethnicity, + data = CPS1988)) R> structure(sapply(cps_bs, AIC, k = log(nrow(CPS1988))), + .Names = 3:10) 3 4 5 6 7 8 9 10 49205 48836 48794 48795 48801 48797 48799 48802 R> R> R> ################################################### R> ### chunk number 30: plm-plot eval=FALSE R> ################################################### R> ## cps <- data.frame(experience = -2:60, education = R> ## with(CPS1988, mean(education[ethnicity == "cauc"])), R> ## ethnicity = "cauc") R> ## cps$yhat1 <- predict(cps_lm, newdata = cps) R> ## cps$yhat2 <- predict(cps_plm, newdata = cps) R> ## R> ## plot(log(wage) ~ jitter(experience, factor = 3), pch = 19, R> ## col = rgb(0.5, 0.5, 0.5, alpha = 0.02), data = CPS1988) R> ## lines(yhat1 ~ experience, data = cps, lty = 2) R> ## lines(yhat2 ~ experience, data = cps) R> ## legend("topleft", c("quadratic", "spline"), lty = c(2,1), R> ## bty = "n") R> R> R> ################################################### R> ### chunk number 31: plm-plot1 R> ################################################### R> cps <- data.frame(experience = -2:60, education = + with(CPS1988, mean(education[ethnicity == "cauc"])), + ethnicity = "cauc") R> cps$yhat1 <- predict(cps_lm, newdata = cps) R> cps$yhat2 <- predict(cps_plm, newdata = cps) R> R> plot(log(wage) ~ jitter(experience, factor = 3), pch = 19, + col = rgb(0.5, 0.5, 0.5, alpha = 0.02), data = CPS1988) R> lines(yhat1 ~ experience, data = cps, lty = 2) R> lines(yhat2 ~ experience, data = cps) R> legend("topleft", c("quadratic", "spline"), lty = c(2,1), + bty = "n") R> R> R> ################################################### R> ### chunk number 32: CPS-int R> ################################################### R> cps_int <- lm(log(wage) ~ experience + I(experience^2) + + education * ethnicity, data = CPS1988) R> coeftest(cps_int) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.313059 0.019590 220.17 <2e-16 experience 0.077520 0.000880 88.06 <2e-16 I(experience^2) -0.001318 0.000019 -69.34 <2e-16 education 0.086312 0.001309 65.94 <2e-16 ethnicityafam -0.123887 0.059026 -2.10 0.036 education:ethnicityafam -0.009648 0.004651 -2.07 0.038 R> R> R> ################################################### R> ### chunk number 33: CPS-int2 eval=FALSE R> ################################################### R> ## cps_int <- lm(log(wage) ~ experience + I(experience^2) + R> ## education + ethnicity + education:ethnicity, R> ## data = CPS1988) R> R> R> ################################################### R> ### chunk number 34: CPS-sep R> ################################################### R> cps_sep <- lm(log(wage) ~ ethnicity / + (experience + I(experience^2) + education) - 1, + data = CPS1988) R> R> R> ################################################### R> ### chunk number 35: CPS-sep-coef R> ################################################### R> cps_sep_cf <- matrix(coef(cps_sep), nrow = 2) R> rownames(cps_sep_cf) <- levels(CPS1988$ethnicity) R> colnames(cps_sep_cf) <- names(coef(cps_lm))[1:4] R> cps_sep_cf (Intercept) experience I(experience^2) education cauc 4.310 0.07923 -0.0013597 0.08575 afam 4.159 0.06190 -0.0009415 0.08654 R> R> R> ################################################### R> ### chunk number 36: CPS-sep-anova R> ################################################### R> anova(cps_sep, cps_lm) Analysis of Variance Table Model 1: log(wage) ~ ethnicity/(experience + I(experience^2) + education) - 1 Model 2: log(wage) ~ experience + I(experience^2) + education + ethnicity Res.Df RSS Df Sum of Sq F Pr(>F) 1 28147 9582 2 28150 9599 -3 -16.8 16.5 1.1e-10 R> R> R> ################################################### R> ### chunk number 37: CPS-sep-visualization-unused eval=FALSE R> ################################################### R> ## ex <- 0:56 R> ## ed <- with(CPS1988, tapply(education, list(ethnicity, R> ## experience), mean))[, as.character(ex)] R> ## fm <- cps_lm R> ## wago <- predict(fm, newdata = data.frame(experience = ex, R> ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) R> ## wagb <- predict(fm, newdata = data.frame(experience = ex, R> ## ethnicity = "afam", education = as.numeric(ed["afam",]))) R> ## plot(log(wage) ~ jitter(experience, factor = 2), R> ## data = CPS1988, pch = ".", col = as.numeric(ethnicity)) R> ## R> ## R> ## plot(log(wage) ~ as.factor(experience), data = CPS1988, R> ## pch = ".") R> ## lines(ex, wago, lwd = 2) R> ## lines(ex, wagb, col = 2, lwd = 2) R> ## fm <- cps_sep R> ## wago <- predict(fm, newdata = data.frame(experience = ex, R> ## ethnicity = "cauc", education = as.numeric(ed["cauc",]))) R> ## wagb <- predict(fm, newdata = data.frame(experience = ex, R> ## ethnicity = "afam", education = as.numeric(ed["afam",]))) R> ## lines(ex, wago, lty = 2, lwd = 2) R> ## lines(ex, wagb, col = 2, lty = 2, lwd = 2) R> R> R> ################################################### R> ### chunk number 38: CPS-region R> ################################################### R> CPS1988$region <- relevel(CPS1988$region, ref = "south") R> cps_region <- lm(log(wage) ~ ethnicity + education + + experience + I(experience^2) + region, data = CPS1988) R> coef(cps_region) (Intercept) ethnicityafam education experience 4.283606 -0.225679 0.084672 0.077656 I(experience^2) regionnortheast regionmidwest regionwest -0.001323 0.131920 0.043789 0.040327 R> R> R> ################################################### R> ### chunk number 39: wls1 R> ################################################### R> jour_wls1 <- lm(log(subs) ~ log(citeprice), data = journals, + weights = 1/citeprice^2) R> R> R> ################################################### R> ### chunk number 40: wls2 R> ################################################### R> jour_wls2 <- lm(log(subs) ~ log(citeprice), data = journals, + weights = 1/citeprice) R> R> R> ################################################### R> ### chunk number 41: journals-wls1 eval=FALSE R> ################################################### R> ## plot(log(subs) ~ log(citeprice), data = journals) R> ## abline(jour_lm) R> ## abline(jour_wls1, lwd = 2, lty = 2) R> ## abline(jour_wls2, lwd = 2, lty = 3) R> ## legend("bottomleft", c("OLS", "WLS1", "WLS2"), R> ## lty = 1:3, lwd = 2, bty = "n") R> R> R> ################################################### R> ### chunk number 42: journals-wls11 R> ################################################### R> plot(log(subs) ~ log(citeprice), data = journals) R> abline(jour_lm) R> abline(jour_wls1, lwd = 2, lty = 2) R> abline(jour_wls2, lwd = 2, lty = 3) R> legend("bottomleft", c("OLS", "WLS1", "WLS2"), + lty = 1:3, lwd = 2, bty = "n") R> R> R> ################################################### R> ### chunk number 43: fgls1 R> ################################################### R> auxreg <- lm(log(residuals(jour_lm)^2) ~ log(citeprice), + data = journals) R> jour_fgls1 <- lm(log(subs) ~ log(citeprice), + weights = 1/exp(fitted(auxreg)), data = journals) R> R> R> ################################################### R> ### chunk number 44: fgls2 R> ################################################### R> gamma2i <- coef(auxreg)[2] R> gamma2 <- 0 R> while(abs((gamma2i - gamma2)/gamma2) > 1e-7) { + gamma2 <- gamma2i + fglsi <- lm(log(subs) ~ log(citeprice), data = journals, + weights = 1/citeprice^gamma2) + gamma2i <- coef(lm(log(residuals(fglsi)^2) ~ + log(citeprice), data = journals))[2] + } R> jour_fgls2 <- lm(log(subs) ~ log(citeprice), data = journals, + weights = 1/citeprice^gamma2) R> R> R> ################################################### R> ### chunk number 45: fgls2-coef R> ################################################### R> coef(jour_fgls2) (Intercept) log(citeprice) 4.7758 -0.5008 R> R> R> ################################################### R> ### chunk number 46: journals-fgls R> ################################################### R> plot(log(subs) ~ log(citeprice), data = journals) R> abline(jour_lm) R> abline(jour_fgls2, lty = 2, lwd = 2) R> R> R> ################################################### R> ### chunk number 47: usmacro-plot eval=FALSE R> ################################################### R> ## data("USMacroG") R> ## plot(USMacroG[, c("dpi", "consumption")], lty = c(3, 1), R> ## plot.type = "single", ylab = "") R> ## legend("topleft", legend = c("income", "consumption"), R> ## lty = c(3, 1), bty = "n") R> R> R> ################################################### R> ### chunk number 48: usmacro-plot1 R> ################################################### R> data("USMacroG") R> plot(USMacroG[, c("dpi", "consumption")], lty = c(3, 1), + plot.type = "single", ylab = "") R> legend("topleft", legend = c("income", "consumption"), + lty = c(3, 1), bty = "n") R> R> R> ################################################### R> ### chunk number 49: usmacro-fit R> ################################################### R> library("dynlm") R> cons_lm1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG) R> cons_lm2 <- dynlm(consumption ~ dpi + L(consumption), + data = USMacroG) R> R> R> ################################################### R> ### chunk number 50: usmacro-summary1 R> ################################################### R> summary(cons_lm1) Time series regression with "ts" data: Start = 1950(2), End = 2000(4) Call: dynlm(formula = consumption ~ dpi + L(dpi), data = USMacroG) Residuals: Min 1Q Median 3Q Max -190.0 -56.7 1.6 49.9 323.9 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -81.0796 14.5081 -5.59 7.4e-08 dpi 0.8912 0.2063 4.32 2.4e-05 L(dpi) 0.0309 0.2075 0.15 0.88 Residual standard error: 87.6 on 200 degrees of freedom Multiple R-squared: 0.996, Adjusted R-squared: 0.996 F-statistic: 2.79e+04 on 2 and 200 DF, p-value: <2e-16 R> R> R> ################################################### R> ### chunk number 51: usmacro-summary2 R> ################################################### R> summary(cons_lm2) Time series regression with "ts" data: Start = 1950(2), End = 2000(4) Call: dynlm(formula = consumption ~ dpi + L(consumption), data = USMacroG) Residuals: Min 1Q Median 3Q Max -101.30 -9.67 1.14 12.69 45.32 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.53522 3.84517 0.14 0.89 dpi -0.00406 0.01663 -0.24 0.81 L(consumption) 1.01311 0.01816 55.79 <2e-16 Residual standard error: 21.5 on 200 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 4.63e+05 on 2 and 200 DF, p-value: <2e-16 R> R> R> ################################################### R> ### chunk number 52: dynlm-plot eval=FALSE R> ################################################### R> ## plot(merge(as.zoo(USMacroG[,"consumption"]), fitted(cons_lm1), R> ## fitted(cons_lm2), 0, residuals(cons_lm1), R> ## residuals(cons_lm2)), screens = rep(1:2, c(3, 3)), R> ## lty = rep(1:3, 2), ylab = c("Fitted values", "Residuals"), R> ## xlab = "Time", main = "") R> ## legend(0.05, 0.95, c("observed", "cons_lm1", "cons_lm2"), R> ## lty = 1:3, bty = "n") R> R> R> ################################################### R> ### chunk number 53: dynlm-plot1 R> ################################################### R> plot(merge(as.zoo(USMacroG[,"consumption"]), fitted(cons_lm1), + fitted(cons_lm2), 0, residuals(cons_lm1), + residuals(cons_lm2)), screens = rep(1:2, c(3, 3)), + lty = rep(1:3, 2), ylab = c("Fitted values", "Residuals"), + xlab = "Time", main = "") R> legend(0.05, 0.95, c("observed", "cons_lm1", "cons_lm2"), + lty = 1:3, bty = "n") R> R> R> ################################################### R> ### chunk number 54: encompassing1 R> ################################################### R> cons_lmE <- dynlm(consumption ~ dpi + L(dpi) + + L(consumption), data = USMacroG) R> R> R> ################################################### R> ### chunk number 55: encompassing2 R> ################################################### R> anova(cons_lm1, cons_lmE, cons_lm2) Analysis of Variance Table Model 1: consumption ~ dpi + L(dpi) Model 2: consumption ~ dpi + L(dpi) + L(consumption) Model 3: consumption ~ dpi + L(consumption) Res.Df RSS Df Sum of Sq F Pr(>F) 1 200 1534001 2 199 73550 1 1460451 3951.4 < 2e-16 3 200 92644 -1 -19094 51.7 1.3e-11 R> R> R> ################################################### R> ### chunk number 56: encompassing3 R> ################################################### R> encomptest(cons_lm1, cons_lm2) Encompassing test Model 1: consumption ~ dpi + L(dpi) Model 2: consumption ~ dpi + L(consumption) Model E: consumption ~ dpi + L(dpi) + L(consumption) Res.Df Df F Pr(>F) M1 vs. ME 199 -1 3951.4 < 2e-16 M2 vs. ME 199 -1 51.7 1.3e-11 R> R> R> ################################################### R> ### chunk number 57: pdata.frame R> ################################################### R> data("Grunfeld", package = "AER") R> library("plm") Loading required package: Formula R> gr <- subset(Grunfeld, firm %in% c("General Electric", + "General Motors", "IBM")) R> pgr <- pdata.frame(gr, index = c("firm", "year")) R> R> R> ################################################### R> ### chunk number 58: plm-pool R> ################################################### R> gr_pool <- plm(invest ~ value + capital, data = pgr, + model = "pooling") R> R> R> ################################################### R> ### chunk number 59: plm-FE R> ################################################### R> gr_fe <- plm(invest ~ value + capital, data = pgr, + model = "within") R> summary(gr_fe) Oneway (individual) effect Within Model Call: plm(formula = invest ~ value + capital, data = pgr, model = "within") Balanced Panel: n = 3, T = 20, N = 60 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -167.33 -26.14 2.09 26.84 201.68 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.1049 0.0163 6.42 3.3e-08 capital 0.3453 0.0244 14.16 < 2e-16 Total Sum of Squares: 1890000 Residual Sum of Squares: 244000 R-Squared: 0.871 Adj. R-Squared: 0.861 F-statistic: 185.407 on 2 and 55 DF, p-value: <2e-16 R> R> R> ################################################### R> ### chunk number 60: plm-pFtest R> ################################################### R> pFtest(gr_fe, gr_pool) F test for individual effects data: invest ~ value + capital F = 57, df1 = 2, df2 = 55, p-value = 4e-14 alternative hypothesis: significant effects R> R> R> ################################################### R> ### chunk number 61: plm-RE R> ################################################### R> gr_re <- plm(invest ~ value + capital, data = pgr, + model = "random", random.method = "walhus") R> summary(gr_re) Oneway (individual) effect Random Effect Model (Wallace-Hussain's transformation) Call: plm(formula = invest ~ value + capital, data = pgr, model = "random", random.method = "walhus") Balanced Panel: n = 3, T = 20, N = 60 Effects: var std.dev share idiosyncratic 4389.3 66.3 0.35 individual 8079.7 89.9 0.65 theta: 0.837 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -187.40 -32.92 6.96 31.43 210.20 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) -109.9766 61.7014 -1.78 0.075 value 0.1043 0.0150 6.95 3.6e-12 capital 0.3448 0.0245 14.06 < 2e-16 Total Sum of Squares: 1990000 Residual Sum of Squares: 258000 R-Squared: 0.87 Adj. R-Squared: 0.866 Chisq: 383.089 on 2 DF, p-value: <2e-16 R> R> R> ################################################### R> ### chunk number 62: plm-plmtest R> ################################################### R> plmtest(gr_pool) Lagrange Multiplier Test - (Honda) for balanced panels data: invest ~ value + capital normal = 15, p-value <2e-16 alternative hypothesis: significant effects R> R> R> ################################################### R> ### chunk number 63: plm-phtest R> ################################################### R> phtest(gr_re, gr_fe) Hausman Test data: invest ~ value + capital chisq = 0.04, df = 2, p-value = 1 alternative hypothesis: one model is inconsistent R> R> R> ################################################### R> ### chunk number 64: EmplUK-data R> ################################################### R> data("EmplUK", package = "plm") R> R> R> ################################################### R> ### chunk number 65: plm-AB R> ################################################### R> empl_ab <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + + log(capital) + lag(log(output), 0:1) | lag(log(emp), 2:99), + data = EmplUK, index = c("firm", "year"), + effect = "twoways", model = "twosteps") R> R> R> ################################################### R> ### chunk number 66: plm-AB-summary R> ################################################### R> summary(empl_ab, robust = FALSE) Twoways effects Two steps model Call: pgmm(formula = log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + lag(log(output), 0:1) | lag(log(emp), 2:99), data = EmplUK, effect = "twoways", model = "twosteps", index = c("firm", "year")) Unbalanced Panel: n = 140, T = 7-9, N = 1031 Number of Observations Used: 611 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.6191 -0.0256 0.0000 -0.0001 0.0332 0.6410 Coefficients: Estimate Std. Error z-value Pr(>|z|) lag(log(emp), 1:2)1 0.4742 0.0853 5.56 2.7e-08 lag(log(emp), 1:2)2 -0.0530 0.0273 -1.94 0.05222 lag(log(wage), 0:1)0 -0.5132 0.0493 -10.40 < 2e-16 lag(log(wage), 0:1)1 0.2246 0.0801 2.81 0.00502 log(capital) 0.2927 0.0395 7.42 1.2e-13 lag(log(output), 0:1)0 0.6098 0.1085 5.62 1.9e-08 lag(log(output), 0:1)1 -0.4464 0.1248 -3.58 0.00035 Sargan test: chisq(25) = 30.11 (p-value = 0.22) Autocorrelation test (1): normal = -2.428 (p-value = 0.0152) Autocorrelation test (2): normal = -0.3325 (p-value = 0.739) Wald test for coefficients: chisq(7) = 372 (p-value = <2e-16) Wald test for time dummies: chisq(6) = 26.9 (p-value = 0.000151) R> R> R> ################################################### R> ### chunk number 67: systemfit R> ################################################### R> library("systemfit") Loading required package: Matrix Please cite the 'systemfit' package as: Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/. If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site: https://r-forge.r-project.org/projects/systemfit/ R> gr2 <- subset(Grunfeld, firm %in% c("Chrysler", "IBM")) R> pgr2 <- pdata.frame(gr2, c("firm", "year")) R> R> R> ################################################### R> ### chunk number 68: SUR R> ################################################### R> gr_sur <- systemfit(invest ~ value + capital, + method = "SUR", data = pgr2) R> summary(gr_sur, residCov = FALSE, equations = FALSE) systemfit results method: SUR N DF SSR detRCov OLS-R2 McElroy-R2 system 40 34 4114 11022 0.929 0.927 N DF SSR MSE RMSE R2 Adj R2 Chrysler 20 17 3002 176.6 13.29 0.913 0.903 IBM 20 17 1112 65.4 8.09 0.952 0.946 Coefficients: Estimate Std. Error t value Pr(>|t|) Chrysler_(Intercept) -5.7031 13.2774 -0.43 0.67293 Chrysler_value 0.0780 0.0196 3.98 0.00096 Chrysler_capital 0.3115 0.0287 10.85 4.6e-09 IBM_(Intercept) -8.0908 4.5216 -1.79 0.09139 IBM_value 0.1272 0.0306 4.16 0.00066 IBM_capital 0.0966 0.0983 0.98 0.33951 R> R> R> ################################################### R> ### chunk number 69: nlme eval=FALSE R> ################################################### R> ## library("nlme") R> ## g1 <- subset(Grunfeld, firm == "Westinghouse") R> ## gls(invest ~ value + capital, data = g1, correlation = corAR1()) R> R> R> > proc.time() user system elapsed 3.513 0.168 3.668 AER/tests/Ch-Basics.Rout.save0000644000176200001440000007116413457447170015414 0ustar liggesusers R version 3.5.2 (2018-12-20) -- "Eggshell Igloo" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: calc1 R> ################################################### R> 1 + 1 [1] 2 R> 2^3 [1] 8 R> R> R> ################################################### R> ### chunk number 3: calc2 R> ################################################### R> log(exp(sin(pi/4)^2) * exp(cos(pi/4)^2)) [1] 1 R> R> R> ################################################### R> ### chunk number 4: vec1 R> ################################################### R> x <- c(1.8, 3.14, 4, 88.169, 13) R> R> R> ################################################### R> ### chunk number 5: length R> ################################################### R> length(x) [1] 5 R> R> R> ################################################### R> ### chunk number 6: vec2 R> ################################################### R> 2 * x + 3 [1] 6.60 9.28 11.00 179.34 29.00 R> 5:1 * x + 1:5 [1] 10.00 14.56 15.00 180.34 18.00 R> R> R> ################################################### R> ### chunk number 7: vec3 R> ################################################### R> log(x) [1] 0.5878 1.1442 1.3863 4.4793 2.5649 R> R> R> ################################################### R> ### chunk number 8: subset1 R> ################################################### R> x[c(1, 4)] [1] 1.80 88.17 R> R> R> ################################################### R> ### chunk number 9: subset2 R> ################################################### R> x[-c(2, 3, 5)] [1] 1.80 88.17 R> R> R> ################################################### R> ### chunk number 10: pattern1 R> ################################################### R> ones <- rep(1, 10) R> even <- seq(from = 2, to = 20, by = 2) R> trend <- 1981:2005 R> R> R> ################################################### R> ### chunk number 11: pattern2 R> ################################################### R> c(ones, even) [1] 1 1 1 1 1 1 1 1 1 1 2 4 6 8 10 12 14 16 18 20 R> R> R> ################################################### R> ### chunk number 12: matrix1 R> ################################################### R> A <- matrix(1:6, nrow = 2) R> R> R> ################################################### R> ### chunk number 13: matrix2 R> ################################################### R> t(A) [,1] [,2] [1,] 1 2 [2,] 3 4 [3,] 5 6 R> R> R> ################################################### R> ### chunk number 14: matrix3 R> ################################################### R> dim(A) [1] 2 3 R> nrow(A) [1] 2 R> ncol(A) [1] 3 R> R> R> ################################################### R> ### chunk number 15: matrix-subset R> ################################################### R> A1 <- A[1:2, c(1, 3)] R> R> R> ################################################### R> ### chunk number 16: matrix4 R> ################################################### R> solve(A1) [,1] [,2] [1,] -1.5 1.25 [2,] 0.5 -0.25 R> R> R> ################################################### R> ### chunk number 17: matrix-solve R> ################################################### R> A1 %*% solve(A1) [,1] [,2] [1,] 1 0 [2,] 0 1 R> R> R> ################################################### R> ### chunk number 18: diag R> ################################################### R> diag(4) [,1] [,2] [,3] [,4] [1,] 1 0 0 0 [2,] 0 1 0 0 [3,] 0 0 1 0 [4,] 0 0 0 1 R> R> R> ################################################### R> ### chunk number 19: matrix-combine1 R> ################################################### R> cbind(1, A1) [,1] [,2] [,3] [1,] 1 1 5 [2,] 1 2 6 R> R> R> ################################################### R> ### chunk number 20: matrix-combine2 R> ################################################### R> rbind(A1, diag(4, 2)) [,1] [,2] [1,] 1 5 [2,] 2 6 [3,] 4 0 [4,] 0 4 R> R> R> ################################################### R> ### chunk number 21: vector-mode R> ################################################### R> x <- c(1.8, 3.14, 4, 88.169, 13) R> R> R> ################################################### R> ### chunk number 22: logical R> ################################################### R> x > 3.5 [1] FALSE FALSE TRUE TRUE TRUE R> R> R> ################################################### R> ### chunk number 23: names R> ################################################### R> names(x) <- c("a", "b", "c", "d", "e") R> x a b c d e 1.80 3.14 4.00 88.17 13.00 R> R> R> ################################################### R> ### chunk number 24: subset-more R> ################################################### R> x[3:5] c d e 4.00 88.17 13.00 R> x[c("c", "d", "e")] c d e 4.00 88.17 13.00 R> x[x > 3.5] c d e 4.00 88.17 13.00 R> R> R> ################################################### R> ### chunk number 25: list1 R> ################################################### R> mylist <- list(sample = rnorm(5), + family = "normal distribution", + parameters = list(mean = 0, sd = 1)) R> mylist $sample [1] 0.3771 -0.9346 2.4302 1.3195 0.4503 $family [1] "normal distribution" $parameters $parameters$mean [1] 0 $parameters$sd [1] 1 R> R> R> ################################################### R> ### chunk number 26: list2 R> ################################################### R> mylist[[1]] [1] 0.3771 -0.9346 2.4302 1.3195 0.4503 R> mylist[["sample"]] [1] 0.3771 -0.9346 2.4302 1.3195 0.4503 R> mylist$sample [1] 0.3771 -0.9346 2.4302 1.3195 0.4503 R> R> R> ################################################### R> ### chunk number 27: list3 R> ################################################### R> mylist[[3]]$sd [1] 1 R> R> R> ################################################### R> ### chunk number 28: logical2 R> ################################################### R> x <- c(1.8, 3.14, 4, 88.169, 13) R> x > 3 & x <= 4 [1] FALSE TRUE TRUE FALSE FALSE R> R> R> ################################################### R> ### chunk number 29: logical3 R> ################################################### R> which(x > 3 & x <= 4) [1] 2 3 R> R> R> ################################################### R> ### chunk number 30: logical4 R> ################################################### R> all(x > 3) [1] FALSE R> any(x > 3) [1] TRUE R> R> R> ################################################### R> ### chunk number 31: logical5 R> ################################################### R> (1.5 - 0.5) == 1 [1] TRUE R> (1.9 - 0.9) == 1 [1] FALSE R> R> R> ################################################### R> ### chunk number 32: logical6 R> ################################################### R> all.equal(1.9 - 0.9, 1) [1] TRUE R> R> R> ################################################### R> ### chunk number 33: logical7 R> ################################################### R> 7 + TRUE [1] 8 R> R> R> ################################################### R> ### chunk number 34: coercion1 R> ################################################### R> is.numeric(x) [1] TRUE R> is.character(x) [1] FALSE R> as.character(x) [1] "1.8" "3.14" "4" "88.169" "13" R> R> R> ################################################### R> ### chunk number 35: coercion2 R> ################################################### R> c(1, "a") [1] "1" "a" R> R> R> ################################################### R> ### chunk number 36: rng1 R> ################################################### R> set.seed(123) R> rnorm(2) [1] -0.5605 -0.2302 R> rnorm(2) [1] 1.55871 0.07051 R> set.seed(123) R> rnorm(2) [1] -0.5605 -0.2302 R> R> R> ################################################### R> ### chunk number 37: rng2 R> ################################################### R> sample(1:5) [1] 5 1 2 3 4 R> sample(c("male", "female"), size = 5, replace = TRUE, + prob = c(0.2, 0.8)) [1] "female" "male" "female" "female" "female" R> R> R> ################################################### R> ### chunk number 38: flow1 R> ################################################### R> x <- c(1.8, 3.14, 4, 88.169, 13) R> if(rnorm(1) > 0) sum(x) else mean(x) [1] 22.02 R> R> R> ################################################### R> ### chunk number 39: flow2 R> ################################################### R> ifelse(x > 4, sqrt(x), x^2) [1] 3.240 9.860 16.000 9.390 3.606 R> R> R> ################################################### R> ### chunk number 40: flow3 R> ################################################### R> for(i in 2:5) { + x[i] <- x[i] - x[i-1] + } R> x[-1] [1] 1.34 2.66 85.51 -72.51 R> R> R> ################################################### R> ### chunk number 41: flow4 R> ################################################### R> while(sum(x) < 100) { + x <- 2 * x + } R> x [1] 14.40 10.72 21.28 684.07 -580.07 R> R> R> ################################################### R> ### chunk number 42: cmeans R> ################################################### R> cmeans <- function(X) { + rval <- rep(0, ncol(X)) + for(j in 1:ncol(X)) { + mysum <- 0 + for(i in 1:nrow(X)) mysum <- mysum + X[i,j] + rval[j] <- mysum/nrow(X) + } + return(rval) + } R> R> R> ################################################### R> ### chunk number 43: colmeans1 R> ################################################### R> X <- matrix(1:20, ncol = 2) R> cmeans(X) [1] 5.5 15.5 R> R> R> ################################################### R> ### chunk number 44: colmeans2 R> ################################################### R> colMeans(X) [1] 5.5 15.5 R> R> R> ################################################### R> ### chunk number 45: colmeans3 eval=FALSE R> ################################################### R> ## X <- matrix(rnorm(2*10^6), ncol = 2) R> ## system.time(colMeans(X)) R> ## system.time(cmeans(X)) R> R> R> ################################################### R> ### chunk number 46: colmeans4 R> ################################################### R> cmeans2 <- function(X) { + rval <- rep(0, ncol(X)) + for(j in 1:ncol(X)) rval[j] <- mean(X[,j]) + return(rval) + } R> R> R> ################################################### R> ### chunk number 47: colmeans5 eval=FALSE R> ################################################### R> ## system.time(cmeans2(X)) R> R> R> ################################################### R> ### chunk number 48: colmeans6 eval=FALSE R> ################################################### R> ## apply(X, 2, mean) R> R> R> ################################################### R> ### chunk number 49: colmeans7 eval=FALSE R> ################################################### R> ## system.time(apply(X, 2, mean)) R> R> R> ################################################### R> ### chunk number 50: formula1 R> ################################################### R> f <- y ~ x R> class(f) [1] "formula" R> R> R> ################################################### R> ### chunk number 51: formula2 R> ################################################### R> x <- seq(from = 0, to = 10, by = 0.5) R> y <- 2 + 3 * x + rnorm(21) R> R> R> ################################################### R> ### chunk number 52: formula3 eval=FALSE R> ################################################### R> ## plot(y ~ x) R> ## lm(y ~ x) R> R> R> ################################################### R> ### chunk number 53: formula3a R> ################################################### R> print(lm(y ~ x)) Call: lm(formula = y ~ x) Coefficients: (Intercept) x 2.26 2.91 R> R> R> ################################################### R> ### chunk number 54: formula3b R> ################################################### R> plot(y ~ x) R> R> R> ################################################### R> ### chunk number 55: formula3c R> ################################################### R> fm <- lm(y ~ x) R> R> R> ################################################### R> ### chunk number 56: mydata1 R> ################################################### R> mydata <- data.frame(one = 1:10, two = 11:20, three = 21:30) R> R> R> ################################################### R> ### chunk number 57: mydata1a R> ################################################### R> mydata <- as.data.frame(matrix(1:30, ncol = 3)) R> names(mydata) <- c("one", "two", "three") R> R> R> ################################################### R> ### chunk number 58: mydata2 R> ################################################### R> mydata$two [1] 11 12 13 14 15 16 17 18 19 20 R> mydata[, "two"] [1] 11 12 13 14 15 16 17 18 19 20 R> mydata[, 2] [1] 11 12 13 14 15 16 17 18 19 20 R> R> R> ################################################### R> ### chunk number 59: attach R> ################################################### R> attach(mydata) R> mean(two) [1] 15.5 R> detach(mydata) R> R> R> ################################################### R> ### chunk number 60: with R> ################################################### R> with(mydata, mean(two)) [1] 15.5 R> R> R> ################################################### R> ### chunk number 61: mydata-subset R> ################################################### R> mydata.sub <- subset(mydata, two <= 16, select = -two) R> R> R> ################################################### R> ### chunk number 62: write-table R> ################################################### R> write.table(mydata, file = "mydata.txt", col.names = TRUE) R> R> R> ################################################### R> ### chunk number 63: read-table R> ################################################### R> newdata <- read.table("mydata.txt", header = TRUE) R> R> R> ################################################### R> ### chunk number 64: save R> ################################################### R> save(mydata, file = "mydata.rda") R> R> R> ################################################### R> ### chunk number 65: load R> ################################################### R> load("mydata.rda") R> R> R> ################################################### R> ### chunk number 66: file-remove R> ################################################### R> file.remove("mydata.rda") [1] TRUE R> R> R> ################################################### R> ### chunk number 67: data R> ################################################### R> data("Journals", package = "AER") R> R> R> ################################################### R> ### chunk number 68: foreign R> ################################################### R> library("foreign") R> write.dta(mydata, file = "mydata.dta") R> R> R> ################################################### R> ### chunk number 69: read-dta R> ################################################### R> mydata <- read.dta("mydata.dta") R> R> R> ################################################### R> ### chunk number 70: cleanup R> ################################################### R> file.remove("mydata.dta") [1] TRUE R> R> R> ################################################### R> ### chunk number 71: factor R> ################################################### R> g <- rep(0:1, c(2, 4)) R> g <- factor(g, levels = 0:1, labels = c("male", "female")) R> g [1] male male female female female female Levels: male female R> R> R> ################################################### R> ### chunk number 72: na1 R> ################################################### R> newdata <- read.table("mydata.txt", na.strings = "-999") R> R> R> ################################################### R> ### chunk number 73: na2 R> ################################################### R> file.remove("mydata.txt") [1] TRUE R> R> R> ################################################### R> ### chunk number 74: oop1 R> ################################################### R> x <- c(1.8, 3.14, 4, 88.169, 13) R> g <- factor(rep(c(0, 1), c(2, 4)), levels = c(0, 1), + labels = c("male", "female")) R> R> R> ################################################### R> ### chunk number 75: oop2 R> ################################################### R> summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.80 3.14 4.00 22.02 13.00 88.17 R> summary(g) male female 2 4 R> R> R> ################################################### R> ### chunk number 76: oop3 R> ################################################### R> class(x) [1] "numeric" R> class(g) [1] "factor" R> R> R> ################################################### R> ### chunk number 77: oop4 R> ################################################### R> summary function (object, ...) UseMethod("summary") R> R> R> ################################################### R> ### chunk number 78: oop5 R> ################################################### R> normsample <- function(n, ...) { + rval <- rnorm(n, ...) + class(rval) <- "normsample" + return(rval) + } R> R> R> ################################################### R> ### chunk number 79: oop6 R> ################################################### R> set.seed(123) R> x <- normsample(10, mean = 5) R> class(x) [1] "normsample" R> R> R> ################################################### R> ### chunk number 80: oop7 R> ################################################### R> summary.normsample <- function(object, ...) { + rval <- c(length(object), mean(object), sd(object)) + names(rval) <- c("sample size","mean","standard deviation") + return(rval) + } R> R> R> ################################################### R> ### chunk number 81: oop8 R> ################################################### R> summary(x) sample size mean standard deviation 10.0000 5.0746 0.9538 R> R> R> ################################################### R> ### chunk number 82: journals-data eval=FALSE R> ################################################### R> ## data("Journals") R> ## Journals$citeprice <- Journals$price/Journals$citations R> ## attach(Journals) R> ## plot(log(subs), log(citeprice)) R> ## rug(log(subs)) R> ## rug(log(citeprice), side = 2) R> ## detach(Journals) R> R> R> ################################################### R> ### chunk number 83: journals-data1 R> ################################################### R> data("Journals") R> Journals$citeprice <- Journals$price/Journals$citations R> attach(Journals) R> plot(log(subs), log(citeprice)) R> rug(log(subs)) R> rug(log(citeprice), side = 2) R> detach(Journals) R> R> R> ################################################### R> ### chunk number 84: plot-formula R> ################################################### R> plot(log(subs) ~ log(citeprice), data = Journals) R> R> R> ################################################### R> ### chunk number 85: graphics1 R> ################################################### R> plot(log(subs) ~ log(citeprice), data = Journals, pch = 20, + col = "blue", ylim = c(0, 8), xlim = c(-7, 4), + main = "Library subscriptions") R> R> R> ################################################### R> ### chunk number 86: graphics2 R> ################################################### R> pdf("myfile.pdf", height = 5, width = 6) R> plot(1:20, pch = 1:20, col = 1:20, cex = 2) R> dev.off() pdf 2 R> R> R> ################################################### R> ### chunk number 87: dnorm-annotate eval=FALSE R> ################################################### R> ## curve(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, R> ## main = "Density of the standard normal distribution") R> ## text(-5, 0.3, expression(f(x) == frac(1, sigma ~~ R> ## sqrt(2*pi)) ~~ e^{-frac((x - mu)^2, 2*sigma^2)}), adj = 0) R> R> R> ################################################### R> ### chunk number 88: dnorm-annotate1 R> ################################################### R> curve(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, + main = "Density of the standard normal distribution") R> text(-5, 0.3, expression(f(x) == frac(1, sigma ~~ + sqrt(2*pi)) ~~ e^{-frac((x - mu)^2, 2*sigma^2)}), adj = 0) R> R> R> ################################################### R> ### chunk number 89: eda1 R> ################################################### R> data("CPS1985") R> str(CPS1985) 'data.frame': 534 obs. of 11 variables: $ wage : num 5.1 4.95 6.67 4 7.5 ... $ education : num 8 9 12 12 12 13 10 12 16 12 ... $ experience: num 21 42 1 4 17 9 27 9 11 9 ... $ age : num 35 57 19 22 35 28 43 27 33 27 ... $ ethnicity : Factor w/ 3 levels "cauc","hispanic",..: 2 1 1 1 1 1 1 1 1 1 ... $ region : Factor w/ 2 levels "south","other": 2 2 2 2 2 2 1 2 2 2 ... $ gender : Factor w/ 2 levels "male","female": 2 2 1 1 1 1 1 1 1 1 ... $ occupation: Factor w/ 6 levels "worker","technical",..: 1 1 1 1 1 1 1 1 1 1 ... $ sector : Factor w/ 3 levels "manufacturing",..: 1 1 1 3 3 3 3 3 1 3 ... $ union : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 1 1 1 ... $ married : Factor w/ 2 levels "no","yes": 2 2 1 1 2 1 1 1 2 1 ... R> R> R> ################################################### R> ### chunk number 90: eda2 R> ################################################### R> head(CPS1985) wage education experience age ethnicity region gender 1 5.10 8 21 35 hispanic other female 1100 4.95 9 42 57 cauc other female 2 6.67 12 1 19 cauc other male 3 4.00 12 4 22 cauc other male 4 7.50 12 17 35 cauc other male 5 13.07 13 9 28 cauc other male occupation sector union married 1 worker manufacturing no yes 1100 worker manufacturing no yes 2 worker manufacturing no no 3 worker other no no 4 worker other no yes 5 worker other yes no R> R> R> ################################################### R> ### chunk number 91: eda3 R> ################################################### R> levels(CPS1985$occupation)[c(2, 6)] <- c("techn", "mgmt") R> attach(CPS1985) R> R> R> ################################################### R> ### chunk number 92: eda4 R> ################################################### R> summary(wage) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.00 5.25 7.78 9.02 11.25 44.50 R> R> R> ################################################### R> ### chunk number 93: eda5 R> ################################################### R> mean(wage) [1] 9.024 R> median(wage) [1] 7.78 R> R> R> ################################################### R> ### chunk number 94: eda6 R> ################################################### R> var(wage) [1] 26.41 R> sd(wage) [1] 5.139 R> R> R> ################################################### R> ### chunk number 95: wage-hist R> ################################################### R> hist(wage, freq = FALSE) R> hist(log(wage), freq = FALSE) R> lines(density(log(wage)), col = 4) R> R> R> ################################################### R> ### chunk number 96: wage-hist1 R> ################################################### R> hist(wage, freq = FALSE) R> hist(log(wage), freq = FALSE) R> lines(density(log(wage)), col = 4) R> R> R> ################################################### R> ### chunk number 97: occ-table R> ################################################### R> summary(occupation) worker techn services office sales mgmt 156 105 83 97 38 55 R> R> R> ################################################### R> ### chunk number 98: occ-table R> ################################################### R> tab <- table(occupation) R> prop.table(tab) occupation worker techn services office sales mgmt 0.29213 0.19663 0.15543 0.18165 0.07116 0.10300 R> R> R> ################################################### R> ### chunk number 99: occ-barpie R> ################################################### R> barplot(tab) R> pie(tab) R> R> R> ################################################### R> ### chunk number 100: occ-barpie R> ################################################### R> par(mar = c(4, 3, 1, 1)) R> barplot(tab, las = 3) R> par(mar = c(2, 3, 1, 3)) R> pie(tab, radius = 1) R> R> R> ################################################### R> ### chunk number 101: xtabs R> ################################################### R> xtabs(~ gender + occupation, data = CPS1985) occupation gender worker techn services office sales mgmt male 126 53 34 21 21 34 female 30 52 49 76 17 21 R> R> R> ################################################### R> ### chunk number 102: spine eval=FALSE R> ################################################### R> ## plot(gender ~ occupation, data = CPS1985) R> R> R> ################################################### R> ### chunk number 103: spine1 R> ################################################### R> plot(gender ~ occupation, data = CPS1985) R> R> R> ################################################### R> ### chunk number 104: wageeduc-cor R> ################################################### R> cor(log(wage), education) [1] 0.3804 R> cor(log(wage), education, method = "spearman") [1] 0.3813 R> R> R> ################################################### R> ### chunk number 105: wageeduc-scatter eval=FALSE R> ################################################### R> ## plot(log(wage) ~ education) R> R> R> ################################################### R> ### chunk number 106: wageeduc-scatter1 R> ################################################### R> plot(log(wage) ~ education) R> R> R> ################################################### R> ### chunk number 107: tapply R> ################################################### R> tapply(log(wage), gender, mean) male female 2.165 1.934 R> R> R> ################################################### R> ### chunk number 108: boxqq1 eval=FALSE R> ################################################### R> ## plot(log(wage) ~ gender) R> R> R> ################################################### R> ### chunk number 109: boxqq2 eval=FALSE R> ################################################### R> ## mwage <- subset(CPS1985, gender == "male")$wage R> ## fwage <- subset(CPS1985, gender == "female")$wage R> ## qqplot(mwage, fwage, xlim = range(wage), ylim = range(wage), R> ## xaxs = "i", yaxs = "i", xlab = "male", ylab = "female") R> ## abline(0, 1) R> R> R> ################################################### R> ### chunk number 110: qq R> ################################################### R> plot(log(wage) ~ gender) R> mwage <- subset(CPS1985, gender == "male")$wage R> fwage <- subset(CPS1985, gender == "female")$wage R> qqplot(mwage, fwage, xlim = range(wage), ylim = range(wage), + xaxs = "i", yaxs = "i", xlab = "male", ylab = "female") R> abline(0, 1) R> R> R> ################################################### R> ### chunk number 111: detach R> ################################################### R> detach(CPS1985) R> R> R> > proc.time() user system elapsed 1.248 0.070 1.306 AER/tests/Ch-Microeconometrics.Rout.save0000644000176200001440000005773613616365071017701 0ustar liggesusers R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: swisslabor-data R> ################################################### R> data("SwissLabor") R> swiss_probit <- glm(participation ~ . + I(age^2), + data = SwissLabor, family = binomial(link = "probit")) R> summary(swiss_probit) Call: glm(formula = participation ~ . + I(age^2), family = binomial(link = "probit"), data = SwissLabor) Deviance Residuals: Min 1Q Median 3Q Max -1.919 -0.970 -0.479 1.021 2.480 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.7491 1.4069 2.66 0.0077 income -0.6669 0.1320 -5.05 4.3e-07 age 2.0753 0.4054 5.12 3.1e-07 education 0.0192 0.0179 1.07 0.2843 youngkids -0.7145 0.1004 -7.12 1.1e-12 oldkids -0.1470 0.0509 -2.89 0.0039 foreignyes 0.7144 0.1213 5.89 3.9e-09 I(age^2) -0.2943 0.0499 -5.89 3.8e-09 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1203.2 on 871 degrees of freedom Residual deviance: 1017.2 on 864 degrees of freedom AIC: 1033 Number of Fisher Scoring iterations: 4 R> R> R> ################################################### R> ### chunk number 3: swisslabor-plot eval=FALSE R> ################################################### R> ## plot(participation ~ age, data = SwissLabor, ylevels = 2:1) R> R> R> ################################################### R> ### chunk number 4: swisslabor-plot-refined R> ################################################### R> plot(participation ~ education, data = SwissLabor, ylevels = 2:1) R> fm <- glm(participation ~ education + I(education^2), data = SwissLabor, family = binomial) R> edu <- sort(unique(SwissLabor$education)) R> prop <- sapply(edu, function(x) mean(SwissLabor$education <= x)) R> lines(predict(fm, newdata = data.frame(education = edu), type = "response") ~ prop, col = 2) R> R> plot(participation ~ age, data = SwissLabor, ylevels = 2:1) R> fm <- glm(participation ~ age + I(age^2), data = SwissLabor, family = binomial) R> ag <- sort(unique(SwissLabor$age)) R> prop <- sapply(ag, function(x) mean(SwissLabor$age <= x)) R> lines(predict(fm, newdata = data.frame(age = ag), type = "response") ~ prop, col = 2) R> R> R> ################################################### R> ### chunk number 5: effects1 R> ################################################### R> fav <- mean(dnorm(predict(swiss_probit, type = "link"))) R> fav * coef(swiss_probit) (Intercept) income age education youngkids 1.241930 -0.220932 0.687466 0.006359 -0.236682 oldkids foreignyes I(age^2) -0.048690 0.236644 -0.097505 R> R> R> ################################################### R> ### chunk number 6: effects2 R> ################################################### R> av <- colMeans(SwissLabor[, -c(1, 7)]) R> av <- data.frame(rbind(swiss = av, foreign = av), + foreign = factor(c("no", "yes"))) R> av <- predict(swiss_probit, newdata = av, type = "link") R> av <- dnorm(av) R> av["swiss"] * coef(swiss_probit)[-7] (Intercept) income age education youngkids 1.495137 -0.265976 0.827628 0.007655 -0.284938 oldkids I(age^2) -0.058617 -0.117384 R> R> R> ################################################### R> ### chunk number 7: effects3 R> ################################################### R> av["foreign"] * coef(swiss_probit)[-7] (Intercept) income age education youngkids 1.136517 -0.202180 0.629115 0.005819 -0.216593 oldkids I(age^2) -0.044557 -0.089229 R> R> R> ################################################### R> ### chunk number 8: mcfadden R> ################################################### R> swiss_probit0 <- update(swiss_probit, formula = . ~ 1) R> 1 - as.vector(logLik(swiss_probit)/logLik(swiss_probit0)) [1] 0.1546 R> R> R> ################################################### R> ### chunk number 9: confusion-matrix R> ################################################### R> table(true = SwissLabor$participation, + pred = round(fitted(swiss_probit))) pred true 0 1 no 337 134 yes 146 255 R> R> R> ################################################### R> ### chunk number 10: confusion-matrix1 R> ################################################### R> tab <- table(true = SwissLabor$participation, + pred = round(fitted(swiss_probit))) R> tabp <- round(100 * c(tab[1,1] + tab[2,2], tab[2,1] + tab[1,2])/sum(tab), digits = 2) R> R> R> ################################################### R> ### chunk number 11: roc-plot eval=FALSE R> ################################################### R> ## library("ROCR") R> ## pred <- prediction(fitted(swiss_probit), R> ## SwissLabor$participation) R> ## plot(performance(pred, "acc")) R> ## plot(performance(pred, "tpr", "fpr")) R> ## abline(0, 1, lty = 2) R> R> R> ################################################### R> ### chunk number 12: roc-plot1 R> ################################################### R> library("ROCR") Loading required package: gplots Attaching package: 'gplots' The following object is masked from 'package:stats': lowess R> pred <- prediction(fitted(swiss_probit), + SwissLabor$participation) R> plot(performance(pred, "acc")) R> plot(performance(pred, "tpr", "fpr")) R> abline(0, 1, lty = 2) R> R> R> ################################################### R> ### chunk number 13: rss R> ################################################### R> deviance(swiss_probit) [1] 1017 R> sum(residuals(swiss_probit, type = "deviance")^2) [1] 1017 R> sum(residuals(swiss_probit, type = "pearson")^2) [1] 866.5 R> R> R> ################################################### R> ### chunk number 14: coeftest eval=FALSE R> ################################################### R> ## coeftest(swiss_probit, vcov = sandwich) R> R> R> ################################################### R> ### chunk number 15: murder R> ################################################### R> data("MurderRates") R> ## murder_logit <- glm(I(executions > 0) ~ time + income + ## IGNORE_RDIFF, excluded due to small numeric deviations on different platforms R> ## noncauc + lfp + southern, data = MurderRates, R> ## family = binomial) R> ## R> ## R> ## ################################################### R> ## ### chunk number 16: murder-coeftest R> ## ################################################### R> ## coeftest(murder_logit) R> ## R> ## R> ## ################################################### R> ## ### chunk number 17: murder2 R> ## ################################################### R> ## murder_logit2 <- glm(I(executions > 0) ~ time + income + R> ## noncauc + lfp + southern, data = MurderRates, R> ## family = binomial, control = list(epsilon = 1e-15, R> ## maxit = 50, trace = FALSE)) R> ## R> ## R> ## ################################################### R> ## ### chunk number 18: murder2-coeftest R> ## ################################################### R> ## coeftest(murder_logit2) R> R> R> ################################################### R> ### chunk number 19: separation R> ################################################### R> table(I(MurderRates$executions > 0), MurderRates$southern) no yes FALSE 9 0 TRUE 20 15 R> R> R> ################################################### R> ### chunk number 20: countreg-pois R> ################################################### R> data("RecreationDemand") R> rd_pois <- glm(trips ~ ., data = RecreationDemand, + family = poisson) R> R> R> ################################################### R> ### chunk number 21: countreg-pois-coeftest R> ################################################### R> coeftest(rd_pois) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.26499 0.09372 2.83 0.0047 quality 0.47173 0.01709 27.60 < 2e-16 skiyes 0.41821 0.05719 7.31 2.6e-13 income -0.11132 0.01959 -5.68 1.3e-08 userfeeyes 0.89817 0.07899 11.37 < 2e-16 costC -0.00343 0.00312 -1.10 0.2713 costS -0.04254 0.00167 -25.47 < 2e-16 costH 0.03613 0.00271 13.34 < 2e-16 R> R> R> ################################################### R> ### chunk number 22: countreg-pois-logLik R> ################################################### R> logLik(rd_pois) 'log Lik.' -1529 (df=8) R> R> R> ################################################### R> ### chunk number 23: countreg-odtest1 R> ################################################### R> dispersiontest(rd_pois) Overdispersion test data: rd_pois z = 2.4, p-value = 0.008 alternative hypothesis: true dispersion is greater than 1 sample estimates: dispersion 6.566 R> R> R> ################################################### R> ### chunk number 24: countreg-odtest2 R> ################################################### R> dispersiontest(rd_pois, trafo = 2) Overdispersion test data: rd_pois z = 2.9, p-value = 0.002 alternative hypothesis: true alpha is greater than 0 sample estimates: alpha 1.316 R> R> R> ################################################### R> ### chunk number 25: countreg-nbin R> ################################################### R> library("MASS") R> rd_nb <- glm.nb(trips ~ ., data = RecreationDemand) R> coeftest(rd_nb) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.12194 0.21430 -5.24 1.6e-07 quality 0.72200 0.04012 18.00 < 2e-16 skiyes 0.61214 0.15030 4.07 4.6e-05 income -0.02606 0.04245 -0.61 0.539 userfeeyes 0.66917 0.35302 1.90 0.058 costC 0.04801 0.00918 5.23 1.7e-07 costS -0.09269 0.00665 -13.93 < 2e-16 costH 0.03884 0.00775 5.01 5.4e-07 R> logLik(rd_nb) 'log Lik.' -825.6 (df=9) R> R> R> ################################################### R> ### chunk number 26: countreg-se R> ################################################### R> round(sqrt(rbind(diag(vcov(rd_pois)), + diag(sandwich(rd_pois)))), digits = 3) (Intercept) quality skiyes income userfeeyes costC costS [1,] 0.094 0.017 0.057 0.02 0.079 0.003 0.002 [2,] 0.432 0.049 0.194 0.05 0.247 0.015 0.012 costH [1,] 0.003 [2,] 0.009 R> R> R> ################################################### R> ### chunk number 27: countreg-sandwich R> ################################################### R> coeftest(rd_pois, vcov = sandwich) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.26499 0.43248 0.61 0.54006 quality 0.47173 0.04885 9.66 < 2e-16 skiyes 0.41821 0.19387 2.16 0.03099 income -0.11132 0.05031 -2.21 0.02691 userfeeyes 0.89817 0.24691 3.64 0.00028 costC -0.00343 0.01470 -0.23 0.81549 costS -0.04254 0.01173 -3.62 0.00029 costH 0.03613 0.00939 3.85 0.00012 R> R> R> ################################################### R> ### chunk number 28: countreg-OPG R> ################################################### R> round(sqrt(diag(vcovOPG(rd_pois))), 3) (Intercept) quality skiyes income userfeeyes 0.025 0.007 0.020 0.010 0.033 costC costS costH 0.001 0.000 0.001 R> R> R> ################################################### R> ### chunk number 29: countreg-plot R> ################################################### R> plot(table(RecreationDemand$trips), ylab = "") R> R> R> ################################################### R> ### chunk number 30: countreg-zeros R> ################################################### R> rbind(obs = table(RecreationDemand$trips)[1:10], exp = round( + sapply(0:9, function(x) sum(dpois(x, fitted(rd_pois)))))) 0 1 2 3 4 5 6 7 8 9 obs 417 68 38 34 17 13 11 2 8 1 exp 277 146 68 41 30 23 17 13 10 7 R> R> R> ################################################### R> ### chunk number 31: countreg-pscl R> ################################################### R> library("pscl") Classes and Methods for R developed in the Political Science Computational Laboratory Department of Political Science Stanford University Simon Jackman hurdle and zeroinfl functions by Achim Zeileis R> R> R> ################################################### R> ### chunk number 32: countreg-zinb R> ################################################### R> rd_zinb <- zeroinfl(trips ~ . | quality + income, + data = RecreationDemand, dist = "negbin") R> R> R> ################################################### R> ### chunk number 33: countreg-zinb-summary R> ################################################### R> summary(rd_zinb) Call: zeroinfl(formula = trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") Pearson residuals: Min 1Q Median 3Q Max -1.0889 -0.2004 -0.0570 -0.0451 40.0139 Count model coefficients (negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 1.09663 0.25668 4.27 1.9e-05 quality 0.16891 0.05303 3.19 0.0014 skiyes 0.50069 0.13449 3.72 0.0002 income -0.06927 0.04380 -1.58 0.1138 userfeeyes 0.54279 0.28280 1.92 0.0549 costC 0.04044 0.01452 2.79 0.0053 costS -0.06621 0.00775 -8.55 < 2e-16 costH 0.02060 0.01023 2.01 0.0441 Log(theta) 0.19017 0.11299 1.68 0.0924 Zero-inflation model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) 5.743 1.556 3.69 0.00022 quality -8.307 3.682 -2.26 0.02404 income -0.258 0.282 -0.92 0.35950 Theta = 1.209 Number of iterations in BFGS optimization: 26 Log-likelihood: -722 on 12 Df R> R> R> ################################################### R> ### chunk number 34: countreg-zinb-expected R> ################################################### R> round(colSums(predict(rd_zinb, type = "prob")[,1:10])) 0 1 2 3 4 5 6 7 8 9 433 47 35 27 20 16 12 10 8 7 R> R> R> ################################################### R> ### chunk number 35: countreg-hurdle R> ################################################### R> rd_hurdle <- hurdle(trips ~ . | quality + income, + data = RecreationDemand, dist = "negbin") R> summary(rd_hurdle) Call: hurdle(formula = trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") Pearson residuals: Min 1Q Median 3Q Max -1.610 -0.207 -0.185 -0.164 12.111 Count model coefficients (truncated negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 0.8419 0.3828 2.20 0.0278 quality 0.1717 0.0723 2.37 0.0176 skiyes 0.6224 0.1901 3.27 0.0011 income -0.0571 0.0645 -0.88 0.3763 userfeeyes 0.5763 0.3851 1.50 0.1345 costC 0.0571 0.0217 2.63 0.0085 costS -0.0775 0.0115 -6.71 1.9e-11 costH 0.0124 0.0149 0.83 0.4064 Log(theta) -0.5303 0.2611 -2.03 0.0423 Zero hurdle model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) -2.7663 0.3623 -7.64 2.3e-14 quality 1.5029 0.1003 14.98 < 2e-16 income -0.0447 0.0785 -0.57 0.57 Theta: count = 0.588 Number of iterations in BFGS optimization: 18 Log-likelihood: -765 on 12 Df R> R> R> ################################################### R> ### chunk number 36: countreg-hurdle-expected R> ################################################### R> round(colSums(predict(rd_hurdle, type = "prob")[,1:10])) 0 1 2 3 4 5 6 7 8 9 417 74 42 27 19 14 10 8 6 5 R> R> R> ################################################### R> ### chunk number 37: tobit1 R> ################################################### R> data("Affairs") R> aff_tob <- tobit(affairs ~ age + yearsmarried + + religiousness + occupation + rating, data = Affairs) R> summary(aff_tob) Call: tobit(formula = affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 8.1742 2.7414 2.98 0.0029 age -0.1793 0.0791 -2.27 0.0234 yearsmarried 0.5541 0.1345 4.12 3.8e-05 religiousness -1.6862 0.4038 -4.18 3.0e-05 occupation 0.3261 0.2544 1.28 0.2000 rating -2.2850 0.4078 -5.60 2.1e-08 Log(scale) 2.1099 0.0671 31.44 < 2e-16 Scale: 8.25 Gaussian distribution Number of Newton-Raphson Iterations: 4 Log-likelihood: -706 on 7 Df Wald-statistic: 67.7 on 5 Df, p-value: 3.1e-13 R> R> R> ################################################### R> ### chunk number 38: tobit2 R> ################################################### R> aff_tob2 <- update(aff_tob, right = 4) R> summary(aff_tob2) Call: tobit(formula = affairs ~ age + yearsmarried + religiousness + occupation + rating, right = 4, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 70 80 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 7.9010 2.8039 2.82 0.00483 age -0.1776 0.0799 -2.22 0.02624 yearsmarried 0.5323 0.1412 3.77 0.00016 religiousness -1.6163 0.4244 -3.81 0.00014 occupation 0.3242 0.2539 1.28 0.20162 rating -2.2070 0.4498 -4.91 9.3e-07 Log(scale) 2.0723 0.1104 18.77 < 2e-16 Scale: 7.94 Gaussian distribution Number of Newton-Raphson Iterations: 4 Log-likelihood: -500 on 7 Df Wald-statistic: 42.6 on 5 Df, p-value: 4.5e-08 R> R> R> ################################################### R> ### chunk number 39: tobit3 R> ################################################### R> linearHypothesis(aff_tob, c("age = 0", "occupation = 0"), + vcov = sandwich) Linear hypothesis test Hypothesis: age = 0 occupation = 0 Model 1: restricted model Model 2: affairs ~ age + yearsmarried + religiousness + occupation + rating Note: Coefficient covariance matrix supplied. Res.Df Df Chisq Pr(>Chisq) 1 596 2 594 2 4.91 0.086 R> R> R> ################################################### R> ### chunk number 40: numeric-response R> ################################################### R> SwissLabor$partnum <- as.numeric(SwissLabor$participation) - 1 R> R> R> ################################################### R> ### chunk number 41: kleinspady eval=FALSE R> ################################################### R> ## library("np") R> ## swiss_bw <- npindexbw(partnum ~ income + age + education + R> ## youngkids + oldkids + foreign + I(age^2), data = SwissLabor, R> ## method = "kleinspady", nmulti = 5) R> R> R> ################################################### R> ### chunk number 42: kleinspady-bw eval=FALSE R> ################################################### R> ## summary(swiss_bw) R> R> R> ################################################### R> ### chunk number 43: kleinspady-summary eval=FALSE R> ################################################### R> ## swiss_ks <- npindex(bws = swiss_bw, gradients = TRUE) R> ## summary(swiss_ks) R> R> R> ################################################### R> ### chunk number 44: probit-confusion R> ################################################### R> table(Actual = SwissLabor$participation, Predicted = + round(predict(swiss_probit, type = "response"))) Predicted Actual 0 1 no 337 134 yes 146 255 R> R> R> ################################################### R> ### chunk number 45: bw-tab R> ################################################### R> data("BankWages") R> edcat <- factor(BankWages$education) R> levels(edcat)[3:10] <- rep(c("14-15", "16-18", "19-21"), + c(2, 3, 3)) R> tab <- xtabs(~ edcat + job, data = BankWages) R> prop.table(tab, 1) job edcat custodial admin manage 8 0.245283 0.754717 0.000000 12 0.068421 0.926316 0.005263 14-15 0.008197 0.959016 0.032787 16-18 0.000000 0.367089 0.632911 19-21 0.000000 0.033333 0.966667 R> R> R> ################################################### R> ### chunk number 46: bw-plot eval=FALSE R> ################################################### R> ## plot(job ~ edcat, data = BankWages, off = 0) R> R> R> ################################################### R> ### chunk number 47: bw-plot1 R> ################################################### R> plot(job ~ edcat, data = BankWages, off = 0) R> box() R> R> R> ################################################### R> ### chunk number 48: bw-multinom R> ################################################### R> library("nnet") R> bank_mnl <- multinom(job ~ education + minority, + data = BankWages, subset = gender == "male", trace = FALSE) R> R> R> ################################################### R> ### chunk number 49: bw-multinom-coeftest R> ################################################### R> coeftest(bank_mnl) z test of coefficients: Estimate Std. Error z value Pr(>|z|) admin:(Intercept) -4.761 1.173 -4.06 4.9e-05 admin:education 0.553 0.099 5.59 2.3e-08 admin:minorityyes -0.427 0.503 -0.85 0.3957 manage:(Intercept) -30.775 4.479 -6.87 6.4e-12 manage:education 2.187 0.295 7.42 1.2e-13 manage:minorityyes -2.536 0.934 -2.71 0.0066 R> R> R> ################################################### R> ### chunk number 50: bw-polr R> ################################################### R> library("MASS") R> bank_polr <- polr(job ~ education + minority, + data = BankWages, subset = gender == "male", Hess = TRUE) R> coeftest(bank_polr) z test of coefficients: Estimate Std. Error z value Pr(>|z|) education 0.8700 0.0931 9.35 < 2e-16 minorityyes -1.0564 0.4120 -2.56 0.01 custodial|admin 7.9514 1.0769 7.38 1.5e-13 admin|manage 14.1721 1.4744 9.61 < 2e-16 R> R> R> ################################################### R> ### chunk number 51: bw-AIC R> ################################################### R> AIC(bank_mnl) [1] 249.5 R> AIC(bank_polr) [1] 268.6 R> R> R> > proc.time() user system elapsed 2.316 0.823 2.117 AER/tests/Ch-Validation.Rout.save0000644000176200001440000004607213461526527016301 0ustar liggesusers R version 3.6.0 (2019-04-26) -- "Planting of a Tree" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: ps-summary R> ################################################### R> data("PublicSchools") R> summary(PublicSchools) Expenditure Income Min. :259 Min. : 5736 1st Qu.:315 1st Qu.: 6670 Median :354 Median : 7597 Mean :373 Mean : 7608 3rd Qu.:426 3rd Qu.: 8286 Max. :821 Max. :10851 NA's :1 R> R> R> ################################################### R> ### chunk number 3: ps-plot eval=FALSE R> ################################################### R> ## ps <- na.omit(PublicSchools) R> ## ps$Income <- ps$Income / 10000 R> ## plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) R> ## ps_lm <- lm(Expenditure ~ Income, data = ps) R> ## abline(ps_lm) R> ## id <- c(2, 24, 48) R> ## text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) R> R> R> ################################################### R> ### chunk number 4: ps-plot1 R> ################################################### R> ps <- na.omit(PublicSchools) R> ps$Income <- ps$Income / 10000 R> plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) R> ps_lm <- lm(Expenditure ~ Income, data = ps) R> abline(ps_lm) R> id <- c(2, 24, 48) R> text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) R> R> R> ################################################### R> ### chunk number 5: ps-lmplot eval=FALSE R> ################################################### R> ## plot(ps_lm, which = 1:6) R> R> R> ################################################### R> ### chunk number 6: ps-lmplot1 R> ################################################### R> plot(ps_lm, which = 1:6) R> R> R> ################################################### R> ### chunk number 7: ps-hatvalues eval=FALSE R> ################################################### R> ## ps_hat <- hatvalues(ps_lm) R> ## plot(ps_hat) R> ## abline(h = c(1, 3) * mean(ps_hat), col = 2) R> ## id <- which(ps_hat > 3 * mean(ps_hat)) R> ## text(id, ps_hat[id], rownames(ps)[id], pos = 1, xpd = TRUE) R> R> R> ################################################### R> ### chunk number 8: ps-hatvalues1 R> ################################################### R> ps_hat <- hatvalues(ps_lm) R> plot(ps_hat) R> abline(h = c(1, 3) * mean(ps_hat), col = 2) R> id <- which(ps_hat > 3 * mean(ps_hat)) R> text(id, ps_hat[id], rownames(ps)[id], pos = 1, xpd = TRUE) R> R> R> ################################################### R> ### chunk number 9: influence-measures1 eval=FALSE R> ################################################### R> ## influence.measures(ps_lm) R> R> R> ################################################### R> ### chunk number 10: which-hatvalues R> ################################################### R> which(ps_hat > 3 * mean(ps_hat)) Alaska Washington DC 2 48 R> R> R> ################################################### R> ### chunk number 11: influence-measures2 R> ################################################### R> summary(influence.measures(ps_lm)) Potentially influential observations of lm(formula = Expenditure ~ Income, data = ps) : dfb.1_ dfb.Incm dffit cov.r cook.d hat Alaska -2.39_* 2.52_* 2.65_* 0.55_* 2.31_* 0.21_* Mississippi 0.07 -0.07 0.08 1.14_* 0.00 0.08 Washington DC 0.66 -0.71 -0.77_* 1.01 0.28 0.13_* R> R> R> ################################################### R> ### chunk number 12: ps-noinf eval=FALSE R> ################################################### R> ## plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) R> ## abline(ps_lm) R> ## id <- which(apply(influence.measures(ps_lm)$is.inf, 1, any)) R> ## text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) R> ## ps_noinf <- lm(Expenditure ~ Income, data = ps[-id,]) R> ## abline(ps_noinf, lty = 2) R> R> R> ################################################### R> ### chunk number 13: ps-noinf1 R> ################################################### R> plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) R> abline(ps_lm) R> id <- which(apply(influence.measures(ps_lm)$is.inf, 1, any)) R> text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) R> ps_noinf <- lm(Expenditure ~ Income, data = ps[-id,]) R> abline(ps_noinf, lty = 2) R> R> R> ################################################### R> ### chunk number 14: journals-age R> ################################################### R> data("Journals") R> journals <- Journals[, c("subs", "price")] R> journals$citeprice <- Journals$price/Journals$citations R> journals$age <- 2000 - Journals$foundingyear R> R> R> ################################################### R> ### chunk number 15: journals-lm R> ################################################### R> jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) R> R> R> ################################################### R> ### chunk number 16: bptest1 R> ################################################### R> bptest(jour_lm) studentized Breusch-Pagan test data: jour_lm BP = 9.8, df = 1, p-value = 0.002 R> R> R> ################################################### R> ### chunk number 17: bptest2 R> ################################################### R> bptest(jour_lm, ~ log(citeprice) + I(log(citeprice)^2), + data = journals) studentized Breusch-Pagan test data: jour_lm BP = 11, df = 2, p-value = 0.004 R> R> R> ################################################### R> ### chunk number 18: gqtest R> ################################################### R> gqtest(jour_lm, order.by = ~ citeprice, data = journals) Goldfeld-Quandt test data: jour_lm GQ = 1.7, df1 = 88, df2 = 88, p-value = 0.007 alternative hypothesis: variance increases from segment 1 to 2 R> R> R> ################################################### R> ### chunk number 19: resettest R> ################################################### R> resettest(jour_lm) RESET test data: jour_lm RESET = 1.4, df1 = 2, df2 = 176, p-value = 0.2 R> R> R> ################################################### R> ### chunk number 20: raintest R> ################################################### R> raintest(jour_lm, order.by = ~ age, data = journals) Rainbow test data: jour_lm Rain = 1.8, df1 = 90, df2 = 88, p-value = 0.004 R> R> R> ################################################### R> ### chunk number 21: harvtest R> ################################################### R> harvtest(jour_lm, order.by = ~ age, data = journals) Harvey-Collier test data: jour_lm HC = 5.1, df = 177, p-value = 9e-07 R> R> R> ################################################### R> ### chunk number 22: R> ################################################### R> library("dynlm") R> R> R> ################################################### R> ### chunk number 23: usmacro-dynlm R> ################################################### R> data("USMacroG") R> consump1 <- dynlm(consumption ~ dpi + L(dpi), + data = USMacroG) R> R> R> ################################################### R> ### chunk number 24: dwtest R> ################################################### R> dwtest(consump1) Durbin-Watson test data: consump1 DW = 0.087, p-value <2e-16 alternative hypothesis: true autocorrelation is greater than 0 R> R> R> ################################################### R> ### chunk number 25: Box-test R> ################################################### R> Box.test(residuals(consump1), type = "Ljung-Box") Box-Ljung test data: residuals(consump1) X-squared = 176, df = 1, p-value <2e-16 R> R> R> ################################################### R> ### chunk number 26: bgtest R> ################################################### R> bgtest(consump1) Breusch-Godfrey test for serial correlation of order up to 1 data: consump1 LM test = 193, df = 1, p-value <2e-16 R> R> R> ################################################### R> ### chunk number 27: vcov R> ################################################### R> vcov(jour_lm) (Intercept) log(citeprice) (Intercept) 3.126e-03 -6.144e-05 log(citeprice) -6.144e-05 1.268e-03 R> vcovHC(jour_lm) (Intercept) log(citeprice) (Intercept) 0.003085 0.000693 log(citeprice) 0.000693 0.001188 R> R> R> ################################################### R> ### chunk number 28: coeftest R> ################################################### R> coeftest(jour_lm, vcov = vcovHC) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.7662 0.0555 85.8 <2e-16 log(citeprice) -0.5331 0.0345 -15.5 <2e-16 R> R> R> ################################################### R> ### chunk number 29: sandwiches R> ################################################### R> t(sapply(c("const", "HC0", "HC1", "HC2", "HC3", "HC4"), + function(x) sqrt(diag(vcovHC(jour_lm, type = x))))) (Intercept) log(citeprice) const 0.05591 0.03561 HC0 0.05495 0.03377 HC1 0.05526 0.03396 HC2 0.05525 0.03412 HC3 0.05555 0.03447 HC4 0.05536 0.03459 R> R> R> ################################################### R> ### chunk number 30: ps-anova R> ################################################### R> ps_lm <- lm(Expenditure ~ Income, data = ps) R> ps_lm2 <- lm(Expenditure ~ Income + I(Income^2), data = ps) R> anova(ps_lm, ps_lm2) Analysis of Variance Table Model 1: Expenditure ~ Income Model 2: Expenditure ~ Income + I(Income^2) Res.Df RSS Df Sum of Sq F Pr(>F) 1 48 181015 2 47 150986 1 30030 9.35 0.0037 R> R> R> ################################################### R> ### chunk number 31: ps-waldtest R> ################################################### R> waldtest(ps_lm, ps_lm2, vcov = vcovHC(ps_lm2, type = "HC4")) Wald test Model 1: Expenditure ~ Income Model 2: Expenditure ~ Income + I(Income^2) Res.Df Df F Pr(>F) 1 48 2 47 1 0.08 0.77 R> R> R> ################################################### R> ### chunk number 32: vcovHAC R> ################################################### R> rbind(SE = sqrt(diag(vcov(consump1))), + QS = sqrt(diag(kernHAC(consump1))), + NW = sqrt(diag(NeweyWest(consump1)))) (Intercept) dpi L(dpi) SE 14.51 0.2063 0.2075 QS 94.11 0.3893 0.3669 NW 100.83 0.4230 0.3989 R> R> R> ################################################### R> ### chunk number 33: solow-lm R> ################################################### R> data("OECDGrowth") R> solow_lm <- lm(log(gdp85/gdp60) ~ log(gdp60) + + log(invest) + log(popgrowth + .05), data = OECDGrowth) R> summary(solow_lm) Call: lm(formula = log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth + 0.05), data = OECDGrowth) Residuals: Min 1Q Median 3Q Max -0.1840 -0.0399 -0.0078 0.0451 0.3188 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.9759 1.0216 2.91 0.0093 log(gdp60) -0.3429 0.0565 -6.07 9.8e-06 log(invest) 0.6501 0.2020 3.22 0.0048 log(popgrowth + 0.05) -0.5730 0.2904 -1.97 0.0640 Residual standard error: 0.133 on 18 degrees of freedom Multiple R-squared: 0.746, Adjusted R-squared: 0.704 F-statistic: 17.7 on 3 and 18 DF, p-value: 1.34e-05 R> R> R> ################################################### R> ### chunk number 34: solow-plot eval=FALSE R> ################################################### R> ## plot(solow_lm) R> R> R> ################################################### R> ### chunk number 35: solow-lts R> ################################################### R> library("MASS") R> solow_lts <- lqs(log(gdp85/gdp60) ~ log(gdp60) + + log(invest) + log(popgrowth + .05), data = OECDGrowth, + psamp = 13, nsamp = "exact") R> R> R> ################################################### R> ### chunk number 36: solow-smallresid R> ################################################### R> smallresid <- which( + abs(residuals(solow_lts)/solow_lts$scale[2]) <= 2.5) R> R> R> ################################################### R> ### chunk number 37: solow-nohighlev R> ################################################### R> X <- model.matrix(solow_lm)[,-1] R> Xcv <- cov.rob(X, nsamp = "exact") R> nohighlev <- which( + sqrt(mahalanobis(X, Xcv$center, Xcv$cov)) <= 2.5) R> R> R> ################################################### R> ### chunk number 38: solow-goodobs R> ################################################### R> goodobs <- unique(c(smallresid, nohighlev)) R> R> R> ################################################### R> ### chunk number 39: solow-badobs R> ################################################### R> rownames(OECDGrowth)[-goodobs] [1] "Canada" "USA" "Turkey" "Australia" R> R> R> ################################################### R> ### chunk number 40: solow-rob R> ################################################### R> solow_rob <- update(solow_lm, subset = goodobs) R> summary(solow_rob) Call: lm(formula = log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth + 0.05), data = OECDGrowth, subset = goodobs) Residuals: Min 1Q Median 3Q Max -0.15454 -0.05548 -0.00651 0.03159 0.26773 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.7764 1.2816 2.95 0.0106 log(gdp60) -0.4507 0.0569 -7.93 1.5e-06 log(invest) 0.7033 0.1906 3.69 0.0024 log(popgrowth + 0.05) -0.6504 0.4190 -1.55 0.1429 Residual standard error: 0.107 on 14 degrees of freedom Multiple R-squared: 0.853, Adjusted R-squared: 0.822 F-statistic: 27.1 on 3 and 14 DF, p-value: 4.3e-06 R> R> R> ################################################### R> ### chunk number 41: quantreg R> ################################################### R> library("quantreg") Loading required package: SparseM Attaching package: 'SparseM' The following object is masked from 'package:base': backsolve Attaching package: 'quantreg' The following object is masked from 'package:survival': untangle.specials R> R> R> ################################################### R> ### chunk number 42: cps-lad R> ################################################### R> library("quantreg") R> data("CPS1988") R> cps_f <- log(wage) ~ experience + I(experience^2) + education R> cps_lad <- rq(cps_f, data = CPS1988) R> summary(cps_lad) Call: rq(formula = cps_f, data = CPS1988) tau: [1] 0.5 Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 4.24088 0.02190 193.67805 0.00000 experience 0.07744 0.00115 67.50041 0.00000 I(experience^2) -0.00130 0.00003 -49.97891 0.00000 education 0.09429 0.00140 67.57171 0.00000 R> R> R> ################################################### R> ### chunk number 43: cps-rq R> ################################################### R> cps_rq <- rq(cps_f, tau = c(0.25, 0.75), data = CPS1988) R> summary(cps_rq) Call: rq(formula = cps_f, tau = c(0.25, 0.75), data = CPS1988) tau: [1] 0.25 Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 3.78227 0.02866 131.95189 0.00000 experience 0.09156 0.00152 60.26474 0.00000 I(experience^2) -0.00164 0.00004 -45.39065 0.00000 education 0.09321 0.00185 50.32520 0.00000 Call: rq(formula = cps_f, tau = c(0.25, 0.75), data = CPS1988) tau: [1] 0.75 Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 4.66005 0.02023 230.39734 0.00000 experience 0.06377 0.00097 65.41364 0.00000 I(experience^2) -0.00099 0.00002 -44.15591 0.00000 education 0.09434 0.00134 70.65855 0.00000 R> R> R> ################################################### R> ### chunk number 44: cps-rqs R> ################################################### R> cps_rq25 <- rq(cps_f, tau = 0.25, data = CPS1988) R> cps_rq75 <- rq(cps_f, tau = 0.75, data = CPS1988) R> anova(cps_rq25, cps_rq75) Quantile Regression Analysis of Deviance Table Model: log(wage) ~ experience + I(experience^2) + education Joint Test of Equality of Slopes: tau in { 0.25 0.75 } Df Resid Df F value Pr(>F) 1 3 56307 115 <2e-16 R> R> R> ################################################### R> ### chunk number 45: cps-rq-anova R> ################################################### R> anova(cps_rq25, cps_rq75, joint = FALSE) Quantile Regression Analysis of Deviance Table Model: log(wage) ~ experience + I(experience^2) + education Tests of Equality of Distinct Slopes: tau in { 0.25 0.75 } Df Resid Df F value Pr(>F) experience 1 56309 339.41 <2e-16 I(experience^2) 1 56309 329.74 <2e-16 education 1 56309 0.35 0.55 R> R> R> ################################################### R> ### chunk number 46: rqbig R> ################################################### R> cps_rqbig <- rq(cps_f, tau = seq(0.05, 0.95, by = 0.05), + data = CPS1988) R> cps_rqbigs <- summary(cps_rqbig) Warning message: In summary.rq(xi, U = U, ...) : 18 non-positive fis R> R> R> ################################################### R> ### chunk number 47: rqbig-plot eval=FALSE R> ################################################### R> ## plot(cps_rqbigs) R> R> R> ################################################### R> ### chunk number 48: rqbig-plot1 R> ################################################### R> plot(cps_rqbigs) R> R> R> > proc.time() user system elapsed 16.529 0.105 16.620 AER/tests/Ch-Intro.Rout.save0000644000176200001440000002357413517437412015300 0ustar liggesusers R version 3.5.2 (2018-12-20) -- "Eggshell Igloo" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: journals-data R> ################################################### R> data("Journals", package = "AER") R> R> R> ################################################### R> ### chunk number 3: journals-dim R> ################################################### R> dim(Journals) [1] 180 10 R> names(Journals) [1] "title" "publisher" "society" "price" [5] "pages" "charpp" "citations" "foundingyear" [9] "subs" "field" R> R> R> ################################################### R> ### chunk number 4: journals-plot eval=FALSE R> ################################################### R> ## plot(log(subs) ~ log(price/citations), data = Journals) R> R> R> ################################################### R> ### chunk number 5: journals-lm eval=FALSE R> ################################################### R> ## j_lm <- lm(log(subs) ~ log(price/citations), data = Journals) R> ## abline(j_lm) R> R> R> ################################################### R> ### chunk number 6: journals-lmplot R> ################################################### R> plot(log(subs) ~ log(price/citations), data = Journals) R> j_lm <- lm(log(subs) ~ log(price/citations), data = Journals) R> abline(j_lm) R> R> R> ################################################### R> ### chunk number 7: journals-lm-summary R> ################################################### R> summary(j_lm) Call: lm(formula = log(subs) ~ log(price/citations), data = Journals) Residuals: Min 1Q Median 3Q Max -2.7248 -0.5361 0.0372 0.4662 1.8481 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.7662 0.0559 85.2 <2e-16 log(price/citations) -0.5331 0.0356 -15.0 <2e-16 Residual standard error: 0.75 on 178 degrees of freedom Multiple R-squared: 0.557, Adjusted R-squared: 0.555 F-statistic: 224 on 1 and 178 DF, p-value: <2e-16 R> R> R> ################################################### R> ### chunk number 8: cps-data R> ################################################### R> data("CPS1985", package = "AER") R> cps <- CPS1985 R> R> R> ################################################### R> ### chunk number 9: cps-data1 eval=FALSE R> ################################################### R> ## data("CPS1985", package = "AER") R> ## cps <- CPS1985 R> R> R> ################################################### R> ### chunk number 10: cps-reg R> ################################################### R> library("quantreg") Loading required package: SparseM Attaching package: 'SparseM' The following object is masked from 'package:base': backsolve Attaching package: 'quantreg' The following object is masked from 'package:survival': untangle.specials R> cps_lm <- lm(log(wage) ~ experience + I(experience^2) + + education, data = cps) R> cps_rq <- rq(log(wage) ~ experience + I(experience^2) + + education, data = cps, tau = seq(0.2, 0.8, by = 0.15)) R> R> R> ################################################### R> ### chunk number 11: cps-predict R> ################################################### R> cps2 <- data.frame(education = mean(cps$education), + experience = min(cps$experience):max(cps$experience)) R> cps2 <- cbind(cps2, predict(cps_lm, newdata = cps2, + interval = "prediction")) R> cps2 <- cbind(cps2, + predict(cps_rq, newdata = cps2, type = "")) R> R> R> ################################################### R> ### chunk number 12: rq-plot eval=FALSE R> ################################################### R> ## plot(log(wage) ~ experience, data = cps) R> ## for(i in 6:10) lines(cps2[,i] ~ experience, R> ## data = cps2, col = "red") R> R> R> ################################################### R> ### chunk number 13: rq-plot1 R> ################################################### R> plot(log(wage) ~ experience, data = cps) R> for(i in 6:10) lines(cps2[,i] ~ experience, + data = cps2, col = "red") R> R> R> ################################################### R> ### chunk number 14: srq-plot eval=FALSE R> ################################################### R> ## plot(summary(cps_rq)) R> R> R> ################################################### R> ### chunk number 15: srq-plot1 R> ################################################### R> try(plot(summary(cps_rq))) Warning messages: 1: In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : Solution may be nonunique 2: In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : Solution may be nonunique R> R> R> ################################################### R> ### chunk number 16: bkde-fit R> ################################################### R> library("KernSmooth") KernSmooth 2.23 loaded Copyright M. P. Wand 1997-2009 R> cps_bkde <- bkde2D(cbind(cps$experience, log(cps$wage)), + bandwidth = c(3.5, 0.5), gridsize = c(200, 200)) R> R> R> ################################################### R> ### chunk number 17: bkde-plot eval=FALSE R> ################################################### R> ## image(cps_bkde$x1, cps_bkde$x2, cps_bkde$fhat, R> ## col = rev(gray.colors(10, gamma = 1)), R> ## xlab = "experience", ylab = "log(wage)") R> ## box() R> ## lines(fit ~ experience, data = cps2) R> ## lines(lwr ~ experience, data = cps2, lty = 2) R> ## lines(upr ~ experience, data = cps2, lty = 2) R> R> R> ################################################### R> ### chunk number 18: bkde-plot1 R> ################################################### R> image(cps_bkde$x1, cps_bkde$x2, cps_bkde$fhat, + col = rev(gray.colors(10, gamma = 1)), + xlab = "experience", ylab = "log(wage)") R> box() R> lines(fit ~ experience, data = cps2) R> lines(lwr ~ experience, data = cps2, lty = 2) R> lines(upr ~ experience, data = cps2, lty = 2) R> R> R> ################################################### R> ### chunk number 19: install eval=FALSE R> ################################################### R> ## install.packages("AER") R> R> R> ################################################### R> ### chunk number 20: library R> ################################################### R> library("AER") R> R> R> ################################################### R> ### chunk number 21: objects R> ################################################### R> objects() [1] "CPS1985" "Journals" "cps" "cps2" "cps_bkde" [6] "cps_lm" "cps_rq" "i" "j_lm" R> R> R> ################################################### R> ### chunk number 22: search R> ################################################### R> search() [1] ".GlobalEnv" "package:KernSmooth" [3] "package:quantreg" "package:SparseM" [5] "package:AER" "package:survival" [7] "package:sandwich" "package:lmtest" [9] "package:zoo" "package:car" [11] "package:carData" "package:stats" [13] "package:graphics" "package:grDevices" [15] "package:utils" "package:datasets" [17] "package:methods" "Autoloads" [19] "package:base" R> R> R> ################################################### R> ### chunk number 23: assignment R> ################################################### R> x <- 2 R> objects() [1] "CPS1985" "Journals" "cps" "cps2" "cps_bkde" [6] "cps_lm" "cps_rq" "i" "j_lm" "x" R> R> R> ################################################### R> ### chunk number 24: remove R> ################################################### R> remove(x) R> objects() [1] "CPS1985" "Journals" "cps" "cps2" "cps_bkde" [6] "cps_lm" "cps_rq" "i" "j_lm" R> R> R> ################################################### R> ### chunk number 25: log eval=FALSE R> ################################################### R> ## log(16, 2) R> ## log(x = 16, 2) R> ## log(16, base = 2) R> ## log(base = 2, x = 16) R> R> R> ################################################### R> ### chunk number 26: q eval=FALSE R> ################################################### R> ## q() R> R> R> ################################################### R> ### chunk number 27: apropos R> ################################################### R> apropos("help") [1] "help" "help.request" "help.search" "help.start" R> R> R> > proc.time() user system elapsed 1.441 0.069 1.493 AER/tests/Ch-Validation.R0000644000176200001440000002602213517437276014611 0ustar liggesusers################################################### ### chunk number 1: setup ################################################### options(prompt = "R> ", continue = "+ ", width = 64, digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, twofig = function() {par(mfrow = c(1,2))}, threefig = function() {par(mfrow = c(1,3))}, fourfig = function() {par(mfrow = c(2,2))}, sixfig = function() {par(mfrow = c(3,2))})) library("AER") suppressWarnings(RNGversion("3.5.0")) set.seed(1071) ################################################### ### chunk number 2: ps-summary ################################################### data("PublicSchools") summary(PublicSchools) ################################################### ### chunk number 3: ps-plot eval=FALSE ################################################### ## ps <- na.omit(PublicSchools) ## ps$Income <- ps$Income / 10000 ## plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) ## ps_lm <- lm(Expenditure ~ Income, data = ps) ## abline(ps_lm) ## id <- c(2, 24, 48) ## text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 4: ps-plot1 ################################################### ps <- na.omit(PublicSchools) ps$Income <- ps$Income / 10000 plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) ps_lm <- lm(Expenditure ~ Income, data = ps) abline(ps_lm) id <- c(2, 24, 48) text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 5: ps-lmplot eval=FALSE ################################################### ## plot(ps_lm, which = 1:6) ################################################### ### chunk number 6: ps-lmplot1 ################################################### plot(ps_lm, which = 1:6) ################################################### ### chunk number 7: ps-hatvalues eval=FALSE ################################################### ## ps_hat <- hatvalues(ps_lm) ## plot(ps_hat) ## abline(h = c(1, 3) * mean(ps_hat), col = 2) ## id <- which(ps_hat > 3 * mean(ps_hat)) ## text(id, ps_hat[id], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 8: ps-hatvalues1 ################################################### ps_hat <- hatvalues(ps_lm) plot(ps_hat) abline(h = c(1, 3) * mean(ps_hat), col = 2) id <- which(ps_hat > 3 * mean(ps_hat)) text(id, ps_hat[id], rownames(ps)[id], pos = 1, xpd = TRUE) ################################################### ### chunk number 9: influence-measures1 eval=FALSE ################################################### ## influence.measures(ps_lm) ################################################### ### chunk number 10: which-hatvalues ################################################### which(ps_hat > 3 * mean(ps_hat)) ################################################### ### chunk number 11: influence-measures2 ################################################### summary(influence.measures(ps_lm)) ################################################### ### chunk number 12: ps-noinf eval=FALSE ################################################### ## plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) ## abline(ps_lm) ## id <- which(apply(influence.measures(ps_lm)$is.inf, 1, any)) ## text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ## ps_noinf <- lm(Expenditure ~ Income, data = ps[-id,]) ## abline(ps_noinf, lty = 2) ################################################### ### chunk number 13: ps-noinf1 ################################################### plot(Expenditure ~ Income, data = ps, ylim = c(230, 830)) abline(ps_lm) id <- which(apply(influence.measures(ps_lm)$is.inf, 1, any)) text(ps[id, 2:1], rownames(ps)[id], pos = 1, xpd = TRUE) ps_noinf <- lm(Expenditure ~ Income, data = ps[-id,]) abline(ps_noinf, lty = 2) ################################################### ### chunk number 14: journals-age ################################################### data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations journals$age <- 2000 - Journals$foundingyear ################################################### ### chunk number 15: journals-lm ################################################### jour_lm <- lm(log(subs) ~ log(citeprice), data = journals) ################################################### ### chunk number 16: bptest1 ################################################### bptest(jour_lm) ################################################### ### chunk number 17: bptest2 ################################################### bptest(jour_lm, ~ log(citeprice) + I(log(citeprice)^2), data = journals) ################################################### ### chunk number 18: gqtest ################################################### gqtest(jour_lm, order.by = ~ citeprice, data = journals) ################################################### ### chunk number 19: resettest ################################################### resettest(jour_lm) ################################################### ### chunk number 20: raintest ################################################### raintest(jour_lm, order.by = ~ age, data = journals) ################################################### ### chunk number 21: harvtest ################################################### harvtest(jour_lm, order.by = ~ age, data = journals) ################################################### ### chunk number 22: ################################################### library("dynlm") ################################################### ### chunk number 23: usmacro-dynlm ################################################### data("USMacroG") consump1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG) ################################################### ### chunk number 24: dwtest ################################################### dwtest(consump1) ################################################### ### chunk number 25: Box-test ################################################### Box.test(residuals(consump1), type = "Ljung-Box") ################################################### ### chunk number 26: bgtest ################################################### bgtest(consump1) ################################################### ### chunk number 27: vcov ################################################### vcov(jour_lm) vcovHC(jour_lm) ################################################### ### chunk number 28: coeftest ################################################### coeftest(jour_lm, vcov = vcovHC) ################################################### ### chunk number 29: sandwiches ################################################### t(sapply(c("const", "HC0", "HC1", "HC2", "HC3", "HC4"), function(x) sqrt(diag(vcovHC(jour_lm, type = x))))) ################################################### ### chunk number 30: ps-anova ################################################### ps_lm <- lm(Expenditure ~ Income, data = ps) ps_lm2 <- lm(Expenditure ~ Income + I(Income^2), data = ps) anova(ps_lm, ps_lm2) ################################################### ### chunk number 31: ps-waldtest ################################################### waldtest(ps_lm, ps_lm2, vcov = vcovHC(ps_lm2, type = "HC4")) ################################################### ### chunk number 32: vcovHAC ################################################### rbind(SE = sqrt(diag(vcov(consump1))), QS = sqrt(diag(kernHAC(consump1))), NW = sqrt(diag(NeweyWest(consump1)))) ################################################### ### chunk number 33: solow-lm ################################################### data("OECDGrowth") solow_lm <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth + .05), data = OECDGrowth) summary(solow_lm) ################################################### ### chunk number 34: solow-plot eval=FALSE ################################################### ## plot(solow_lm) ################################################### ### chunk number 35: solow-lts ################################################### library("MASS") solow_lts <- lqs(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth + .05), data = OECDGrowth, psamp = 13, nsamp = "exact") ################################################### ### chunk number 36: solow-smallresid ################################################### smallresid <- which( abs(residuals(solow_lts)/solow_lts$scale[2]) <= 2.5) ################################################### ### chunk number 37: solow-nohighlev ################################################### X <- model.matrix(solow_lm)[,-1] Xcv <- cov.rob(X, nsamp = "exact") nohighlev <- which( sqrt(mahalanobis(X, Xcv$center, Xcv$cov)) <= 2.5) ################################################### ### chunk number 38: solow-goodobs ################################################### goodobs <- unique(c(smallresid, nohighlev)) ################################################### ### chunk number 39: solow-badobs ################################################### rownames(OECDGrowth)[-goodobs] ################################################### ### chunk number 40: solow-rob ################################################### solow_rob <- update(solow_lm, subset = goodobs) summary(solow_rob) ################################################### ### chunk number 41: quantreg ################################################### library("quantreg") ################################################### ### chunk number 42: cps-lad ################################################### library("quantreg") data("CPS1988") cps_f <- log(wage) ~ experience + I(experience^2) + education cps_lad <- rq(cps_f, data = CPS1988) summary(cps_lad) ################################################### ### chunk number 43: cps-rq ################################################### cps_rq <- rq(cps_f, tau = c(0.25, 0.75), data = CPS1988) summary(cps_rq) ################################################### ### chunk number 44: cps-rqs ################################################### cps_rq25 <- rq(cps_f, tau = 0.25, data = CPS1988) cps_rq75 <- rq(cps_f, tau = 0.75, data = CPS1988) anova(cps_rq25, cps_rq75) ################################################### ### chunk number 45: cps-rq-anova ################################################### anova(cps_rq25, cps_rq75, joint = FALSE) ################################################### ### chunk number 46: rqbig ################################################### cps_rqbig <- rq(cps_f, tau = seq(0.05, 0.95, by = 0.05), data = CPS1988) cps_rqbigs <- summary(cps_rqbig) ################################################### ### chunk number 47: rqbig-plot eval=FALSE ################################################### ## plot(cps_rqbigs) ################################################### ### chunk number 48: rqbig-plot1 ################################################### plot(cps_rqbigs) AER/vignettes/0000755000176200001440000000000013616365107012701 5ustar liggesusersAER/vignettes/AER.Rnw0000644000176200001440000003241313463423425014001 0ustar liggesusers\documentclass[nojss]{jss} %% need no \usepackage{Sweave} \usepackage{thumbpdf} %% new commands \newcommand{\class}[1]{``\code{#1}''} \newcommand{\fct}[1]{\code{#1()}} \SweaveOpts{engine=R, eps=FALSE, keep.source = TRUE} <>= options(prompt = "R> ", digits = 4, show.signif.stars = FALSE) @ %%\VignetteIndexEntry{Applied Econometrics with R: Package Vignette and Errata} %%\VignettePackage{AER} %%\VignetteDepends{AER} %%\VignetteKeywords{econometrics, statistical software, R} \author{Christian Kleiber\\Universit\"at Basel \And Achim Zeileis\\Universit\"at Innsbruck} \Plainauthor{Christian Kleiber, Achim Zeileis} \title{Applied Econometrics with \proglang{R}:\\Package Vignette and Errata} \Plaintitle{Applied Econometrics with R: Package Vignette and Errata} \Shorttitle{\pkg{AER}: Package Vignette and Errata} \Keywords{econometrics, statistical software, \proglang{R}} \Plainkeywords{econometrics, statistical software, R} \Abstract{ ``Applied Econometrics with \proglang{R}'' \citep[Springer-Verlag, ISBN~978-0-387-77316-2, pp.~vii+222]{aer:Kleiber+Zeileis:2008} is the first book on applied econometrics using the \proglang{R}~system for statistical computing and graphics \citep{aer:R:2019}. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics, such as logit, probit, tobit models as well as regression models for count data, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization and an introduction to \proglang{R} tools enabling reproducible econometric research. The methods are presented by illustrating, among other things, the fitting of wage equations, growth regressions, dynamic regressions and time series models as well as various models of microeconometrics. The book is accompanied by the \proglang{R} package \pkg{AER} \citep{aer:Kleiber+Zeileis:2019} which contains some new \proglang{R} functionality, some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the book, as well as further worked examples, e.g., from popular textbooks. This vignette provides an overview of the package contents and contains a list of errata for the book. } \Address{ Christian Kleiber\\ Faculty of Business and Economics\\ Universit\"at Basel\\ Peter Merian-Weg 6\\ 4002 Basel, Switzerland\\ E-mail: \email{Christian.Kleiber@unibas.ch}\\ URL: \url{https://wwz.unibas.ch/en/kleiber/}\\ Achim Zeileis\\ Department of Statistics\\ Faculty of Economics and Statistics\\ Universit\"at Innsbruck\\ Universit\"atsstr.~15\\ 6020 Innsbruck, Austria\\ E-mail: \email{Achim.Zeileis@R-project.org}\\ URL: \url{https://eeecon.uibk.ac.at/~zeileis/} } \begin{document} \section{Package overview} \subsection[R code from the book]{\proglang{R} code from the book} The full \proglang{R} code from the book is provided in the demos for the package \pkg{AER}. The source scripts can be found in the \code{demo} directory of the package and executed interactively by calling \fct{demo}, as in % <>= demo("Ch-Intro", package = "AER") @ % One demo per chapter is provided: \begin{itemize} \item \code{Ch-Intro} (Chapter~1: Introduction), \item \code{Ch-Basics} (Chapter~2: Basics), \item \code{Ch-LinearRegression} (Chapter~3: Linear Regression), \item \code{Ch-Validation} (Chapter~4: Diagnostics and Alternative Methods of Regression), \item \code{Ch-Microeconometrics} (Chapter~5: Models of Microeconometrics), \item \code{Ch-TimeSeries} (Chapter~6: Time Series), \item \code{Ch-Programming} (Chapter~7: Programming Your Own Analysis). \end{itemize} This list of demos is also shown by \code{demo(package = "AER")}. The same scripts are contained in the \code{tests} directory of the package so that they are automatically checked and compared with the desired output provided in \code{.Rout.save} files. To make the code fully reproducible and to avoid some lengthy computations in the daily checks, a few selected code chunks are commented out in the scripts. Also, for technical reasons, some graphics code chunks are repeated, once commented out and once without comments. \subsection{Data sets} The \pkg{AER} package includes some 100 data sets from leading applied econometrics journals and popular econometrics textbooks. Many data sets have been obtained from the data archive of the \emph{Journal of Applied Econometrics} and the (now defunct) data archive of the \emph{Journal of Business \& Economic Statistics} (see note below). Some of these are used in recent textbooks, among them \cite{aer:Baltagi:2002}, \cite{aer:Davidson+MacKinnon:2004}, \cite{aer:Greene:2003}, \cite{aer:Stock+Watson:2007}, and \cite{aer:Verbeek:2004}. In addition, we provide all further data sets from \cite{aer:Baltagi:2002}, \cite{aer:Franses:1998}, \cite{aer:Greene:2003}, \cite{aer:Stock+Watson:2007}, and \cite{aer:Winkelmann+Boes:2009}. Selected data sets from \cite{aer:Franses+vanDijk+Opschoor:2014} are also included. Detailed information about the source of each data set, descriptions of the variables included, and usually also examples for typical analyses are provided on the respective manual pages. A full list of all data sets in \pkg{AER} can be obtained via % <>= data(package = "AER") @ % In addition, manual pages corresponding to selected textbooks are available. They list all data sets from the respective book and provide extensive code for replicating many of the empirical examples. See, for example, <>= help("Greene2003", package = "AER") @ for data sets and code for \cite{aer:Greene:2003}. Currently available manual pages are: \begin{itemize} \item \code{Baltagi2002} for \cite{aer:Baltagi:2002}, \item \code{CameronTrivedi1998} for \cite{aer:Cameron+Trivedi:1998}, \item \code{Franses1998} for \cite{aer:Franses:1998}, \item \code{Greene2003} for \cite{aer:Greene:2003}, \item \code{StockWatson2007} for \cite{aer:Stock+Watson:2007}. \item \code{WinkelmannBoes2009} for \cite{aer:Winkelmann+Boes:2009}. \end{itemize} \subsection[New R functions]{New \proglang{R} functions} \pkg{AER} provides a few new \proglang{R} functions extending or complementing methods previously available in \proglang{R}: \begin{itemize} \item \fct{tobit} is a convenience interface to \fct{survreg} from package \pkg{survival} for fitting tobit regressions to censored data. In addition to the fitting function itself, the usual set of accessor and extractor functions is provided, e.g., \fct{print}, \fct{summary}, \fct{logLik}, etc. For more details see \code{?tobit}. \item \fct{ivreg} fits instrumental-variable regressions via two-stage least squares. It provides a formula interface and calls the workhorse function \fct{ivreg.fit} which in turn calls \fct{lm.fit} twice. In addition to the fitting functions, the usual set of accessor and extractor functions is provided, e.g., \fct{print}, \fct{summary}, \fct{anova}, etc. For more details see \code{?ivreg}, \code{?ivreg.fit}, and \code{?summary.ivreg}, respectively. \item \fct{dispersiontest} tests the null hypothesis of equidispersion in Poisson regressions against the alternative of overdispersion and/or underdispersion. For more details see \code{?dispersiontest}. \end{itemize} \section{Errata and comments} Below we list the errors that have been found in the book so far. Please report any further errors you find to us. We also provide some comments, for example on functions whose interface has changed. \begin{itemize} \item p.~5--9, 46--53: There are now very minor differences in the plots pertaining to Example~2 (Determinants of wages) in Chapter~1.1 and Chapter~2.8 (Exploratory Data Analysis with \proglang{R}) due to a missing observation. Specifically, the version of the \code{CPS1985} data used for the book contained only 533~observations, the original observation~1 had been omitted inadvertently. \item p.~38, 48, 85: By default there is less rounding in calls to \code{summary()} starting from \proglang{R}~3.4.0. \item p.~63--65, 130, 143: The function \fct{linear.hypothesis} from the \pkg{car} package is now defunct, it has been replaced by \fct{linearHypothesis} starting from \pkg{car}~2.0-0. \item p.~85--86: Due to a bug in the \code{summary()} method for ``\code{plm}'' objects, the degrees of freedom reported for the $F$~statistics were interchanged and thus the $p$~values were not correct. Therefore, the $p$~values printed in the book at the end of \code{summary(gr_fe)} and \code{summary(gr_re)} are not correct, they should both be \code{< 2.22e-16}. Using \pkg{plm} 1.1-1 or higher, the code produces the correct output. Also the degrees-of-freedom adjustment in the $p$~values for the coefficient tests in \code{summary(gr_re)} were corrected. \item pp.~88--89: As of version 1.3-1 of the \pkg{plm} package, summaries of ``\code{pgmm}'' objects provide robust standard errors by default. The output presented on pp.~88--89 is still available, but now requires \code{summary(empl_ab, robust = FALSE)}. Also, the formula interface for \fct{pgmm} has changed: as of version 1.7-0 of the \pkg{plm} package, the function \fct{dynformula} is deprecated. Instead, lags should now be specified via the package's \fct{lag} function. In addition, instruments should now be specified via a two-part formula. Using the new interface, the function call for the Arellano-Bond example is % <>= empl_ab <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + lag(log(output), 0:1) | lag(log(emp), 2:99), data = EmplUK, index = c("firm", "year"), effect = "twoways", model = "twosteps") @ % \item p.~92: Exercise~6 cannot be solved using \code{PSID1982} since that data set only contains a cross-section while Hausman-Taylor requires panel data. A panel version has been available in the \pkg{plm} package under the name \code{Wages}; we have now added \code{PSID7682} to \pkg{AER} for completeness (and consistent naming conventions). Use \code{PSID7682} for the exercise. \item pp.~98--100: \proglang{R} only provides a function \code{dffits()} but not \code{dffit()} as claimed on p.~99. Somewhat confusingly the corresponding column in the output of \code{influence.measures()} (as shown on p.~100) is called \code{dffit} by \proglang{R} (rather than \code{dffits}). \item p.~141: The log-likelihood for the tobit model lacked a minus sign. The correct version is % \[ \ell(\beta, \sigma^2) = \sum_{y_i > 0} \left( \log\phi\{(y_i - x_i^\top \beta)/\sigma\} - \log\sigma \right) + \sum_{y_i = 0} \log \Phi( - x_i^\top \beta /\sigma). \] % \item p.~149: The standard error (and hence the corresponding $z$~test) of \code{admin|manage} in the output of \code{coeftest(bank_polr)} is wrong, it should be \code{1.4744}. This was caused by an inconsistency between \fct{polr} and its \fct{vcov} method which has now been improved in the \pkg{MASS} package ($\ge$ 7.3-6). \item p.~167: The truncation lag parameter in the output of \code{kpss.test(log(PepperPrice[, "white"]))} is wrong, it should be \code{5} instead of \code{3}, also leading to a somewhat smaller test statistic and larger $p$~value. This has now been corrected in the \pkg{tseries} package ($\ge$ 0.10-46). \item p.~169: The comment regarding the output from the Johansen test is in error. The null hypothesis of no cointegration is not rejected at the 10\% level. Nonetheless, the table corresponding to Case~2 in \citet[][p.~420]{aer:Juselius:2006} reveals that the trace statistic is significant at the 15\% level, thus the Johansen test weakly confirms the initial two-step approach. \item p.~179: For consistency, the GARCH code should be preceded by \code{data("MarkPound")}. \item p.~192: The likelihood for the generalized production function was in error (code and computations were correct though). The correct likelihood for the model is % \[ \mathcal{L} = \prod_{i=1}^n \left\{ \frac{1}{\sigma} \phi \left(\frac{\varepsilon_i}{\sigma}\right) \cdot \frac{1 + \theta Y_i}{Y_i} \right\} . \] % giving the log-likelihood % \[ \ell = \sum_{i=1}^n \left\{ \log (1 + \theta Y_i) - \log Y_i \right\} - n \log \sigma + \sum_{i=1}^n \log \phi (\varepsilon_i/\sigma) . \] \item p.~205: The reference for Henningsen (2008) should be: %% FIXME: will be package vignette \begin{quote} Henningsen A (2008). ``Demand Analysis with the Almost Ideal Demand System in \proglang{R}: Package \pkg{micEcon},'' Unpublished. URL~\url{http://CRAN.R-project.org/package=micEcon}. \end{quote} \end{itemize} \emph{Note:} Currently, all links on manual pages corresponding to data sets taken from the Journal of Business \& Economic Statistics (JBES) archive are broken (data sets \code{MarkPound}, and \code{RecreationDemand}). The reason is the redesign of the American Statistical Association (ASA) website, rendering the old ASA data archive nonfunctional. The ASA journals manager currently appears to supply data on a case-by-case basis. The problem awaits a more permanent solution. \bibliography{aer} \end{document} AER/vignettes/aer.bib0000644000176200001440000000676613463421232014135 0ustar liggesusers@Book{aer:Baltagi:2002, author = {Badi H. Baltagi}, title = {Econometrics}, edition = {3rd}, year = {2002}, pages = {401}, publisher = {Springer-Verlag}, address = {New York}, url = {https://www.springer.com/us/book/9783662046937} } @Book{aer:Cameron+Trivedi:1998, author = {A. Colin Cameron and Pravin K. Trivedi}, title = {Regression Analysis of Count Data}, year = {1998}, pages = {411}, publisher = {Cambridge University Press}, address = {Cambridge} } @Book{aer:Davidson+MacKinnon:2004, author = {Russell Davidson and James G. MacKinnon}, title = {Econometric Theory and Methods}, year = {2004}, pages = {750}, publisher = {Oxford University Press}, address = {Oxford} } @Book{aer:Franses:1998, author = {Philip Hans Franses}, title = {Time Series Models for Business and Economic Forecasting}, publisher = {Cambridge University Press}, year = {1998}, address = {Cambridge} } @Book{aer:Franses+vanDijk+Opschoor:2014, author = {Philip Hans Franses and Dick van Dijk and Anne Opschoor}, title = {Time Series Models for Business and Economic Forecasting}, edition = {2nd}, publisher = {Cambridge University Press}, year = {2014}, address = {Cambridge}, url = {http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition} } @Book{aer:Greene:2003, author = {William H. Greene}, title = {Econometric Analysis}, edition = {5th}, year = {2003}, pages = {1026}, publisher = {Prentice Hall}, address = {Upper Saddle River, NJ}, url = {http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm} } @Book{aer:Juselius:2006, author = {Katarina Juselius}, title = {The Cointegrated {VAR} Model}, year = {2006}, publisher = {Oxford University Press}, address = {Oxford} } @Book{aer:Kleiber+Zeileis:2008, title = {Applied Econometrics with \proglang{R}}, author = {Christian Kleiber and Achim Zeileis}, year = {2008}, publisher = {Springer-Verlag}, address = {New York}, note = {{ISBN} 978-0-387-77316-2}, } @Manual{aer:Kleiber+Zeileis:2019, title = {\pkg{AER}: Applied Econometrics with \proglang{R}}, author = {Christian Kleiber and Achim Zeileis}, year = {2019}, note = {\proglang{R}~package version~1.2-7}, url = {https://CRAN.R-project.org/package=AER} } @Manual{aer:R:2019, title = {\proglang{R}: {A} Language and Environment for Statistical Computing}, author = {{\proglang{R} Core Team}}, organization = {\proglang{R} Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2019}, url = {https://www.R-project.org/} } @Book{aer:Stock+Watson:2007, author = {James H. Stock and Mark W. Watson}, title = {Introduction to Econometrics}, year = {2007}, edition = {2nd}, publisher = {Addison-Wesley}, address = {Reading, MA} } @Book{aer:Verbeek:2004, author = {Marno Verbeek}, title = {A Guide to Modern Econometrics}, edition = {2nd}, year = {2004}, publisher = {John Wiley \& Sons}, address = {Hoboken, NJ} } @Book{aer:Winkelmann+Boes:2009, author = {Rainer Winkelmann and Stefan Boes}, title = {Analysis of Microdata}, edition = {2nd}, year = {2009}, publisher = {Springer-Verlag}, address = {Berlin and Heidelberg} } AER/vignettes/Sweave-journals.Rnw0000644000176200001440000000103412225031401016432 0ustar liggesusers%\VignetteIndexEntry{Sweave Example: Linear Regression for Economics Journals Data} \documentclass[a4paper]{article} \begin{document} We fit a linear regression for the economic journals demand model. <<>>= data("Journals", package = "AER") journals_lm <- lm(log(subs) ~ log(price/citations), data = Journals) journals_lm @ A scatter plot with the fitted regression line is shown below. \begin{center} <>= plot(log(subs) ~ log(price/citations), data = Journals) abline(journals_lm) @ \end{center} \end{document} AER/NEWS0000644000176200001440000002767613616354577011423 0ustar liggesusersChanges in Version 1.2-8 o Some examples from the AER book lead to small numeric differences across different platforms. Hence, these have been excluded now from tests/Ch-*.R with an IGNORE_RDIFF comment for CRAN. o The examples on the manual pages for the books CameronTrivedi1998, Greene2003, WinkelmannBoes2009 are now excluded from testing to reduce the computational demands on CRAN. The corresponding analyses are mostly available on the manual pages for the underlying data sets, though. Changes in Version 1.2-8 o Starting from survival >= 3.1-6 the survival provides (or corrected) some standard S3 methods for "survreg" objects: fitted(), nobs(), weights(), vcov(). Previously, these were registered by AER" in order to work for "tobit" objects. For now, AER registers the methods for "tobit" objects. In the future, the methods will be dropped by AER altogether and inherited from survival (when AER depends on survival >= 3.1-6). Changes in Version 1.2-7 o Diagnostic tests ivdiag() for _weighted_ instrumental variables regression was incorrect as weights were erroneously ignored (detected and tested by Jonathan Siverskog). o vcov() method for "survreg" objects in "survival" currently (version 2.44-1.1) provides no row/column names. Hence, a new vcov() method for "tobit" objects was added in "AER" mimicking the naming conventions from survival:::summary.survreg. Analogously, a bread() method was added for "tobit" objects that calls the vcov() method internally. o linearHypothesis() method for tobit() objects no longer assumes that a scale parameter was estimated as part of the model. This enables the somewhat unusual but possible case of summary(tobit(..., dist = "exponential")). o In examples/demos/tests with calls to set.seed(...) the RNG version is now fixed to suppressWarnings(RNGversion("3.5.0")) to keep results exactly reproducible after fixing RNG problems in R 3.6.0. o New errata item in vignette("AER", package = "AER"): The formula interface for pgmm() has changed. As of "plm" version 1.7-0, the function dynformula() is deprecated. Examples, tests, and demos have been adapted accordingly. o In tests/Ch-Intro.R some quantreg computations are now wrapped into try() because the summary method yields non-finite standard errors on some platforms. o In ?CameronTrivedi1998 one version of the negbin-negbin hurdle model is now wrapped into try() because evaluating the log-likelihood at the given start values becomes too unstable on some platforms. Changes in Version 1.2-6 o Added update() method for "ivreg" objects that correctly handles the two-part right-hand side of the formula (suggested by Matthieu Stigler). o Use pdata.frame() instead of plm.data() as preparation for modeling with "plm" and certain "systemfit" functions (requires systemfit 1.1-20). o The model.matrix() method for "ivreg" objects erroneously dropped the dimension of single-column projected regressor matrices. This propagated to the estfun() method and hence lead to problems with sandwich covariances (reported by Justus Winkelmann). o In a bug fix the survival package changed the internal structure of survreg()$y starting from survival 2.42-7. This leads to incorrect summary() output for "tobit" objects which has been worked around now. Thanks to Terry Therneau for pointing out the problem and suggesting a fix. Changes in Version 1.2-5 o Support for aliased coefficients in ivreg() (suggested and tested by Liviu Andronic). o New data sets GoldSilver, MotorCycles2 and MSCISwitzerland, taken from Franses, van Dijk, Opschoor (2014): "Time Series Models for Business and Economic Forecasting", 2nd ed. For replication of the corresponding examples, several packages were added to the list of 'suggested' packages (including fGarch, forecast, longmemo, rugarch, vars). o Small improvements in DESCRIPTION/Imports and NAMESPACE for R CMD check. Changes in Version 1.2-4 o Reference output updated for recent versions of R. Changes in Version 1.2-3 o Package "splines" is loaded explicitly if needed (rather than assuming that it is loaded along with "survival"). o Some URLs in the manual pages had been outdated and are updated (or omitted) now. Changes in Version 1.2-2 o Another bug fix in the new summary(ivreg_object, diagnostics = TRUE). If sandwich standard errors (or other vcov) were used, the chi-squared form rather than the F form of the diagnostic Wald tests was computed and hence the p-values were incorrect. o If there is more than one endogenous variable, summary(ivreg_object, diagnostics = TRUE) now reports separate tests of weak instruments for each endogenous variable. Changes in Version 1.2-1 o Bug fix in the new summary(ivreg_object, diagnostics = TRUE). If there is more than one endogenous variable, the degrees of freedom (and hence the associated p-values) for the Sargan test were too large. o The examples employing "rgl" for 3d visualizations (e.g., for the SIC33 data) are not tested anymore in R CMD check (as "rgl" currently has some problems on CRAN's checks for OS X). Changes in Version 1.2-0 o The summary() method for ivreg() now has a diagnostics=FALSE argument. If set to TRUE, three diagnostic tests are performed: an F test of the first stage regression for weak instruments, a Wu-Hausman test for endogeneity, and a Sargan test of overidentifying restrictions (only if there are more instruments than regressors). o Added new data set EquationCitations provided by Fawcett & Higginson (2012, PNAS). o Changes in Depends/Imports/Suggests due to new CRAN check requirements. In particular, the "Formula" package is now only imported (but not loaded into the search path). Changes in Version 1.1-9 o Recompressed data sets in package to reduce file storage requirements. o ivreg() failed when used without instruments. Now fixed. o The summary() for ivreg() displayed the degrees of freedom of the overall Wald test incorrectly (although the p-value was computed correctly). o Some technical changes for new R 2.14.0, e.g., adding Authors@R in DESCRIPTION, recompressing data, etc. Changes in Version 1.1-8 o The hat values for instrumental variables regressions are now computed in the hatvalues() method and not within ivreg.fit() to save computation time for large data sets. o Added nobs() method for "survreg" objects (and thus "tobit" objects). Modified "ivreg" objects so that default nobs() methods works. o Labeling in coeftest() method for "multinom" objects with binary responses has been fixed. o Example 21.4 in ?Greene2003 now employs the scaled regressor fincome/10000. Changes in Version 1.1-7 o Adapted some example in ?Greene2003 in order to work both with the old and new "dynlm" package. dynlm() now provides convenient support for linear time trends via dynlm(y ~ trend(y)) etc. Changes in Version 1.1-6 o Adapted code/examples/tests to new car version which has deprecated linear.hypothesis() in favor of linearHypothesis(). Changes in Version 1.1-5 o CPS1985 now has 534 observations (not 533 as in prior releases), the original observation 1 had been omitted inadvertently. See also the errata in vignette("AER", package = "AER"). o Data and examples for Winkelmann and Boes (2009), "Analysis of Microdata" (2nd ed.) have been added. For details and extensive (but not quite complete) replication code see help("WinkelmannBoes2009") as well as help("GSS7402") and help("GSOEP9402"). o As announced in the changes for version 1.1-0 of the "AER" package, the variable "earnings" has now been removed from the PSID1976 (aka Mroz) data. In 1.1-0 it was renamed to "wage" to avoid confusion with other data sets. o The coeftest() method for "polr" objects used to return wrong standard errors (and hence wrong z tests) for the last intercept. This was caused by an inconsistency between the summary() and vcov() methods for "polr" objects which has been improved in recent versions of the "MASS" package. The correct results are computed by coeftest() for "polr" objects computed with MASS version >= 7.3-6. See also the errata in vignette("AER", package = "AER") o The paper describing the various versions of the Grunfeld data has been accepted for publication in the German Economic Review. An updated version of the manuscript and associated replication files -- mostly based on data("Grunfeld", package = "AER") -- is available from http://statmath.wu.ac.at/~zeileis/grunfeld/. o Added lrtest() method for "fitdistr" objects with intelligible model name (instead of the usual formula for formula-based models). Changes in Version 1.1-4 o ivreg() now uses the "Formula" package (>= 0.2-0) for processing of its model formulas. However, this only affects the internal computations, the user interface has remained unchanged. o Numerous spelling improvements in the documentation (thanks to the new aspell() functionality in base R). Changes in Version 1.1-3 o Added PSID7682 data set which contains the full Cornwell & Rupert (1988) panel data for the years 1976-1982. This should be used for estimation of the Hausman-Taylor model in Exercise 6 from Chapter 3 (instead of PSID1982 which does not provide panel data but only the cross-section for 1982). See the errata and the manual page for more details. o Fixed overall Wald test in summary() for "tobit" models with intercept only. Changes in Version 1.1-2 o New errata item in vignette("AER", package = "AER"): The comment regarding the output from the Johansen test (p. 169) is in error. The null hypothesis of no cointegration is not rejected at the 10% level (only at 15% level). o Enhancements of the CigarettesSW examples from Stock & Watson. o Fixed overall Wald test in summary() for "tobit" models without intercept. o Improved "rgl" code in the SIC33 example. o The variable "gender" in the Parade2005 data set was wrong for observation 70. It is now "male" (not "female"). Changes in Version 1.1-1 o A new improved version of the "plm" package is available from CRAN (version 1.1-1). This fixes a bug in the summary() of "plm" objects, see the vignette/errata for details. Furthermore, there is now a vcovHC() method for "panelmodel" objects: It gives equivalent results to pvcovHC() but is now the recommended user interface and hence used in the examples of some manual pages (see e.g. ?Fatalities). Changes in Version 1.1-0 o Some variable names in the PSID1976 (aka Mroz) data have been renamed: "earnings" is now called "wage" (to avoid confusion with other data sets), the previous variable "wage" is renamed as "repwage" (reported wage). Currently, "earnings" is kept; it will be removed in future releases. o Documentation of the Grunfeld data has been enhanced and updated. Much more details are available in a recent technical report: Kleiber and Zeileis (2008), "The Grunfeld Data at 50", available from http://epub.wu-wien.ac.at/. o Multinomial logit examples using Yves Croissant's "mlogit" package have been added for the TravelMode and BankWages data sets. o Vignette/errata updated. Changes in Version 1.0-1 o Small changes for R 2.8.0. Changes in Version 1.0-0 o official version accompanying the release of the book (contains all code from the book in demos and tests) o See the new vignette("AER", package = "AER") for an overview of the package and a list of errata. Changes in Version 0.9-0 o release of the version used for compiling the final version of the book for Springer Changes in Version 0.2-0 o first CRAN release of the AER package AER/R/0000755000176200001440000000000013616365107011072 5ustar liggesusersAER/R/tobit.R0000644000176200001440000002334013561127470012336 0ustar liggesusers## Dedicated methods for "tobit" objects that really should be inherited ## from "survival". However, some versions of "survival" did not provide ## these at all or had bugs. With survival >= 3.1-6 the methods in ## "survival" are ok. So for now we still keep the "tobit" methods but ## might remove them in future versions. fitted.tobit <- function(object, ...) predict(object, type = "response", se.fit = FALSE) nobs.tobit <- function(object, ...) length(object$linear.predictors) weights.tobit <- function(object, ...) model.weights(model.frame(object)) vcov.tobit <- function(object, ...) { vc <- NextMethod() if(is.null(colnames(vc))) { nam <- names(object$coefficients) nam <- if(length(nam) == ncol(vc)) { nam } else if(length(nam) == ncol(vc) - 1L) { c(nam, "Log(scale)") } else { c(nam, names(object$scale)) } colnames(vc) <- rownames(vc) <- nam } return(vc) } bread.tobit <- function(x, ...) { length(x$linear.predictors) * vcov(x) } ## "survival" chose not to include this deviance() method ## so this needs to be provided even if "survival" >= 3.1-6 ## is required. deviance.survreg <- function(object, ...) sum(residuals(object, type = "deviance")^2) ## convenience tobit() interface to survreg() tobit <- function(formula, left = 0, right = Inf, dist = "gaussian", subset = NULL, data = list(), ...) { ## remember original environment oenv <- environment(formula) oformula <- eval(formula) ## process censoring stopifnot(all(left < right)) lfin <- any(is.finite(left)) rfin <- any(is.finite(right)) ## formula processing: replace dependent variable ## original y <- formula[[2]] if(lfin & rfin) { ## interval censoring formula[[2]] <- call("Surv", call("ifelse", call(">=", y, substitute(right)), substitute(right), call("ifelse", call("<=", y, substitute(left)), substitute(left), y)), time2 = substitute(right), call("ifelse", call(">=", y, substitute(right)), 0, call("ifelse", call("<=", y, substitute(left)), 2, 1)), type = "interval") } else if(!rfin) { ## left censoring formula[[2]] <- call("Surv", call("ifelse", call("<=", y, substitute(left)), substitute(left), y), call(">", y, substitute(left)) , type = "left") } else { ## right censoring formula[[2]] <- call("Surv", call("ifelse", call(">=", y, substitute(right)), substitute(right), y), call("<", y, substitute(right)) , type = "right") } ## call survreg cl <- ocl <- match.call() cl$formula <- formula cl$left <- NULL cl$right <- NULL cl$dist <- dist cl[[1]] <- as.name("survreg") rval <- eval(cl, oenv) ## slightly modify result class(rval) <- c("tobit", class(rval)) ocl$formula <- oformula rval$call <- ocl rval$formula <- formula return(rval) } ## add printing and summary methods that are more similar to ## the corresponding methods for lm objects print.tobit <- function(x, digits = max(3, getOption("digits") - 3), ...) { ## failure if(!is.null(x$fail)) { cat("tobit/survreg failed.", x$fail, "\n") return(invisible(x)) } ## call cat("\nCall:\n") cat(paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") ## coefficients coef <- x$coefficients if(any(nas <- is.na(coef))) { if (is.null(names(coef))) names(coef) <- paste("b", 1:length(coef), sep = "") cat("Coefficients: (", sum(nas), " not defined because of singularities)\n", sep = "") } else cat("Coefficients:\n") print.default(format(coef, digits = digits), print.gap = 2, quote = FALSE) ## scale if(nrow(x$var) == length(coef)) cat("\nScale fixed at", format(x$scale, digits = digits), "\n") else if (length(x$scale) == 1) cat("\nScale:", format(x$scale, digits = digits), "\n") else { cat("\nScale:\n") print(format(x$scale, digits = digits), ...) } ## return cat("\n") invisible(x) } summary.tobit <- function(object, correlation = FALSE, symbolic.cor = FALSE, vcov. = NULL, ...) { ## failure if(!is.null(object$fail)) { warning("tobit/survreg failed.", object$fail, " No summary provided\n") return(invisible(object)) } ## rank if(all(is.na(object$coefficients))) { warning("This model has zero rank --- no summary is provided") return(invisible(object)) } ## vcov if(is.null(vcov.)) vcov. <- vcov(object) else { if(is.function(vcov.)) vcov. <- vcov.(object) } ## coefmat coef <- coeftest(object, vcov. = vcov., ...) attr(coef, "method") <- NULL ## Wald test nc <- length(coef(object)) has_intercept <- attr(terms(object), "intercept") > 0.5 wald <- if(nc <= has_intercept) NULL else linearHypothesis(object, if(has_intercept) cbind(0, diag(nc-1)) else diag(nc), vcov. = vcov.)[2,3] ## instead of: waldtest(object, vcov = vcov.) ## correlation correlation <- if(correlation) cov2cor(vcov.) else NULL ## distribution dist <- object$dist if(is.character(dist)) sd <- survreg.distributions[[dist]] else sd <- dist if(length(object$parms)) pprint <- paste(sd$name, "distribution: parmameters =", object$parms) else pprint <- paste(sd$name, "distribution") ## number of observations ## (incorporating "bug fix" change for $y in survival 2.42-7) surv_table <- function(y) { if(!inherits(y, "Surv")) y <- y$y type <- attr(y, "type") if(is.null(type) || (type == "left" && any(y[, 2L] > 1))) type <- "old" y <- switch(type, "left" = 2 - y[, 2L], "interval" = y[, 3L], y[, 2L] ) table(factor(y, levels = c(2, 1, 0, 3), labels = c("Left-censored", "Uncensored", "Right-censored", "Interval-censored"))) } nobs <- surv_table(object$y) nobs <- c("Total" = sum(nobs), nobs[1:3]) rval <- object[match(c("call", "df", "loglik", "iter", "na.action", "idf", "scale"), names(object), nomatch = 0)] rval <- c(rval, list(coefficients = coef, correlation = correlation, symbolic.cor = symbolic.cor, parms = pprint, n = nobs, wald = wald)) class(rval) <- "summary.tobit" return(rval) } print.summary.tobit <- function(x, digits = max(3, getOption("digits") - 3), ...) { ## call cat("\nCall:\n") cat(paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") ## observations and censoring if(length(x$na.action)) cat("Observations: (", naprint(x$na.action), ")\n", sep = "") else cat("Observations:\n") print(x$n) ## coefficients if(any(nas <- is.na(x$coefficients[,1]))) cat("\nCoefficients: (", sum(nas), " not defined because of singularities)\n", sep = "") else cat("\nCoefficients:\n") printCoefmat(x$coefficients, digits = digits, ...) ## scale if("Log(scale)" %in% rownames(x$coefficients)) cat("\nScale:", format(x$scale, digits = digits), "\n") else cat("\nScale fixed at", format(x$scale, digits = digits), "\n") ## logLik and Chi-squared test cat(paste("\n", x$parms, "\n", sep = "")) cat("Number of Newton-Raphson Iterations:", format(trunc(x$iter)), "\n") cat("Log-likelihood:", formatC(x$loglik[2], digits = digits), "on", x$df, "Df\n") if(!is.null(x$wald)) cat("Wald-statistic:", formatC(x$wald, digits = digits), "on", sum(x$df) - x$idf, "Df, p-value:", format.pval(pchisq(x$wald, sum(x$df) - x$idf, lower.tail = FALSE)), "\n") ## correlation correl <- x$correlation if (!is.null(correl)) { p <- NCOL(correl) if (p > 1) { cat("\nCorrelation of Coefficients:\n") if (is.logical(x$symbolic.cor) && x$symbolic.cor) { print(symnum(correl, abbr.colnames = NULL)) } else { correl <- format(round(correl, 2), nsmall = 2, digits = digits) correl[!lower.tri(correl)] <- "" print(correl[-1, -p, drop = FALSE], quote = FALSE) } } } ## return cat("\n") invisible(x) } ## as the apparent y ~ ... and actual Surv(y) ~ ... formula ## differ, some standard functionality has to be done by work-arounds formula.tobit <- function(x, ...) x$formula model.frame.tobit <- function(formula, ...) { Call <- formula$call Call[[1]] <- as.name("model.frame") Call <- Call[match(c("", "formula", "data", "weights", "subset", "na.action"), names(Call), 0)] dots <- list(...) nargs <- dots[match(c("data", "na.action", "subset"), names(dots), 0)] Call[names(nargs)] <- nargs Call$formula <- formula$formula env <- environment(formula$terms) if(is.null(env)) env <- parent.frame() eval(Call, env) } update.tobit <- function(object, formula., ..., evaluate = TRUE) { call <- object$call extras <- match.call(expand.dots = FALSE)$... if(!missing(formula.)) { ff <- formula(object) ff[[2]] <- call$formula[[2]] call$formula <- update.formula(ff, formula.) } if (length(extras) > 0) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } if(evaluate) eval(call, parent.frame()) else call } waldtest.tobit <- function(object, ..., test = c("Chisq", "F"), name = NULL) { if(is.null(name)) name <- function(x) paste(deparse(x$call$formula), collapse="\n") waldtest.default(object, ..., test = match.arg(test), name = name) } lrtest.tobit <- function(object, ..., name = NULL) { if(is.null(name)) name <- function(x) paste(deparse(x$call$formula), collapse="\n") lrtest.default(object, ..., name = name) } linearHypothesis.tobit <- function(model, hypothesis.matrix, rhs = NULL, vcov. = NULL, ...) { if(is.null(vcov.)) { vcov. <- vcov(model) } else { if(is.function(vcov.)) vcov. <- vcov.(model) } if("Log(scale)" %in% rownames(vcov.)) vcov. <- vcov.[-nrow(vcov.), -ncol(vcov.)] model$formula <- model$call$formula car::linearHypothesis.default(model, hypothesis.matrix = hypothesis.matrix, rhs = rhs, vcov. = vcov., ...) } AER/R/dispersiontest.R0000644000176200001440000000404012415720605014264 0ustar liggesusersdispersiontest <- function(object, trafo = NULL, alternative = c("greater", "two.sided", "less")) { if(!inherits(object, "glm") || family(object)$family != "poisson") stop("only Poisson GLMs can be tested") alternative <- match.arg(alternative) otrafo <- trafo if(is.numeric(otrafo)) trafo <- function(x) x^otrafo y <- if(is.null(object$y)) model.response(model.frame(object)) else object$y yhat <- fitted(object) aux <- ((y - yhat)^2 - y)/yhat if(is.null(trafo)) { STAT <- sqrt(length(aux)) * mean(aux)/sd(aux) NVAL <- c(dispersion = 1) EST <- c(dispersion = mean(aux) + 1) } else { auxreg <- lm(aux ~ 0 + I(trafo(yhat)/yhat)) STAT <- as.vector(summary(auxreg)$coef[1,3]) NVAL <- c(alpha = 0) EST <- c(alpha = as.vector(coef(auxreg)[1])) } rval <- list(statistic = c(z = STAT), p.value = switch(alternative, "greater" = pnorm(STAT, lower.tail = FALSE), "two.sided" = pnorm(abs(STAT), lower.tail = FALSE)*2, "less" = pnorm(STAT)), estimate = EST, null.value = NVAL, alternative = alternative, method = switch(alternative, "greater" = "Overdispersion test", "two.sided" = "Dispersion test", "less" = "Underdispersion test"), data.name = deparse(substitute(object))) class(rval) <- "htest" return(rval) } ## NB. score tests a la DCluster now implemented in countreg ## ## TODO: ## LRT for Poi vs NB2. ## fix DCluster::test.nb.pois() and pscl::odTest() ## proposed interface: ## poistest(object, object2 = NULL) ## where either a "negbin" and a "glm" object have to be ## supplied or only one of them, then update via either ## cl <- object$call ## cl[[1]] <- as.name("glm.nb") ## cl$link <- object$family$link ## cl$family <- NULL ## or ## cl <- object$call ## cl[[1]] <- as.name("glm") ## cl$family <- call("poisson") ## cl$family$link <- object$family$link ## cl$link <- NULL ## cl$init.theta <- NULL ## and evaluate the call "cl" appropriately. AER/R/ivreg.R0000644000176200001440000003737713421602702012337 0ustar liggesusersivreg <- function(formula, instruments, data, subset, na.action, weights, offset, contrasts = NULL, model = TRUE, y = TRUE, x = FALSE, ...) { ## set up model.frame() call cl <- match.call() if(missing(data)) data <- environment(formula) mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "data", "subset", "na.action", "weights", "offset"), names(mf), 0) mf <- mf[c(1, m)] mf$drop.unused.levels <- TRUE ## handle instruments for backward compatibility if(!missing(instruments)) { formula <- Formula::as.Formula(formula, instruments) cl$instruments <- NULL cl$formula <- formula(formula) } else { formula <- Formula::as.Formula(formula) } stopifnot(length(formula)[1] == 1L, length(formula)[2] %in% 1:2) ## try to handle dots in formula has_dot <- function(formula) inherits(try(terms(formula), silent = TRUE), "try-error") if(has_dot(formula)) { f1 <- formula(formula, rhs = 1) f2 <- formula(formula, lhs = 0, rhs = 2) if(!has_dot(f1) & has_dot(f2)) formula <- Formula::as.Formula(f1, update(formula(formula, lhs = 0, rhs = 1), f2)) } ## call model.frame() mf$formula <- formula mf[[1]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) ## extract response, terms, model matrices Y <- model.response(mf, "numeric") mt <- terms(formula, data = data) mtX <- terms(formula, data = data, rhs = 1) X <- model.matrix(mtX, mf, contrasts) if(length(formula)[2] < 2L) { mtZ <- NULL Z <- NULL } else { mtZ <- delete.response(terms(formula, data = data, rhs = 2)) Z <- model.matrix(mtZ, mf, contrasts) } ## weights and offset weights <- model.weights(mf) offset <- model.offset(mf) if(is.null(offset)) offset <- 0 if(length(offset) == 1) offset <- rep(offset, NROW(Y)) offset <- as.vector(offset) ## call default interface rval <- ivreg.fit(X, Y, Z, weights, offset, ...) ## enhance information stored in fitted model object rval$call <- cl rval$formula <- formula(formula) rval$terms <- list(regressors = mtX, instruments = mtZ, full = mt) rval$na.action <- attr(mf, "na.action") rval$levels <- .getXlevels(mt, mf) rval$contrasts <- list(regressors = attr(X, "contrasts"), instruments = attr(Z, "contrasts")) if(model) rval$model <- mf if(y) rval$y <- Y if(x) rval$x <- list(regressors = X, instruments = Z, projected = rval$x) else rval$x <- NULL class(rval) <- "ivreg" return(rval) } ivreg.fit <- function(x, y, z, weights, offset, ...) { ## model dimensions n <- NROW(y) p <- ncol(x) ## defaults if(missing(z)) z <- NULL if(missing(weights)) weights <- NULL if(missing(offset)) offset <- rep(0, n) ## sanity checks stopifnot(n == nrow(x)) if(!is.null(z)) stopifnot(n == nrow(z)) if(!is.null(weights)) stopifnot(n == NROW(weights)) stopifnot(n == NROW(offset)) ## project regressors x on image of instruments z if(!is.null(z)) { if(ncol(z) < ncol(x)) warning("more regressors than instruments") auxreg <- if(is.null(weights)) lm.fit(z, x, ...) else lm.wfit(z, x, weights, ...) xz <- as.matrix(auxreg$fitted.values) # pz <- z %*% chol2inv(auxreg$qr$qr) %*% t(z) colnames(xz) <- colnames(x) } else { xz <- x # pz <- diag(NROW(x)) # colnames(pz) <- rownames(pz) <- rownames(x) } ## main regression fit <- if(is.null(weights)) lm.fit(xz, y, offset = offset, ...) else lm.wfit(xz, y, weights, offset = offset, ...) ## model fit information ok <- which(!is.na(fit$coefficients)) yhat <- drop(x[, ok, drop = FALSE] %*% fit$coefficients[ok]) names(yhat) <- names(y) res <- y - yhat ucov <- chol2inv(fit$qr$qr[1:length(ok), 1:length(ok), drop = FALSE]) colnames(ucov) <- rownames(ucov) <- names(fit$coefficients[ok]) rss <- if(is.null(weights)) sum(res^2) else sum(weights * res^2) ## hat <- diag(x %*% ucov %*% t(x) %*% pz) ## names(hat) <- rownames(x) rval <- list( coefficients = fit$coefficients, residuals = res, fitted.values = yhat, weights = weights, offset = if(identical(offset, rep(0, n))) NULL else offset, n = n, nobs = if(is.null(weights)) n else sum(weights > 0), rank = fit$rank, df.residual = fit$df.residual, cov.unscaled = ucov, sigma = sqrt(rss/fit$df.residual), ## NOTE: Stata divides by n here and uses z tests rather than t tests... # hatvalues = hat, x = xz ) return(rval) } vcov.ivreg <- function(object, ...) object$sigma^2 * object$cov.unscaled bread.ivreg <- function (x, ...) x$cov.unscaled * x$nobs estfun.ivreg <- function (x, ...) { xmat <- model.matrix(x) if(any(alias <- is.na(coef(x)))) xmat <- xmat[, !alias, drop = FALSE] wts <- weights(x) if(is.null(wts)) wts <- 1 res <- residuals(x) rval <- as.vector(res) * wts * xmat attr(rval, "assign") <- NULL attr(rval, "contrasts") <- NULL return(rval) } hatvalues.ivreg <- function(model, ...) { xz <- model.matrix(model, component = "projected") x <- model.matrix(model, component = "regressors") z <- model.matrix(model, component = "instruments") solve_qr <- function(x) chol2inv(qr.R(qr(x))) diag(x %*% solve_qr(xz) %*% t(x) %*% z %*% solve_qr(z) %*% t(z)) } terms.ivreg <- function(x, component = c("regressors", "instruments"), ...) x$terms[[match.arg(component)]] model.matrix.ivreg <- function(object, component = c("projected", "regressors", "instruments"), ...) { component <- match.arg(component, c("projected", "regressors", "instruments")) if(!is.null(object$x)) rval <- object$x[[component]] else if(!is.null(object$model)) { X <- model.matrix(object$terms$regressors, object$model, contrasts = object$contrasts$regressors) Z <- if(is.null(object$terms$instruments)) NULL else model.matrix(object$terms$instruments, object$model, contrasts = object$contrasts$instruments) w <- weights(object) XZ <- if(is.null(Z)) { X } else if(is.null(w)) { lm.fit(Z, X)$fitted.values } else { lm.wfit(Z, X, w)$fitted.values } if(is.null(dim(XZ))) { XZ <- matrix(XZ, ncol = 1L, dimnames = list(names(XZ), colnames(X))) attr(XZ, "assign") <- attr(X, "assign") } rval <- switch(component, "regressors" = X, "instruments" = Z, "projected" = XZ) } else stop("not enough information in fitted model to return model.matrix") return(rval) } predict.ivreg <- function(object, newdata, na.action = na.pass, ...) { if(missing(newdata)) fitted(object) else { mf <- model.frame(delete.response(object$terms$full), newdata, na.action = na.action, xlev = object$levels) X <- model.matrix(delete.response(object$terms$regressors), mf, contrasts = object$contrasts$regressors) ok <- !is.na(object$coefficients) drop(X[, ok, drop = FALSE] %*% object$coefficients[ok]) } } print.ivreg <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall:\n", deparse(x$call), "\n\n", sep = "") cat("Coefficients:\n") print.default(format(coef(x), digits = digits), print.gap = 2, quote = FALSE) cat("\n") invisible(x) } summary.ivreg <- function(object, vcov. = NULL, df = NULL, diagnostics = FALSE, ...) { ## weighted residuals res <- object$residuals y <- object$fitted.values + res n <- NROW(res) w <- object$weights if(is.null(w)) w <- rep(1, n) res <- res * sqrt(w) ## R-squared rss <- sum(res^2) if(attr(object$terms$regressors, "intercept")) { tss <- sum(w * (y - weighted.mean(y, w))^2) dfi <- 1 } else { tss <- sum(w * y^2) dfi <- 0 } r.squared <- 1 - rss/tss adj.r.squared <- 1 - (1 - r.squared) * ((n - dfi)/object$df.residual) ## degrees of freedom (for z vs. t test) if(is.null(df)) df <- object$df.residual if(!is.finite(df)) df <- 0 if(df > 0 & (df != object$df.residual)) { df <- object$df.residual } ## covariance matrix if(is.null(vcov.)) vc <- vcov(object) else { if(is.function(vcov.)) vc <- vcov.(object) else vc <- vcov. } ## Wald test of each coefficient cf <- lmtest::coeftest(object, vcov. = vc, df = df, ...) attr(cf, "method") <- NULL class(cf) <- "matrix" ## Wald test of all coefficients Rmat <- if(attr(object$terms$regressors, "intercept")) cbind(0, diag(length(na.omit(coef(object)))-1)) else diag(length(na.omit(coef(object)))) waldtest <- car::linearHypothesis(object, Rmat, vcov. = vcov., test = ifelse(df > 0, "F", "Chisq"), singular.ok = TRUE) waldtest <- c(waldtest[2,3], waldtest[2,4], abs(waldtest[2,2]), if(df > 0) waldtest[2,1] else NULL) ## diagnostic tests diag <- if(diagnostics) ivdiag(object, vcov. = vcov.) else NULL rval <- list( call = object$call, terms = object$terms, residuals = res, weights <- object$weights, coefficients = cf, sigma = object$sigma, df = c(object$rank, if(df > 0) df else Inf, object$rank), ## aliasing r.squared = r.squared, adj.r.squared = adj.r.squared, waldtest = waldtest, vcov = vc, diagnostics = diag) class(rval) <- "summary.ivreg" return(rval) } print.summary.ivreg <- function(x, digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"), ...) { cat("\nCall:\n") cat(paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat(if(!is.null(x$weights) && diff(range(x$weights))) "Weighted ", "Residuals:\n", sep = "") if(NROW(x$residuals) > 5L) { nam <- c("Min", "1Q", "Median", "3Q", "Max") rq <- if(length(dim(x$residuals)) == 2) structure(apply(t(x$residuals), 1, quantile), dimnames = list(nam, dimnames(x$residuals)[[2]])) else structure(quantile(x$residuals), names = nam) print(rq, digits = digits, ...) } else { print(x$residuals, digits = digits, ...) } cat("\nCoefficients:\n") printCoefmat(x$coefficients, digits = digits, signif.stars = signif.stars, signif.legend = signif.stars & is.null(x$diagnostics), na.print = "NA", ...) if(!is.null(x$diagnostics)) { cat("\nDiagnostic tests:\n") printCoefmat(x$diagnostics, cs.ind = 1L:2L, tst.ind = 3L, has.Pvalue = TRUE, P.values = TRUE, digits = digits, signif.stars = signif.stars, na.print = "NA", ...) } cat("\nResidual standard error:", format(signif(x$sigma, digits)), "on", x$df[2L], "degrees of freedom\n") cat("Multiple R-Squared:", formatC(x$r.squared, digits = digits)) cat(",\tAdjusted R-squared:", formatC(x$adj.r.squared, digits = digits), "\nWald test:", formatC(x$waldtest[1L], digits = digits), "on", x$waldtest[3L], if(length(x$waldtest) > 3L) c("and", x$waldtest[4L]) else NULL, "DF, p-value:", format.pval(x$waldtest[2L], digits = digits), "\n\n") invisible(x) } anova.ivreg <- function(object, object2, test = "F", vcov = NULL, ...) { rval <- waldtest(object, object2, test = test, vcov = vcov) if(is.null(vcov)) { head <- attr(rval, "heading") head[1] <- "Analysis of Variance Table\n" rss <- sapply(list(object, object2), function(x) sum(residuals(x)^2)) dss <- c(NA, -diff(rss)) rval <- cbind(rval, cbind("RSS" = rss, "Sum of Sq" = dss))[,c(1L, 5L, 2L, 6L, 3L:4L)] attr(rval, "heading") <- head class(rval) <- c("anova", "data.frame") } return(rval) } update.ivreg <- function (object, formula., ..., evaluate = TRUE) { if(is.null(call <- getCall(object))) stop("need an object with call component") extras <- match.call(expand.dots = FALSE)$... if(!missing(formula.)) call$formula <- formula(update(Formula(formula(object)), formula.)) if(length(extras)) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if(any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } if(evaluate) eval(call, parent.frame()) else call } ivdiag <- function(obj, vcov. = NULL) { ## extract data y <- model.response(model.frame(obj)) x <- model.matrix(obj, component = "regressors") z <- model.matrix(obj, component = "instruments") w <- weights(obj) ## endogenous/instrument variables endo <- which(!(colnames(x) %in% colnames(z))) inst <- which(!(colnames(z) %in% colnames(x))) if((length(endo) <= 0L) | (length(inst) <= 0L)) stop("no endogenous/instrument variables") ## return value rval <- matrix(NA, nrow = length(endo) + 2L, ncol = 4L) colnames(rval) <- c("df1", "df2", "statistic", "p-value") rownames(rval) <- c(if(length(endo) > 1L) paste0("Weak instruments (", colnames(x)[endo], ")") else "Weak instruments", "Wu-Hausman", "Sargan") ## convenience functions lmfit <- function(x, y, w = NULL) { rval <- if(is.null(w)) lm.fit(x, y) else lm.wfit(x, y, w) rval$x <- x rval$y <- y return(rval) } rss <- function(obj, weights = NULL) if(is.null(weights)) sum(obj$residuals^2) else sum(weights * obj$residuals^2) wald <- function(obj0, obj1, vcov. = NULL, weights = NULL) { df <- c(obj1$rank - obj0$rank, obj1$df.residual) if(!is.function(vcov.)) { w <- ((rss(obj0, w) - rss(obj1, w)) / df[1L]) / (rss(obj1, w)/df[2L]) } else { if(NCOL(obj0$coefficients) > 1L) { cf0 <- structure(as.vector(obj0$coefficients), .Names = c(outer(rownames(obj0$coefficients), colnames(obj0$coefficients), paste, sep = ":"))) cf1 <- structure(as.vector(obj1$coefficients), .Names = c(outer(rownames(obj1$coefficients), colnames(obj1$coefficients), paste, sep = ":"))) } else { cf0 <- obj0$coefficients cf1 <- obj1$coefficients } cf0 <- na.omit(cf0) cf1 <- na.omit(cf1) ovar <- which(!(names(cf1) %in% names(cf0))) vc <- vcov.(lm(obj1$y ~ 0 + obj1$x, weights = w)) w <- t(cf1[ovar]) %*% solve(vc[ovar,ovar]) %*% cf1[ovar] w <- w / df[1L] } pval <- pf(w, df[1L], df[2L], lower.tail = FALSE) c(df, w, pval) } # Test for weak instruments for(i in seq_along(endo)) { aux0 <- lmfit(z[, -inst, drop = FALSE], x[, endo[i]], w) aux1 <- lmfit(z, x[, endo[i]], w) rval[i, ] <- wald(aux0, aux1, vcov. = vcov., weights = w) } ## Wu-Hausman test for endogeneity if(length(endo) > 1L) aux1 <- lmfit(z, x[, endo], w) xfit <- as.matrix(aux1$fitted.values) colnames(xfit) <- paste("fit", colnames(xfit), sep = "_") auxo <- lmfit( x, y, w) auxe <- lmfit(cbind(x, xfit), y, w) rval[nrow(rval) - 1L, ] <- wald(auxo, auxe, vcov. = vcov., weights = w) ## Sargan test of overidentifying restrictions r <- residuals(obj) auxs <- lmfit(z, r, w) rssr <- if(is.null(w)) sum((r - mean(r))^2) else sum(w * (r - weighted.mean(r, w))^2) rval[nrow(rval), 1L] <- length(inst) - length(endo) if(rval[nrow(rval), 1L] > 0L) { rval[nrow(rval), 3L] <- length(r) * (1 - rss(auxs, w)/rssr) rval[nrow(rval), 4L] <- pchisq(rval[nrow(rval), 3L], rval[nrow(rval), 1L], lower.tail = FALSE) } return(rval) } ## If #Instruments = #Regressors then ## b = (Z'X)^{-1} Z'y ## and solves the estimating equations ## Z' (y - X beta) = 0 ## For ## cov(y) = Omega ## the following holds ## cov(b) = (Z'X)^{-1} Z' Omega Z (X'Z)^{-1} ## ## Generally: ## b = (X' P_Z X)^{-1} X' P_Z y ## with estimating equations ## X' P_Z (y - X beta) = 0 ## where P_Z is the usual projector (hat matrix wrt Z) and ## cov(b) = (X' P_Z X)^{-1} X' P_Z Omega P_Z X (X' P_Z X)^{-1} ## Thus meat is X' P_Z Omega P_Z X and bread i (X' P_Z X)^{-1} ## ## See ## http://www.stata.com/support/faqs/stat/2sls.html AER/R/coeftest-methods.R0000644000176200001440000000432512270216037014466 0ustar liggesuserscoeftest.multinom <- function(x, vcov. = NULL, df = NULL, ...) { ## extract coefficients est <- coef(x) if(!is.null(dim(est))) { est <- structure(as.vector(t(est)), names = as.vector(t(outer(rownames(est), colnames(est), paste, sep = ":")))) } ## process vcov. if(is.null(vcov.)) vc <- vcov(x) else { if(is.function(vcov.)) vc <- vcov.(x) else vc <- vcov. } se <- sqrt(diag(vc)) tval <- as.vector(est)/se ## process degrees of freedom if(is.null(df)) df <- Inf if(is.finite(df) && df > 0) { pval <- 2 * pt(abs(tval), df = df, lower.tail = FALSE) cnames <- c("Estimate", "Std. Error", "t value", "Pr(>|t|)") mthd <- "t" } else { pval <- 2 * pnorm(abs(tval), lower.tail = FALSE) cnames <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)") mthd <- "z" } rval <- cbind(est, se, tval, pval) colnames(rval) <- cnames class(rval) <- "coeftest" attr(rval, "method") <- paste(mthd, "test of coefficients") return(rval) } coeftest.polr <- function(x, vcov. = NULL, df = NULL, ...) { ## extract coefficients est <- c(x$coefficients, x$zeta) ## process vcov. if(is.null(vcov.)) vc <- vcov(x) else { if(is.function(vcov.)) vc <- vcov.(x) else vc <- vcov. } se <- sqrt(diag(vc)) tval <- as.vector(est)/se ## process degrees of freedom if(is.null(df)) df <- Inf if(is.finite(df) && df > 0) { pval <- 2 * pt(abs(tval), df = df, lower.tail = FALSE) cnames <- c("Estimate", "Std. Error", "t value", "Pr(>|t|)") mthd <- "t" } else { pval <- 2 * pnorm(abs(tval), lower.tail = FALSE) cnames <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)") mthd <- "z" } rval <- cbind(est, se, tval, pval) colnames(rval) <- cnames class(rval) <- "coeftest" attr(rval, "method") <- paste(mthd, "test of coefficients") return(rval) } lrtest.fitdistr <- function(object, ..., name = NULL) { if(is.null(name)) name <- function(x) if(is.null(names(x$estimate))) { paste(round(x$estimate, digits = max(getOption("digits") - 3, 2)), collapse = ", ") } else { paste(names(x$estimate), "=", round(x$estimate, digits = max(getOption("digits") - 3, 2)), collapse = ", ") } lrtest.default(object, ..., name = name) } AER/MD50000644000176200001440000003316013616730105011176 0ustar liggesusers2bcabea9f451c3b29939cb63493730e7 *DESCRIPTION 2a204f006caa299b52cae788a9d49849 *NAMESPACE a28b335ba78fe5f681106cdb4f26c95c *NEWS 3dd3d9497975612509b765b789beac40 *R/coeftest-methods.R c939ea5a5fc99674637505396b5fc14c *R/dispersiontest.R 2c19235454151119634609618004cfe6 *R/ivreg.R 10093c55c4eecc4256e10965b0f52dbc *R/tobit.R 79cf90d1985e9f552d59fd59d1a6b405 *build/vignette.rds ec0b0d3ec197fe9683db2a001bb3f9d2 *data/Affairs.rda c5a8698a990d99c3dbd470816318e68d *data/ArgentinaCPI.rda 22550d68fee8c7a614e5b5aac55a72cd *data/BankWages.rda 7c8e8591c19391144d2a0b99241c202f *data/BenderlyZwick.rda 48430186b406a514c7681ced22b29b4b *data/BondYield.rda 028591cc4b422aeed8bced9f2b7f5ee2 *data/CASchools.rda 68d1cb50e07cf1a62f2d9939e9909da4 *data/CPS1985.rda 90ac4f29f732b504204a65660c42d47e *data/CPS1988.rda 2967e1e6a91cfeaf5b5b5a9b1920b322 *data/CPSSW04.rda 8a73097d16c4fa694f7a81988f78ea18 *data/CPSSW3.rda f7fb76f3c644893e80cbd1985c2afd9f *data/CPSSW8.rda e57c7cbec9bbad71d7b6ddc0c9079ae2 *data/CPSSW9204.rda fc840ff7c20901263e9ac31bbf47d727 *data/CPSSW9298.rda 16a2316f6212b5fc7da7de2674261815 *data/CPSSWEducation.rda 05c53499dc6eec9125dfad24907b2a3f *data/CartelStability.rda bc6d0903203904dc22c681ee029a09e2 *data/ChinaIncome.rda 4557c66a97e22cd02aa2b157049db753 *data/CigarettesB.rda e114797cfe4bb0a9fe5b650c27f75529 *data/CigarettesSW.rda 26bc525757c9d9ef92ca218b7760a496 *data/CollegeDistance.rda 1ae258bc2dd5d32f03d7a5c9ebe90059 *data/ConsumerGood.rda c9e871d910156c9839e97cd8181f4a64 *data/CreditCard.rda b6737c670442b35abd3b93a53b4b4615 *data/DJFranses.rda cd91e236abaa44b739926ce4327c47fa *data/DJIA8012.rda 4f038b7a2b8ab3648fd4e21024087d57 *data/DoctorVisits.rda a18e434fd037772cd8b4dacafe71d6f6 *data/DutchAdvert.rda 4899218072eff278abd818e3c5cfff40 *data/DutchSales.rda 9b1b4b8c35fab8ce43e0ae84eb7e2794 *data/Electricity1955.rda 6e20d1ebea98e22731a57f8f35b2f05f *data/Electricity1970.rda c41eb90643144a032738d377d65822cb *data/EquationCitations.rda 8ff68a3096571ec38503ba6c98ab69ea *data/Equipment.rda 05677ef2c6024202c43668c0278fe69b *data/EuroEnergy.rda f2294fb4ba27b9d4f30a4a2599573224 *data/Fatalities.rda 6adb2a16a546e26caa9acc99c4005431 *data/Fertility.rda 04a353b69660bf37da1fce7771cc3373 *data/Fertility2.rda a47c8cb3d3fef7ea4df2d87a7a2ff146 *data/FrozenJuice.rda 3448280f60c11ffced3a320f6deecb21 *data/GSOEP9402.rda d19153ec871f2d0d9bad2c0b91aecd09 *data/GSS7402.rda 20d1d451b995b8664c664fdfd5f8c1b3 *data/GermanUnemployment.rda 1b4c43d46868074bf54e328722d2cdb5 *data/GoldSilver.rda 41a6551255f980a35b8c7da62d28582d *data/GrowthDJ.rda 5329750ce8f4d150a72ffd293c07d8ce *data/GrowthSW.rda 3b6637657f0c35c4a30381d692b96c4a *data/Grunfeld.rda 99c51de059a838e2a541066d4db4ba4e *data/Guns.rda 0b0094f408844aa5efe1cfbb0c82806e *data/HMDA.rda 8201359c4bcfa44571e0bb130537afba *data/HealthInsurance.rda d8228ea1cec07c1b2cc95565a69100f7 *data/HousePrices.rda c42519711fe04b51e4879645bcdd9d6f *data/Journals.rda b107954cc1e93cea0d9a6cb2fdac4e68 *data/KleinI.rda 7e0c302425664058c7e151488755265a *data/Longley.rda 7bb4a3a0c6c8b872b4e3b01f91502006 *data/MASchools.rda 1792aa2a61db27edcd58fedb13bccf65 *data/MSCISwitzerland.rda b1f1f1a836bce7ac5c8fe58ed941e796 *data/ManufactCosts.rda 8db9f80eff304407e09a3b6fa9e90f69 *data/MarkDollar.rda 2b5de56c1771db48b143864cfcd72605 *data/MarkPound.rda 2cc09dc37535962c14f76e92bc61d64c *data/Medicaid1986.rda aa16d417c697ee6dc3c2fe6e46fde005 *data/Mortgage.rda 634e6a833631b3dc0ae2917e5bd01676 *data/MotorCycles.rda b65c1e89eff37ea227830d40918e450e *data/MotorCycles2.rda 3e18818abf73e02b12b494566e702a34 *data/Municipalities.rda 5d264a431069e30f751f3e88c445f842 *data/MurderRates.rda b331c91efb5efc87f605f5b7fccea66e *data/NMES1988.rda dc7ea2f99b24a9ed4c32e93fe1cf685e *data/NYSESW.rda d596315415f6e197b67dd8ce5f433695 *data/NaturalGas.rda 0e18d7aad9844c8c29e4d8baf875e3b8 *data/OECDGas.rda 16bc0301f855a055e45fcf7c5504adbc *data/OECDGrowth.rda 2181496be632fc179bb77d7346f697a7 *data/OlympicTV.rda 330e03a8fdd1e9ce3d7c64eee6b245b1 *data/OrangeCounty.rda 3126a057766cae8e24a5f9460fb328bf *data/PSID1976.rda a844ad9e8dc527f87fe06a228c50190a *data/PSID1982.rda ad675e5450594d681a6942f8c6da780e *data/PSID7682.rda ccfdbbdd809435b0f096fdc81ff66fde *data/Parade2005.rda 41a2e1893190a821b7f5e566569ab122 *data/PepperPrice.rda 3fe4b8ed39cc8f09bb1235397ad580f4 *data/PhDPublications.rda 2891a5d7abf7732e378633603714bf69 *data/ProgramEffectiveness.rda 22f0bfea99d0b1bb963e71ff86f66451 *data/RecreationDemand.rda 43ed656ee7a03ccb9433c1b0fcd16a62 *data/ResumeNames.rda ce0a17428cd5958e86c955cb15bd7ed3 *data/SIC33.rda c9e85b303188e8f9f94a025329976fbd *data/STAR.rda 7abed0c7caf06bc84b9a2eac2010b7e7 *data/ShipAccidents.rda c85002047f1877b4a35fbc2e24b847ae *data/SmokeBan.rda 4c6fcbf29e05102999aec9b350461bea *data/SportsCards.rda dcc1da187555c393420ea430c18219a5 *data/StrikeDuration.rda 567311b649098ae6e6b4afe469266b7e *data/SwissLabor.rda 407e1cdb393a6ae33ace5f0639461548 *data/TeachingRatings.rda f358fdec755fd27d76b11523484d39c3 *data/TechChange.rda 0214212df48344736e254581c96f82da *data/TradeCredit.rda 17457fcc925058e69666780f182673bf *data/TravelMode.rda 2942b23ea98c0bdbb190aa40363cc7a7 *data/UKInflation.rda 4efb79b1ce3476488529f304f197edc3 *data/UKNonDurables.rda 1178a62bc38554e729728b9cb1907a56 *data/USAirlines.rda e87ed7d56294def0fca1039ea9d4915b *data/USConsump1950.rda dcf531bbc4d58d1cda19c02d4ecb30be *data/USConsump1979.rda 43aaec6d14461fff2613bf1a1e267e9c *data/USConsump1993.rda 6c3b826d7b0631305757628e3b3d1995 *data/USCrudes.rda 2f05fb33719fe3db1768cdeb98d3c520 *data/USGasB.rda f7876d95223a50e9bdd59b6a4bbb13a7 *data/USGasG.rda d71c56e37ac7121987b5852c19a97f19 *data/USInvest.rda a6b7997682536499504a7a2924fe78e2 *data/USMacroB.rda 8d54455d3a5775e48dea322256441ca5 *data/USMacroG.rda 3d5372c3d0b68696168d967d88be0d59 *data/USMacroSW.rda 84cc8ab1945fd766910c449013866f54 *data/USMacroSWM.rda 6384a641e57d3590640edc09cabdef16 *data/USMacroSWQ.rda cf8019fe7e2211b6a238d2747fd03620 *data/USMoney.rda fd5ee4de97a9a9a29db0fe6f8c6233d9 *data/USProdIndex.rda 18dd3ce889570fd42ea685a188ffb003 *data/USSeatBelts.rda eab72f00b81d83ccd892cfadca34cbfb *data/USStocksSW.rda 28f96273e6946c2683c2c27b4a36898c *data/WeakInstrument.rda 78a5e0fe8f528aeb571b7d5501ebcfb0 *data/datalist c71c48f13243f39d8c1443d52f6525cd *demo/00Index d97965b7e0b6c86be84644aeb506dd83 *demo/Ch-Basics.R 0310dc1872229bebd805012f37305868 *demo/Ch-Intro.R ce79593f13ea25f8dcea3e8b0e86c6c0 *demo/Ch-LinearRegression.R 79c942c3c83e4035fa97c568c939c590 *demo/Ch-Microeconometrics.R 24ffe6baf6f33cbe2bce4320f59b97e6 *demo/Ch-Programming.R 35d8d28d238efa520dbbc50785a55cc8 *demo/Ch-TimeSeries.R 3e90c714c97233d018c6b616fc7c61e4 *demo/Ch-Validation.R be1c63e8b99a84caf2b69089584c3882 *inst/CITATION d320a0b0d4260e95b7f54aa7328e0d1d *inst/doc/AER.R ece1f99e848b513eaee01c83a01d912d *inst/doc/AER.Rnw 5228081101f58b91ccc679d1d34c27c5 *inst/doc/AER.pdf 55645a67fdf53c524f1352c9ba6a5910 *inst/doc/Sweave-journals.R 043acb4ba18e012bfc0c60717156d385 *inst/doc/Sweave-journals.Rnw 0eadbd3339a40e3c773041f898e20fd5 *inst/doc/Sweave-journals.pdf 6955f77b9eb0055824b5d521feb2455a *man/Affairs.Rd 60062af9c93b24cac2b25377dac5e222 *man/ArgentinaCPI.Rd 3617226a597ceb88dc058af8c2e545c1 *man/Baltagi2002.Rd 4c64198cc6ad5689d4c0eca87ad3ab5c *man/BankWages.Rd efecbe83ff32c1034b7bf64455c764a2 *man/BenderlyZwick.Rd 3d193966356f54b8877a777c80bd76f6 *man/BondYield.Rd efafc0f9f858f879f259c0453fe66949 *man/CASchools.Rd 356cdd226d939950a6d9c9effc7016f8 *man/CPS1985.Rd 4008ca71755918fcb811451086192cca *man/CPS1988.Rd adf8734b0d57d79707ff7c8147c67aac *man/CPSSW.Rd 45490c39f9ef0008513e05fd0e4815b7 *man/CameronTrivedi1998.Rd 171262b9838be0f53717c7849d148067 *man/CartelStability.Rd 30a7589b2ba71fbd36977ca87e913860 *man/ChinaIncome.Rd cdc5730180e062893c138fd2901f9193 *man/CigarettesB.Rd 143396c2730fd42f60cd81406782770f *man/CigarettesSW.Rd 4ca48c726409424e3f1aefaaef6b11d8 *man/CollegeDistance.Rd aca94e85997fd6c37cdd4ea0520009d6 *man/ConsumerGood.Rd c71ab901dd51cd800879f891c24a08b9 *man/CreditCard.Rd ea5a4e5332109264bd36abea390b08fe *man/DJFranses.Rd da44a2f2450254516333e21f646bb92d *man/DJIA8012.Rd 7d1f0ec4515443b68417d4062bdd61cb *man/DoctorVisits.Rd dd3b5d79204dd747f0b2bafc4fcd3da6 *man/DutchAdvert.Rd 7289cda1e03ae07c24773ab8f922776e *man/DutchSales.Rd 1c67de99cc85c020c6342e7b29f724cb *man/Electricity1955.Rd 10acb9ae3f2559939eb58eef8a0257fb *man/Electricity1970.Rd 8cc46e5ee4dd0f56ba7d00676e2a2bd3 *man/EquationCitations.Rd 45c0e4f347873eb32698828f441feaf0 *man/Equipment.Rd ab5f2a4d9ad6a0a7534a9b8eccbee1f3 *man/EuroEnergy.Rd db79598603da2f3cf7c956165cd0ea28 *man/Fatalities.Rd 7c7eefb57dd2dbdb2fb4e1ca10d8aeae *man/Fertility.Rd 904781049293273a1ab2d20d8d448da2 *man/Franses1998.Rd 2849a01bd2934f5752e415d15219b0c7 *man/FrozenJuice.Rd 3d172f461ec99f9ae239dd570e3a3a75 *man/GSOEP9402.Rd 4ecf9acbc3522d8a348f953951decc95 *man/GSS7402.Rd 7bf20d8a813160c889fd7697e63f8bb2 *man/GermanUnemployment.Rd 5003a43fd9d74969fffa8c4a0b79b275 *man/GoldSilver.Rd fbce93f282509a6f2d1c3c16d7b3987f *man/Greene2003.Rd 52a150f4696edd6dcd103977288262d9 *man/GrowthDJ.Rd bf5417c5c842b9ab575851990514dfb8 *man/GrowthSW.Rd 7de4046d1276bdf65d01fe7c1f3547af *man/Grunfeld.Rd fd0681261e0aba5bfcf5ec60c9511f9f *man/Guns.Rd 11c77404c24500db3e75ad6d724d9bf6 *man/HMDA.Rd cca440199e06e60a28acf081b8e80404 *man/HealthInsurance.Rd e8970093ff06e7b7433e12a66c5aad05 *man/HousePrices.Rd e80684b5f684e7126bd75bba70ce106b *man/Journals.Rd 766829114a4b292c1f9e42d82a4672f6 *man/KleinI.Rd 16da462d93863ab069c549a868160546 *man/Longley.Rd e2e9097328a005d747b8037a24e04719 *man/MASchools.Rd 0975ada40a40507fff503d6423b18e61 *man/MSCISwitzerland.Rd 61510348cb957b1ab181152fcf021ab6 *man/ManufactCosts.Rd 594ad139d2bfeff7e3317e7c614b0694 *man/MarkDollar.Rd 070a195dae0b487c188345dbf5b81511 *man/MarkPound.Rd a762e77161434ea8b76d0e651d6b2e7d *man/Medicaid1986.Rd 25cd0df43df78e3c99b8dbf2da16f278 *man/Mortgage.Rd 958a617bff70fb950d343595676fb5ee *man/MotorCycles.Rd 76a2176ac57a0c76bce9e2d183a8d98a *man/MotorCycles2.Rd dc1b52380811dc1866343e32b41816d2 *man/Municipalities.Rd 0b130af0c82280b23c0c00e168e6db1a *man/MurderRates.Rd 299f9664100c41586a1778f5c98fe239 *man/NMES1988.Rd 256bed985f5d3f35baba7910b2fbe8f9 *man/NYSESW.Rd 2d8fcd90fb4bec614c47adb396fe1482 *man/NaturalGas.Rd bcea0c2d6886108cefee5424427c5e61 *man/OECDGas.Rd 49a03bae86ac9b04719b6ebce4d6368a *man/OECDGrowth.Rd 8f90a553c913136affcdc5772104ad13 *man/OlympicTV.Rd 8c2ae32e2201320f7c29ad34beb124f2 *man/OrangeCounty.Rd 7adf8c80ee5992d818ccb5ce6f8bf0a6 *man/PSID1976.Rd d066e983510ba1d4b50f046967866b96 *man/PSID1982.Rd d0d69bff394f787f8ee8b82843dedd55 *man/PSID7682.Rd 6a986a4a57e927c35e4fada0b9ab7458 *man/Parade2005.Rd de9d141a4a516ec70e6285b58aa06d77 *man/PepperPrice.Rd 1ab5bfb5aeb0d7eddf01cc64f457d25c *man/PhDPublications.Rd da54ba41002fdb537ede6bf4d5cd299b *man/ProgramEffectiveness.Rd 5afc39cbe51cea1fe76be6dd22a7c999 *man/RecreationDemand.Rd 397a0086742c451a00fcd20a35104d55 *man/ResumeNames.Rd dc8981942608d0ca0c82fb9a1a49dcff *man/SIC33.Rd c54bda1930e7b462271ddeefd9f9d0c2 *man/STAR.Rd babeb5ea59d2c8af2ed00bd0f7eab082 *man/ShipAccidents.Rd 076205d8dfc49748020a5ef3aaf2c620 *man/SmokeBan.Rd e294144d4deef9b62c5161a28c8704ac *man/SportsCards.Rd e2b007aaaa892460625bb871ffac40f8 *man/StockWatson2007.Rd d6c70c3c90d5e5bea5f65b76e2166e85 *man/StrikeDuration.Rd d6939d092f785fc73e32cee26c3f2469 *man/SwissLabor.Rd 77af7a8927c3f56e0d7a28eed1a1abf9 *man/TeachingRatings.Rd 172c8d25bad81c7a60f2d037d2374263 *man/TechChange.Rd d9b29a0e0525d10e6a75ecb77b9b8c2f *man/TradeCredit.Rd 0467187ab71fd88d10167b7462ee1663 *man/TravelMode.Rd 4135ec98f20e266dd7344f4a8de1a225 *man/UKInflation.Rd c255f954fd83e016e09a74d941759037 *man/UKNonDurables.Rd 4151f6f8104e3eeb5f7cf9434b58033a *man/USAirlines.Rd a9f647920cd22f4dc11b176919d1a879 *man/USConsump1950.Rd 5d398fc5cdb0729efe293be1d52d3df3 *man/USConsump1979.Rd aaf82b99c08976ebbc4c0ccc5a1a82e0 *man/USConsump1993.Rd 0f2e4f6b40585ab23a07e8bea1ce46ad *man/USCrudes.Rd 26c158f5f9e23835039be002bcaf5b28 *man/USGasB.Rd d6249a7e020811c5b34ddb742a820db0 *man/USGasG.Rd 7d4f1bf77d9ee7e0865c1cc743b57e83 *man/USInvest.Rd 4b7cd39c93401ab570889d96d0ffefab *man/USMacroB.Rd a76e81bb48960010b878c815b2b67fca *man/USMacroG.Rd 9e7f2fc0e112b31bcc1af86ad6090df2 *man/USMacroSW.Rd 1ad188b409f1d92c9c30f2db27769dfa *man/USMacroSWM.Rd 7917c3220154ca951ec8329b6162c79a *man/USMacroSWQ.Rd 80d6e5dc3b32be519d6e78d7e4076a4a *man/USMoney.Rd 2586374f2eeb930827e00314c043301c *man/USProdIndex.Rd cea363bdf59add644251b6a83bf3745a *man/USSeatBelts.Rd f80e40d0fe8e8b83a15c0041e64aa30d *man/USStocksSW.Rd bb36bfc98d5ff4fddb215e6d00b17b8c *man/WeakInstrument.Rd 68cba156e11bff745c607bf51048b45f *man/WinkelmannBoes2009.Rd 1a5d21429e8ad0e04095a91cb7abdac0 *man/dispersiontest.Rd c11ba4bc897fa906f13ebf7143d807c7 *man/ivreg.Rd 3dd6caf5430813b2787bcc8e6d1ab9c0 *man/ivreg.fit.Rd 89d13021a279d1d8674b17a46be0a5b1 *man/summary.ivreg.Rd cc34a837cad33dbd00a528de7a7512ab *man/tobit.Rd 09ab183ef26bd0d986d93d298dde09a7 *tests/Ch-Basics.R 4b89f7f36dd4ed2a858f1d525cb9f058 *tests/Ch-Basics.Rout.save 97db6390838efa25b157d29ed12198ae *tests/Ch-Intro.R 38997875ecf0dc04356b71349aed0bb7 *tests/Ch-Intro.Rout.save 80d7adfc7e48fb83f771a2793e3e9bca *tests/Ch-LinearRegression.R 6ccbffc47d1e2834614baed2510e5697 *tests/Ch-LinearRegression.Rout.save f84fc6b28efc9cdfc7d5bb47cf60770c *tests/Ch-Microeconometrics.R fec23c902abaa7ec1c1269b4f0a1fb47 *tests/Ch-Microeconometrics.Rout.save d144d7fe92194751159165e9388ffefc *tests/Ch-Programming.R 4fdec635b74951f05e9810cffe9d7979 *tests/Ch-Programming.Rout.save 0f131b293703601b4a2433497dbd4bd4 *tests/Ch-TimeSeries.R eb5925857dacdcdef00c711805acb90b *tests/Ch-TimeSeries.Rout.save 197cd6a78b21d8f86395b83e93c74bbe *tests/Ch-Validation.R 31090f663339964fa782ac560bc0f935 *tests/Ch-Validation.Rout.save ece1f99e848b513eaee01c83a01d912d *vignettes/AER.Rnw 043acb4ba18e012bfc0c60717156d385 *vignettes/Sweave-journals.Rnw 290f9aa4f0ebe92b19f3e68a93ec054c *vignettes/aer.bib AER/inst/0000755000176200001440000000000013616365107011646 5ustar liggesusersAER/inst/doc/0000755000176200001440000000000013616365107012413 5ustar liggesusersAER/inst/doc/AER.Rnw0000644000176200001440000003241313463423425013513 0ustar liggesusers\documentclass[nojss]{jss} %% need no \usepackage{Sweave} \usepackage{thumbpdf} %% new commands \newcommand{\class}[1]{``\code{#1}''} \newcommand{\fct}[1]{\code{#1()}} \SweaveOpts{engine=R, eps=FALSE, keep.source = TRUE} <>= options(prompt = "R> ", digits = 4, show.signif.stars = FALSE) @ %%\VignetteIndexEntry{Applied Econometrics with R: Package Vignette and Errata} %%\VignettePackage{AER} %%\VignetteDepends{AER} %%\VignetteKeywords{econometrics, statistical software, R} \author{Christian Kleiber\\Universit\"at Basel \And Achim Zeileis\\Universit\"at Innsbruck} \Plainauthor{Christian Kleiber, Achim Zeileis} \title{Applied Econometrics with \proglang{R}:\\Package Vignette and Errata} \Plaintitle{Applied Econometrics with R: Package Vignette and Errata} \Shorttitle{\pkg{AER}: Package Vignette and Errata} \Keywords{econometrics, statistical software, \proglang{R}} \Plainkeywords{econometrics, statistical software, R} \Abstract{ ``Applied Econometrics with \proglang{R}'' \citep[Springer-Verlag, ISBN~978-0-387-77316-2, pp.~vii+222]{aer:Kleiber+Zeileis:2008} is the first book on applied econometrics using the \proglang{R}~system for statistical computing and graphics \citep{aer:R:2019}. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics, such as logit, probit, tobit models as well as regression models for count data, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization and an introduction to \proglang{R} tools enabling reproducible econometric research. The methods are presented by illustrating, among other things, the fitting of wage equations, growth regressions, dynamic regressions and time series models as well as various models of microeconometrics. The book is accompanied by the \proglang{R} package \pkg{AER} \citep{aer:Kleiber+Zeileis:2019} which contains some new \proglang{R} functionality, some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the book, as well as further worked examples, e.g., from popular textbooks. This vignette provides an overview of the package contents and contains a list of errata for the book. } \Address{ Christian Kleiber\\ Faculty of Business and Economics\\ Universit\"at Basel\\ Peter Merian-Weg 6\\ 4002 Basel, Switzerland\\ E-mail: \email{Christian.Kleiber@unibas.ch}\\ URL: \url{https://wwz.unibas.ch/en/kleiber/}\\ Achim Zeileis\\ Department of Statistics\\ Faculty of Economics and Statistics\\ Universit\"at Innsbruck\\ Universit\"atsstr.~15\\ 6020 Innsbruck, Austria\\ E-mail: \email{Achim.Zeileis@R-project.org}\\ URL: \url{https://eeecon.uibk.ac.at/~zeileis/} } \begin{document} \section{Package overview} \subsection[R code from the book]{\proglang{R} code from the book} The full \proglang{R} code from the book is provided in the demos for the package \pkg{AER}. The source scripts can be found in the \code{demo} directory of the package and executed interactively by calling \fct{demo}, as in % <>= demo("Ch-Intro", package = "AER") @ % One demo per chapter is provided: \begin{itemize} \item \code{Ch-Intro} (Chapter~1: Introduction), \item \code{Ch-Basics} (Chapter~2: Basics), \item \code{Ch-LinearRegression} (Chapter~3: Linear Regression), \item \code{Ch-Validation} (Chapter~4: Diagnostics and Alternative Methods of Regression), \item \code{Ch-Microeconometrics} (Chapter~5: Models of Microeconometrics), \item \code{Ch-TimeSeries} (Chapter~6: Time Series), \item \code{Ch-Programming} (Chapter~7: Programming Your Own Analysis). \end{itemize} This list of demos is also shown by \code{demo(package = "AER")}. The same scripts are contained in the \code{tests} directory of the package so that they are automatically checked and compared with the desired output provided in \code{.Rout.save} files. To make the code fully reproducible and to avoid some lengthy computations in the daily checks, a few selected code chunks are commented out in the scripts. Also, for technical reasons, some graphics code chunks are repeated, once commented out and once without comments. \subsection{Data sets} The \pkg{AER} package includes some 100 data sets from leading applied econometrics journals and popular econometrics textbooks. Many data sets have been obtained from the data archive of the \emph{Journal of Applied Econometrics} and the (now defunct) data archive of the \emph{Journal of Business \& Economic Statistics} (see note below). Some of these are used in recent textbooks, among them \cite{aer:Baltagi:2002}, \cite{aer:Davidson+MacKinnon:2004}, \cite{aer:Greene:2003}, \cite{aer:Stock+Watson:2007}, and \cite{aer:Verbeek:2004}. In addition, we provide all further data sets from \cite{aer:Baltagi:2002}, \cite{aer:Franses:1998}, \cite{aer:Greene:2003}, \cite{aer:Stock+Watson:2007}, and \cite{aer:Winkelmann+Boes:2009}. Selected data sets from \cite{aer:Franses+vanDijk+Opschoor:2014} are also included. Detailed information about the source of each data set, descriptions of the variables included, and usually also examples for typical analyses are provided on the respective manual pages. A full list of all data sets in \pkg{AER} can be obtained via % <>= data(package = "AER") @ % In addition, manual pages corresponding to selected textbooks are available. They list all data sets from the respective book and provide extensive code for replicating many of the empirical examples. See, for example, <>= help("Greene2003", package = "AER") @ for data sets and code for \cite{aer:Greene:2003}. Currently available manual pages are: \begin{itemize} \item \code{Baltagi2002} for \cite{aer:Baltagi:2002}, \item \code{CameronTrivedi1998} for \cite{aer:Cameron+Trivedi:1998}, \item \code{Franses1998} for \cite{aer:Franses:1998}, \item \code{Greene2003} for \cite{aer:Greene:2003}, \item \code{StockWatson2007} for \cite{aer:Stock+Watson:2007}. \item \code{WinkelmannBoes2009} for \cite{aer:Winkelmann+Boes:2009}. \end{itemize} \subsection[New R functions]{New \proglang{R} functions} \pkg{AER} provides a few new \proglang{R} functions extending or complementing methods previously available in \proglang{R}: \begin{itemize} \item \fct{tobit} is a convenience interface to \fct{survreg} from package \pkg{survival} for fitting tobit regressions to censored data. In addition to the fitting function itself, the usual set of accessor and extractor functions is provided, e.g., \fct{print}, \fct{summary}, \fct{logLik}, etc. For more details see \code{?tobit}. \item \fct{ivreg} fits instrumental-variable regressions via two-stage least squares. It provides a formula interface and calls the workhorse function \fct{ivreg.fit} which in turn calls \fct{lm.fit} twice. In addition to the fitting functions, the usual set of accessor and extractor functions is provided, e.g., \fct{print}, \fct{summary}, \fct{anova}, etc. For more details see \code{?ivreg}, \code{?ivreg.fit}, and \code{?summary.ivreg}, respectively. \item \fct{dispersiontest} tests the null hypothesis of equidispersion in Poisson regressions against the alternative of overdispersion and/or underdispersion. For more details see \code{?dispersiontest}. \end{itemize} \section{Errata and comments} Below we list the errors that have been found in the book so far. Please report any further errors you find to us. We also provide some comments, for example on functions whose interface has changed. \begin{itemize} \item p.~5--9, 46--53: There are now very minor differences in the plots pertaining to Example~2 (Determinants of wages) in Chapter~1.1 and Chapter~2.8 (Exploratory Data Analysis with \proglang{R}) due to a missing observation. Specifically, the version of the \code{CPS1985} data used for the book contained only 533~observations, the original observation~1 had been omitted inadvertently. \item p.~38, 48, 85: By default there is less rounding in calls to \code{summary()} starting from \proglang{R}~3.4.0. \item p.~63--65, 130, 143: The function \fct{linear.hypothesis} from the \pkg{car} package is now defunct, it has been replaced by \fct{linearHypothesis} starting from \pkg{car}~2.0-0. \item p.~85--86: Due to a bug in the \code{summary()} method for ``\code{plm}'' objects, the degrees of freedom reported for the $F$~statistics were interchanged and thus the $p$~values were not correct. Therefore, the $p$~values printed in the book at the end of \code{summary(gr_fe)} and \code{summary(gr_re)} are not correct, they should both be \code{< 2.22e-16}. Using \pkg{plm} 1.1-1 or higher, the code produces the correct output. Also the degrees-of-freedom adjustment in the $p$~values for the coefficient tests in \code{summary(gr_re)} were corrected. \item pp.~88--89: As of version 1.3-1 of the \pkg{plm} package, summaries of ``\code{pgmm}'' objects provide robust standard errors by default. The output presented on pp.~88--89 is still available, but now requires \code{summary(empl_ab, robust = FALSE)}. Also, the formula interface for \fct{pgmm} has changed: as of version 1.7-0 of the \pkg{plm} package, the function \fct{dynformula} is deprecated. Instead, lags should now be specified via the package's \fct{lag} function. In addition, instruments should now be specified via a two-part formula. Using the new interface, the function call for the Arellano-Bond example is % <>= empl_ab <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + lag(log(output), 0:1) | lag(log(emp), 2:99), data = EmplUK, index = c("firm", "year"), effect = "twoways", model = "twosteps") @ % \item p.~92: Exercise~6 cannot be solved using \code{PSID1982} since that data set only contains a cross-section while Hausman-Taylor requires panel data. A panel version has been available in the \pkg{plm} package under the name \code{Wages}; we have now added \code{PSID7682} to \pkg{AER} for completeness (and consistent naming conventions). Use \code{PSID7682} for the exercise. \item pp.~98--100: \proglang{R} only provides a function \code{dffits()} but not \code{dffit()} as claimed on p.~99. Somewhat confusingly the corresponding column in the output of \code{influence.measures()} (as shown on p.~100) is called \code{dffit} by \proglang{R} (rather than \code{dffits}). \item p.~141: The log-likelihood for the tobit model lacked a minus sign. The correct version is % \[ \ell(\beta, \sigma^2) = \sum_{y_i > 0} \left( \log\phi\{(y_i - x_i^\top \beta)/\sigma\} - \log\sigma \right) + \sum_{y_i = 0} \log \Phi( - x_i^\top \beta /\sigma). \] % \item p.~149: The standard error (and hence the corresponding $z$~test) of \code{admin|manage} in the output of \code{coeftest(bank_polr)} is wrong, it should be \code{1.4744}. This was caused by an inconsistency between \fct{polr} and its \fct{vcov} method which has now been improved in the \pkg{MASS} package ($\ge$ 7.3-6). \item p.~167: The truncation lag parameter in the output of \code{kpss.test(log(PepperPrice[, "white"]))} is wrong, it should be \code{5} instead of \code{3}, also leading to a somewhat smaller test statistic and larger $p$~value. This has now been corrected in the \pkg{tseries} package ($\ge$ 0.10-46). \item p.~169: The comment regarding the output from the Johansen test is in error. The null hypothesis of no cointegration is not rejected at the 10\% level. Nonetheless, the table corresponding to Case~2 in \citet[][p.~420]{aer:Juselius:2006} reveals that the trace statistic is significant at the 15\% level, thus the Johansen test weakly confirms the initial two-step approach. \item p.~179: For consistency, the GARCH code should be preceded by \code{data("MarkPound")}. \item p.~192: The likelihood for the generalized production function was in error (code and computations were correct though). The correct likelihood for the model is % \[ \mathcal{L} = \prod_{i=1}^n \left\{ \frac{1}{\sigma} \phi \left(\frac{\varepsilon_i}{\sigma}\right) \cdot \frac{1 + \theta Y_i}{Y_i} \right\} . \] % giving the log-likelihood % \[ \ell = \sum_{i=1}^n \left\{ \log (1 + \theta Y_i) - \log Y_i \right\} - n \log \sigma + \sum_{i=1}^n \log \phi (\varepsilon_i/\sigma) . \] \item p.~205: The reference for Henningsen (2008) should be: %% FIXME: will be package vignette \begin{quote} Henningsen A (2008). ``Demand Analysis with the Almost Ideal Demand System in \proglang{R}: Package \pkg{micEcon},'' Unpublished. URL~\url{http://CRAN.R-project.org/package=micEcon}. \end{quote} \end{itemize} \emph{Note:} Currently, all links on manual pages corresponding to data sets taken from the Journal of Business \& Economic Statistics (JBES) archive are broken (data sets \code{MarkPound}, and \code{RecreationDemand}). The reason is the redesign of the American Statistical Association (ASA) website, rendering the old ASA data archive nonfunctional. The ASA journals manager currently appears to supply data on a case-by-case basis. The problem awaits a more permanent solution. \bibliography{aer} \end{document} AER/inst/doc/Sweave-journals.pdf0000644000176200001440000004262413616365107016203 0ustar liggesusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 1224 /Filter /FlateDecode /N 19 /First 134 >> stream xW[s8~_i t2N$Nwn' ȶv1rH \[˹q}AP"D: C"D.rDaŰH@>&Dp %(G @؍zw:/# *WE}`omesr%!c# Yx= t}(-@&`̤X Z釀S# "f ,Qa L# łf L.*VJoY*q/G{1`D<.T9-4TL}V&#*e.-|SƩvJg7)dWw '8 Oc^ ,rx .R\`^ }r;ӌ4;ќ0~;8q}[A6P DG 7O,c \J9t|]갨 6TW/'r2B++o6it \Rٕx[J4F,Jvj菻r)tO;A4t7IkW81n{kMendstream endobj 21 0 obj << /Subtype /XML /Type /Metadata /Length 1329 >> stream 2020-02-04T23:03:51+01:00 2020-02-04T23:03:51+01:00 TeX Untitled endstream endobj 22 0 obj << /Filter /FlateDecode /Length 3000 >> stream x\K#>78A\,JYej}vϪ%[V% a 6z}U52X^<ë;;p\ ?Â2TSK;WCMF\nvߍ7RS/ {k|RN9Sjϧaes)>W?^kEx˟U[R")k)~e0c+Rd^k *)̿tCpj2NēKw{U,+.A/8Jn;]-5K)jvxV]Q;oX jnfI|Eżoƛx?djy\R$@L保=@9H0`@S g/-b-AAǛ}1|/iѻ> mT8~}P7}.O_Be>m|1 N37,mlp9ۛe c.6lp̻>~)_[l{Ն/Jnav{WbE:j618fdWoӍLfÑC(1nɭM󇏒_FYLۚ~~ӆzxrR_- _`3[%B0!wT9<։ʢ/74k]ŠѤ <2fԖw?Tl )6dct/3}(Fa>c7~ȯF[C'}}$_,|  w\! L ,g1PmOc3hhO<4bmk8l?Kxl^NS>]C)WZ=]Aebv}pL#ҡZTwugSbX$x&MG_a)DuZҹ@:#A(xL a@&!nVqZcUqsZNCgRJCɤ39a&5D,+@Y4{Q ez!&hUvW 0BH╨D6( % }CTZ3amMQi5b5|Cfqz?p 1G3>K!tJ&zäChyrjp!<8=Ђ@p$j>!,G-ǀFU( ;qWGZNH+#ɞh"\B%B9ۊ ypN%#:NZD1iS>:xy4lϩ<[YYnܴ\mf"[~N&HeO @BDkJs<\i/ ;vMq*0b^%/qAy&&uC)wEe޸Vk+ދ` Xl*E)2@5E 2(I*Sw*_]cU N.O-Qi! DpBC]DtJ6e\AUHZ951D$zV8 F(*2z8UzB(u[z$;Uw2Kz=ٚ U0:a/vW U0<'5qtd(W7,LKɀ ;iM xxsX#";;q6*qhEr߇"L2,Z%)kD 21fdE vC8+ dFSxw 4m=EVBJ)ckb!TZT+h^ h@A~+p4鄆 a ʾʩ*7 AR(IZhJQzCR NX4Ѕ"W9{A ԚeʫDmʘMUi%iJRC"T0X@K]ZWZj '8sLd/[Zᚢo$C ڌg*!)<rci-S31+oާ!Cy#), W#[Od`5M!G@HǚQ|.gw}Q`Nk ?iE}fY w,ժG*I9YE!(V3' iY/&kTŤɪ{j9Yɪy׫Usyҡd G1<4gHǵ?N1,qMlސ^+~*Ny,~65zfm#&iwo Rǫ'WWwo/UnZT(}=w߇OBعm_]bP@@5$o5hxendstream endobj 23 0 obj << /Filter /FlateDecode /Subtype /Type1C /Length 2823 >> stream xWkTSW!*hUT-S;"Vw$wD$;  CZ֡CmUS]Ts k3:{r{ޗG8;<5`vߥ_,;#䍆&Y=E^Wn_z@PtISrdIi}q т"ߕ+^[t_"6G(Ē( E+D ފS(\$55'B#Ǿp(u"N]"Gʤ іX4wLTͲh\JT$OQ(S#"1q%;|| b+"vDXCZ]b@l$6[7 1 fr΄74)O;,s9:ϵMPr}R'6MǪGsvF}ԟϾ2ίg@pӿz Rd lЯkϺ͵AW { []> %A$mmq$ YŪ R\՝ҩ4{ !$t#'t&2T븋Zu `lNB|tO˂389h)D-%eU.L x%t~BwVKͼ:M@4zig,<<ӏ~&@'t8z]1A|Pk: ]pZi 6C,+Ôa@qZlf;!TuSpfAss8/L(iD\uz+49MEE׀BSh$X; WADX2JM˰ %6Jf@FΣY9)FC7iDbÛ1h?eC׫o 'o6!!t޹VQkSs 25r=) uYEFef_hϠ4⌧F! vEp* ˊtZF}MmWDEO|ŀ9\o>qmPٺ3[n!"D=-=PPT+|HVC*_9ٌ`nZ @aw񺠸*YR(cOn"sKeAHfCoyL h4׆MrWJhaH<^N˗ $W\b xYY_Ӿ$Wb#q0#Dak{]WϠ ԯ "r,E >23m5gm:F*5R?2ƩSkN@x-@r:Hv&$ Ic'ڡJS]W&)>{-Fr‰mF?q ë(p*@Cw^r,yWr(ϜnC f_|=:E~C_Z<t] c˃m3*7,ǁϊ4 `z|Ԓyx zED݈_[zH;aA Q͊v6]tCZ~㨙6Fݙ3]J8\eRT64UcZB_`į*W(#7y4^Ek]Ch5VנWZGOu+ r,ꪍSZ gmۊj8A*WA^SUZ[\WcN ˋ`"Z" J|֮};,fgC%% BlKݚQTM~t֬smpXP\n!յzNwBΰ" ]~KGN_b "9QXCSG<z @v E-*k0d}X:ah}AQP=A&Y2)9ɜlk GaM}k,YZx +Ms$d-]stip-NIr 6[gZc<-h k\\:IsA fU5Oمy vY}&0`e䡤+|;]zy2H}AzZA$$ns$܆ֱR@څJ3PeG̮8MLv3M2 ݦĿdPendstream endobj 24 0 obj << /Filter /FlateDecode /Subtype /Type1C /Length 3050 >> stream xW tSU>!@ ޣs*,R^D^bQ( }&iӴMڼw6MҴiBZ;@p\Cu+,zsu5' :ìkee嬽wDB LZ~Ӻ͛O>,~%=(*rEӖ/{Gg\ BH?0-n48һ@PU%EŊs,^dvg=/K YbI($+(+TY-V(*76'_R#-U[((kąYeREKqm9I*byzYX.%祲U*EunBqƒҲrɜy3]t6A$6/īkcrbXE&\b-XODL&Se]D!$$%&3y4B% r.˅WK]>2H0UJ~I'gM>;0ݞ?ZƚSNrMbk!p- @AhK*R] ?n[2_EqqTW@BrkTA9&ҡMݔ- hPt!!r\ZKF(#\2Ň [HG;;}ɼm v3r5J`'c"Eo;i"@w@kRJMX t9!>.97jQҏ8߅YL}Oh,vzM^bǫ_\+. y'мTR;hI:& MAhz-q{P//Bt(#6u }Cu+0`lKP):D{IŦ %Z=`ʹt f[ܮibd?!i6XZ{*`K%vWpVОLOiI ĮvpZ@#%{l:u<{nWO hziBn -jKX|1G 3[Z.k* ؍Zj0Qu]{۷9Qx|;wުte1wI`KfG9ȫrqX Ǡ\OW;v¬6٘ ;hIg2Nj*#׫mL<1|Nb|Cه_d0rMYne4VZ@>p!LMZ:sΩ:\.V T=tg=oO|>o]9H9;?=ʸ15<^<}%_^rVz $2H~mi|k9|!gξy(3>ATjwVN~>wdn'Y8o<;:,da_o1ӯ8y 5Vĸ,^R8#!6x ;&zo+7z %'gh6K][k2K~ѝCCgҁaj9C Bg"Y!$h#?.J_ ͆{Gڗ_wcv5:1T4ݦߨȷHђ2yErU*4UvrV]YA'niQvF&$ϟ9x{_lxoG7:zhEn܀U.aG;iK&?w/ w{GzG]4E ~TꪎQ8JOMjhY%9JEߦ2[#_Rϝ~bΞ 0 0aG ?0y^OvCG 1),W17_ݸf{Dw5!ݩ2ye&#.=ns_E)9n`Iן  ]w t{"ӖVWA >VUuڂ70G־wX~k(w/P7Dё1~qNacHXG ڸ阓 +5s_y]hO)Dw_Cd,|8ݷQe&඿/D-y؎S@ojhP};o~n< >S|G/۴@o`tZ :9R@Ռڿc_W1ˠW8!!Jn5j{siO oR|j02zkQ+Z& Q4MhwҲjf 6z}zm١;+ʊ5}M.p!|!W: I]1@ 6{Hp"g^T'E?=Bw?MFSRi55lf0fZ:"M/ӡ ZF\wj#d`Ժ]e 0诏EJvuƲjT&j|njy{/E^~(;v۠=Jxw H>jojmNNk7*ᰇA O?Ozz 1Ul JILP ^+:3ZZxSmFVCKTȹ_~kqRߒIΦ*'W0/WWư4=tBЮ[P^SF E|k0k@Yݮmkjv{1yJyy.{ѧk~MJ/ڂ$^BT.A>]tvSpvCP]&v[ьkчIj> stream xV TS>!ȠBcy(jW%Mj ZGPS@jZD-PK *ZIPpj5FQK18Q/Q2*:/rrs:$JpYss=.?bܙÜ-;xEOC:W;P7\vYZ+s'M 8'F~wjqZ0`1;#δxx]ǹX^xë_(h<782Av<`PrTv>6Wm,0F' ]zHpR\~Gtkt ԙM̙ٸ\b-∼9oOGhT`⯓j! `6̴ڼ`%ݚҸ%{s/\QjqjSXr;s PK(X8t^]{d}{Ϧ5U4\~Ϊ`@Ti*j˵G)pnxKXKLS\d8mFYCW7OA"sn.{ PCvS/G\^o-Xхɩ\aZ`/ZOZآ C)A!;MD̟573 8=*Nri2}mLNH )Qڐ xwj}tE3Mr*jl7̠_Oyq%%`bSm!fmz{nY6s-,‘,8u cktw+n@%ZVAEQX*tB^q,EF5uq6*?d~MZHG@0[k Ǘ<-цϤf[2ZɭG)Î7Dw6bG>D=vL QZ /mG.H̫ YA6_ܟ^LE!/ǣ'G<H6 %P5V7Fo5mG$Sy{b&CFcd kB_JwԾfLQ69;֋xװІ{d4q: N@ǜS+%Oڸ.[ ߑ5S{.Х|hC!rw-)9d8| }MVuH{}W5tMAVOp;1¹Nx1!cKKBv]3N8}MWng)G|ioSvc/WT w056Hi^¨|Zrf`/uKMtu TVZvj,(̀XY8 G=M߱QV7 Etd@N Әe,4C1K0J>d'w7*^tP-?ĈE.|]XOy$Τ0AO BgZP(Y$.jrV>yv2Z'Ɛʍ:fΜGW1$xIz`bhwx%L~:]2DDh=~cjG)gs]f@zjkF3QF$,&1xt|[ٱ|"0<]*tsBLNx2ǰ#' LNt GxK2'n ?Rغ5oJܹc>t:Yt ݦUD4j;"`}# J:S Je+>zHgMSZV^~^.R'ȩ؛OgxPiSu$3iiҲrzd=P/s_9@j7& 3KQs^zwP V?2RBiO.O0Ry>[ũD/ЉG䑼7tz; MsBs&1s򬣣scx_mKmI Pڋ,~~` W^^qN-"ZQ."ڨ5%Tsn.BzܿRNK?e D7n;(?F7#`*<ZsMS{)"w)( {7 {6Ԑ zlleee @a OQ%@ۋ5Ptuv|~e]"$'D" +H<'&[kjRlhd-.#@Hg;v(HJ)twFedT$O 7%GR!{.lrN.*l|L6MCљ6 61eX-%#one(8|sBBƐJYoqg?[#K*aA%&rʹja677;E z endstream endobj 26 0 obj << /Filter /FlateDecode /Length 160 >> stream x]O10 @N. 2D! }I:gׁm_Xց$i,Ÿ> stream xTkTT Dz;P^F# PAƑ03 Q+Z_|(-b|54̈%65=^>{-"$H$ Wi T&\y{`;csLkϳ&YK9 vP;C-!|Ǯ_Sޚ/R CTyjYx(Pis*]~T6}{|].ᅤAsu9y7ԙ <<}Z"!qD<@D'D$q"l3 EH{0B"gk]!pKk|;^5(_-݃6G/2)[egJԦfJ7Q LbײP,oCP.s˖@< z,5C07b]BM3Ifc"D48A,01!(6+#~B1$Eex~?jwlLs60(0YyҮK2_uw*-@QKQz~%p;< ļaK~ OC4YVj9/w+@[$&}:y@ԁSh O댁yBs?΢9ۗ;޵S_ Dž~Z]3G_-~{xoIu$Nk{N`_)|]buڍ6OPu1!yO NzvߑC uWpX#fgey9#5FDw3;N V eڦIAa{ڟ:5?}1A'V3({Qn~&$D+UWVW.>IOz!4B`~/!yLbQ7t= K%o1!': q;a].Od%o?;݂Wt LW 4]S]F>i+7ѦsՐd _L :Q lX @4ejNۜ>F"y9^/1-AŕBX71PZYZ7ϵ-z14%F`%^{a(HpJ|`S[)j}1krio<endstream endobj 28 0 obj << /Type /XRef /Length 55 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Info 3 0 R /Root 2 0 R /Size 29 /ID [<2449da7cfaab904aff1934c1b71020c6><8de4d0744dc85a098c492c981b3090c5>] >> stream xcb&F~ c k3F"\v Hpx0  endstream endobj startxref 17486 %%EOF AER/inst/doc/AER.pdf0000644000176200001440000025404513616365107013527 0ustar liggesusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 4192 /Filter /FlateDecode /N 68 /First 553 >> stream x[w6 MzZN34x䬝Z-Nd,}}~ e.CgZ w Yi V29` Ke)9&b&L3 LT*&MP*aJZ̈́eJpL9zN%S5*e̴SIp4,$I\3Z3 ̴Aab9̙HFb.I%aI1b)a %Up5U@B 2-Y EE$Pb7j,nA.!3Kh%*"")3-7=Q 5Ld%Rұh@V&dZI` -^U 7E kЋƞ1J F@6Z$׿2~W,2l3aZ˼:(@S{@]H(e80N=üo A=8rrXj ~aGZ?}~_? ]zBw"|MIQz*|U/04LC4LC4LCi.4X]uԥKWI]D OD OD OD Ob.qm.!N0۫~z:jɛ7k^8Ut5+^alM&c g SECbV͉R#ɥ,0$oW{s%6AOv[R8KmJ;oe.ֆ t` z8Bw4f߫n%Lٮ[Г0m+唸> yq9oAp|?)WWs>b3 ~qQK>/9Hj׼<ՇJtGbOBEvIOA#- BcK2ßeW%uR%8s',?WplZl8`'?y|L -ndmɐUҍci8z)ȑ2Ƴ&YB*X϶TR?Y8yJ0vp,s0Ԧb ѿC^T1z&@v5p5C3fz[ѳ !iz 'JSGPI?;nq G+G'G$=O)2uO W!Yu_𗐱 S+jItuuAfEˢ/|99d /^,{)lQx| QVXpgo$"4F߬|v𽛇 $e~U=^y tZ|(. Ӟzt눮UFU^K?;rY7-m!-.ӂ[9izKgkMzʘ{T}*0*'oIӏ&2bX'8o:s4DZZRYVzĆst<骤GP?t2(1׵繃yaL>uL 0"a["N퀉=KJrn}G3,gj$B5nz^bh5]z//=w5[]aZ+.g''ZQfmZ\R>3X)ЋG-GoWquV8"x3yѦK0]CG},SKw[>L6鴴zZ~Nܞ;x}VF⮾ԡ؝u7SSw18nbܛnljt:ilD!nO@*ljHתњB& "mH f~u#^WL˛կ&yrKr)FG*Q ڥR[Bws)evlR40CR [I+%+"4Gڠ!58)H|K8cܴYED4J}N$0wHi9RwBj낼~z|N) dnaV ?򰫗`sBZ[Aamڦ,]ƉFo{}ڷ3&IYqwi<:Q ݺfynK~],m./^ TszLjVg%3XBqXֹe_0^;34mκ߮_ %Dc Z.ceV4#b'bgoZAk;p W[7bF&CWwnj骆Ղm*H$gzwtƲ^g[zJ3T=WNE=Z֤n*;4|L ڑ#(h ODF !1Ta #H!*ט|`O]b} -Cn 5vq-1dJj+0Nqivu.fMmk᷽L˚$=|z#mM,Z}Q kX7ƺ8X/Gg9XS̫J[Xe֡}X}"i_iVLKendstream endobj 70 0 obj << /Subtype /XML /Type /Metadata /Length 1674 >> stream GPL Ghostscript 9.50 econometrics, statistical software, R 2020-02-04T23:03:51+01:00 2020-02-04T23:03:51+01:00 LaTeX with hyperref Applied Econometrics with R: Package Vignette and ErrataChristian Kleiber, Achim Zeileis endstream endobj 71 0 obj << /Type /ObjStm /Length 2487 /Filter /FlateDecode /N 68 /First 578 >> stream xZR}?_я35V55U' d<8F*bl1!ڒe˲ \K{ڻw^1fR( S1ox ylh#V T2 4V Qq˔=SQZzpb-P Jx%: ŴEʹwEHRmfCy`S"3VhO>)3wRHfqe@=Ŭ2, :PˬYRP5@DWR0R2g4.gA9. sIt /A^+u5 H*\̂uW(&1ʤgB+РTĺC[ 4[jTeTu@Or\4cz$5 PRk%7Ձ8~tR/D&VF7! g+Kng);WLٗ<+f+ՊU.q/j~gb$T,BQN>*PI;qe婴6! >t/7A:amqp\􋴴uY` )fgjH[x"EDt&ɛoF"GZ0R%0Clah`+btv,w?mx#=hgIsS0*Y8.1E~͗䐘5U\÷rɠ_f-j%C{<%yؿ @ To}!tן70Őq oWi/0l.%n$HSxlGǯ^ܮ7#:Xr rN1ôaѴi-_ 3pai^Y44^DglN-ݲÚvi'%Fa^¡tU duC8hz&*MLWIP$1&q0}J19b?2kBB\C(hqD0iD ʙ31, ?d1!aDbPƈ@@9$Z=EC'cuh]S_+ZGNȪ1-\}T|QuXY_cg((#\x5T?u˻Eނ|sʤס>+*P.Xi9҉2'U&`TJ?ZJ)&cR\KA\⌜a +4< s tbi;!SjH` {?dMBql,gRЉ#Lrژ"8äO|Ť43L'=;ṷ`U9[ ;zs4l@ iܩ#`Z,A$65Yp$ipQ$Bσ :tXpe` &D)&LVELxLb@`ͨhAI]P(e|"( R" pbZ{<٠cHA,\tLˠTPMt (zZWjºS981ϰ* (:PQj 0 \7HC9Pn3q !@(9b: ASb1(J5 r ((I)$- ݽiQ ރjuըkUS& WOoizm(xSsPVTaõbWkpMc {t"4[l+$_JGi'uc<.tt#{o;܊C$Ys4O3QvH<[f {l cIUVd3s2akgr߽z{JɎO5רw;tvda׵ݙtؽ\]a(AZ]I6t^38_ e]M 1KбmIq1r'uA7&-\bf)L1g͂P̨ If7;('SݑӡmNԩo[Աq 4sTw$uI:'ZZ m,Ѷ{朎aM!(ԳѻJ6 { y Cr:O2]YyT=.endstream endobj 140 0 obj << /Filter /FlateDecode /Length 5493 >> stream x\IsGv#GtTm ܗ#$á3C>X M Ie~K./)J*Tgd&ڨYoWyOϵ3Mn.7__`q6ڻ3T+7[7Y%?ajfUHzz|۞_Y1ZKB79rͽ2ʄa?xhtOX͛mk4Y:<@~L?o{֖z[?nO$WmlS2+Xs4l*GoK`sM؜2C8LFeNւxm0QVk^!٫^P,ȬY;9CSYT'"T_j/׮] B TIx9*7:}Tl)jCъˎsXϥ %Ry&DcR+ Ngڕ69 Ҝa uis=/?(d}f)T}N[?*!ަlHHk' 3TFR*̦m/ a^ 7i 3k3]ӟz&o1BFHnTYh/FP2uXe X&iZ;lbEjKگi-[Ŗ*_RMVb/~CE *Aඉ^h&-~(z|:=8뙝s ׳. c7gޘ'=1<&ƌ9 ,m,eZSEh.rliwY⢕m ε3>2,qF,Rl22@Kx%cx=Vn=7D1_4Cۣ)"-p"W Dt"In5No^x1oEzav^=Ώ~L y:G 7t l 8SȮQ 事DhP K +rBˇ+~ $rna*_J`<[80, ԒBRY<KݣN"Rd>'"VJ5f@XYAk bd V^"}p38MέSI Psh7eLi`]h{N<աa:gcAG!nzsQW/hmȢqƘ9 B~T ~]~lP tQ8@)'@О ў @Fg؇\$2UI(_{A[@!,,tKvό5Z(kXF4v>xXasZ~ŗ>'XC=@6!i/]Tm*_ \t)]@:@bCoR;U{m2ȨPR ^BIAQڶ$׀M?"j;w?$[[2 32A]? Kϻeu/6!3[@.B"B\Ȫ8Jҽa$l%in4:޹Fv@dFhށB՘,$3i; ȕxe7W3)W4#ئ:!̡a{&O`GrE ȕo/U*O]ߔ&/')D(pg%ؓɘJ\Zqϡ.Xt&W%p4DK\V5: ==w!9A%٧|CZ\Rt/+m,q=Ay!R,;2ѡHF۞kZ_ȊSF퐓 Yt3x0l vC$ c`Ô;ψ%KfQ@[H^$ѱ׎{u/ _N"دMii)*4g$d@>$WiJ3 iu^dGHP ]J劗e XiWjL_yMx4z CMekmC uoNxowg/i88Y/TGc_qo} _xl@j G=~;ߗa?o)0Q qϖ[;"CjA@sAźAFi  0 (.!r2"0Dp/ q҈p=&A E,q7]JF4=X VE5D D#[QzhтB`.-XĢREZD҈մڌ[ E~[9yHtV jQExiۋ6I-xxGuqm3+k{"A7)b>#|E;,Ln9~O#gȨ$fd]jhQ؇]D.HXҢCvFq~k qq5 \ގ7'x5и\@㨖tAm0:0k2Ar-vT1 z gR!.z;Eu׌S67(L': 6ZG(5J\[cӛmy iٸ` aʷ0]h{4)IQ|D[>a=Wڃ\|⮱m |E<&x&Pc`rm,j|%Y<0zsgaB>9%_=y/.L@>9GX'69$Cyx0U͞>Ѽ: { =}z?+SvhJW'$1jekgue; ~i5W]2e¢6\(L)ˑ]W,&ً},F}Ft=JŐ<3Q@da cʹ$R#ۂTp@+ŏC ۣs^K67,mrHN.6ʉ9JV`n,Gw`-G0qH wXs kŒ0 bע2G=g;$!sX̰<RXTPuWNdATAc`pR'~ |Y$L8牃in=CQ=n3GVnidr>4#,ʃrGsȯmpxQ1' сoDx/􉻋48m0Ә;]{$KC²܃囹N;6ïVboOaD]D]qK*D7(p^9r|dJN8S{:,JCq7<\x,CvF(3Q&-gWcׁۉŪP!$ u(ƣ.?AfdW_#S N?YЩ'+4o&9j;y1R- 1Mc1n`@pczdkۣӞl\QpS1DE2GCc@Cxszt)od{J%켖7psw:M|mP>[;re͊K+'e5_֣md0-"5ܻ8^4Eм|򴶥,8\)]XZ̷? $n[1?ԹpAИ(a1 Ϗ]:n.tU8@hɕ&쌦<tr| e.:Y|6R5=52 3FsȊ*zm' JȰڣy?zirG1[K5LVoȯ/VDP+@[edKxm?ÛP۾e+(-5-H{O2rm R 2t!ptƒ.-p#tbeaʭhco1UkFѠQF,].T7\AtA&9ť5 uޮpH*)KUAgH\ѯH;308 >Xňt{@BN}ۈdc8i[buh@ OmۜWUQ}ie %q{uQb(MNb7,0;$! hhDw<W/\$"86v bj 5z{zp%c?oW!hK4QO̻Lڌûf]`ݿK;W}r?Xx?OÉ!ye|EI4s7?>h{BMߝ]ӟ s׬iLOyk~z*P Gz9S 1>f7=+=hI~sG=;1ӑWnbjgFjgFA߱kά^ZD%*L%/4ЕM& q˲vw^l!*@q`鿄>6j G騫s5>1OW_/U <]f &˭R`YAd~QdXU {^Ռ0moV^H%kjh5[!5阊M)l* % Ю.j!'1!Rzq`VZXendstream endobj 141 0 obj << /Filter /FlateDecode /Length 5860 >> stream x][s\7r~gGIgqlUR%nZ̲+È(DVC?Nwh`p(Q3tC 7࿟I 'wϥ=ޝ듿HiWC)(O_ OSd7}u䯫31!l4gk1Ya ? $e _i&껵rB6kf]^[;)js}]5(W? ɨ3[ ߯s;Eyvī2}a$|w9h^V ⭃!i}I wL)8\wNne' 扫WyV?6ϵy9{7컭ۼ#pa3?h QB(8?Zz&iU4T^{aV0`ԫq HTƻ^x c֭vi&+ Gh_\o` 5lJ(-9p*m NN=yNY<3AH%n(60 o"3ьf].C3|?Ohc! a>=SZ!h/jWþ G_o#fryQ!I Ld[!J<\| YB j @"H S?I^VG(8EC< ƹ}Ccf $LlgyJh>U|1~}7u쑅c;gMǢpH@ Xr(,B?/g@sA6HHGK?C w8!ng|qГsس3BÓZ n7qA^J"gTJ<hj*wJ\w˺4o MR>X"6S4J3:SH5!x`A0)֞bВQkTd5n6qf@(%mըO ގ{FCtaʵ*{B,dy::Fb8fbH{+fav6ZBiC W3cDc`Š6{Vo"8l2D=5v!Lo}ψmh2/D/@%+Gw ̾hYk4@ S,k)  (=>Bz mW&ٞ!.hFo,F2SĬu9>9f",u3-;eYo:Tkj,.Y.zDoJ{(AۜN(%& -nVlUf4czN pAs3W0ɿ$TNsҞ1`XBo-7yҊؤ"ߑ*{sW[/wϥ;mp~?u%qo'w8R딭[ >[V_'*$䍀yL]"#liiO` %VڑɨLr``޽cÐP6JjwTUU/[̒ qt(ږE`cYaz[?0ۃ&wt\1sez{ 1Iv])"!YsMkG'mS .FUէi.A+.`[}q@)wM,dFpCqM̖ɛI!W=ϔhlPEjL"YPi4CZuJ+͒AxoM(zkƂ* ېERV}쁔 :dʲUJ=lMW `Hu$#KBy n՘+P7  #{zd$N.aYo3;-{Aγ!7'K$ж=4ą.q?Y@8ED ƒmX7ufJf8P!U eP>H;E74$r,ΣctY"u0v 7u)2\$u^o='îɹ31E~(ۗH=$GuHxdKE}ŅPODF ܡ=Sy,Za0mZ$<yve 9W~y(.1YEhT m]4)`8zR8kб(ӞU׎=3"U(\fE1]]Ԭ7 qv>'bl,x1#я_~_4f*0wwfp&۱ g5:"$UwF"&=y2>T뚑aT)+])V(5 deO2˓|!J)vPB K"dwMg%Yg6w֩\gա~ޏ`RC7m3&4 &:Z,i')uglX' ; ׏hԌFʺSZT/VhHד0DN+jNG[}8Oq  8G1/W l8A+=E?^WM 9a~ gKQ)a,>-coX8:cqRrZz\VFOTJ?Q5Z&,.ʜVyo2 2ŧp1Qh0#<[I_lL)/CL_>]چt\JoR +N@@ |-bꦹ^SfS_pq NVoZ:>šfnuTW ü<Ǜ8*Z/fAMD~fdC }iSXZ7 4̮crB͚A`Mr0(헶lhj0+}HQlfۙ= xn"13M#Op/=fg GKŏ f @r(XE ģggQ`-USb!ڟ/SfQe"jqf{; REd@rz`&ov$Ss k&0 \NqEdVEY0(Ϩ:A_?;H“>v_r%cҰk*1bg!բlL7.^0,jV8vV{! 6oڨg&#RRjޤQ-\wu 6GuUѦMsEmHSFn"Db e}ePb<}-7P~Zf#.ؕg3ǿjEpm|,1vGr $ p},˒ΕXth.jMiw퓝chnC`s6bnO9dyio\"ꞻOin7u.qK3e[I*Y\ ERɸ"ޞV&{@G(zq|qr*e.|QsR[!r{ ])tտw]b_wj`ּv`ON;qw:uxZjAwi\)ScXxB7Om_aEXu'BM~14;puxoT-/f0 Am4EFcػc {GE/ .Tﵷxqu`Jg'YXt;mcYSuh>W`a"`ًH_A|zY=By!uFw.("m=xZxRo aL8Mš%Wx ֹ(줜{ `3dݐv3o-RD>˃^ՁXgfN< B@htTwGK8I|o-q ,a+[~Z%<}"(# -A.?N5k>ر;~ur|,e/3 ǏC{7cK擰#JIk{ /qqoW,*U##lkEÀmn./'ɳendstream endobj 142 0 obj << /Filter /FlateDecode /Subtype /Type1C /Length 3700 >> stream xW TW֮TVq?WWWT\[MvPvޠЊ(q ,DG$&&s}59sc׋d5mzHCd^09 O]aL3\b'pݑ^CL-):$(8e1.SƹLtwtY⣎  Wҋ0Cc\Fm[BBux̄cƹ$,  p񎌈uYty ="÷FDFG0 :+"kۺ9scbԋ4n ^:|ISfeF2˘WfYɬbV3~f63b0s7ƛx0$f!a0KLf:3D֛Ed4{\ٹ^*||TvWJJܸ Zz}z}nwq߬~R.X"-I|fnW D.;3~tѹ9\JWht:ia "KhāVy+ƫ 3ǒ9Ҧ ;NVX$ (Í(K<<3Dc/%P !@${:O"uLJkcfBX'|VYքXr#y# S&IAƣN#,@@'t* ZUFs/8Y;S-W kQc]ᒡ]nZ'u'Zqr~,p&dq%[ǐ1Cod * Z-t2~&UzPʿd1ʀcHA$-jS {J޸c/„;dizkWѫyKmᓝG/L\,RL'hJ/8.nVD.uY_`r iDhWሿƁ4PeȮ{α`je%6+ bHqxD "Ñ8@CټI׃*ɓŕ8V'/oxކC5j'0pNxv V=w0z:^'cL#inwl?D3CljLz`^XNx]ZE/u WYJ'iS}?~07$ven$Nc޾wo!Ꝕr%yFaoPɔK̒Y%qI.fPd!'7-3;7bKApm G`l!ۜGӏFkW<jCnHX62 PQ# #3Z:2'T!J.-'ws!FZȫTNg~. ɕǑ2WGr[&v+W*taK:#񉴄v%f=#,2-g_0U 'Env[dR]tmF m0egA=)!<T(cusiP,,_ḫ (2P:udH}ʼs 9gOSB3hd7aU2FKﱶ;<+%?=/$AϝA & Vcuh|(CxZaR~ Ki9j`'8vl*IdQӀͲ`[Ux78dOQ7'5hmά^*KmfL$6;r qXT? "0զNq*#)I4". K!Tq;laRlp=@^O.rE7t` DtthZu2@lφu'f}u6vz(WuPb( vcԻ'7KO ՈC'OW{ XlkCal9Dϥ~J"l$Ⱥ%kR~>m*6=_Pmp,ɼsz*^R 9ZR!~)3%Q9I}p*e=gemɋIfV_jz(, }UCq6LHއn6Ա `⸮wKZpE#Ni `kN|WҟSK9DH8u*yo-O/oΞ'pӕ[wx]z|E,!= <-)M$"vFulhƉ5cAY yRpCq!2,h~wDl:p{S][ԬViM}@rܰM{=1)UUvGLO,,|?q͏}1L_P V5E.'axOSѹcp֏/B|\O&Qʗ|_-x،-h$K>[W#ማGR(¢NaoQhN5E3|4!oM\vwOeF^J>3E/f EY=n@rVgU˜Q[pJJd脟PH?Hcm[4a!M!mGMmCPK8ҸqNgfm([he%ŗmK-+I-DRH*Umi `+wq\A@i`'z4@{ϙb$@ag^N^mF= c$;/nz7mV/؝_rJ2LL<߉ 읃+IZ}2T7jBa&uz'+l@ R;eSϯz^ib7jŽ[X鋇Mq9%M)+ݼјÎt}<˱D|BYvKTU+Ag\* ɳ*as~YtF_|].1N׹ڌiLMKQK=._Pu+^4n%x{XmXpxg~-pKI8IЦS$9o޵L=z&"'BU<a8a-MpkhԨ#"nWW><qkiuw#;5>^=Pho GDu}.+2!^5Br{sijl4N}}lG ȭoI+2! t(9Z 0mJ Dq[b׵xn{SV cˀ\ o>߻O1?ޙ-ڟݗ&٧;~]٬Kjӓ(_XƹNeo1n))S e)5QV0h{endstream endobj 143 0 obj << /Filter /FlateDecode /Subtype /Type1C /Length 8230 >> stream xy XSJz*=u*j)* 32O"H0C TE묵U֡jNͽ `{y<&dz#,zQ@a)M aX#-joZq ŞaXk~ thHY!1;|"x:gά vӦLcg==;=|s c7f_ddɓ&EL }~v#wz{- =μI!AHp;/5~;(Zk(d-K>_,rtΕQ=Vx:z}o'΁6oĸIL6}Κ=g{o3Ej.5ZKGMޡzj L6P㨍xj@mS-jL-P˨rj5ZI͠VQ3Ի5MQ)T՟B@jeM7(zS6-5J DMqCz T>:r  K--,f񕥳YlSZID2{>o`+oZeoÀ37(aSoqg; <`zy{>熌4jмơC{sXo{'΁$n]pWFL16zS7WXu$m6+t{!?c5V̎8Pg761