plm/0000755000176200001440000000000013623756670011061 5ustar liggesusersplm/NAMESPACE0000755000176200001440000001645513603734271012305 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method("$",pdata.frame) S3method("$<-",pdata.frame) S3method("[",pdata.frame) S3method("[[",pdata.frame) S3method(Between,default) S3method(Between,matrix) S3method(Between,pseries) S3method(Within,default) S3method(Within,matrix) S3method(Within,pseries) S3method(alias,pdata.frame) S3method(alias,plm) S3method(as.Formula,pFormula) S3method(as.data.frame,pdata.frame) S3method(as.list,pdata.frame) S3method(as.matrix,pseries) S3method(between,default) S3method(between,matrix) S3method(between,pseries) S3method(coef,panelmodel) S3method(coef,pgmm) S3method(coef,summary.plm.list) S3method(detect.lindep,data.frame) S3method(detect.lindep,matrix) S3method(detect.lindep,plm) S3method(deviance,panelmodel) S3method(df.residual,panelmodel) S3method(diff,pseries) S3method(ercomp,formula) S3method(ercomp,pdata.frame) S3method(ercomp,plm) S3method(fitted,panelmodel) S3method(fitted,plm) S3method(fixef,plm) S3method(formula,pdata.frame) S3method(formula,plm) S3method(has.intercept,Formula) S3method(has.intercept,default) S3method(has.intercept,formula) S3method(has.intercept,panelmodel) S3method(has.intercept,plm) S3method(index,panelmodel) S3method(index,pdata.frame) S3method(index,pindex) S3method(index,pseries) S3method(is.pbalanced,data.frame) S3method(is.pbalanced,default) S3method(is.pbalanced,panelmodel) S3method(is.pbalanced,pdata.frame) S3method(is.pbalanced,pgmm) S3method(is.pbalanced,pseries) S3method(is.pconsecutive,data.frame) S3method(is.pconsecutive,default) S3method(is.pconsecutive,panelmodel) S3method(is.pconsecutive,pdata.frame) S3method(is.pconsecutive,pseries) S3method(lag,pseries) S3method(lead,pseries) S3method(make.pbalanced,data.frame) S3method(make.pbalanced,pdata.frame) S3method(make.pbalanced,pseries) S3method(make.pconsecutive,data.frame) S3method(make.pconsecutive,pdata.frame) S3method(make.pconsecutive,pseries) S3method(model.frame,pFormula) S3method(model.frame,pdata.frame) S3method(model.matrix,pFormula) S3method(model.matrix,pcce) S3method(model.matrix,pdata.frame) S3method(model.matrix,plm) S3method(nobs,panelmodel) S3method(nobs,pgmm) S3method(pFtest,formula) S3method(pFtest,plm) S3method(pbgtest,formula) S3method(pbgtest,panelmodel) S3method(pbltest,formula) S3method(pbltest,plm) S3method(pbnftest,formula) S3method(pbnftest,panelmodel) S3method(pbsytest,formula) S3method(pbsytest,panelmodel) S3method(pcdtest,formula) S3method(pcdtest,panelmodel) S3method(pcdtest,pseries) S3method(pdim,data.frame) S3method(pdim,default) S3method(pdim,panelmodel) S3method(pdim,pdata.frame) S3method(pdim,pgmm) S3method(pdim,pseries) S3method(pdwtest,formula) S3method(pdwtest,panelmodel) S3method(phtest,formula) S3method(phtest,panelmodel) S3method(plmtest,formula) S3method(plmtest,plm) S3method(plot,plm) S3method(plot,pseries) S3method(plot,summary.pseries) S3method(pmodel.response,data.frame) S3method(pmodel.response,formula) S3method(pmodel.response,pcce) S3method(pmodel.response,plm) S3method(pooltest,formula) S3method(pooltest,plm) S3method(predict,plm) S3method(print,ercomp) S3method(print,fixef) S3method(print,panelmodel) S3method(print,pdata.frame) S3method(print,pdim) S3method(print,piest) S3method(print,plm.list) S3method(print,pseries) S3method(print,purtest) S3method(print,pvar) S3method(print,summary.fixef) S3method(print,summary.pcce) S3method(print,summary.pggls) S3method(print,summary.pgmm) S3method(print,summary.pht) S3method(print,summary.piest) S3method(print,summary.plm) S3method(print,summary.plm.list) S3method(print,summary.pmg) S3method(print,summary.pseries) S3method(print,summary.purtest) S3method(print,summary.pvcm) S3method(punbalancedness,data.frame) S3method(punbalancedness,panelmodel) S3method(punbalancedness,pdata.frame) S3method(pvar,data.frame) S3method(pvar,matrix) S3method(pvar,pdata.frame) S3method(pvar,pseries) S3method(pwaldtest,plm) S3method(pwaldtest,pvcm) S3method(pwartest,formula) S3method(pwartest,panelmodel) S3method(pwfdtest,formula) S3method(pwfdtest,panelmodel) S3method(pwtest,formula) S3method(pwtest,panelmodel) S3method(ranef,plm) S3method(residuals,panelmodel) S3method(residuals,pcce) S3method(residuals,pggls) S3method(residuals,plm) S3method(residuals,pmg) S3method(summary,fixef) S3method(summary,pcce) S3method(summary,pggls) S3method(summary,pgmm) S3method(summary,pht) S3method(summary,piest) S3method(summary,plm) S3method(summary,plm.list) S3method(summary,pmg) S3method(summary,pseries) S3method(summary,purtest) S3method(summary,pvcm) S3method(terms,panelmodel) S3method(update,panelmodel) S3method(vcov,panelmodel) S3method(vcovBK,plm) S3method(vcovDC,plm) S3method(vcovG,pcce) S3method(vcovG,plm) S3method(vcovHC,pcce) S3method(vcovHC,pgmm) S3method(vcovHC,plm) S3method(vcovNW,pcce) S3method(vcovNW,plm) S3method(vcovSCC,pcce) S3method(vcovSCC,plm) S3method(within_intercept,plm) export(Between) export(Within) export(aneweytest) export(between) export(cipstest) export(cortab) export(detect.lindep) export(detect_lin_dep) export(dynformula) export(ercomp) export(fixef) export(has.intercept) export(index) export(is.pbalanced) export(is.pconsecutive) export(is.pseries) export(lag) export(lead) export(make.pbalanced) export(make.pconsecutive) export(maxLik) export(mtest) export(nobs) export(pFormula) export(pFtest) export(pbgtest) export(pbltest) export(pbnftest) export(pbsytest) export(pcce) export(pcdtest) export(pdata.frame) export(pdim) export(pdwtest) export(pggls) export(pgmm) export(pgrangertest) export(pht) export(phtest) export(piest) export(pldv) export(plm) export(plm.data) export(plmtest) export(pmg) export(pmodel.response) export(pooltest) export(punbalancedness) export(purtest) export(pvar) export(pvcm) export(pwaldtest) export(pwartest) export(pwfdtest) export(pwtest) export(r.squared) export(ranef) export(sargan) export(vcovBK) export(vcovDC) export(vcovG) export(vcovHC) export(vcovHC.plm) export(vcovNW) export(vcovSCC) export(within_intercept) import(Formula) importFrom(MASS,ginv) importFrom(Rdpack,reprompt) importFrom(bdsmatrix,bdsmatrix) importFrom(grDevices,heat.colors) importFrom(grDevices,rainbow) importFrom(graphics,abline) importFrom(graphics,axis) importFrom(graphics,barplot) importFrom(graphics,legend) importFrom(graphics,lines) importFrom(graphics,plot) importFrom(graphics,points) importFrom(lattice,xyplot) importFrom(lmtest,bgtest) importFrom(lmtest,dwtest) importFrom(maxLik,maxLik) importFrom(nlme,fixef) importFrom(nlme,lme) importFrom(nlme,ranef) importFrom(sandwich,vcovHC) importFrom(stats,alias) importFrom(stats,approx) importFrom(stats,as.formula) importFrom(stats,coef) importFrom(stats,coefficients) importFrom(stats,cor) importFrom(stats,delete.response) importFrom(stats,deviance) importFrom(stats,df.residual) importFrom(stats,dnorm) importFrom(stats,fitted) importFrom(stats,formula) importFrom(stats,lag) importFrom(stats,lm) importFrom(stats,lm.fit) importFrom(stats,model.frame) importFrom(stats,model.matrix) importFrom(stats,model.response) importFrom(stats,model.weights) importFrom(stats,na.omit) importFrom(stats,nobs) importFrom(stats,pchisq) importFrom(stats,pf) importFrom(stats,pnorm) importFrom(stats,printCoefmat) importFrom(stats,pt) importFrom(stats,qnorm) importFrom(stats,reshape) importFrom(stats,resid) importFrom(stats,residuals) importFrom(stats,sd) importFrom(stats,setNames) importFrom(stats,terms) importFrom(stats,update) importFrom(stats,var) importFrom(stats,vcov) importFrom(zoo,index) plm/THANKS0000644000176200001440000000076311322656370011770 0ustar liggesusersWe've benefited from comments and bug reports from many people on previous versions of the package. We would like to thank especially : Arne Henningsen, Christian Kleiber, Katarzyna Kopczewska, Ott Toomet and Achim Zeileis. The package was greatly improved during the revision of the Journal of Statistical Software article, thanks to the helpful comments of three anonymous referees. We especially thank the co-editor of this journal, Achim Zeileis, for many interesting comments and suggestions. plm/data/0000755000176200001440000000000013623646153011764 5ustar liggesusersplm/data/LaborSupply.rda0000644000176200001440000004227413623647325014743 0ustar liggesusers7zXZi"6!XD}])TW"nRʟoÞuDlxd&MyoX S(Dadk+z 9M[XtZ#9cajM6۸.l=a `Q˟!9v[o x1-J@70oNLź7S,:6A ],g=>{@_zNߜRچglf?-J_xyWFh:$ڻ3 + [`7]erGƐlNLB1Cwtta$,iy*iDؙS< qW}Z.2R 3 *'_u!dv@*4ń0ҁ69Y2آ.`3VT% ,O<3fvE ypMdLl#SAGeG#:/c MŽJ/s^)пir[La<2FnYϳWi?[}N#ԩ ޷*#(}޶9:Ntxy(_(=,KQL[̩вoڽnn3K@p Ɍ"X2ި髰ɯ*א}׻z#X):n_ڶ#-0c5C<w"Q?=b̍jq, >Ok"&DV)P9juޜO,msAP%bMq0X&f Чz&UJwmc+S {h`mĐHO2D `7X)NI/ J!Phuݲ IQxp-0R)Xl@T<($]Y1ލomc[ J(&Y$qn>r'e*?~IʒRnXvfdqni%|gpж.f$bn/Q3 zaϵ#}āxtdi [י0 B0u ߁Ex)hq >1:k4Jl6d"k‡lӨLXB:0B6~EѴ&ytj,6qN À*05s9c&61\|n]C+`)m%&d@mIpUTǻ8_;6: DIgir$)S<|yRŚdR2Wgҗdҋpiŏ`ȽL}=M*G{GpGNlSLj-V|)I*I_8Eoo`IS JMǀ.ijVK ۅ_i{w`r>8;~ߣ>Amu>wXbǻl+3K (v~.poI.޽RDT.n w}S/㧓?R"Q*4°w1aGHD8F?~sBj",7lu2ܔk3m5Љ~+>2IHxe 0(Dlif%''Syn9͖ƈeL=?o W^)C&x"gMυ3LXigwұtaedQJp7I±#`(.sLO3]Yl̍+;)DQja!szXr2SeF /0OP+Yge=pe?t䧸 t8mJvtC`ma`fw P l x>>jNNsqTrlsuSri ~&9qepiUZ~u4Kc'"Lp9bQ 9f$- N<>Wk-gKب (c*J8Wk ̻\!BLLH/ b Y75YRkv.jU l5 L{Lpm$|kMSbd5_c%^pg bM^-8J@)WeǗVFogAa5sS 5($cOGx"i~RKdf ZxB͊?6Kz_U,O˙w4Պ0sf>´r-v{`Lb&TNX 7ň~3$9ci[/01H5r69Pjj$'i<%mVwlr= }uNA9ZT,`%֫V'\Ck[pTHZpAAFqADNaVBrJBZbRbwAoi\j9E+ T&|icx圐P Bm h5/H.e$ iF6iJM(rp慙#3jCŲG(@M:$vĽ ݡb4U5]9P0c{ (SGJMh;Jk4! 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Please follow the links to view the function's original documentation. } \details{ \itemize{ \item index \item fixef \item ranef \item vcovHC \item nobs \item maxLik } } \keyword{internal} plm/man/Hedonic.Rd0000755000176200001440000000303413503144006013454 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Hedonic} \alias{Hedonic} \title{Hedonic Prices of Census Tracts in the Boston Area} \format{A dataframe containing: \describe{ \item{mv}{median value of owner--occupied homes} \item{crim}{crime rate} \item{zn}{proportion of 25,000 square feet residential lots} \item{indus}{proportion of no--retail business acres} \item{chas}{is the tract bounds the Charles River?} \item{nox}{annual average nitrogen oxide concentration in parts per hundred million} \item{rm}{average number of rooms} \item{age}{proportion of owner units built prior to 1940} \item{dis}{weighted distances to five employment centers in the Boston area} \item{rad}{index of accessibility to radial highways} \item{tax}{full value property tax rate ($/$10,000)} \item{ptratio}{pupil/teacher ratio} \item{blacks}{proportion of blacks in the population} \item{lstat}{proportion of population that is lower status} \item{townid}{town identifier} }} \source{ Online complements to Baltagi (2001): \url{http://www.wiley.com/legacy/wileychi/baltagi/} Online complements to Baltagi (2013): \url{http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452} } \description{ A cross-section } \details{ \emph{number of observations} : 506 \emph{observation} : regional \emph{country} : United States } \references{ \insertRef{BALT:01}{plm} \insertRef{BALT:13}{plm} \insertRef{BESL:KUH:WELS:80}{plm} \insertRef{HARR:RUBI:78}{plm} } \keyword{datasets} plm/man/is.pconsecutive.Rd0000644000176200001440000001615613602224251015233 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/is.pconsecutive_pbalanced.R \name{is.pconsecutive} \alias{is.pconsecutive} \alias{is.pconsecutive.default} \alias{is.pconsecutive.data.frame} \alias{is.pconsecutive.pseries} \alias{is.pconsecutive.pdata.frame} \alias{is.pconsecutive.panelmodel} \title{Check if time periods are consecutive} \usage{ is.pconsecutive(x, ...) \method{is.pconsecutive}{default}(x, id, time, na.rm.tindex = FALSE, ...) \method{is.pconsecutive}{data.frame}(x, index = NULL, na.rm.tindex = FALSE, ...) \method{is.pconsecutive}{pseries}(x, na.rm.tindex = FALSE, ...) \method{is.pconsecutive}{pdata.frame}(x, na.rm.tindex = FALSE, ...) \method{is.pconsecutive}{panelmodel}(x, na.rm.tindex = FALSE, ...) } \arguments{ \item{x}{usually, an object of class \code{pdata.frame}, \code{data.frame}, \code{pseries}, or an estimated \code{panelmodel}; for the default method \code{x} can also be an arbitrary vector or \code{NULL}, see \strong{Details},} \item{\dots}{further arguments.} \item{id, time}{only relevant for default method: vectors specifying the id and time dimensions, i. e. a sequence of individual and time identifiers, each as stacked time series,} \item{na.rm.tindex}{logical indicating whether any \code{NA} values in the time index are removed before consecutiveness is evaluated (defaults to \code{FALSE}),} \item{index}{only relevant for \code{data.frame} interface; if \code{NULL}, the first two columns of the data.frame are assumed to be the index variables; if not \code{NULL}, both dimensions ('individual', 'time') need to be specified by \code{index} for \code{is.pconsecutive} on data frames, for further details see \code{\link[=pdata.frame]{pdata.frame()}},} } \value{ A named \code{logical} vector (names are those of the individuals). The i-th element of the returned vector corresponds to the i-th individual. The values of the i-th element can be: \item{list("TRUE")}{if the i-th individual has consecutive time periods,} \item{list("FALSE")}{if the i-th individual has non-consecutive time periods,} \item{list("NA")}{if there are any NA values in time index of the i-th the individual; see also argument \code{na.rm.tindex} to remove those.} } \description{ This function checks for each individual if its associated time periods are consecutive (no "gaps" in time dimension per individual) } \details{ (p)data.frame, pseries and estimated panelmodel objects can be tested if their time periods are consecutive per individual. For evaluation of consecutiveness, the time dimension is interpreted to be numeric, and the data are tested for being a regularly spaced sequence with distance 1 between the time periods for each individual (for each individual the time dimension can be interpreted as sequence t, t+1, t+2, \ldots{} where t is an integer). As such, the "numerical content" of the time index variable is considered for consecutiveness, not the "physical position" of the various observations for an individuals in the (p)data.frame/pseries (it is not about "neighbouring" rows). If the object to be evaluated is a pseries or a pdata.frame, the time index is coerced from factor via as.character to numeric, i.e. the series \verb{as.numeric(as.character(index()[[2]]))]} is evaluated for gaps. The default method also works for argument \code{x} being an arbitrary vector (see \strong{Examples}), provided one can supply arguments \code{id} and \code{time}, which need to ordered as stacked time series. As only \code{id} and \code{time} are really necessary for the default method to evaluate the consecutiveness, \code{x = NULL} is also possible. However, if the vector \code{x} is also supplied, additional input checking for equality of the lengths of \code{x}, \code{id} and \code{time} is performed, which is safer. For the data.frame interface, the data is ordered in the appropriate way (stacked time series) before the consecutiveness is evaluated. For the pdata.frame and pseries interface, ordering is not performed because both data types are already ordered in the appropriate way when created. Note: Only the presence of the time period itself in the object is tested, not if there are any other variables. \code{NA} values in individual index are not examined but silently dropped - In this case, it is not clear which individual is meant by id value \code{NA}, thus no statement about consecutiveness of time periods for those "\code{NA}-individuals" is possible. } \examples{ data("Grunfeld", package = "plm") is.pconsecutive(Grunfeld) is.pconsecutive(Grunfeld, index=c("firm", "year")) # delete 2nd row (2nd time period for first individual) # -> non consecutive Grunfeld_missing_period <- Grunfeld[-2, ] is.pconsecutive(Grunfeld_missing_period) all(is.pconsecutive(Grunfeld_missing_period)) # FALSE # delete rows 1 and 2 (1st and 2nd time period for first individual) # -> consecutive Grunfeld_missing_period_other <- Grunfeld[-c(1,2), ] is.pconsecutive(Grunfeld_missing_period_other) # all TRUE # delete year 1937 (3rd period) for _all_ individuals Grunfeld_wo_1937 <- Grunfeld[Grunfeld$year != 1937, ] is.pconsecutive(Grunfeld_wo_1937) # all FALSE # pdata.frame interface pGrunfeld <- pdata.frame(Grunfeld) pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) is.pconsecutive(pGrunfeld) # all TRUE is.pconsecutive(pGrunfeld_missing_period) # first FALSE, others TRUE # panelmodel interface (first, estimate some models) mod_pGrunfeld <- plm(inv ~ value + capital, data = Grunfeld) mod_pGrunfeld_missing_period <- plm(inv ~ value + capital, data = Grunfeld_missing_period) is.pconsecutive(mod_pGrunfeld) is.pconsecutive(mod_pGrunfeld_missing_period) nobs(mod_pGrunfeld) # 200 nobs(mod_pGrunfeld_missing_period) # 199 # pseries interface pinv <- pGrunfeld$inv pinv_missing_period <- pGrunfeld_missing_period$inv is.pconsecutive(pinv) is.pconsecutive(pinv_missing_period) # default method for arbitrary vectors or NULL inv <- Grunfeld$inv inv_missing_period <- Grunfeld_missing_period$inv is.pconsecutive(inv, id = Grunfeld$firm, time = Grunfeld$year) is.pconsecutive(inv_missing_period, id = Grunfeld_missing_period$firm, time = Grunfeld_missing_period$year) # (not run) demonstrate mismatch lengths of x, id, time # is.pconsecutive(x = inv_missing_period, id = Grunfeld$firm, time = Grunfeld$year) # only id and time are needed for evaluation is.pconsecutive(NULL, id = Grunfeld$firm, time = Grunfeld$year) } \seealso{ \code{\link[=make.pconsecutive]{make.pconsecutive()}} to make data consecutive (and, as an option, balanced at the same time) and \code{\link[=make.pbalanced]{make.pbalanced()}} to make data balanced.\cr \code{\link[=pdim]{pdim()}} to check the dimensions of a 'pdata.frame' (and other objects), \code{\link[=pvar]{pvar()}} to check for individual and time variation of a 'pdata.frame' (and other objects), \code{\link[=lag]{lag()}} for lagged (and leading) values of a 'pseries' object.\cr \code{\link[=pseries]{pseries()}}, \code{\link[=data.frame]{data.frame()}}, \code{\link[=pdata.frame]{pdata.frame()}}, for class 'panelmodel' see \code{\link[=plm]{plm()}} and \code{\link[=pgmm]{pgmm()}}. } \author{ Kevin Tappe } \keyword{attribute} plm/man/index.plm.Rd0000644000176200001440000000520313503144006013776 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_pdata.frame.R \name{index.plm} \alias{index.plm} \alias{index.pindex} \alias{index.pdata.frame} \alias{index.pseries} \alias{index.panelmodel} \title{Extract the indexes of panel data} \usage{ \method{index}{pindex}(x, which = NULL, ...) \method{index}{pdata.frame}(x, which = NULL, ...) \method{index}{pseries}(x, which = NULL, ...) \method{index}{panelmodel}(x, which = NULL, ...) } \arguments{ \item{x}{an object of class \code{"pindex"}, \code{"pdata.frame"}, \code{"pseries"} or \code{"panelmodel"},} \item{which}{the index(es) to be extracted (see details),} \item{\dots}{further arguments.} } \value{ A vector or an object of class \code{c("pindex","data.frame")} containing either one index, individual and time index, or (any combination of) individual, time and group indexes. } \description{ This function extracts the information about the structure of the individual and time dimensions of panel data. Grouping information can also be extracted if the panel data were created with a grouping variable. } \details{ Panel data are stored in a \code{"pdata.frame"} which has an \code{"index"} attribute. Fitted models in \code{"plm"} have a \code{"model"} element which is also a \code{"pdata.frame"} and therefore also has an \code{"index"} attribute. Finally, each series, once extracted from a \code{"pdata.frame"}, becomes of class \code{"pseries"}, which also has this \code{"index"} attribute. \code{"index"} methods are available for all these objects. The argument \code{"which"} indicates which index should be extracted. If \code{which = NULL}, all indexes are extracted. \code{"which"} can also be a vector of length 1, 2, or 3 (3 only if the pdata frame was constructed with an additional group index) containing either characters (the names of the individual variable and/or of the time variable and/or the group variable or \code{"id"} and \code{"time"}) and \code{"group"} or integers (1 for the individual index, 2 for the time index, and 3 for the group index (the latter only if the pdata frame was constructed with such).) } \examples{ data("Grunfeld", package = "plm") Gr <- pdata.frame(Grunfeld, index = c("firm", "year")) m <- plm(inv ~ value + capital, data = Gr) index(Gr, "firm") index(Gr, "time") index(Gr$inv, c(2, 1)) index(m, "id") # with additional group index data("Produc", package = "plm") pProduc <- pdata.frame(Produc, index = c("state", "year", "region")) index(pProduc, 3) index(pProduc, "region") index(pProduc, "group") } \seealso{ \code{\link[=pdata.frame]{pdata.frame()}}, \code{\link[=plm]{plm()}} } \author{ Yves Croissant } \keyword{attribute} plm/man/Gasoline.Rd0000755000176200001440000000201513503144006013642 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Gasoline} \alias{Gasoline} \title{Gasoline Consumption} \format{A data frame containing : \describe{ \item{country}{a factor with 18 levels} \item{year}{the year} \item{lgaspcar}{logarithm of motor gasoline consumption per car} \item{lincomep}{logarithm of real per-capita income} \item{lrpmg}{logarithm of real motor gasoline price} \item{lcarpcap}{logarithm of the stock of cars per capita} }} \source{ Online complements to Baltagi (2001): \url{http://www.wiley.com/legacy/wileychi/baltagi/} Online complements to Baltagi (2013): \url{http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452} } \description{ A panel of 18 observations from 1960 to 1978 } \details{ \emph{total number of observations} : 342 \emph{observation} : country \emph{country} : OECD } \references{ \insertRef{BALT:01}{plm} \insertRef{BALT:13}{plm} \insertRef{BALT:GRIF:83}{plm} } \keyword{datasets} plm/man/Produc.Rd0000755000176200001440000000224713503144006013344 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Produc} \alias{Produc} \title{US States Production} \format{A data frame containing : \describe{ \item{state}{the state} \item{year}{the year} \item{region}{the region} \item{pcap}{public capital stock} \item{hwy}{highway and streets} \item{water}{water and sewer facilities} \item{util}{other public buildings and structures} \item{pc}{private capital stock} \item{gsp}{gross state product} \item{emp}{labor input measured by the employment in non--agricultural payrolls} \item{unemp}{state unemployment rate} }} \source{ Online complements to Baltagi (2001): \url{http://www.wiley.com/legacy/wileychi/baltagi/} Online complements to Baltagi (2013): \url{http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452} } \description{ A panel of 48 observations from 1970 to 1986 } \details{ \emph{total number of observations} : 816 \emph{observation} : regional \emph{country} : United States } \references{ \insertRef{BALT:01}{plm} \insertRef{BALT:13}{plm} \insertRef{BALT:PINN:95}{plm} \insertRef{MUNN:90}{plm} } \keyword{datasets} plm/man/phtest.Rd0000755000176200001440000000623313602224251013417 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_general.R \name{phtest} \alias{phtest} \alias{phtest.formula} \alias{phtest.panelmodel} \title{Hausman Test for Panel Models} \usage{ phtest(x, ...) \method{phtest}{formula}( x, data, model = c("within", "random"), method = c("chisq", "aux"), index = NULL, vcov = NULL, ... ) \method{phtest}{panelmodel}(x, x2, ...) } \arguments{ \item{x}{an object of class \code{"panelmodel"} or \code{"formula"},} \item{\dots}{further arguments to be passed on. For the formula method, place argument \code{effect} here to compare e.g. twoway models (\code{effect = "twoways"}) Note: Argument \code{effect} is not respected in the panelmodel method.} \item{data}{a \code{data.frame},} \item{model}{a character vector containing the names of two models (length(model) must be 2),} \item{method}{one of \code{"chisq"} or \code{"aux"},} \item{index}{an optional vector of index variables,} \item{vcov}{an optional covariance function,} \item{x2}{an object of class \code{"panelmodel"},} } \value{ An object of class \code{"htest"}. } \description{ Specification test for panel models. } \details{ The Hausman test (sometimes also called Durbin--Wu--Hausman test) is based on the difference of the vectors of coefficients of two different models. The \code{panelmodel} method computes the original version of the test based on a quadratic form \insertCite{HAUS:78}{plm}. The \code{formula} method, if \code{method="chisq"} (default), computes the original version of the test based on a quadratic form; if \code{method="aux"} then the auxiliary-regression-based version in Wooldridge (2010, Sec. 10.7.3.) is computed instead \insertCite{@WOOL:10 Sec.10.7.3}{plm}. Only the latter can be robustified by specifying a robust covariance estimator as a function through the argument \code{vcov} (see \strong{Examples}). The equivalent tests in the \strong{one-way} case using a between model (either "within vs. between" or "random vs. between") \insertCite{@see @HAUS:TAYL:81 or @BALT:13 Sec.4.3}{plm} can also be performed by \code{phtest}, but only for \code{test = "chisq"}, not for the regression-based test. NB: These equivalent tests using the between model do not extend to the two-ways case. There are, however, some other equivalent tests, \insertCite{@see @KANG:85 or @BALT:13 Sec.4.3.7}{plm}, but those are unsupported by \code{phtest}. } \examples{ data("Gasoline", package = "plm") form <- lgaspcar ~ lincomep + lrpmg + lcarpcap wi <- plm(form, data = Gasoline, model = "within") re <- plm(form, data = Gasoline, model = "random") phtest(wi, re) phtest(form, data = Gasoline) phtest(form, data = Gasoline, method = "aux") # robust Hausman test (regression-based) phtest(form, data = Gasoline, method = "aux", vcov = vcovHC) # robust Hausman test with vcov supplied as a # function and additional parameters phtest(form, data = Gasoline, method = "aux", vcov = function(x) vcovHC(x, method="white2", type="HC3")) } \references{ \insertRef{HAUS:78}{plm} \insertRef{HAUS:TAYL:81}{plm} \insertRef{KANG:85}{plm} \insertRef{WOOL:10}{plm} \insertRef{BALT:13}{plm} } \author{ Yves Croissant, Giovanni Millo } \keyword{htest} plm/man/vcovNW.Rd0000644000176200001440000000647213602224251013334 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_vcovG.R \name{vcovNW} \alias{vcovNW} \alias{vcovNW.plm} \alias{vcovNW.pcce} \title{\insertCite{NEWE:WEST:87;textual}{plm} Robust Covariance Matrix Estimator} \usage{ vcovNW(x, ...) \method{vcovNW}{plm}( x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), maxlag = NULL, wj = function(j, maxlag) 1 - j/(maxlag + 1), ... ) \method{vcovNW}{pcce}( x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), maxlag = NULL, wj = function(j, maxlag) 1 - j/(maxlag + 1), ... ) } \arguments{ \item{x}{an object of class \code{"plm"} or \code{"pcce"}} \item{\dots}{further arguments} \item{type}{the weighting scheme used, one of \code{"HC0"}, \code{"sss"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, see Details,} \item{maxlag}{either \code{NULL} or a positive integer specifying the maximum lag order before truncation} \item{wj}{weighting function to be applied to lagged terms,} } \value{ An object of class \code{"matrix"} containing the estimate of the covariance matrix of coefficients. } \description{ Nonparametric robust covariance matrix estimators \emph{a la Newey and West} for panel models with serial correlation. } \details{ \code{vcovNW} is a function for estimating a robust covariance matrix of parameters for a panel model according to the \insertCite{NEWE:WEST:87;textual}{plm} method. The function works as a restriction of the \insertCite{DRIS:KRAA:98;textual}{plm} covariance (see \code{\link[=vcovSCC]{vcovSCC()}}) to no cross--sectional correlation. Weighting schemes specified by \code{type} are analogous to those in \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} in package \CRANpkg{sandwich} and are justified theoretically (although in the context of the standard linear model) by \insertCite{MACK:WHIT:85;textual}{plm} and \insertCite{CRIB:04;textual}{plm} \insertCite{@see @ZEIL:04}{plm}. The main use of \code{vcovNW} is to be an argument to other functions, e.g. for Wald--type testing: argument \code{vcov.} to \code{coeftest()}, argument \code{vcov} to \code{waldtest()} and other methods in the \CRANpkg{lmtest} package; and argument \code{vcov.} to \code{linearHypothesis()} in the \CRANpkg{car} package (see the examples). Notice that the \code{vcov} and \code{vcov.} arguments allow to supply a function (which is the safest) or a matrix \insertCite{@see @ZEIL:04, 4.1-2 and examples below}{plm}. } \examples{ library(lmtest) library(car) data("Produc", package="plm") zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling") ## standard coefficient significance test coeftest(zz) ## NW robust significance test, default coeftest(zz, vcov.=vcovNW) ## idem with parameters, pass vcov as a function argument coeftest(zz, vcov.=function(x) vcovNW(x, type="HC1", maxlag=4)) ## joint restriction test waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovNW) ## test of hyp.: 2*log(pc)=log(emp) linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovNW) } \references{ \insertRef{CRIB:04}{plm} \insertRef{DRIS:KRAA:98}{plm} \insertRef{MACK:WHIT:85}{plm} \insertRef{NEWE:WEST:87}{plm} \insertRef{ZEIL:04}{plm} } \seealso{ \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} from the \CRANpkg{sandwich} package for weighting schemes (\code{type} argument). } \author{ Giovanni Millo } \keyword{regression} plm/man/Crime.Rd0000755000176200001440000000716513503144006013153 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Crime} \alias{Crime} \title{Crime in North Carolina} \format{A data frame containing : \describe{ \item{county}{county identifier} \item{year}{year from 1981 to 1987} \item{crmrte}{crimes committed per person} \item{prbarr}{'probability' of arrest} \item{prbconv}{'probability' of conviction} \item{prbpris}{'probability' of prison sentence} \item{avgsen}{average sentence, days} \item{polpc}{police per capita} \item{density}{people per square mile} \item{taxpc}{tax revenue per capita} \item{region}{factor. One of 'other', 'west' or 'central'.} \item{smsa}{factor. (Also called "urban".) Does the individual reside in a SMSA (standard metropolitan statistical area)?} \item{pctmin}{percentage minority in 1980} \item{wcon}{weekly wage in construction} \item{wtuc}{weekly wage in transportation, utilities, communications} \item{wtrd}{weekly wage in wholesale and retail trade} \item{wfir}{weekly wage in finance, insurance and real estate} \item{wser}{weekly wage in service industry} \item{wmfg}{weekly wage in manufacturing} \item{wfed}{weekly wage in federal government} \item{wsta}{weekly wage in state government} \item{wloc}{weekly wage in local government} \item{mix}{offence mix: face-to-face/other} \item{pctymle}{percentage of young males (between ages 15 to 24)} \item{lcrmrte}{log of crimes committed per person} \item{lprbarr}{log of 'probability' of arrest} \item{lprbconv}{log of 'probability' of conviction} \item{lprbpris}{log of 'probability' of prison sentence} \item{lavgsen}{log of average sentence, days} \item{lpolpc}{log of police per capita} \item{ldensity}{log of people per square mile} \item{ltaxpc}{log of tax revenue per capita} \item{lpctmin}{log of percentage minority in 1980} \item{lwcon}{log of weekly wage in construction} \item{lwtuc}{log of weekly wage in transportation, utilities, communications} \item{lwtrd}{log of weekly wage in wholesale and retail trade} \item{lwfir}{log of weekly wage in finance, insurance and real estate} \item{lwser}{log of weekly wage in service industry} \item{lwmfg}{log of weekly wage in manufacturing} \item{lwfed}{log of weekly wage in federal government} \item{lwsta}{log of weekly wage in state government} \item{lwloc}{log of weekly wage in local government} \item{lmix}{log of offence mix: face-to-face/other} \item{lpctymle}{log of percentage of young males (between ages 15 to 24)}}} \source{ Journal of Applied Econometrics Data Archive (complements Baltagi (2006)): \url{http://qed.econ.queensu.ca/jae/2006-v21.4/baltagi/} Online complements to Baltagi (2001): \url{http://www.wiley.com/legacy/wileychi/baltagi/} Online complements to Baltagi (2013): \url{http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452} See also Journal of Applied Econometrics data archive entry for Baltagi (2006) at \url{http://qed.econ.queensu.ca/jae/2006-v21.4/baltagi/}. } \description{ a panel of 90 observational units (counties) from 1981 to 1987 } \details{ \emph{total number of observations} : 630 \emph{observation} : regional \emph{country} : United States The variables l* (lcrmrte, lprbarr, ...) contain the pre-computed logarithms of the base variables as found in the original data set. Note that these values slightly differ from what R's log() function yields for the base variables. In order to reproduce examples from the literature, the pre-computed logs need to be used, otherwise the results differ slightly. } \references{ \insertRef{CORN:TRUM:94}{plm} \insertRef{BALT:06}{plm} \insertRef{BALT:01}{plm} \insertRef{BALT:13}{plm} } \keyword{datasets} plm/man/piest.Rd0000644000176200001440000000266713602224251013240 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_pi.R \name{piest} \alias{piest} \alias{print.piest} \alias{summary.piest} \alias{print.summary.piest} \title{Chamberlain estimator and test for fixed effects} \usage{ piest(formula, data, subset, na.action, index = NULL, robust = TRUE, ...) \method{print}{piest}(x, ...) \method{summary}{piest}(object, ...) \method{print}{summary.piest}(x, ...) } \arguments{ \item{formula}{a symbolic description for the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{\link[=lm]{lm()}},} \item{na.action}{see \code{\link[=lm]{lm()}},} \item{index}{the indexes,} \item{robust}{if \code{FALSE}, the error as assumed to be spherical, otherwise, a robust estimation of the covariance matrix is computed,} \item{\dots}{further arguments.} \item{object, x}{an object of class \code{"plm"},} } \value{ An object of class \code{"piest"}. } \description{ General estimator useful for testing the within specification } \details{ The Chamberlain method consists on using the covariates of all the periods as regressors. It allows to test the within specification. } \examples{ data("RiceFarms", package = "plm") pirice <- piest(log(goutput) ~ log(seed) + log(totlabor) + log(size), RiceFarms, index = "id") summary(pirice) } \references{ \insertRef{CHAM:82}{plm} } \seealso{ \code{\link[=aneweytest]{aneweytest()}} } \author{ Yves Croissant } \keyword{htest} plm/man/model.frame.pdata.frame.Rd0000644000176200001440000000747113602224251016464 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_model.extract.R \name{model.frame.pdata.frame} \alias{model.frame.pdata.frame} \alias{formula.pdata.frame} \alias{model.matrix.plm} \alias{model.matrix.pdata.frame} \title{model.frame and model.matrix for panel data} \usage{ \method{model.frame}{pdata.frame}( formula, data = NULL, ..., lhs = NULL, rhs = NULL, dot = "previous" ) \method{formula}{pdata.frame}(x, ...) \method{model.matrix}{plm}(object, ...) \method{model.matrix}{pdata.frame}( object, model = c("pooling", "within", "Between", "Sum", "between", "mean", "random", "fd"), effect = c("individual", "time", "twoways", "nested"), rhs = 1, theta = NULL, cstcovar.rm = NULL, ... ) } \arguments{ \item{data}{a \code{formula}, see \strong{Details},} \item{\dots}{further arguments.} \item{lhs}{inherited from package \code{\link[Formula:Formula]{Formula::Formula()}} (see there),} \item{rhs}{inherited from package \code{\link[Formula:Formula]{Formula::Formula()}} (see there),} \item{dot}{inherited from package \code{\link[Formula:Formula]{Formula::Formula()}} (see there),} \item{x}{a \code{model.frame}} \item{object, formula}{an object of class \code{"pdata.frame"} or an estimated model object of class \code{"plm"},} \item{model}{one of \code{"pooling"}, \code{"within"}, \code{"Sum"}, \code{"Between"}, \code{"between"}, \verb{"random",} \code{"fd"} and \code{"ht"},} \item{effect}{the effects introduced in the model, one of \code{"individual"}, \code{"time"}, \code{"twoways"} or \code{"nested"},} \item{theta}{the parameter for the transformation if \code{model = "random"},} \item{cstcovar.rm}{remove the constant columns, one of \verb{"none", "intercept", "covariates", "all")},} } \value{ The \code{model.frame} methods return a \code{pdata.frame}.\cr The \code{model.matrix} methods return a \code{matrix}. } \description{ Methods to create model frame and model matrix for panel data. } \details{ The \code{lhs} and \code{rhs} arguments are inherited from \code{Formula}, see there for more details.\cr The \code{model.frame} methods return a \code{pdata.frame} object suitable as an input to plm's \code{model.matrix}.\cr The \code{model.matrix} methods builds a model matrix with transformations performed as specified by the \code{model} and \code{effect} arguments (and \code{theta} if \code{model = "random"} is requested), in this case the supplied \code{data} argument should be a model frame created by plm's \code{model.frame} method. If not, it is tried to construct the model frame from the data. Constructing the model frame first ensures proper NA handling, see \strong{Examples}. } \examples{ # First, make a pdata.frame data("Grunfeld", package = "plm") pGrunfeld <- pdata.frame(Grunfeld) # then make a model frame from a pFormula and a pdata.frame #pform <- pFormula(inv ~ value + capital) #mf <- model.frame(pform, data = pGrunfeld) form <- inv ~ value mf <- model.frame(pGrunfeld, form) # then construct the (transformed) model matrix (design matrix) # from formula and model frame #modmat <- model.matrix(pform, data = mf, model = "within") modmat <- model.matrix(mf, model = "within") ## retrieve model frame and model matrix from an estimated plm object #fe_model <- plm(pform, data = pGrunfeld, model = "within") fe_model <- plm(form, data = pGrunfeld, model = "within") model.frame(fe_model) model.matrix(fe_model) # same as constructed before all.equal(mf, model.frame(fe_model), check.attributes = FALSE) # TRUE all.equal(modmat, model.matrix(fe_model), check.attributes = FALSE) # TRUE } \seealso{ \code{\link[=pmodel.response]{pmodel.response()}} for (transformed) response variable.\cr \code{\link[Formula:Formula]{Formula::Formula()}} from package \code{Formula}, especially for the \code{lhs} and \code{rhs} arguments. } \author{ Yves Croissant } \keyword{classes} plm/man/pFtest.Rd0000755000176200001440000000314213503144006013350 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_general.R \name{pFtest} \alias{pFtest} \alias{pFtest.formula} \alias{pFtest.plm} \title{F Test for Individual and/or Time Effects} \usage{ pFtest(x, ...) \method{pFtest}{formula}(x, data, ...) \method{pFtest}{plm}(x, z, ...) } \arguments{ \item{x}{an object of class \code{"plm"} or of class \code{"formula"},} \item{\dots}{further arguments.} \item{data}{a \code{data.frame},} \item{z}{an object of class \code{"plm"},} } \value{ An object of class \code{"htest"}. } \description{ Test of individual and/or time effects based on the comparison of the \code{within} and the \code{pooling} model. } \details{ For the \code{plm} method, the argument of this function is two \code{plm} objects, the first being a within model, the second a pooling model. The effects tested are either individual, time or twoways, depending on the effects introduced in the within model. } \examples{ data("Grunfeld", package="plm") gp <- plm(inv ~ value + capital, data = Grunfeld, model = "pooling") gi <- plm(inv ~ value + capital, data = Grunfeld, effect = "individual", model = "within") gt <- plm(inv ~ value + capital, data = Grunfeld, effect = "time", model = "within") gd <- plm(inv ~ value + capital, data = Grunfeld, effect = "twoways", model = "within") pFtest(gi, gp) pFtest(gt, gp) pFtest(gd, gp) pFtest(inv ~ value + capital, data = Grunfeld, effect = "twoways") } \seealso{ \code{\link[=plmtest]{plmtest()}} for Lagrange multiplier tests of individuals and/or time effects. } \author{ Yves Croissant } \keyword{htest} plm/man/nobs.plm.Rd0000644000176200001440000000344413602224251013636 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_methods.R \name{nobs.plm} \alias{nobs.plm} \alias{nobs.panelmodel} \alias{nobs.pgmm} \title{Extract Total Number of Observations Used in Estimated Panelmodel} \usage{ \method{nobs}{panelmodel}(object, ...) \method{nobs}{pgmm}(object, ...) } \arguments{ \item{object}{a \code{panelmodel} object for which the number of total observations is to be extracted,} \item{\dots}{further arguments.} } \value{ A single number, normally an integer. } \description{ This function extracts the total number of 'observations' from a fitted panel model. } \details{ The number of observations is usually the length of the residuals vector. Thus, \code{nobs} gives the number of observations actually used by the estimation procedure. It is not necessarily the number of observations of the model frame (number of rows in the model frame), because sometimes the model frame is further reduced by the estimation procedure. This is e.g. the case for first--difference models estimated by \code{plm(..., model = "fd")} where the model frame does not yet contain the differences (see also \strong{Examples}). } \examples{ # estimate a panelmodel data("Produc", package = "plm") z <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=Produc, model="random", subset = gsp > 5000) nobs(z) # total observations used in estimation pdim(z)$nT$N # same information pdim(z) # more information about the dimensions (no. of individuals and time periods) # illustrate difference between nobs and pdim for first-difference model data("Grunfeld", package = "plm") fdmod <- plm(inv ~ value + capital, data = Grunfeld, model = "fd") nobs(fdmod) # 190 pdim(fdmod)$nT$N # 200 } \seealso{ \code{\link[=pdim]{pdim()}} } \keyword{attribute} plm/man/pggls.Rd0000755000176200001440000000746513602224251013234 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_ggls.R \name{pggls} \alias{pggls} \alias{summary.pggls} \alias{print.summary.pggls} \alias{residuals.pggls} \title{General FGLS Estimators} \usage{ pggls( formula, data, subset, na.action, effect = c("individual", "time"), model = c("within", "random", "pooling", "fd"), index = NULL, ... ) \method{summary}{pggls}(object, ...) \method{print}{summary.pggls}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) \method{residuals}{pggls}(object, ...) } \arguments{ \item{formula}{a symbolic description of the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{\link[=lm]{lm()}},} \item{na.action}{see \code{\link[=lm]{lm()}},} \item{effect}{the effects introduced in the model, one of \code{"individual"} or \code{"time"},} \item{model}{one of \code{"within"}, \code{"pooling"}, \code{"random"} or \code{"fd"},} \item{index}{the indexes, see \code{\link[=pdata.frame]{pdata.frame()}},} \item{\dots}{further arguments.} \item{object, x}{an object of class \code{pggls},} \item{digits}{digits,} \item{width}{the maximum length of the lines in the print output,} } \value{ An object of class \code{c("pggls","panelmodel")} containing: \item{coefficients}{the vector of coefficients,} \item{residuals}{the vector of residuals,} \item{fitted.values}{the vector of fitted values,} \item{vcov}{the covariance matrix of the coefficients,} \item{df.residual}{degrees of freedom of the residuals,} \item{model}{a data.frame containing the variables used for the estimation,} \item{call}{the call,} \item{sigma}{the estimated intragroup (or cross-sectional, if \code{effect = "time"}) covariance of errors,} } \description{ General FGLS estimators for panel data (balanced or unbalanced) } \details{ \code{pggls} is a function for the estimation of linear panel models by general feasible generalized least squares, either with or without fixed effects. General FGLS is based on a two-step estimation process: first a model is estimated by OLS (\code{model = "pooling"}), fixed effects (\code{model = "within"}) or first differences (\code{model = "fd"}), then its residuals are used to estimate an error covariance matrix for use in a feasible-GLS analysis. This framework allows the error covariance structure inside every group (if \code{effect = "individual"}, else symmetric) of observations to be fully unrestricted and is therefore robust against any type of intragroup heteroskedasticity and serial correlation. Conversely, this structure is assumed identical across groups and thus general FGLS estimation is inefficient under groupwise heteroskedasticity. Note also that this method requires estimation of \eqn{T(T+1)/2} variance parameters, thus efficiency requires N >> T (if \code{effect = "individual"}, else the opposite). Setting \code{model = "random"} or \code{model = "pooling"}, both produce an unrestricted FGLS model as in Wooldridge, Ch. 10.5, although the former is deprecated and included only for retro--compatibility reasons. If \code{model = "within"} (the default) then a FEGLS (fixed effects GLS, see ibid.) is estimated; if \code{model = "fd"} a FDGLS (first-difference GLS). } \examples{ data("Produc", package = "plm") zz_wi <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "within") summary(zz_wi) zz_pool <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "pooling") summary(zz_pool) zz_fd <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "fd") summary(zz_fd) } \references{ \insertRef{IM:SEUN:SCHM:WOOL:99}{plm} \insertRef{KIEF:80}{plm} \insertRef{WOOL:02}{plm} \insertRef{WOOL:10}{plm} } \author{ Giovanni Millo } \keyword{regression} plm/man/pgmm.Rd0000755000176200001440000001500413602224251013044 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_gmm.R \name{pgmm} \alias{pgmm} \alias{coef.pgmm} \alias{summary.pgmm} \alias{print.summary.pgmm} \title{Generalized Method of Moments (GMM) Estimation for Panel Data} \usage{ pgmm( formula, data, subset, na.action, effect = c("twoways", "individual"), model = c("onestep", "twosteps"), collapse = FALSE, lost.ts = NULL, transformation = c("d", "ld"), fsm = NULL, index = NULL, ... ) \method{coef}{pgmm}(object, ...) \method{summary}{pgmm}(object, robust = TRUE, time.dummies = FALSE, ...) \method{print}{summary.pgmm}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) } \arguments{ \item{formula}{a symbolic description for the model to be estimated. The preferred interface is now to indicate a multi--part formula, the first two parts describing the covariates and the GMM instruments and, if any, the third part the 'normal' instruments,} \item{data}{a \code{data.frame} (neither factors nor character vectors will be accepted in \code{data.frame}),} \item{subset}{see \code{\link[=lm]{lm()}},} \item{na.action}{see \code{\link[=lm]{lm()}},} \item{effect}{the effects introduced in the model, one of \code{"twoways"} (the default) or \code{"individual"},} \item{model}{one of \code{"onestep"} (the default) or \code{"twosteps"},} \item{collapse}{if \code{TRUE}, the GMM instruments are collapsed,} \item{lost.ts}{the number of lost time series: if \code{NULL}, this is automatically computed. Otherwise, it can be defined by the user as a numeric vector of length 1 or 2. The first element is the number of lost time series in the model in difference, the second one in the model in level. If the second element is missing, it is set to the first one minus one,} \item{transformation}{the kind of transformation to apply to the model: either \code{"d"} (the default value) for the "difference GMM" model or \code{"ld"} for the "system GMM",} \item{fsm}{the matrix for the one step estimator: one of \code{"I"} (identity matrix) or \code{"G"} (\eqn{=D'D} where \eqn{D} is the first--difference operator) if \code{transformation="d"}, one of \code{"GI"} or \code{"full"} if \code{transformation="ld"},} \item{index}{the indexes,} \item{\dots}{further arguments.} \item{object, x}{an object of class \code{"pgmm"},} \item{robust}{if \code{TRUE}, robust inference is performed in the summary,} \item{time.dummies}{if \code{TRUE}, the estimated coefficients of time dummies are present in the table of coefficients,} \item{digits}{digits,} \item{width}{the maximum length of the lines in the print output,} } \value{ An object of class \code{c("pgmm","panelmodel")}, which has the following elements: \item{coefficients}{the vector (or the list for fixed effects) of coefficients,} \item{residuals}{the vector of residuals,} \item{vcov}{the covariance matrix of the coefficients,} \item{fitted.values}{the vector of fitted values,} \item{df.residual}{degrees of freedom of the residuals,} \item{model}{a list containing the variables used for the estimation for each individual,} \item{W}{a list containing the instruments for each individual (two lists in case of "sys--GMM"),} \item{A1}{the weighting matrix for the one--step estimator,} \item{A2}{the weighting matrix for the two--steps estimator,} \item{call}{the call.} It has \code{print}, \code{summary} and \code{print.summary} methods. } \description{ Generalized method of moments estimation for static or dynamic models with panel data. } \details{ \code{pgmm} estimates a model for panel data with a generalized method of moments (GMM) estimator. The description of the model to estimate is provided with a multi--part formula which is (or which is coerced to) a \code{Formula} object. The first right--hand side part describes the covariates. The second one, which is mandatory, describes the GMM instruments. The third one, which is optional, describes the 'normal' instruments. By default, all the variables of the model which are not used as GMM instruments are used as normal instruments with the same lag structure as the one specified in the model. \code{y~lag(y, 1:2)+lag(x1, 0:1)+lag(x2, 0:2) | lag(y, 2:99)} is similar to \code{y~lag(y, 1:2)+lag(x1, 0:1)+lag(x2, 0:2) | lag(y, 2:99) | lag(x1, 0:1)+lag(x2, 0:2)} and indicates that all lags from 2 of \code{y} are used as GMM instruments. \code{transformation} indicates how the model should be transformed for the estimation. \code{"d"} gives the "difference GMM" model \insertCite{@see @AREL:BOND:91}{plm}, \code{"ld"} the "system GMM" model \insertCite{@see @BLUN:BOND:98}{plm}. \code{pgmm} is an attempt to adapt GMM estimators available within the DPD library for GAUSS \insertCite{@see @AREL:BOND:98}{plm} and Ox \insertCite{@see @DOOR:AREL:BOND:12}{plm} and within the xtabond2 library for Stata \insertCite{@see @ROOD:09}{plm}. } \examples{ data("EmplUK", package = "plm") ## Arellano and Bond (1991), table 4 col. b z1 <- 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, effect = "twoways", model = "twosteps") summary(z1, robust = FALSE) ## Blundell and Bond (1998) table 4 (cf. DPD for OX p. 12 col. 4) z2 <- pgmm(log(emp) ~ lag(log(emp), 1)+ lag(log(wage), 0:1) + lag(log(capital), 0:1) | lag(log(emp), 2:99) + lag(log(wage), 2:99) + lag(log(capital), 2:99), data = EmplUK, effect = "twoways", model = "onestep", transformation = "ld") summary(z2, robust = TRUE) \dontrun{ ## Same with the old formula or dynformula interface ## Arellano and Bond (1991), table 4, col. b z1 <- pgmm(log(emp) ~ log(wage) + log(capital) + log(output), lag.form = list(2,1,0,1), data = EmplUK, effect = "twoways", model = "twosteps", gmm.inst = ~log(emp), lag.gmm = list(c(2,99))) summary(z1, robust = FALSE) ## Blundell and Bond (1998) table 4 (cf DPD for OX p. 12 col. 4) z2 <- pgmm(dynformula(log(emp) ~ log(wage) + log(capital), list(1,1,1)), data = EmplUK, effect = "twoways", model = "onestep", gmm.inst = ~log(emp) + log(wage) + log(capital), lag.gmm = c(2,99), transformation = "ld") summary(z2, robust = TRUE) } } \references{ \insertAllCited{} } \seealso{ \code{\link[=sargan]{sargan()}} for the Hansen--Sargan test and \code{\link[=mtest]{mtest()}} for Arellano--Bond's test of serial correlation. \code{\link[=dynformula]{dynformula()}} for dynamic formulas (deprecated). } \author{ Yves Croissant } \keyword{regression} plm/man/SumHes.Rd0000755000176200001440000000150113503144006013304 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{SumHes} \alias{SumHes} \title{The Penn World Table, v. 5} \format{A data frame containing : \describe{ \item{year}{the year} \item{country}{the country name (factor)} \item{opec}{OPEC member?} \item{com}{communist regime? } \item{pop}{country's population (in thousands)} \item{gdp}{real GDP per capita (in 1985 US dollars)} \item{sr}{saving rate (in percent)}}} \source{ Online supplements to Hayashi (2000). \url{http://fhayashi.fc2web.com/datasets.htm} } \description{ A panel of 125 observations from 1960 to 1985 } \details{ \emph{total number of observations} : 3250 \emph{observation} : country \emph{country} : World } \references{ \insertRef{HAYA:00}{plm} \insertRef{SUMM:HEST:91}{plm} } \keyword{datasets} plm/man/pbltest.Rd0000755000176200001440000000422713602224251013566 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pbltest} \alias{pbltest} \alias{pbltest.formula} \alias{pbltest.plm} \title{Baltagi and Li Serial Dependence Test For Random Effects Models} \usage{ pbltest(x, ...) \method{pbltest}{formula}(x, data, alternative = c("twosided", "onesided"), index = NULL, ...) \method{pbltest}{plm}(x, alternative = c("twosided", "onesided"), ...) } \arguments{ \item{x}{a model formula or an estimated random--effects model of class \code{plm} ,} \item{\dots}{further arguments.} \item{data}{for the formula interface only: a \code{data.frame},} \item{alternative}{one of \code{"twosided"}, \code{"onesided"}. Selects either \eqn{H_A: \rho \neq 0} or \eqn{H_A: \rho = 0} (i.e., the Normal or the Chi-squared version of the test),} \item{index}{the index of the \code{data.frame},} } \value{ An object of class \code{"htest"}. } \description{ \insertCite{BALT:LI:95;textual}{plm}'s Lagrange multiplier test for AR(1) or MA(1) idiosyncratic errors in panel models with random effects. } \details{ This is a Lagrange multiplier test for the null of no serial correlation, against the alternative of either an AR(1) or an MA(1) process, in the idiosyncratic component of the error term in a random effects panel model (as the analytical expression of the test turns out to be the same under both alternatives, \insertCite{@see @BALT:LI:95 and @BALT:LI:97}{plm}. The \code{alternative} argument, defaulting to \code{twosided}, allows testing for positive serial correlation only, if set to \code{onesided}. } \examples{ data("Grunfeld", package = "plm") # formula interface pbltest(inv ~ value + capital, data = Grunfeld) # plm interface re_mod <- plm(inv ~ value + capital, data = Grunfeld, model = "random") pbltest(re_mod) pbltest(re_mod, alternative = "onesided") } \references{ \insertRef{BALT:LI:95}{plm} \insertRef{BALT:LI:97}{plm} } \seealso{ \code{\link[=pdwtest]{pdwtest()}}, \code{bgtest}, \code{\link[=pbsytest]{pbsytest()}}, \code{\link[=pwartest]{pwartest()}} and \code{\link[=pwfdtest]{pwfdtest()}} for other serial correlation tests for panel models. } \author{ Giovanni Millo } \keyword{htest} plm/man/vcovSCC.Rd0000644000176200001440000000743313602224251013416 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_vcovG.R \name{vcovSCC} \alias{vcovSCC} \alias{vcovSCC.plm} \alias{vcovSCC.pcce} \title{\insertCite{DRIS:KRAA:98;textual}{plm} Robust Covariance Matrix Estimator} \usage{ vcovSCC(x, ...) \method{vcovSCC}{plm}( x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), cluster = "time", maxlag = NULL, inner = c("cluster", "white", "diagavg"), wj = function(j, maxlag) 1 - j/(maxlag + 1), ... ) \method{vcovSCC}{pcce}( x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), cluster = "time", maxlag = NULL, inner = c("cluster", "white", "diagavg"), wj = function(j, maxlag) 1 - j/(maxlag + 1), ... ) } \arguments{ \item{x}{an object of class \code{"plm"} or \code{"pcce"}} \item{\dots}{further arguments} \item{type}{the weighting scheme used, one of \code{"HC0"}, \code{"sss"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, see Details,} \item{cluster}{switch for vcovG; set at \code{"time"} here,} \item{maxlag}{either \code{NULL} or a positive integer specifying the maximum lag order before truncation} \item{inner}{the function to be applied to the residuals inside the sandwich: \code{"cluster"} for SCC, \code{"white"} for Newey-West, (\code{"diagavg"} for compatibility reasons)} \item{wj}{weighting function to be applied to lagged terms,} } \value{ An object of class \code{"matrix"} containing the estimate of the covariance matrix of coefficients. } \description{ Nonparametric robust covariance matrix estimators \emph{a la Driscoll and Kraay} for panel models with cross-sectional \emph{and} serial correlation. } \details{ \code{vcovSCC} is a function for estimating a robust covariance matrix of parameters for a panel model according to the \insertCite{DRIS:KRAA:98;textual}{plm} method, which is consistent with cross--sectional and serial correlation in a T-asymptotic setting and irrespective of the N dimension. The use with random effects models is undocumented. Weighting schemes specified by \code{type} are analogous to those in \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} in package \CRANpkg{sandwich} and are justified theoretically (although in the context of the standard linear model) by \insertCite{MACK:WHIT:85;textual}{plm} and \insertCite{CRIB:04;textual}{plm} \insertCite{@see @ZEIL:04}{plm}). The main use of \code{vcovSCC} is to be an argument to other functions, e.g. for Wald--type testing: argument \code{vcov.} to \code{coeftest()}, argument \code{vcov} to \code{waldtest()} and other methods in the \CRANpkg{lmtest} package; and argument \code{vcov.} to \code{linearHypothesis()} in the \CRANpkg{car} package (see the examples). Notice that the \code{vcov} and \code{vcov.} arguments allow to supply a function (which is the safest) or a matrix \insertCite{@see @ZEIL:04, 4.1-2 and examples below}{plm}. } \examples{ library(lmtest) library(car) data("Produc", package="plm") zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling") ## standard coefficient significance test coeftest(zz) ## SCC robust significance test, default coeftest(zz, vcov.=vcovSCC) ## idem with parameters, pass vcov as a function argument coeftest(zz, vcov.=function(x) vcovSCC(x, type="HC1", maxlag=4)) ## joint restriction test waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovSCC) ## test of hyp.: 2*log(pc)=log(emp) linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovSCC) } \references{ \insertRef{CRIB:04}{plm} \insertRef{DRIS:KRAA:98}{plm} \insertRef{HOEC:07}{plm} \insertRef{MACK:WHIT:85}{plm} \insertRef{ZEIL:04}{plm} } \seealso{ \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} from the \CRANpkg{sandwich} package for weighting schemes (\code{type} argument). } \author{ Giovanni Millo, partially ported from Daniel Hoechle's (2007) Stata code } \keyword{regression} plm/man/punbalancedness.Rd0000644000176200001440000001224213602224251015247 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_misc.R \name{punbalancedness} \alias{punbalancedness} \alias{punbalancedness.pdata.frame} \alias{punbalancedness.data.frame} \alias{punbalancedness.panelmodel} \title{Measures for Unbalancedness of Panel Data} \usage{ punbalancedness(x, ...) \method{punbalancedness}{pdata.frame}(x, ...) \method{punbalancedness}{data.frame}(x, index = NULL, ...) \method{punbalancedness}{panelmodel}(x, ...) } \arguments{ \item{x}{a \code{panelmodel}, a \code{data.frame}, or a \code{pdata.frame} object,} \item{\dots}{further arguments.} \item{index}{only relevant for \code{data.frame} interface, for details see \code{\link[=pdata.frame]{pdata.frame()}},} } \value{ A named numeric containing either two or three entries, depending on the panel structure inputted: \itemize{ \item For the two-dimensional panel structure, the entries are called \code{gamma} and \code{nu}, \item For a nested panel structure, the entries are called \code{c1}, \code{c2}, \code{c3}. } } \description{ This function reports unbalancedness measures for panel data as defined in \insertCite{AHRE:PINC:81;textual}{plm} and \insertCite{BALT:SONG:JUNG:01;textual}{plm}. } \details{ \code{punbalancedness} returns measures for the unbalancedness of a panel data set. \itemize{ \item For two-dimensional data:\cr The two measures of \insertCite{AHRE:PINC:81;textual}{plm} are calculated, called "gamma" (\eqn{\gamma}) and "nu" (\eqn{\nu}). } If the panel data are balanced, both measures equal 1. The more "unbalanced" the panel data, the lower the measures (but > 0). The upper and lower bounds as given in \insertCite{AHRE:PINC:81;textual}{plm} are:\cr \eqn{0 < \gamma, \nu \le 1}, and for \eqn{\nu} more precisely \eqn{\frac{1}{n} < \nu \le 1}{1/n < \nu \le 1}, with \eqn{n} being the number of individuals (as in \code{pdim(x)$nT$n}). \itemize{ \item For nested panel data (meaning including a grouping variable):\cr The extension of the above measures by \insertCite{BALT:SONG:JUNG:01;textual}{plm}, p. 368, are calculated:\cr \itemize{ \item c1: measure of subgroup (individual) unbalancedness, \item c2: measure of time unbalancedness, \item c3: measure of group unbalancedness due to each group size. } } Values are 1 if the data are balanced and become smaller as the data become more unbalanced. An application of the measure "gamma" is found in e. g. \insertCite{BALT:SONG:JUNG:01;textual}{plm}, pp. 488-491, and \insertCite{BALT:CHAN:94;textual}{plm}, pp. 78--87, where it is used to measure the unbalancedness of various unbalanced data sets used for Monte Carlo simulation studies. Measures c1, c2, c3 are used for similar purposes in \insertCite{BALT:SONG:JUNG:01;textual}{plm}. In the two-dimensional case, \code{punbalancedness} uses output of \code{\link[=pdim]{pdim()}} to calculate the two unbalancedness measures, so inputs to \code{punbalancedness} can be whatever \code{pdim} works on. \code{pdim} returns detailed information about the number of individuals and time observations (see \code{\link[=pdim]{pdim()}}). } \note{ Calling \code{punbalancedness} on an estimated \code{panelmodel} object and on the corresponding \verb{(p)data.frame} used for this estimation does not necessarily yield the same result (true also for \code{pdim}). When called on an estimated \code{panelmodel}, the number of observations (individual, time) actually used for model estimation are taken into account. When called on a \verb{(p)data.frame}, the rows in the \verb{(p)data.frame} are considered, disregarding any NA values in the dependent or independent variable(s) which would be dropped during model estimation. } \examples{ # Grunfeld is a balanced panel, Hedonic is an unbalanced panel data(list=c("Grunfeld", "Hedonic"), package="plm") # Grunfeld has individual and time index in first two columns punbalancedness(Grunfeld) # c(1,1) indicates balanced panel pdim(Grunfeld)$balanced # TRUE # Hedonic has individual index in column "townid" (in last column) punbalancedness(Hedonic, index="townid") # c(0.472, 0.519) pdim(Hedonic, index="townid")$balanced # FALSE # punbalancedness on estimated models plm_mod_pool <- plm(inv ~ value + capital, data = Grunfeld) punbalancedness(plm_mod_pool) plm_mod_fe <- plm(inv ~ value + capital, data = Grunfeld[1:99, ], model = "within") punbalancedness(plm_mod_fe) # replicate results for panel data design no. 1 in Ahrens/Pincus (1981), p. 234 ind_d1 <- c(1,1,1,2,2,2,3,3,3,3,3,4,4,4,4,4,4,4,5,5,5,5,5,5,5) time_d1 <- c(1,2,3,1,2,3,1,2,3,4,5,1,2,3,4,5,6,7,1,2,3,4,5,6,7) df_d1 <- data.frame(individual = ind_d1, time = time_d1) punbalancedness(df_d1) # c(0.868, 0.887) # example for a nested panel structure with a third index variable # specifying a group (states are grouped by region) and without grouping data("Produc", package = "plm") punbalancedness(Produc, index = c("state", "year", "region")) punbalancedness(Produc, index = c("state", "year")) } \references{ \insertRef{AHRE:PINC:81}{plm} \insertRef{BALT:CHAN:94}{plm} \insertRef{BALT:SONG:JUNG:01}{plm} \insertRef{BALT:SONG:JUNG:02}{plm} } \seealso{ \code{\link[=nobs]{nobs()}}, \code{\link[=pdim]{pdim()}}, \code{\link[=pdata.frame]{pdata.frame()}} } \author{ Kevin Tappe } \keyword{attribute} plm/man/make.pbalanced.Rd0000644000176200001440000001641413602224251014734 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/make.pconsecutive_pbalanced.R \name{make.pbalanced} \alias{make.pbalanced} \alias{make.pbalanced.pdata.frame} \alias{make.pbalanced.pseries} \alias{make.pbalanced.data.frame} \title{Make data balanced} \usage{ make.pbalanced( x, balance.type = c("fill", "shared.times", "shared.individuals"), ... ) \method{make.pbalanced}{pdata.frame}( x, balance.type = c("fill", "shared.times", "shared.individuals"), ... ) \method{make.pbalanced}{pseries}( x, balance.type = c("fill", "shared.times", "shared.individuals"), ... ) \method{make.pbalanced}{data.frame}( x, balance.type = c("fill", "shared.times", "shared.individuals"), index = NULL, ... ) } \arguments{ \item{x}{an object of class \code{pdata.frame}, \code{data.frame}, or \code{pseries};} \item{balance.type}{character, one of \code{"fill"}, \code{"shared.times"}, or \code{"shared.individuals"}, see \strong{Details},} \item{\dots}{further arguments.} \item{index}{only relevant for \code{data.frame} interface; if \code{NULL}, the first two columns of the data.frame are assumed to be the index variables; if not \code{NULL}, both dimensions ('individual', 'time') need to be specified by \code{index} as character of length 2 for data frames, for further details see \code{\link[=pdata.frame]{pdata.frame()}},} } \value{ An object of the same class as the input \code{x}, i.e. a pdata.frame, data.frame or a pseries which is made balanced based on the index variables. The returned data are sorted as a stacked time series. } \description{ This function makes the data balanced, i.e. each individual has the same time periods, by filling in or dropping observations } \details{ (p)data.frame and pseries objects are made balanced, meaning each individual has the same time periods. Depending on the value of \code{balance.type}, the balancing is done in different ways: \itemize{ \item \code{balance.type = "fill"} (default): The union of available time periods over all individuals is taken (w/o \code{NA} values). Missing time periods for an individual are identified and corresponding rows (elements for pseries) are inserted and filled with \code{NA} for the non--index variables (elements for a pseries). This means, only time periods present for at least one individual are inserted, if missing. \item \code{balance.type = "shared.times"}: The intersect of available time periods over all individuals is taken (w/o \code{NA} values). Thus, time periods not available for all individuals are discarded, i. e., only time periods shared by all individuals are left in the result). \item \code{balance.type = "shared.individuals"}: All available time periods are kept and those individuals are dropped for which not all time periods are available, i. e., only individuals shared by all time periods are left in the result (symmetric to \code{"shared.times"}). } The data are not necessarily made consecutive (regular time series with distance 1), because balancedness does not imply consecutiveness. For making the data consecutive, use \code{\link[=make.pconsecutive]{make.pconsecutive()}} (and, optionally, set argument \code{balanced = TRUE} to make consecutive and balanced, see also \strong{Examples} for a comparison of the two functions. Note: Rows of (p)data.frames (elements for pseries) with \code{NA} values in individual or time index are not examined but silently dropped before the data are made balanced. In this case, it cannot be inferred which individual or time period is meant by the missing value(s) (see also \strong{Examples}). Especially, this means: \code{NA} values in the first/last position of the original time periods for an individual are dropped, which are usually meant to depict the beginning and ending of the time series for that individual. Thus, one might want to check if there are any \code{NA} values in the index variables before applying make.pbalanced, and especially check for \code{NA} values in the first and last position for each individual in original data and, if so, maybe set those to some meaningful begin/end value for the time series. } \examples{ # take data and make it unbalanced # by deletion of 2nd row (2nd time period for first individual) data("Grunfeld", package = "plm") nrow(Grunfeld) # 200 rows Grunfeld_missing_period <- Grunfeld[-2, ] pdim(Grunfeld_missing_period)$balanced # check if balanced: FALSE make.pbalanced(Grunfeld_missing_period) # make it balanced (by filling) make.pbalanced(Grunfeld_missing_period, balance.type = "shared.times") # (shared periods) nrow(make.pbalanced(Grunfeld_missing_period)) nrow(make.pbalanced(Grunfeld_missing_period, balance.type = "shared.times")) # more complex data: # First, make data unbalanced (and non-consecutive) # by deletion of 2nd time period (year 1936) for all individuals # and more time periods for first individual only Grunfeld_unbalanced <- Grunfeld[Grunfeld$year != 1936, ] Grunfeld_unbalanced <- Grunfeld_unbalanced[-c(1,4), ] pdim(Grunfeld_unbalanced)$balanced # FALSE all(is.pconsecutive(Grunfeld_unbalanced)) # FALSE g_bal <- make.pbalanced(Grunfeld_unbalanced) pdim(g_bal)$balanced # TRUE unique(g_bal$year) # all years but 1936 nrow(g_bal) # 190 rows head(g_bal) # 1st individual: years 1935, 1939 are NA # NA in 1st, 3rd time period (years 1935, 1937) for first individual Grunfeld_NA <- Grunfeld Grunfeld_NA[c(1, 3), "year"] <- NA g_bal_NA <- make.pbalanced(Grunfeld_NA) head(g_bal_NA) # years 1935, 1937: NA for non-index vars nrow(g_bal_NA) # 200 # pdata.frame interface pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) make.pbalanced(Grunfeld_missing_period) # pseries interface make.pbalanced(pGrunfeld_missing_period$inv) # comparison to make.pconsecutive g_consec <- make.pconsecutive(Grunfeld_unbalanced) all(is.pconsecutive(g_consec)) # TRUE pdim(g_consec)$balanced # FALSE head(g_consec, 22) # 1st individual: no years 1935/6; 1939 is NA; # other indviduals: years 1935-1954, 1936 is NA nrow(g_consec) # 198 rows g_consec_bal <- make.pconsecutive(Grunfeld_unbalanced, balanced = TRUE) all(is.pconsecutive(g_consec_bal)) # TRUE pdim(g_consec_bal)$balanced # TRUE head(g_consec_bal) # year 1936 is NA for all individuals nrow(g_consec_bal) # 200 rows head(g_bal) # no year 1936 at all nrow(g_bal) # 190 rows } \seealso{ \code{\link[=is.pbalanced]{is.pbalanced()}} to check if data are balanced; \code{\link[=is.pconsecutive]{is.pconsecutive()}} to check if data are consecutive; \code{\link[=make.pconsecutive]{make.pconsecutive()}} to make data consecutive (and, optionally, also balanced).\cr \code{\link[=punbalancedness]{punbalancedness()}} for two measures of unbalancedness, \code{\link[=pdim]{pdim()}} to check the dimensions of a 'pdata.frame' (and other objects), \code{\link[=pvar]{pvar()}} to check for individual and time variation of a 'pdata.frame' (and other objects), \code{\link[=lag]{lag()}} for lagging (and leading) values of a 'pseries' object.\cr \code{\link[=pseries]{pseries()}}, \code{\link[=data.frame]{data.frame()}}, \code{\link[=pdata.frame]{pdata.frame()}}. } \author{ Kevin Tappe } \keyword{attribute} plm/man/pht.Rd0000644000176200001440000000743513602224251012705 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R \name{pht} \alias{pht} \alias{summary.pht} \alias{print.summary.pht} \title{Hausman--Taylor Estimator for Panel Data} \usage{ pht( formula, data, subset, na.action, model = c("ht", "am", "bms"), index = NULL, ... ) \method{summary}{pht}(object, ...) \method{print}{summary.pht}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), subset = NULL, ... ) } \arguments{ \item{formula}{a symbolic description for the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{\link[=lm]{lm()}} for \code{"plm"}, a character or numeric vector indicating a subset of the table of coefficient to be printed for \code{"print.summary.plm"},} \item{na.action}{see \code{\link[=lm]{lm()}},} \item{model}{one of \code{"ht"} for Hausman--Taylor, \code{"am"} for Amemiya--MaCurdy and \code{"bms"} for Breusch--Mizon--Schmidt,} \item{index}{the indexes,} \item{\dots}{further arguments.} \item{object, x}{an object of class \code{"plm"},} \item{digits}{digits,} \item{width}{the maximum length of the lines in the print output,} } \value{ An object of class \code{c("pht", "plm", "panelmodel")}. A \code{"pht"} object contains the same elements as \code{plm} object, with a further argument called \code{varlist} which describes the typology of the variables. It has \code{summary} and \code{print.summary} methods. } \description{ The Hausman--Taylor estimator is an instrumental variable estimator without external instruments (function deprecated). } \details{ \code{pht} estimates panels models using the Hausman--Taylor estimator, Amemiya--MaCurdy estimator, or Breusch--Mizon--Schmidt estimator, depending on the argument \code{model}. The model is specified as a two--part formula, the second part containing the exogenous variables. } \note{ The function \code{pht} is deprecated. Please use function \code{plm} to estimate Taylor--Hausman models like this with a three-part formula as shown in the example:\cr \verb{plm(, random.method = "ht", model = "random", inst.method = "baltagi")}. The Amemiya--MaCurdy estimator and the Breusch--Mizon--Schmidt estimator is computed likewise with \code{plm}. } \examples{ ## replicates Baltagi (2005, 2013), table 7.4 ## preferred way with plm() data("Wages", package = "plm") ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp ^ 2), data = Wages, index = 595, random.method = "ht", model = "random", inst.method = "baltagi") summary(ht) am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp ^ 2), data = Wages, index = 595, random.method = "ht", model = "random", inst.method = "am") summary(am) ## deprecated way with pht() for HT #ht <- pht(lwage ~ wks + south + smsa + married + exp + I(exp^2) + # bluecol + ind + union + sex + black + ed | # sex + black + bluecol + south + smsa + ind, # data = Wages, model = "ht", index = 595) #summary(ht) # deprecated way with pht() for AM #am <- pht(lwage ~ wks + south + smsa + married + exp + I(exp^2) + # bluecol + ind + union + sex + black + ed | # sex + black + bluecol + south + smsa + ind, # data = Wages, model = "am", index = 595) #summary(am) } \references{ \insertCite{AMEM:MACU:86}{plm} \insertCite{BALT:13}{plm} \insertCite{BREU:MIZO:SCHM:89}{plm} \insertCite{HAUS:TAYL:81}{plm} } \author{ Yves Croissant } \keyword{regression} plm/man/vcovHC.plm.Rd0000644000176200001440000001251113602224251014060 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_vcovG.R \name{vcovHC.plm} \alias{vcovHC.plm} \alias{vcovHC.pcce} \alias{vcovHC.pgmm} \title{Robust Covariance Matrix Estimators} \usage{ \method{vcovHC}{plm}( x, method = c("arellano", "white1", "white2"), type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), cluster = c("group", "time"), ... ) \method{vcovHC}{pcce}( x, method = c("arellano", "white1", "white2"), type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), cluster = c("group", "time"), ... ) \method{vcovHC}{pgmm}(x, ...) } \arguments{ \item{x}{an object of class \code{"plm"} which should be the result of a random effects or a within model or a model of class \code{"pgmm"} or an object of class \code{"pcce"},} \item{method}{one of \code{"arellano"}, \code{"white1"}, \code{"white2"},} \item{type}{the weighting scheme used, one of \code{"HC0"}, \code{"sss"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, see Details,} \item{cluster}{one of \code{"group"}, \code{"time"},} \item{\dots}{further arguments.} } \value{ An object of class \code{"matrix"} containing the estimate of the asymptotic covariance matrix of coefficients. } \description{ Robust covariance matrix estimators \emph{a la White} for panel models. } \details{ \code{vcovHC} is a function for estimating a robust covariance matrix of parameters for a fixed effects or random effects panel model according to the White method \insertCite{WHIT:80,WHIT:84b,AREL:87}{plm}. Observations may be clustered by \code{"group"} (\code{"time"}) to account for serial (cross-sectional) correlation. All types assume no intragroup (serial) correlation between errors and allow for heteroskedasticity across groups (time periods). As for the error covariance matrix of every single group of observations, \code{"white1"} allows for general heteroskedasticity but no serial (cross--sectional) correlation; \code{"white2"} is \code{"white1"} restricted to a common variance inside every group (time period) \insertCite{@see @GREE:03, Sec. 13.7.1-2, @GREE:12, Sec. 11.6.1-2 and @WOOL:02, Sec. 10.7.2}{plm}; \code{"arellano"} \insertCite{@see ibid. and the original ref. @AREL:87}{plm} allows a fully general structure w.r.t. heteroskedasticity and serial (cross--sectional) correlation. Weighting schemes specified by \code{type} are analogous to those in \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} in package \CRANpkg{sandwich} and are justified theoretically (although in the context of the standard linear model) by \insertCite{MACK:WHIT:85;textual}{plm} and \insertCite{CRIB:04;textual}{plm} \insertCite{ZEIL:04}{plm}. \code{type = "sss"} employs the small sample correction as used by Stata. The main use of \code{vcovHC} is to be an argument to other functions, e.g. for Wald--type testing: argument \code{vcov.} to \code{coeftest()}, argument \code{vcov} to \code{waldtest()} and other methods in the \CRANpkg{lmtest} package; and argument \code{vcov.} to \code{linearHypothesis()} in the \CRANpkg{car} package (see the examples). Notice that the \code{vcov} and \code{vcov.} arguments allow to supply a function (which is the safest) or a matrix \insertCite{@ZEIL:04, 4.1-2 and examples below}{plm}. A special procedure for \code{pgmm} objects, proposed by \insertCite{WIND:05;textual}{plm}, is also provided. } \note{ The function \code{pvcovHC} is deprecated. Use \code{vcovHC} for the same functionality. } \examples{ library(lmtest) library(car) data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "random") ## standard coefficient significance test coeftest(zz) ## robust significance test, cluster by group ## (robust vs. serial correlation) coeftest(zz, vcov.=vcovHC) ## idem with parameters, pass vcov as a function argument coeftest(zz, vcov.=function(x) vcovHC(x, method="arellano", type="HC1")) ## idem, cluster by time period ## (robust vs. cross-sectional correlation) coeftest(zz, vcov.=function(x) vcovHC(x, method="arellano", type="HC1", cluster="group")) ## idem with parameters, pass vcov as a matrix argument coeftest(zz, vcov.=vcovHC(zz, method="arellano", type="HC1")) ## joint restriction test waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovHC) ## test of hyp.: 2*log(pc)=log(emp) linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovHC) ## Robust inference for CCE models data("Produc", package = "plm") ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p") summary(ccepmod, vcov = vcovHC) ## Robust inference for GMM models data("EmplUK", package="plm") ar <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + lag(log(capital), 2) + log(output) + lag(log(output),2) | lag(log(emp), 2:99), data = EmplUK, effect = "twoways", model = "twosteps") rv <- vcovHC(ar) mtest(ar, order = 2, vcov = rv) } \references{ \insertRef{AREL:87}{plm} \insertRef{CRIB:04}{plm} \insertRef{GREE:03}{plm} \insertRef{GREE:12}{plm} \insertRef{MACK:WHIT:85}{plm} \insertRef{WIND:05}{plm} \insertRef{WHIT:84b}{plm} chap. 6 \insertRef{WHIT:80}{plm} \insertRef{WOOL:02}{plm} \insertRef{ZEIL:04}{plm} } \seealso{ \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} from the \CRANpkg{sandwich} package for weighting schemes (\code{type} argument). } \author{ Giovanni Millo & Yves Croissant } \keyword{regression} plm/man/Cigar.Rd0000755000176200001440000000220713503144006013131 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Cigar} \alias{Cigar} \title{Cigarette Consumption} \format{A data frame containing : \describe{ \item{state}{state abbreviation} \item{year}{the year} \item{price}{price per pack of cigarettes} \item{pop}{population} \item{pop16}{population above the age of 16} \item{cpi}{consumer price index (1983=100)} \item{ndi}{per capita disposable income} \item{sales}{cigarette sales in packs per capita} \item{pimin}{minimum price in adjoining states per pack of cigarettes} }} \source{ Online complements to Baltagi (2001): \url{http://www.wiley.com/legacy/wileychi/baltagi/} Online complements to Baltagi (2013): \url{http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452} } \description{ a panel of 46 observations from 1963 to 1992 } \details{ \emph{total number of observations} : 1380 \emph{observation} : regional \emph{country} : United States } \references{ \insertRef{BALT:01}{plm} \insertRef{BALT:13}{plm} \insertRef{BALT:LEVI:92}{plm} \insertRef{BALT:GRIF:XION:00}{plm} } \keyword{datasets} plm/man/pdwtest.Rd0000755000176200001440000000476313503144006013607 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pdwtest} \alias{pdwtest} \alias{pdwtest.panelmodel} \alias{pdwtest.formula} \title{Durbin--Watson Test for Panel Models} \usage{ pdwtest(x, ...) \method{pdwtest}{panelmodel}(x, ...) \method{pdwtest}{formula}(x, data, ...) } \arguments{ \item{x}{an object of class \code{"panelmodel"} or of class \code{"formula"},} \item{\dots}{further arguments to be passed on to \code{dwtest}, e.g. \code{alternative}, see \code{\link[lmtest:dwtest]{lmtest::dwtest()}} for further details.} \item{data}{a \code{data.frame},} } \value{ An object of class \code{"htest"}. } \description{ Test of serial correlation for (the idiosyncratic component of) the errors in panel models. } \details{ This Durbin--Watson test uses the auxiliary model on (quasi-)demeaned data taken from a model of class \code{plm} which may be a \code{pooling} (the default), \code{random} or \code{within} model. It performs a Durbin--Watson test (using \code{dwtest} from package \CRANpkg{lmtest} on the residuals of the (quasi-)demeaned model, which should be serially uncorrelated under the null of no serial correlation in idiosyncratic errors. The function takes the demeaned data, estimates the model and calls \code{dwtest}. Thus, this test does not take the panel structure of the residuals into consideration; it shall not be confused with the generalized Durbin-Watson test for panels in \code{pbnftest}. } \examples{ data("Grunfeld", package = "plm") g <- plm(inv ~ value + capital, data = Grunfeld, model="random") pdwtest(g) pdwtest(g, alternative="two.sided") ## formula interface pdwtest(inv ~ value + capital, data=Grunfeld, model="random") } \references{ \insertRef{DURB:WATS:50}{plm} \insertRef{DURB:WATS:51}{plm} \insertRef{DURB:WATS:71}{plm} \insertRef{WOOL:02}{plm} \insertRef{WOOL:10}{plm} } \seealso{ \code{\link[lmtest:dwtest]{lmtest::dwtest()}} for the Durbin--Watson test in \CRANpkg{lmtest}, \code{\link[=pbgtest]{pbgtest()}} for the analogous Breusch--Godfrey test for panel models, \code{\link[lmtest:bgtest]{lmtest::bgtest()}} for the Breusch--Godfrey test for serial correlation in the linear model. \code{\link[=pbltest]{pbltest()}}, \code{\link[=pbsytest]{pbsytest()}}, \code{\link[=pwartest]{pwartest()}} and \code{\link[=pwfdtest]{pwfdtest()}} for other serial correlation tests for panel models. For the Durbin-Watson test generalized to panel data models see \code{\link[=pbnftest]{pbnftest()}}. } \author{ Giovanni Millo } \keyword{htest} plm/man/LaborSupply.Rd0000755000176200001440000000136413503144006014363 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{LaborSupply} \alias{LaborSupply} \title{Wages and Hours Worked} \format{A data frame containing : \describe{ \item{lnhr}{log of annual hours worked} \item{lnwg}{log of hourly wage} \item{kids}{number of children} \item{age}{age} \item{disab}{bad health} \item{id}{id} \item{year}{year} }} \source{ Online complements to Ziliak (1997). Journal of Business Economics and Statistics web site: \url{http://amstat.tandfonline.com/loi/ubes20/}. } \description{ A panel of 532 observations from 1979 to 1988 } \details{ \emph{number of observations} : 5320 } \references{ \insertRef{CAME:TRIV:05}{plm} \insertRef{ZILI:97}{plm} } \keyword{datasets} plm/man/summary.plm.Rd0000644000176200001440000001102113602224251014360 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_plm.list.R, R/tool_methods.R \name{summary.plm.list} \alias{summary.plm.list} \alias{coef.summary.plm.list} \alias{print.summary.plm.list} \alias{summary.plm} \alias{print.summary.plm} \title{Summary for plm objects} \usage{ \method{summary}{plm.list}(object, ...) \method{coef}{summary.plm.list}(object, eq = NULL, ...) \method{print}{summary.plm.list}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) \method{summary}{plm}(object, vcov = NULL, ...) \method{print}{summary.plm}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), subset = NULL, ... ) } \arguments{ \item{object}{an object of class \code{"plm"},} \item{\dots}{further arguments.} \item{eq}{the selected equation for list objects} \item{x}{an object of class \code{"summary.plm"},} \item{digits}{number of digits for printed output,} \item{width}{the maximum length of the lines in the printed output,} \item{vcov}{a variance--covariance matrix furnished by the user or a function to calculate one (see \strong{Examples}),} \item{subset}{a character or numeric vector indicating a subset of the table of coefficients to be printed for \code{"print.summary.plm"},} } \value{ An object of class \code{c("summary.plm", "plm", "panelmodel")}. Some of its elements are carried over from the associated plm object and described there (\code{\link[=plm]{plm()}}). The following elements are new or changed relative to the elements of a plm object: \item{fstatistic}{'htest' object: joint test of significance of coefficients (F or Chi-square test) (robust statistic in case of supplied argument \code{vcov}, see \code{\link[=pwaldtest]{pwaldtest()}} for details),} \item{coefficients}{a matrix with the estimated coefficients, standard errors, t--values, and p--values, if argument \code{vcov} was set to non-\code{NULL} the standard errors (and t-- and p--values) in their respective robust variant,} \item{vcov}{the "regular" variance--covariance matrix of the coefficients (class "matrix"),} \item{rvcov}{only present if argument \code{vcov} was set to non-\code{NULL}: the furnished variance--covariance matrix of the coefficients (class "matrix"),} \item{r.squared}{a named numeric containing the R-squared ("rsq") and the adjusted R-squared ("adjrsq") of the model,} \item{df}{an integer vector with 3 components, (p, n-p, p*), where p is the number of estimated (non-aliased) coefficients of the model, n-p are the residual degrees of freedom (n being number of observations), and p* is the total number of coefficients (incl. any aliased ones).} } \description{ The summary method for plm objects generates some more information about estimated plm models. } \details{ The \code{summary} method for plm objects (\code{summary.plm}) creates an object of class \code{c("summary.plm", "plm", "panelmodel")} that extends the plm object it is run on with various information about the estimated model like (inferential) statistics, see \strong{Value}. It has an associated print method (\code{print.summary.plm}). } \examples{ data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year")) summary(zz) # summary with a funished vcov, passed as matrix, as function, and # as function with additional argument data("Grunfeld", package = "plm") wi <- plm(inv ~ value + capital, data = Grunfeld, model="within", effect = "individual") summary(wi, vcov = vcovHC(wi)) summary(wi, vcov = vcovHC) summary(wi, vcov = function(x) vcovHC(x, method = "white2")) # extract F statistic wi_summary <- summary(wi) Fstat <- wi_summary[["fstatistic"]] # extract estimates and p-values est <- wi_summary[["coefficients"]][ , "Estimate"] pval <- wi_summary[["coefficients"]][ , "Pr(>|t|)"] # print summary only for coefficent "value" print(wi_summary, subset = "value") } \seealso{ \code{\link[=plm]{plm()}} for estimation of various models; \code{\link[=vcovHC]{vcovHC()}} for an example of a robust estimation of variance--covariance matrix; \code{\link[=r.squared]{r.squared()}} for the function to calculate R-squared; \code{\link[stats:print.power.htest]{stats::print.power.htest()}} for some information about class "htest"; \code{\link[=fixef]{fixef()}} to compute the fixed effects for "within" (=fixed effects) models and \code{\link[=within_intercept]{within_intercept()}} for an "overall intercept" for such models; \code{\link[=pwaldtest]{pwaldtest()}} } \author{ Yves Croissant } \keyword{regression} plm/man/plm-package.Rd0000644000176200001440000000462313603717463014304 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{package} \name{plm-package} \alias{plm-package} \title{plm package: linear models for panel data} \description{ plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models and to make (robust) inference. } \details{ For a gentle and comprehensive introduction to the package, please see the package's vignette. The main functions to estimate models are: \itemize{ \item \code{plm}: panel data estimators using \code{lm} on transformed data, \item \code{pvcm}: variable coefficients models \item \code{pgmm}: generalized method of moments (GMM) estimation for panel data, \item \code{pggls}: estimation of general feasible generalized least squares models, \item \code{pmg}: mean groups (MG), demeaned MG and common correlated effects (CCEMG) estimators, \item \code{pcce}: estimators for common correlated effects mean groups (CCEMG) and pooled (CCEP) for panel data with common factors, \item \code{pldv}: panel estimators for limited dependent variables. } Next to the model estimation functions, the package offers several functions for statistical tests related to panel data/models. Multiple functions for (robust) variance--covariance matrices are at hand as well. The package also provides data sets to demonstrate functions and to replicate some text book/paper results. Use \code{data(package="plm")} to view a list of available data sets in the package. } \examples{ data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year")) summary(zz) # replicates some results from Baltagi (2013), table 3.1 data("Grunfeld", package = "plm") p <- plm(inv ~ value + capital, data = Grunfeld, model="pooling") wi <- plm(inv ~ value + capital, data = Grunfeld, model="within", effect = "twoways") swar <- plm(inv ~ value + capital, data = Grunfeld, model="random", effect = "twoways") amemiya <- plm(inv ~ value + capital, data = Grunfeld, model = "random", random.method = "amemiya", effect = "twoways") walhus <- plm(inv ~ value + capital, data = Grunfeld, model = "random", random.method = "walhus", effect = "twoways") } \keyword{package} plm/man/plm-deprecated.Rd0000644000176200001440000000447413602224251015000 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deprecated.R, R/detect_lin_dep_alias.R \name{plm-deprecated} \alias{plm-deprecated} \alias{pvcovHC} \alias{plm.data} \alias{dynformula} \alias{pFormula} \alias{as.Formula.pFormula} \alias{model.frame.pFormula} \alias{model.matrix.pFormula} \alias{detect_lin_dep} \title{Deprecated functions of plm} \usage{ pvcovHC(x, ...) plm.data(x, indexes = NULL) dynformula(formula, lag.form = NULL, diff.form = NULL, log.form = NULL) pFormula(object) \method{as.Formula}{pFormula}(x, ...) \method{as.Formula}{pFormula}(x, ...) \method{model.frame}{pFormula}(formula, data, ..., lhs = NULL, rhs = NULL) \method{model.matrix}{pFormula}( object, data, model = c("pooling", "within", "Between", "Sum", "between", "mean", "random", "fd"), effect = c("individual", "time", "twoways", "nested"), rhs = 1, theta = NULL, cstcovar.rm = NULL, ... ) detect_lin_dep(object, ...) } \arguments{ \item{\dots}{further arguments.} \item{indexes}{a vector (of length one or two) indicating the (individual and time) indexes (see Details);} \item{formula}{a formula,} \item{lag.form}{a list containing the lag structure of each variable in the formula,} \item{diff.form}{a vector (or a list) of logical values indicating whether variables should be differenced,} \item{log.form}{a vector (or a list) of logical values indicating whether variables should be in logarithms.} \item{object, x}{an object of class \code{"plm"},} \item{data}{a \code{data.frame},} \item{lhs}{see Formula} \item{rhs}{see Formula} \item{model}{see plm} \item{effect}{see plm} \item{theta}{the parameter of transformation for the random effect model} \item{cstcovar.rm}{remove the constant columns or not} } \description{ \code{dynformula}, \code{pht}, \code{plm.data}, and \code{pvcovHC} are deprecated functions which could be removed from \pkg{plm} in a near future. } \details{ \code{dynformula} was used to construct a dynamic formula which was the first argument of \code{pgmm}. \code{pgmm} uses now multi-part formulas. \code{pht} estimates the Hausman-Taylor model, which can now be estimated using the more general \code{plm} function. \code{plm.data} is replaced by \code{pdata.frame}. \code{pvcovHV} is replaced by \code{vcovHC}. \code{detect_lin_dep} is replaced by \code{detect.lindep}. } plm/man/purtest.Rd0000644000176200001440000001557213604466415013635 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_uroot.R \name{purtest} \alias{purtest} \alias{print.purtest} \alias{summary.purtest} \alias{print.summary.purtest} \title{Unit root tests for panel data} \usage{ purtest( object, data = NULL, index = NULL, test = c("levinlin", "ips", "madwu", "Pm", "invnormal", "logit", "hadri"), exo = c("none", "intercept", "trend"), lags = c("SIC", "AIC", "Hall"), pmax = 10, Hcons = TRUE, q = NULL, dfcor = FALSE, fixedT = TRUE, ips.stat = NULL, ... ) \method{print}{purtest}(x, ...) \method{summary}{purtest}(object, ...) \method{print}{summary.purtest}(x, ...) } \arguments{ \item{object, x}{Either a \code{"data.frame"} or a matrix containing the time series (individuals as columns), a \code{"pseries"} object, a formula; a \code{"purtest"} object for the print and summary methods,} \item{data}{a \code{"data.frame"} or a \code{"pdata.frame"} object (required for formula interface, see Details and Examples),} \item{index}{the indexes,} \item{test}{the test to be computed: one of \code{"levinlin"} for \insertCite{LEVIN:LIN:CHU:02;textual}{plm}, \code{"ips"} for \insertCite{IM:PESAR:SHIN:03;textual}{plm}, \code{"madwu"} for \insertCite{MADDA:WU:99;textual}{plm}, \code{"Pm"} , \code{"invnormal"}, or \code{"logit"} for various tests as in \insertCite{CHOI:01;textual}{plm}, or \code{"hadri"} for \insertCite{HADR:00;textual}{plm}, see Details,} \item{exo}{the exogenous variables to introduce in the augmented Dickey--Fuller (ADF) regressions, one of: no exogenous variables (\code{"none"}), individual intercepts (\code{"intercept"}), or individual intercepts and trends (\code{"trend"}), but see Details,} \item{lags}{the number of lags to be used for the augmented Dickey-Fuller regressions: either an integer (the number of lags for all time series), a vector of integers (one for each time series), or a character string for an automatic computation of the number of lags, based on the AIC (\code{"AIC"}), the SIC (\code{"SIC"}), or on the method by \insertCite{HALL:94;textual}{plm} (\code{"Hall"}); argument is irrelevant for \code{test = "hadri"},} \item{pmax}{maximum number of lags (irrelevant for \code{test = "hadri"}),} \item{Hcons}{logical, only relevant for \code{test = "hadri"}, indicating whether the heteroskedasticity-consistent test of \insertCite{HADR:00;textual}{plm} should be computed,} \item{q}{the bandwidth for the estimation of the long-run variance (only relevant for \code{test = "levinlin"}, the default (`q = NULL``) gives the value as suggested by the authors as round(3.21 * T^(1/3))),} \item{dfcor}{logical, indicating whether the standard deviation of the regressions is to be computed using a degrees-of-freedom correction,} \item{fixedT}{logical, indicating whether the individual ADF regressions are to be computed using the same number of observations (irrelevant for \code{test = "hadri"}),} \item{ips.stat}{\code{NULL} or character of length 1 to request a specific IPS statistic, one of \code{"Wtbar"} (also default if \code{ips.stat = NULL}), \code{"Ztbar"}, \code{"tbar"},} \item{\dots}{further arguments (can set argument 'p.approx' to be passed on to non-exported function 'padf' to either "MacKinnon1994" or "MacKinnon1996" to force a specific method for p-value approximation, the latter only being possible if package 'urca' is installed).} } \value{ For purtest: An object of class \code{"purtest"}: a list with the elements \code{"statistic"} (a \code{"htest"} object), \code{"call"}, \code{"args"}, \code{"idres"} (containing results from the individual regressions), and \code{"adjval"} (containing the simulated means and variances needed to compute the statistic), \code{"sigma2"} short-run and long-run variance (for \code{"test = levinlin"}, otherwise NULL). } \description{ \code{purtest} implements several testing procedures that have been proposed to test unit root hypotheses with panel data. } \details{ All these tests except \code{"hadri"} are based on the estimation of augmented Dickey-Fuller (ADF) regressions for each time series. A statistic is then computed using the t-statistics associated with the lagged variable. The Hadri residual-based LM statistic is the cross-sectional average of the individual KPSS statistics \insertCite{KWIA:PHIL:SCHM:SHIN:92}{plm}, standardized by their asymptotic mean and standard deviation. Several Fisher-type tests that combine p-values from tests based on ADF regressions per individual are available: \itemize{ \item \code{"madwu"} is the inverse chi-squared test \insertCite{MADDA:WU:99}{plm}, also called P test by \insertCite{CHOI:01;textual}{plm}. \item \code{"Pm"} is the modified P test proposed by \insertCite{CHOI:01;textual}{plm} for large N, \item \code{"invnormal"} is the inverse normal test by \insertCite{CHOI:01}{plm}, and \item \code{"logit"} is the logit test by \insertCite{CHOI:01}{plm}. } The individual p-values for the Fisher-type tests are approximated as described in \insertCite{MACK:96;textual}{plm} if the package 'urca' (\insertCite{PFAFF:08;textual}{plm}) is available, otherwise as described in \insertCite{MACK:94;textual}{plm}. For the test statistic tbar of the test of Im/Pesaran/Shin (2003) (\verb{ips.stat = "tbar}), no p-value is given but 1\%, 5\%, and 10\% critical values are interpolated from paper's tabulated values via inverse distance weighting (printed and contained in the returned value's element statistic$ips.tbar.crit). Hadri's test, the test of Levin/Lin/Chu, and the tbar statistic of Im/Pesaran/Shin are not applicable to unbalanced panels; the tbar statistic is not applicable when \code{lags > 0} is given. The exogeneous instruments of the tests (where applicable) can be specified in several ways, depending on how the data is handed over to the function: \itemize{ \item For the \code{formula}/\code{data} interface (if \code{data} is a \code{data.frame}, an additional \code{index} argument should be specified); the formula should be of the form: \code{y ~ 0}, \code{y ~ 1}, or \code{y ~ trend} for a test with no exogenous variables, with an intercept, or with individual intercepts and time trend, respectively. The \code{exo} argument is ignored in this case. \item For the \code{data.frame}, \code{matrix}, and \code{pseries} interfaces: in these cases, the exogenous variables are specified using the \code{exo} argument. } With the associated \code{summary} and \code{print} methods, additional information can be extracted/displayed (see also Value). } \examples{ data("Grunfeld", package = "plm") y <- data.frame(split(Grunfeld$inv, Grunfeld$firm)) # individuals in columns purtest(y, pmax = 4, exo = "intercept", test = "madwu") ## same via formula interface purtest(inv ~ 1, data = Grunfeld, index = c("firm", "year"), pmax = 4, test = "madwu") } \references{ \insertAllCited{} } \seealso{ \code{\link[=cipstest]{cipstest()}} } \author{ Yves Croissant and for "Pm", "invnormal", and "logit" Kevin Tappe } \keyword{htest} plm/man/cortab.Rd0000644000176200001440000000104213503144006013347 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_cd.R \name{cortab} \alias{cortab} \title{Cross--sectional correlation matrix} \usage{ cortab(x, grouping, groupnames = NULL, value = "statistic", ...) } \arguments{ \item{x}{an object of class \code{pseries}} \item{grouping}{grouping variable,} \item{groupnames}{a character vector of group names,} \item{value}{to complete} \item{\dots}{further arguments} } \value{ A matrix } \description{ Computes the cross--sectional correlation matrix } \keyword{htest} plm/man/pbgtest.Rd0000755000176200001440000000657013602224251013564 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pbgtest} \alias{pbgtest} \alias{pbgtest.panelmodel} \alias{pbgtest.formula} \title{Breusch--Godfrey Test for Panel Models} \usage{ pbgtest(x, ...) \method{pbgtest}{panelmodel}(x, order = NULL, type = c("Chisq", "F"), ...) \method{pbgtest}{formula}( x, order = NULL, type = c("Chisq", "F"), data, model = c("pooling", "random", "within"), ... ) } \arguments{ \item{x}{an object of class \code{"panelmodel"} or of class \code{"formula"},} \item{\dots}{further arguments (see \code{\link[lmtest:bgtest]{lmtest::bgtest()}}).} \item{order}{an integer indicating the order of serial correlation to be tested for. \code{NULL} (default) uses the minimum number of observations over the time dimension (see also section \strong{Details} below),} \item{type}{type of test statistic to be calculated; either \code{"Chisq"} (default) for the Chi-squared test statistic or \code{"F"} for the F test statistic,} \item{data}{only relevant for formula interface: data set for which the respective panel model (see \code{model}) is to be evaluated,} \item{model}{only relevant for formula interface: compute test statistic for model \code{pooling} (default), \code{random}, or \code{within}. When \code{model} is used, the \code{data} argument needs to be passed as well,} } \value{ An object of class \code{"htest"}. } \description{ Test of serial correlation for (the idiosyncratic component of) the errors in panel models. } \details{ This Lagrange multiplier test uses the auxiliary model on (quasi-)demeaned data taken from a model of class \code{plm} which may be a \code{pooling} (default for formula interface), \code{random} or \code{within} model. It performs a Breusch--Godfrey test (using \code{bgtest} from package \CRANpkg{lmtest} on the residuals of the (quasi-)demeaned model, which should be serially uncorrelated under the null of no serial correlation in idiosyncratic errors, as illustrated in \insertCite{WOOL:10;textual}{plm}. The function takes the demeaned data, estimates the model and calls \code{bgtest}. Unlike most other tests for serial correlation in panels, this one allows to choose the order of correlation to test for. } \note{ The argument \code{order} defaults to the minimum number of observations over the time dimension, while for \code{lmtest::bgtest} it defaults to \code{1}. } \examples{ data("Grunfeld", package = "plm") g <- plm(inv ~ value + capital, data = Grunfeld, model = "random") # panelmodel interface pbgtest(g) pbgtest(g, order = 4) # formula interface pbgtest(inv ~ value + capital, data = Grunfeld, model = "random") # F test statistic (instead of default type="Chisq") pbgtest(g, type="F") pbgtest(inv ~ value + capital, data = Grunfeld, model = "random", type = "F") } \references{ \insertRef{BREU:78}{plm} \insertRef{GODF:78}{plm} \insertRef{WOOL:02}{plm} \insertRef{WOOL:10}{plm} \insertRef{WOOL:13}{plm} Sec. 12.2, pp. 421--422. } \seealso{ For the original test in package \CRANpkg{lmtest} see \code{\link[lmtest:bgtest]{lmtest::bgtest()}}. See \code{\link[=pdwtest]{pdwtest()}} for the analogous panel Durbin--Watson test. See \code{\link[=pbltest]{pbltest()}}, \code{\link[=pbsytest]{pbsytest()}}, \code{\link[=pwartest]{pwartest()}} and \code{\link[=pwfdtest]{pwfdtest()}} for other serial correlation tests for panel models. } \author{ Giovanni Millo } \keyword{htest} plm/man/pdim.Rd0000755000176200001440000000635413602224251013045 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_pdata.frame.R \name{pdim} \alias{pdim} \alias{pdim.default} \alias{pdim.data.frame} \alias{pdim.pdata.frame} \alias{pdim.pseries} \alias{pdim.panelmodel} \alias{pdim.pgmm} \alias{print.pdim} \title{Check for the Dimensions of the Panel} \usage{ pdim(x, ...) \method{pdim}{default}(x, y, ...) \method{pdim}{data.frame}(x, index = NULL, ...) \method{pdim}{pdata.frame}(x, ...) \method{pdim}{pseries}(x, ...) \method{pdim}{panelmodel}(x, ...) \method{pdim}{pgmm}(x, ...) \method{print}{pdim}(x, ...) } \arguments{ \item{x}{a \code{data.frame}, a \code{pdata.frame}, a \code{pseries}, a \code{panelmodel}, or a \code{pgmm} object,} \item{\dots}{further arguments.} \item{y}{a vector,} \item{index}{see \code{\link[=pdata.frame]{pdata.frame()}},} } \value{ An object of class \code{pdim} containing the following elements: \item{nT}{a list containing \code{n}, the number of individuals, \code{T}, the number of time observations, \code{N} the total number of observations,} \item{Tint}{a list containing two vectors (of type integer): \code{Ti} gives the number of observations for each individual and \code{nt} gives the number of individuals observed for each period,} \item{balanced}{a logical value: \code{TRUE} for a balanced panel, \code{FALSE} for an unbalanced panel,} \item{panel.names}{a list of character vectors: \code{id.names} contains the names of each individual and \code{time.names} contains the names of each period.} } \description{ This function checks the number of individuals and time observations in the panel and whether it is balanced or not. } \details{ \code{pdim} is called by the estimation functions and can be also used stand-alone. } \note{ Calling \code{pdim} on an estimated \code{panelmodel} object and on the corresponding \verb{(p)data.frame} used for this estimation does not necessarily yield the same result. When called on an estimated \code{panelmodel}, the number of observations (individual, time) actually used for model estimation are taken into account. When called on a \verb{(p)data.frame}, the rows in the \verb{(p)data.frame} are considered, disregarding any NA values in the dependent or independent variable(s) which would be dropped during model estimation. } \examples{ # There are 595 individuals data("Wages", package = "plm") pdim(Wages, 595) # Gasoline contains two variables which are individual and time # indexes and are the first two variables data("Gasoline", package="plm") pdim(Gasoline) # Hedonic is an unbalanced panel, townid is the individual index data("Hedonic", package = "plm") pdim(Hedonic, "townid") # An example of the panelmodel method data("Produc", package = "plm") z <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=Produc, model="random", subset = gsp > 5000) pdim(z) } \seealso{ \code{\link[=is.pbalanced]{is.pbalanced()}} to just determine balancedness of data (slightly faster than \code{pdim}),\cr \code{\link[=punbalancedness]{punbalancedness()}} for measures of unbalancedness,\cr \code{\link[=nobs]{nobs()}}, \code{\link[=pdata.frame]{pdata.frame()}},\cr \code{\link[=pvar]{pvar()}} to check for each variable if it varies cross-sectionally and over time. } \author{ Yves Croissant } \keyword{attribute} plm/man/pvcm.Rd0000755000176200001440000000642313602224251013056 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_vcm.R \name{pvcm} \alias{pvcm} \alias{summary.pvcm} \alias{print.summary.pvcm} \title{Variable Coefficients Models for Panel Data} \usage{ pvcm( formula, data, subset, na.action, effect = c("individual", "time"), model = c("within", "random"), index = NULL, ... ) \method{summary}{pvcm}(object, ...) \method{print}{summary.pvcm}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) } \arguments{ \item{formula}{a symbolic description for the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{lm},} \item{na.action}{see \code{lm},} \item{effect}{the effects introduced in the model: one of \code{"individual"}, \code{"time"},} \item{model}{one of \code{"within"}, \code{"random"},} \item{index}{the indexes, see \code{\link[=pdata.frame]{pdata.frame()}},} \item{\dots}{further arguments.} \item{object, x}{an object of class \code{"pvcm"},} \item{digits}{digits,} \item{width}{the maximum length of the lines in the print output,} } \value{ An object of class \code{c("pvcm", "panelmodel")}, which has the following elements: \item{coefficients}{the vector (or the data frame for fixed effects) of coefficients,} \item{residuals}{the vector of residuals,} \item{fitted.values}{the vector of fitted values,} \item{vcov}{the covariance matrix of the coefficients (a list for fixed effects),} \item{df.residual}{degrees of freedom of the residuals,} \item{model}{a data frame containing the variables used for the estimation,} \item{call}{the call,} \item{Delta}{the estimation of the covariance matrix of the coefficients (random effect models only),} \item{std.error}{a data frame containing standard errors for all coefficients for each individual (within models only).} \code{pvcm} objects have \code{print}, \code{summary} and \code{print.summary} methods. } \description{ Estimators for random and fixed effects models with variable coefficients. } \details{ \code{pvcm} estimates variable coefficients models. Time or individual effects are introduced, respectively, if \code{effect = "time"} or \code{effect = "individual"} (the default value). Coefficients are assumed to be fixed if \code{model = "within"} and random if \code{model = "random"}. In the first case, a different model is estimated for each individual (or time period). In the second case, the \insertCite{SWAM:70;textual}{plm} model is estimated. It is a generalized least squares model which uses the results of the previous model. } \examples{ data("Produc", package = "plm") zw <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "within") zr <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "random") ## replicate Greene (2012), p. 419, table 11.14 summary(pvcm(log(gsp) ~ log(pc) + log(hwy) + log(water) + log(util) + log(emp) + unemp, data = Produc, model = "random")) \dontrun{ # replicate Swamy (1970), p. 166, table 5.2 data(Grunfeld, package = "AER") # 11 firm Grunfeld data needed from package AER gw <- pvcm(invest ~ value + capital, data = Grunfeld, index = c("firm", "year")) } } \references{ \insertRef{SWAM:70}{plm} } \author{ Yves Croissant } \keyword{regression} plm/man/mtest.Rd0000755000176200001440000000237113503144006013242 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_gmm.R \name{mtest} \alias{mtest} \title{Arellano--Bond test of Serial Correlation} \usage{ mtest(object, order = 1, vcov = NULL) } \arguments{ \item{object}{an object of class \code{"pgmm"},} \item{order}{the order of the serial correlation (1 or 2),} \item{vcov}{a matrix of covariance for the coefficients or a function to compute it.} } \value{ An object of class \code{"htest"}. } \description{ Test of serial correlation for models estimated by GMM } \details{ The Arellano--Bond test is a test of correlation based on the residuals of the estimation. By default, the computation is done with the standard covariance matrix of the coefficients. A robust estimator of this covariance matrix can be supplied with the \code{vcov} argument. } \examples{ data("EmplUK", package = "plm") ar <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + lag(log(capital), 0:2) + lag(log(output), 0:2) | lag(log(emp), 2:99), data = EmplUK, effect = "twoways", model = "twosteps") mtest(ar, order = 1) mtest(ar, order = 2, vcov = vcovHC) } \references{ \insertCite{AREL:BOND:91}{plm} } \seealso{ \code{\link[=pgmm]{pgmm()}} } \author{ Yves Croissant } \keyword{htest} plm/man/pmg.Rd0000644000176200001440000000663113602224251012672 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_mg.R \name{pmg} \alias{pmg} \alias{summary.pmg} \alias{print.summary.pmg} \alias{residuals.pmg} \title{Mean Groups (MG), Demeaned MG and CCE MG estimators} \usage{ pmg( formula, data, subset, na.action, model = c("mg", "cmg", "dmg"), index = NULL, trend = FALSE, ... ) \method{summary}{pmg}(object, ...) \method{print}{summary.pmg}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) \method{residuals}{pmg}(object, ...) } \arguments{ \item{formula}{a symbolic description of the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{\link[=lm]{lm()}},} \item{na.action}{see \code{\link[=lm]{lm()}},} \item{model}{one of \code{c("mg", "cmg", "dmg")},} \item{index}{the indexes, see \code{\link[=pdata.frame]{pdata.frame()}},} \item{trend}{logical specifying whether an individual-specific trend has to be included,} \item{\dots}{further arguments.} \item{object, x}{an object of class \code{pmg},} \item{digits}{digits,} \item{width}{the maximum length of the lines in the print output,} } \value{ An object of class \code{c("pmg", "panelmodel")} containing: \item{coefficients}{the vector of coefficients,} \item{residuals}{the vector of residuals,} \item{fitted.values}{the vector of fitted values,} \item{vcov}{the covariance matrix of the coefficients,} \item{df.residual}{degrees of freedom of the residuals,} \item{model}{a data.frame containing the variables used for the estimation,} \item{call}{the call,} \item{sigma}{always \code{NULL}, \code{sigma} is here only for compatibility reasons (to allow using the same \code{summary} and \code{print} methods as \code{pggls}),} \item{indcoef}{the matrix of individual coefficients from separate time series regressions.} } \description{ Mean Groups (MG), Demeaned MG (DMG) and Common Correlated Effects MG (CCEMG) estimators for heterogeneous panel models, possibly with common factors (CCEMG) } \details{ \code{pmg} is a function for the estimation of linear panel models with heterogeneous coefficients by the Mean Groups estimator. \code{model = "mg"} specifies the standard Mean Groups estimator, based on the average of individual time series regressions. If \code{model = "dmg"} the data are demeaned cross-sectionally, which is believed to reduce the influence of common factors (and is akin to what is done in homogeneous panels when \code{model = "within"} and \code{effect = "time"}). Lastly, if \code{model = "cmg"} the CCEMG estimator is employed: this latter is consistent under the hypothesis of unobserved common factors and idiosyncratic factor loadings; it works by augmenting the model by cross-sectional averages of the dependent variable and regressors in order to account for the common factors, and adding individual intercepts and possibly trends. } \examples{ data("Produc", package = "plm") ## Mean Groups estimator mgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc) summary(mgmod) ## demeaned Mean Groups dmgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "dmg") summary(dmgmod) ## Common Correlated Effects Mean Groups ccemgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "cmg") summary(ccemgmod) } \references{ \insertRef{PESA:06}{plm} } \author{ Giovanni Millo } \keyword{regression} plm/man/EmplUK.Rd0000755000176200001440000000121513503144006013237 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{EmplUK} \alias{EmplUK} \title{Employment and Wages in the United Kingdom} \format{A data frame containing : \describe{ \item{firm}{firm index} \item{year}{year} \item{sector}{the sector of activity} \item{emp}{employment} \item{wage}{wages} \item{capital}{capital} \item{output}{output} }} \source{ \insertRef{AREL:BOND:91}{plm} } \description{ An unbalanced panel of 140 observations from 1976 to 1984 } \details{ \emph{total number of observations} : 1031 \emph{observation} : firms \emph{country} : United Kingdom } \keyword{datasets} plm/man/pwartest.Rd0000755000176200001440000000461513503144006013762 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pwartest} \alias{pwartest} \alias{pwartest.formula} \alias{pwartest.panelmodel} \title{Wooldridge Test for AR(1) Errors in FE Panel Models} \usage{ pwartest(x, ...) \method{pwartest}{formula}(x, data, ...) \method{pwartest}{panelmodel}(x, ...) } \arguments{ \item{x}{an object of class \code{formula} or of class \code{panelmodel},} \item{\dots}{further arguments to be passed on to \code{vcovHC} (see Details and Examples).} \item{data}{a \code{data.frame},} } \value{ An object of class \code{"htest"}. } \description{ Test of serial correlation for (the idiosyncratic component of) the errors in fixed--effects panel models. } \details{ As \insertCite{WOOL:10;textual}{plm}, Sec. 10.5.4 observes, under the null of no serial correlation in the errors, the residuals of a FE model must be negatively serially correlated, with \eqn{cor(\hat{u}_{it}, \hat{u}_{is})=-1/(T-1)} for each \eqn{t,s}. He suggests basing a test for this null hypothesis on a pooled regression of FE residuals on their first lag: \eqn{\hat{u}_{i,t} = \alpha + \delta \hat{u}_{i,t-1} + \eta_{i,t}}. Rejecting the restriction \eqn{\delta = -1/(T-1)} makes us conclude against the original null of no serial correlation. \code{pwartest} estimates the \code{within} model and retrieves residuals, then estimates an AR(1) \code{pooling} model on them. The test statistic is obtained by applying a F test to the latter model to test the above restriction on \eqn{\delta}, setting the covariance matrix to \code{vcovHC} with the option \code{method="arellano"} to control for serial correlation. Unlike the \code{\link[=pbgtest]{pbgtest()}} and \code{\link[=pdwtest]{pdwtest()}}, this test does not rely on large--T asymptotics and has therefore good properties in ``short'' panels. Furthermore, it is robust to general heteroskedasticity. } \examples{ data("EmplUK", package = "plm") pwartest(log(emp) ~ log(wage) + log(capital), data = EmplUK) # pass argument 'type' to vcovHC used in test pwartest(log(emp) ~ log(wage) + log(capital), data = EmplUK, type = "HC3") } \references{ \insertRef{WOOL:02}{plm} \insertRef{WOOL:10}{plm} } \seealso{ \code{\link[=pwfdtest]{pwfdtest()}}, \code{\link[=pdwtest]{pdwtest()}}, \code{\link[=pbgtest]{pbgtest()}}, \code{\link[=pbltest]{pbltest()}}, \code{\link[=pbsytest]{pbsytest()}}. } \author{ Giovanni Millo } \keyword{htest} plm/man/r.squared.Rd0000644000176200001440000000304413602224251014006 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_plm.R \name{r.squared} \alias{r.squared} \title{R squared and adjusted R squared for panel models} \usage{ r.squared(object, model = NULL, type = c("cor", "rss", "ess"), dfcor = FALSE) } \arguments{ \item{object}{an object of class \code{"plm"},} \item{model}{on which transformation of the data the R-squared is to be computed. If \code{NULL}, the transformation used to estimate the model is also used for the computation of R squared,} \item{type}{indicates method which is used to compute R squared. One of\cr \code{"rss"} (residual sum of squares),\cr \code{"ess"} (explained sum of squares), or\cr \code{"cor"} (coefficient of correlation between the fitted values and the response),} \item{dfcor}{if \code{TRUE}, the adjusted R squared is computed.} } \value{ A numerical value. The R squared or adjusted R squared of the model estimated on the transformed data, e. g. for the within model the so called "within R squared". } \description{ This function computes R squared or adjusted R squared for plm objects. It allows to define on which transformation of the data the (adjusted) R squared is to be computed and which method for calculation is used. } \examples{ data("Grunfeld", package = "plm") p <- plm(inv ~ value + capital, data = Grunfeld, model = "pooling") r.squared(p) r.squared(p, dfcor = TRUE) } \seealso{ \code{\link[=plm]{plm()}} for estimation of various models; \code{\link[=summary.plm]{summary.plm()}} which makes use of \code{r.squared}. } \keyword{htest} plm/man/Males.Rd0000755000176200001440000000215613603734271013162 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Males} \alias{Males} \title{Wages and Education of Young Males} \format{A data frame containing : \describe{ \item{nr}{identifier} \item{year}{year} \item{school}{years of schooling} \item{exper}{years of experience (computed as \code{age-6-school})} \item{union}{wage set by collective bargaining?} \item{ethn}{a factor with levels \verb{black, hisp, other}} \item{married}{married?} \item{health}{health problem?} \item{wage}{log of hourly wage} \item{industry}{a factor with 12 levels} \item{occupation}{a factor with 9 levels} \item{residence}{a factor with levels \verb{rural_area, north_east, northern_central, south}} }} \source{ Journal of Applied Econometrics data archive \url{http://qed.econ.queensu.ca/jae/1998-v13.2/vella-verbeek/}. } \description{ A panel of 545 observations from 1980 to 1987 } \details{ \emph{total number of observations} : 4360 \emph{observation} : individuals \emph{country} : United States } \references{ \insertRef{VELL:VERB:98}{plm} \insertRef{VERB:04}{plm} } \keyword{datasets} plm/man/vcovDC.Rd0000644000176200001440000000566013602224251013274 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_vcovG.R \name{vcovDC} \alias{vcovDC} \alias{vcovDC.plm} \title{Double-Clustering Robust Covariance Matrix Estimator} \usage{ vcovDC(x, ...) \method{vcovDC}{plm}(x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), ...) } \arguments{ \item{x}{an object of class \code{"plm"} or \code{"pcce"}} \item{\dots}{further arguments} \item{type}{the weighting scheme used, one of \code{"HC0"}, \code{"sss"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, see Details,} } \value{ An object of class \code{"matrix"} containing the estimate of the covariance matrix of coefficients. } \description{ High-level convenience wrapper for double-clustering robust covariance matrix estimators \emph{a la} \insertCite{THOM:11;textual}{plm} and \insertCite{CAME:GELB:MILL:11;textual}{plm} for panel models. } \details{ \code{vcovDC} is a function for estimating a robust covariance matrix of parameters for a panel model with errors clustering along both dimensions. The function is a convenience wrapper simply summing a group- and a time-clustered covariance matrix and subtracting a diagonal one \emph{a la} White. Weighting schemes specified by \code{type} are analogous to those in \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} in package \CRANpkg{sandwich} and are justified theoretically (although in the context of the standard linear model) by \insertCite{MACK:WHIT:85;textual}{plm} and \insertCite{CRIB:04;textual}{plm} \insertCite{@see @ZEIL:04}{plm}. The main use of \code{vcovDC} is to be an argument to other functions, e.g. for Wald-type testing: argument \code{vcov.} to \code{coeftest()}, argument \code{vcov} to \code{waldtest()} and other methods in the \CRANpkg{lmtest} package; and argument \code{vcov.} to \code{linearHypothesis()} in the \CRANpkg{car} package (see the examples). Notice that the \code{vcov} and \code{vcov.} arguments allow to supply a function (which is the safest) or a matrix \insertCite{@see @ZEIL:04, 4.1-2 and examples below}{plm}. } \examples{ library(lmtest) library(car) data("Produc", package="plm") zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling") ## standard coefficient significance test coeftest(zz) ## DC robust significance test, default coeftest(zz, vcov.=vcovDC) ## idem with parameters, pass vcov as a function argument coeftest(zz, vcov.=function(x) vcovDC(x, type="HC1", maxlag=4)) ## joint restriction test waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovDC) ## test of hyp.: 2*log(pc)=log(emp) linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovDC) } \references{ \insertRef{CAME:GELB:MILL:11}{plm} \insertRef{CRIB:04}{plm} \insertRef{MACK:WHIT:85}{plm} \insertRef{THOM:11}{plm} \insertRef{ZEIL:04}{plm} } \seealso{ \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} from the \CRANpkg{sandwich} package for weighting schemes (\code{type} argument). } \author{ Giovanni Millo } \keyword{regression} plm/man/ranef.plm.Rd0000644000176200001440000000374513603205230013771 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_ranfixef.R \name{ranef.plm} \alias{ranef.plm} \title{Extract the Random Effects} \usage{ \method{ranef}{plm}(object, effect = NULL, ...) } \arguments{ \item{object}{an object of class \code{"plm"}, needs to be a fitted random effects model,} \item{effect}{\code{NULL}, \code{"individual"}, or \code{"time"}, the effects to be extracted, see \strong{Details},} \item{\dots}{further arguments (currently not used).} } \value{ A named numeric with the random effects per dimension (individual or time). } \description{ Function to calculate the random effects from a \code{plm} object (random effects model). } \details{ Function \code{ranef} calculates the random effects of a fitted random effects model. For one-way models, the effects of the estimated model are extracted (either individual or time effects). For two-way models, extracting the individual effects is the default (both, argument \code{effect = NULL} and \code{effect = "individual"} will give individual effects). Time effects can be extracted by setting \code{effect = "time"}. Not all random effect model types are supported (yet?). } \examples{ data("Grunfeld", package = "plm") m1 <- plm(inv ~ value + capital, data = Grunfeld, model = "random") ranef(m1) # individual random effects # compare to random effects by ML estimation via lmer from package # lme4 \dontrun{ library(lme4) m2 <- lmer(inv ~ value + capital + (1 | firm), data = Grunfeld) cbind("plm" = ranef(m1), "lmer" = unname(ranef(m2)$firm)) } # two-ways RE model, calculate individual and time random effects data("Cigar", package = "plm") tw <- plm(sales ~ pop + price, data = Cigar, model = "random", effect = "twoways") ranef(tw) # individual random effects ranef(tw, effect = "time") # time random effects } \seealso{ \code{\link[=fixef]{fixef()}} to extract the fixed effects from a fixed effects model (within model). } \author{ Kevin Tappe } \keyword{regression} plm/man/pwfdtest.Rd0000755000176200001440000000744413503144006013754 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pwfdtest} \alias{pwfdtest} \alias{pwfdtest.formula} \alias{pwfdtest.panelmodel} \title{Wooldridge first--difference--based test for AR(1) errors in levels or first--differenced panel models} \usage{ pwfdtest(x, ...) \method{pwfdtest}{formula}(x, data, ..., h0 = c("fd", "fe")) \method{pwfdtest}{panelmodel}(x, ..., h0 = c("fd", "fe")) } \arguments{ \item{x}{an object of class \code{formula} or a \code{"fd"}-model (plm object),} \item{\dots}{further arguments to be passed on to \code{vcovHC} (see Details and Examples).} \item{data}{a \code{data.frame},} \item{h0}{the null hypothesis: one of \code{"fd"}, \code{"fe"},} } \value{ An object of class \code{"htest"}. } \description{ First--differencing--based test of serial correlation for (the idiosyncratic component of) the errors in either levels or first--differenced panel models. } \details{ As \insertCite{WOOL:10;textual}{plm}, Sec. 10.6.3 observes, if the idiosyncratic errors in the model in levels are uncorrelated (which we label hypothesis \code{"fe"}), then the errors of the model in first differences (FD) must be serially correlated with \eqn{cor(\hat{e}_{it}, \hat{e}_{is}) = -0.5} for each \eqn{t,s}. If on the contrary the levels model's errors are a random walk, then there must be no serial correlation in the FD errors (hypothesis \code{"fd"}). Both the fixed effects (FE) and the first--differenced (FD) estimators remain consistent under either assumption, but the relative efficiency changes: FE is more efficient under \code{"fe"}, FD under \code{"fd"}. Wooldridge (ibid.) suggests basing a test for either hypothesis on a pooled regression of FD residuals on their first lag: \eqn{\hat{e}_{i,t}=\alpha + \rho \hat{e}_{i,t-1} + \eta_{i,t}}. Rejecting the restriction \eqn{\rho = -0.5} makes us conclude against the null of no serial correlation in errors of the levels equation (\code{"fe"}). The null hypothesis of no serial correlation in differenced errors (\code{"fd"}) is tested in a similar way, but based on the zero restriction on \eqn{\rho} (\eqn{\rho = 0}). Rejecting \code{"fe"} favours the use of the first--differences estimator and the contrary, although it is possible that both be rejected. \code{pwfdtest} estimates the \code{fd} model (or takes an \code{fd} model as input for the panelmodel interface) and retrieves its residuals, then estimates an AR(1) \code{pooling} model on them. The test statistic is obtained by applying a F test to the latter model to test the relevant restriction on \eqn{\rho}, setting the covariance matrix to \code{vcovHC} with the option \code{method="arellano"} to control for serial correlation. Unlike the \code{pbgtest} and \code{pdwtest}, this test does not rely on large--T asymptotics and has therefore good properties in ''short'' panels. Furthermore, it is robust to general heteroskedasticity. The \code{"fe"} version can be used to test for error autocorrelation regardless of whether the maintained specification has fixed or random effects \insertCite{@see @DRUK:03}{plm}. } \examples{ data("EmplUK" , package = "plm") pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK) pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK, h0 = "fe") # pass argument 'type' to vcovHC used in test pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK, type = "HC3", h0 = "fe") # same with panelmodel interface mod <- plm(log(emp) ~ log(wage) + log(capital), data = EmplUK, model = "fd") pwfdtest(mod) pwfdtest(mod, h0 = "fe") pwfdtest(mod, type = "HC3", h0 = "fe") } \references{ \insertRef{DRUK:03}{plm} \insertRef{WOOL:02}{plm} Sec. 10.6.3, pp. 282--283. \insertRef{WOOL:10}{plm} Sec. 10.6.3, pp. 319--320 } \seealso{ \code{pdwtest}, \code{pbgtest}, \code{pwartest}, } \author{ Giovanni Millo } \keyword{htest} plm/man/pldv.Rd0000644000176200001440000000337313602224251013054 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_ldv.R \name{pldv} \alias{pldv} \title{Panel estimators for limited dependent variables} \usage{ pldv( formula, data, subset, weights, na.action, model = c("fd", "random", "pooling"), index = NULL, R = 20, start = NULL, lower = 0, upper = +Inf, objfun = c("lsq", "lad"), sample = c("cens", "trunc"), ... ) } \arguments{ \item{formula}{a symbolic description for the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{lm},} \item{weights}{see \code{lm},} \item{na.action}{see \code{lm},} \item{model}{one of \code{"fd"}, \code{"random"} or \code{"pooling"},} \item{index}{the indexes, see \code{\link[=pdata.frame]{pdata.frame()}},} \item{R}{the number of points for the gaussian quadrature,} \item{start}{a vector of starting values,} \item{lower}{the lower bound for the censored/truncated dependent variable,} \item{upper}{the upper bound for the censored/truncated dependent variable,} \item{objfun}{the objective function for the fixed effect model, one of \code{"lsq"} for least squares and \code{"lad"} for least absolute deviations,} \item{sample}{\code{"cens"} for a censored (tobit-like) sample, \code{"trunc"} for a truncated sample,} \item{\dots}{further arguments.} } \value{ An object of class \code{c("plm","panelmodel")}. } \description{ Fixed and random effects estimators for truncated or censored limited dependent variable } \details{ \code{pldv} computes two kinds of models : maximum likelihood estimator with an assumed normal distribution for the individual effects and a LSQ/LAD estimator for the first-difference model. } \references{ \insertRef{HONO:92}{plm} } \author{ Yves Croissant } \keyword{regression} plm/man/has.intercept.Rd0000644000176200001440000000225213503144006014650 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_misc.R \name{has.intercept} \alias{has.intercept} \alias{has.intercept.default} \alias{has.intercept.formula} \alias{has.intercept.Formula} \alias{has.intercept.panelmodel} \alias{has.intercept.plm} \title{Check for the presence of an intercept in a formula or in a fitted model} \usage{ has.intercept(object, ...) \method{has.intercept}{default}(object, ...) \method{has.intercept}{formula}(object, ...) \method{has.intercept}{Formula}(object, rhs = NULL, ...) \method{has.intercept}{panelmodel}(object, ...) \method{has.intercept}{plm}(object, part = "first", ...) } \arguments{ \item{object}{a \code{formula}, a \code{Formula} or a fitted model (of class \code{plm} or \code{panelmodel}),} \item{\dots}{further arguments.} \item{rhs, part}{the index of the right hand sides part of the formula for which one wants to check the presence of an intercept (relevant for the \code{Formula} and the \code{plm} methods),} } \value{ a boolean } \description{ The presence of an intercept is checked using the formula which is either provided as the argument of the function or extracted from a fitted model } plm/man/lag.plm.Rd0000644000176200001440000001127713602224251013443 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_transformations.R \name{lag.plm} \alias{lag.plm} \alias{lead} \alias{lag.pseries} \alias{lead.pseries} \alias{diff.pseries} \title{lag, lead, and diff for panel data} \usage{ lead(x, k = 1, ...) \method{lag}{pseries}(x, k = 1, shift = c("time", "row"), ...) \method{lead}{pseries}(x, k = 1, shift = c("time", "row"), ...) \method{diff}{pseries}(x, lag = 1, shift = c("time", "row"), ...) } \arguments{ \item{x}{a \code{pseries} object,} \item{k}{an integer, the number of lags for the \code{lag} and \code{lead} methods (can also be negative). For the \code{lag} method, a positive (negative) \code{k} gives lagged (leading) values. For the \code{lead} method, a positive (negative) \code{k} gives leading (lagged) values, thus, \code{lag(x, k = -1)} yields the same as \code{lead(x, k = 1)}. If \code{k} is an integer with length > 1 (\code{k = c(k1, k2, ...)}), a \code{matrix} with multiple lagged \code{pseries} is returned,} \item{...}{further arguments (currently none evaluated).} \item{shift}{character, either \code{"time"} (default) or \code{"row"} determining how the shifting in the \code{lag}/\code{lead}/\code{diff} functions is performed (see Details and Examples).} \item{lag}{the number of lags for the \code{diff} method, can also be of length > 1 (see argument \code{k}) (only non--negative values in argument \code{lag} are allowed for \code{diff}),} } \value{ \itemize{ \item An object of class \code{pseries}, if the argument specifying the lag has length 1 (argument \code{k} in functions \code{lag} and \code{lead}, argument \code{lag} in function \code{diff}). \item A matrix containing the various series in its columns, if the argument specifying the lag has length > 1. } } \description{ lag, lead, and diff functions for class pseries. } \details{ This set of functions perform lagging, leading (lagging in the opposite direction), and differencing operations on \code{pseries} objects, i. e., they take the panel structure of the data into account by performing the operations per individual. Argument \code{shift} controls the shifting of observations to be used by methods \code{lag}, \code{lead}, and \code{diff}: #' - \code{shift = "time"} (default): Methods respect the numerical value in the time dimension of the index. The time dimension needs to be interpretable as a sequence t, t+1, t+2, \ldots{} where t is an integer (from a technical viewpoint, \code{as.numeric(as.character(index(your_pdata.frame)[[2]]))} needs to result in a meaningful integer). \itemize{ \item \verb{shift = "row": }Methods perform the shifting operation based solely on the "physical position" of the observations, i.e. neighbouring rows are shifted per individual. The value in the time index is not relevant in this case. } For consecutive time periods per individual, a switch of shifting behaviour results in no difference. Different return values will occur for non-consecutive time periods per individual ("holes in time"), see also Examples. } \note{ The sign of \code{k} in \code{lag.pseries} results in inverse behaviour compared to \code{\link[stats:lag]{stats::lag()}} and \code{\link[zoo:lag.zoo]{zoo::lag.zoo()}}. } \examples{ # First, create a pdata.frame data("EmplUK", package = "plm") Em <- pdata.frame(EmplUK) # Then extract a series, which becomes additionally a pseries z <- Em$output class(z) # compute the first and third lag, and the difference lagged twice lag(z) lag(z, 3) diff(z, 2) # compute negative lags (= leading values) lag(z, -1) lead(z, 1) # same as line above identical(lead(z, 1), lag(z, -1)) # TRUE # compute more than one lag and diff at once (matrix returned) lag(z, c(1,2)) diff(z, c(1,2)) ## demonstrate behaviour of shift = "time" vs. shift = "row" # delete 2nd time period for first individual (1978 is missing (not NA)): Em_hole <- Em[-2, ] is.pconsecutive(Em_hole) # check: non-consecutive for 1st individual now # original non-consecutive data: head(Em_hole$emp, 10) # for shift = "time", 1-1979 contains the value of former 1-1977 (2 periods lagged): head(lag(Em_hole$emp, k = 2, shift = "time"), 10) # for shift = "row", 1-1979 contains NA (2 rows lagged (and no entry for 1976): head(lag(Em_hole$emp, k = 2, shift = "row"), 10) } \seealso{ To check if the time periods are consecutive per individual, see \code{\link[=is.pconsecutive]{is.pconsecutive()}}. For further function for 'pseries' objects: \code{\link[=between]{between()}}, \link[=between]{Between()}, \code{\link[=Within]{Within()}}, \code{\link[=summary.pseries]{summary.pseries()}}, \code{\link[=print.summary.pseries]{print.summary.pseries()}}, \code{\link[=as.matrix.pseries]{as.matrix.pseries()}}. } \author{ Yves Croissant and Kevin Tappe } \keyword{classes} plm/man/pcce.Rd0000644000176200001440000000645313602224251013023 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_cce.R \name{pcce} \alias{pcce} \alias{summary.pcce} \alias{print.summary.pcce} \alias{residuals.pcce} \alias{model.matrix.pcce} \alias{pmodel.response.pcce} \title{Common Correlated Effects estimators} \usage{ pcce( formula, data, subset, na.action, model = c("mg", "p"), index = NULL, trend = FALSE, ... ) \method{summary}{pcce}(object, vcov = NULL, ...) \method{print}{summary.pcce}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) \method{residuals}{pcce}(object, type = c("defactored", "standard"), ...) \method{model.matrix}{pcce}(object, ...) \method{pmodel.response}{pcce}(object, ...) } \arguments{ \item{formula}{a symbolic description of the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{lm},} \item{na.action}{see \code{lm},} \item{model}{one of \code{"mg"}, \code{"p"}, selects Mean Groups vs. Pooled CCE model,} \item{index}{the indexes, see \code{\link[=pdata.frame]{pdata.frame()}},} \item{trend}{logical specifying whether an individual-specific trend has to be included,} \item{\dots}{further arguments.} \item{object, x}{an object of class \code{"pcce"},} \item{vcov}{a variance–covariance matrix furnished by the user or a function to calculate one,} \item{digits}{digits,} \item{width}{the maximum length of the lines in the print output,} \item{type}{one of \code{"defactored"} or \code{"standard"},} } \value{ An object of class \code{c("pcce", "panelmodel")} containing: \item{coefficients}{the vector of coefficients,} \item{residuals}{the vector of (defactored) residuals,} \item{stdres}{the vector of (raw) residuals,} \item{tr.model}{the transformed data after projection on H,} \item{fitted.values}{the vector of fitted values,} \item{vcov}{the covariance matrix of the coefficients,} \item{df.residual}{degrees of freedom of the residuals,} \item{model}{a data.frame containing the variables used for the estimation,} \item{call}{the call,} \item{sigma}{always \code{NULL}, \code{sigma} is here only for compatibility reasons (to allow using the same \code{summary} and \code{print} methods as \code{pggls}),} \item{indcoef}{the matrix of individual coefficients from separate time series regressions.} } \description{ Common Correlated Effects Mean Groups (CCEMG) and Pooled (CCEP) estimators for panel data with common factors (balanced or unbalanced) } \details{ \code{pcce} is a function for the estimation of linear panel models by the Common Correlated Effects Mean Groups or Pooled estimator, consistent under the hypothesis of unobserved common factors and idiosyncratic factor loadings. The CCE estimator works by augmenting the model by cross-sectional averages of the dependent variable and regressors in order to account for the common factors, and adding individual intercepts and possibly trends. } \examples{ data("Produc", package = "plm") ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p") summary(ccepmod) summary(ccepmod, vcov = vcovHC) # use argument vcov for robust std. errors ccemgmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="mg") summary(ccemgmod) } \references{ \insertRef{kappesyam11}{plm} } \author{ Giovanni Millo } \keyword{regression} plm/man/pooltest.Rd0000755000176200001440000000300613503144006013753 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_general.R \name{pooltest} \alias{pooltest} \alias{pooltest.plm} \alias{pooltest.formula} \title{Test of Poolability} \usage{ pooltest(x, ...) \method{pooltest}{plm}(x, z, ...) \method{pooltest}{formula}(x, data, ...) } \arguments{ \item{x}{an object of class \code{"plm"} for the plm method; an object of class \code{"formula"} for the formula interface,} \item{\dots}{further arguments passed to plm.} \item{z}{an object of class \code{"pvcm"} obtained with \code{model="within"},} \item{data}{a \code{data.frame},} } \value{ An object of class \code{"htest"}. } \description{ A Chow test for the poolability of the data. } \details{ \code{pooltest} is a \emph{F} test of stability (or Chow test) for the coefficients of a panel model. For argument \code{x}, the estimated \code{plm} object should be a \code{"pooling"} model or a \code{"within"} model (the default); intercepts are assumed to be identical in the first case and different in the second case. } \examples{ data("Gasoline", package = "plm") form <- lgaspcar ~ lincomep + lrpmg + lcarpcap gasw <- plm(form, data = Gasoline, model = "within") gasp <- plm(form, data = Gasoline, model = "pooling") gasnp <- pvcm(form, data = Gasoline, model = "within") pooltest(gasw, gasnp) pooltest(gasp, gasnp) pooltest(form, data = Gasoline, effect = "individual", model = "within") pooltest(form, data = Gasoline, effect = "individual", model = "pooling") } \author{ Yves Croissant } \keyword{htest} plm/man/pdata.frame.Rd0000644000176200001440000001436413602224251014273 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_pdata.frame.R \name{pdata.frame} \alias{pdata.frame} \alias{$<-.pdata.frame} \alias{[.pdata.frame} \alias{[[.pdata.frame} \alias{$.pdata.frame} \alias{print.pdata.frame} \alias{as.list.pdata.frame} \alias{as.data.frame.pdata.frame} \title{data.frame for panel data} \usage{ pdata.frame( x, index = NULL, drop.index = FALSE, row.names = TRUE, stringsAsFactors = default.stringsAsFactors(), replace.non.finite = FALSE, drop.NA.series = FALSE, drop.const.series = FALSE, drop.unused.levels = FALSE ) \method{$}{pdata.frame}(x, name) <- value \method{[}{pdata.frame}(x, i, j, drop) \method{[[}{pdata.frame}(x, y) \method{$}{pdata.frame}(x, y) \method{print}{pdata.frame}(x, ...) \method{as.list}{pdata.frame}(x, keep.attributes = FALSE, ...) \method{as.data.frame}{pdata.frame}( x, row.names = NULL, optional = FALSE, keep.attributes = TRUE, ... ) } \arguments{ \item{x}{a \code{data.frame} for the \code{pdata.frame} function and a \code{pdata.frame} for the methods,} \item{index}{this argument indicates the individual and time indexes. See \strong{Details},} \item{drop.index}{logical, indicates whether the indexes are to be excluded from the resulting pdata.frame,} \item{row.names}{\code{NULL} or logical, indicates whether ``fancy'' row names (a combination of individual index and time index) are to be added to the returned (p)data.frame (\code{NULL} and `FALSE` have the same meaning),} \item{stringsAsFactors}{logical, indicating whether character vectors are to be converted to factors,} \item{replace.non.finite}{logical, indicating whether values for which \code{is.finite()} yields \code{TRUE} are to be replaced by \code{NA} values, except for character variables (defaults to \code{FALSE}),} \item{drop.NA.series}{logical, indicating whether all-NA columns are to be removed from the pdata.frame (defaults to \code{FALSE}),} \item{drop.const.series}{logical, indicating whether constant columns are to be removed from the pdata.frame (defaults to \code{FALSE}),} \item{drop.unused.levels}{logical, indicating whether unused levels of factors are to be dropped (defaults to \code{FALSE}) (unused levels are always dropped from variables serving to construct the index variables),} \item{name}{the name of the \code{data.frame},} \item{value}{the name of the variable to include,} \item{i}{see \code{\link[=Extract]{Extract()}},} \item{j}{see \code{\link[=Extract]{Extract()}},} \item{drop}{see \code{\link[=Extract]{Extract()}},} \item{y}{one of the columns of the \code{data.frame},} \item{\dots}{further arguments.} \item{keep.attributes}{logical, only for as.list and as.data.frame methods, indicating whether the elements of the returned list/columns of the data.frame should have the pdata.frame's attributes added (default: FALSE for as.list, TRUE for as.data.frame),} \item{optional}{see \code{\link[=as.data.frame]{as.data.frame()}},} } \value{ a \code{pdata.frame} object: this is a \code{data.frame} with an \code{index} attribute which is a \code{data.frame} with two variables, the individual and the time indexes, both being factors. The resulting pdata.frame is sorted by the individual index, then by the time index. } \description{ An object of class 'pdata.frame' is a data.frame with an index attribute that describes its individual and time dimensions. } \details{ The \code{index} argument indicates the dimensions of the panel. It can be: \itemize{ \item a vector of two character strings which contains the names of the individual and of the time indexes, \item a character string which is the name of the individual index variable. In this case, the time index is created automatically and a new variable called "time" is added, assuming consecutive and ascending time periods in the order of the original data, \item an integer, the number of individuals. In this case, the data need to be a balanced panel and be organized as a stacked time series (successive blocks of individuals, each block being a time series for the respective individual) assuming consecutive and ascending time periods in the order of the original data. Two new variables are added: "id" and "time" which contain the individual and the time indexes. } The \code{"[["} and \code{"$"} extract a series from the \code{pdata.frame}. The \code{"index"} attribute is then added to the series and a class attribute \code{"pseries"} is added. The \code{"["} method behaves as for \code{data.frame}, except that the extraction is also applied to the \code{index} attribute. A safe way to extract the index attribute is to use the function \code{\link[=index]{index()}} for 'pdata.frames' (and other objects). \code{as.data.frame} removes the index from the \code{pdata.frame} and adds it to each column. \code{as.list} behaves by default identical to \code{\link[base:as.list.data.frame]{base::as.list.data.frame()}} which means it drops the attributes specific to a pdata.frame; if a list of pseries is wanted, the attribute \code{keep.attributes} can to be set to \code{TRUE}. This also makes \code{lapply} work as expected on a pdata.frame (see also \strong{Examples}). } \examples{ # Gasoline contains two variables which are individual and time # indexes data("Gasoline", package = "plm") Gas <- pdata.frame(Gasoline, index = c("country", "year"), drop.index = TRUE) # Hedonic is an unbalanced panel, townid is the individual index data("Hedonic", package = "plm") Hed <- pdata.frame(Hedonic, index = "townid", row.names = FALSE) # In case of balanced panel, it is sufficient to give number of # individuals data set 'Wages' is organized as a stacked time # series data("Wages", package = "plm") Wag <- pdata.frame(Wages, 595) # lapply on a pdata.frame by making it a list of pseries first lapply(as.list(Wag[ , c("ed", "lwage")], keep.attributes = TRUE), lag) } \seealso{ \code{\link[=index]{index()}} to extract the index variables from a 'pdata.frame' (and other objects), \code{\link[=pdim]{pdim()}} to check the dimensions of a 'pdata.frame' (and other objects), \code{\link[=pvar]{pvar()}} to check for each variable if it varies cross-sectionally and over time. To check if the time periods are consecutive per individual, see \code{\link[=is.pconsecutive]{is.pconsecutive()}}. } \author{ Yves Croissant } \keyword{classes} plm/man/ercomp.Rd0000755000176200001440000000544313602224251013377 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_ercomp.R \name{ercomp} \alias{ercomp} \alias{ercomp.plm} \alias{ercomp.pdata.frame} \alias{ercomp.formula} \alias{print.ercomp} \title{Estimation of the error components} \usage{ ercomp(object, ...) \method{ercomp}{plm}(object, ...) \method{ercomp}{pdata.frame}( object, effect = c("individual", "time", "twoways", "nested"), method = NULL, models = NULL, dfcor = NULL, index = NULL, ... ) \method{ercomp}{formula}( object, data, effect = c("individual", "time", "twoways", "nested"), method = NULL, models = NULL, dfcor = NULL, index = NULL, ... ) \method{print}{ercomp}(x, digits = max(3, getOption("digits") - 3), ...) } \arguments{ \item{object}{a \code{formula} or a \code{plm} object,} \item{\dots}{further arguments.} \item{effect}{the effects introduced in the model, see \code{\link[=plm]{plm()}} for details,} \item{method}{method of estimation for the variance components, see \code{\link[=plm]{plm()}} for details,} \item{models}{the models used to estimate the variance components (an alternative to the previous argument),} \item{dfcor}{a numeric vector of length 2 indicating which degree of freedom should be used,} \item{index}{the indexes,} \item{data}{a \code{data.frame},} \item{x}{an \code{ercomp} object,} \item{digits}{digits,} } \value{ An object of class \code{"ercomp"}: a list containing \itemize{ \item \code{sigma2} a named numeric with estimates of the variance components, \item \code{theta} contains the parameter(s) used for the transformation of the variables: For a one-way model, a numeric corresponding to the selected effect (individual or time); for a two-ways model a list of length 3 with the parameters. In case of a balanced model, the numeric has length 1 while for an unbalanced model, the numerics' length equal the number of observations. } } \description{ This function enables the estimation of the variance components of a panel model. } \examples{ data("Produc", package = "plm") # an example of the formula method ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, method = "walhus", effect = "time") # same with the plm method z <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, random.method = "walhus", effect = "time", model = "random") ercomp(z) # a two-ways model ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, method = "amemiya", effect = "twoways") } \references{ \insertRef{AMEM:71}{plm} \insertRef{NERLO:71}{plm} \insertRef{SWAM:AROR:72}{plm} \insertRef{WALL:HUSS:69}{plm} } \seealso{ \code{\link[=plm]{plm()}} where the estimates of the variance components are used if a random effects model is estimated } \author{ Yves Croissant } \keyword{regression} plm/man/pmodel.response.Rd0000644000176200001440000000473413503144006015225 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_model.extract.R \name{pmodel.response} \alias{pmodel.response} \alias{pmodel.response.plm} \alias{pmodel.response.data.frame} \alias{pmodel.response.formula} \title{A function to extract the model.response} \usage{ pmodel.response(object, ...) \method{pmodel.response}{plm}(object, ...) \method{pmodel.response}{data.frame}(object, ...) \method{pmodel.response}{formula}(object, data, ...) } \arguments{ \item{object}{an object of class \code{"plm"}, or a formula of class \code{"pFormula"},} \item{\dots}{further arguments.} \item{data}{a \code{data.frame}} } \value{ A pseries except if model responses' of a \code{"between"} or "fd" model as these models "compress" the data (the number of observations used in estimation is smaller than the original data due to the specific transformation). A numeric is returned for the "between" and "fd" model. } \description{ pmodel.response has several methods to conveniently extract the response of several objects. } \details{ The model response is extracted from a \code{pdata.frame} (where the response must reside in the first column; this is the case for a model frame), a \code{pFormula} + \code{data} or a \code{plm} object, and the transformation specified by \code{effect} and \code{model} is applied to it.\cr Constructing the model frame first ensures proper NA handling and the response being placed in the first column, see also \strong{Examples} for usage. } \examples{ # First, make a pdata.frame data("Grunfeld", package = "plm") pGrunfeld <- pdata.frame(Grunfeld) # then make a model frame from a pFormula and a pdata.frame form <- inv ~ value + capital mf <- model.frame(pGrunfeld, form) # construct (transformed) response of the within model resp <- pmodel.response(form, data = mf, model = "within", effect = "individual") # retrieve (transformed) response directly from model frame resp_mf <- pmodel.response(mf, model = "within", effect = "individual") # retrieve (transformed) response from a plm object, i.e. an estimated model fe_model <- plm(form, data = pGrunfeld, model = "within") pmodel.response(fe_model) # same as constructed before all.equal(resp, pmodel.response(fe_model), check.attributes = FALSE) # TRUE } \seealso{ \code{plm}'s \code{\link[=model.matrix]{model.matrix()}} for (transformed) model matrix and the corresponding \code{\link[=model.frame]{model.frame()}} method to construct a model frame. } \author{ Yves Croissant } \keyword{manip} plm/man/plmtest.Rd0000755000176200001440000000737613602224251013611 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_general.R \name{plmtest} \alias{plmtest} \alias{plmtest.plm} \alias{plmtest.formula} \title{Lagrange FF Multiplier Tests for Panel Models} \usage{ plmtest(x, ...) \method{plmtest}{plm}( x, effect = c("individual", "time", "twoways"), type = c("honda", "bp", "ghm", "kw"), ... ) \method{plmtest}{formula}( x, data, ..., effect = c("individual", "time", "twoways"), type = c("honda", "bp", "ghm", "kw") ) } \arguments{ \item{x}{an object of class \code{"plm"} or a formula of class \code{"formula"},} \item{\dots}{further arguments passed to \code{plmtest}.} \item{effect}{a character string indicating which effects are tested: individual effects (\code{"individual"}), time effects (\code{"time"}) or both (\code{"twoways"}),} \item{type}{a character string indicating the test to be performed: \itemize{ \item \code{"honda"} (default) for \insertCite{HOND:85;textual}{plm}, \item \code{"bp"} for \insertCite{BREU:PAGA:80;textual}{plm}, \item \code{"kw"} for \insertCite{KING:WU:97;textual}{plm}, or \item \code{"ghm"} for \insertCite{GOUR:HOLL:MONF:82;textual}{plm} for unbalanced panel data sets, the respective unbalanced version of the tests are computed, }} \item{data}{a \code{data.frame},} } \value{ An object of class \code{"htest"}. } \description{ Test of individual and/or time effects for panel models. } \details{ These Lagrange multiplier tests use only the residuals of the pooling model. The first argument of this function may be either a pooling model of class \code{plm} or an object of class \code{formula} describing the model. For inputted within (fixed effects) or random effects models, the corresponding pooling model is calculated internally first as the tests are based on the residuals of the pooling model. The \code{"bp"} test for unbalanced panels was derived in \insertCite{BALT:LI:90;textual}{plm} (1990), the \code{"kw"} test for unbalanced panels in \insertCite{BALT:CHAN:LI:98;textual}{plm}. The \code{"ghm"} test and the \code{"kw"} test were extended to two-way effects in \insertCite{BALT:CHAN:LI:92;textual}{plm}. For a concise overview of all these statistics see \insertCite{BALT:03;textual}{plm}, Sec. 4.2, pp. 68--76 (for balanced panels) and Sec. 9.5, pp. 200--203 (for unbalanced panels). } \note{ For the King-Wu statistics (\code{"kw"}), the oneway statistics (\code{"individual"} and \code{"time"}) coincide with the respective Honda statistics (\code{"honda"}); twoway statistics of \code{"kw"} and \code{"honda"} differ. } \examples{ data("Grunfeld", package = "plm") g <- plm(inv ~ value + capital, data = Grunfeld, model = "pooling") plmtest(g) plmtest(g, effect="time") plmtest(inv ~ value + capital, data = Grunfeld, type = "honda") plmtest(inv ~ value + capital, data = Grunfeld, type = "bp") plmtest(inv ~ value + capital, data = Grunfeld, type = "bp", effect = "twoways") plmtest(inv ~ value + capital, data = Grunfeld, type = "ghm", effect = "twoways") plmtest(inv ~ value + capital, data = Grunfeld, type = "kw", effect = "twoways") Grunfeld_unbal <- Grunfeld[1:(nrow(Grunfeld)-1), ] # create an unbalanced panel data set g_unbal <- plm(inv ~ value + capital, data = Grunfeld_unbal, model = "pooling") plmtest(g_unbal) # unbalanced version of test is indicated in output } \references{ \insertRef{BALT:13}{plm} \insertRef{BALT:LI:90}{plm} \insertRef{BALT:CHAN:LI:92}{plm} \insertRef{BALT:CHAN:LI:98}{plm} \insertRef{BREU:PAGA:80}{plm} \insertRef{GOUR:HOLL:MONF:82}{plm} \insertRef{HOND:85}{plm} \insertRef{KING:WU:97}{plm} } \seealso{ \code{\link[=pFtest]{pFtest()}} for individual and/or time effects tests based on the within model. } \author{ Yves Croissant (initial implementation), Kevin Tappe (generalization to unbalanced panels) } \keyword{htest} plm/man/plm.Rd0000755000176200001440000002560513602224251012704 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_plm.R, R/est_plm.list.R, R/tool_methods.R \name{plm} \alias{plm} \alias{print.plm.list} \alias{terms.panelmodel} \alias{vcov.panelmodel} \alias{fitted.panelmodel} \alias{residuals.panelmodel} \alias{df.residual.panelmodel} \alias{coef.panelmodel} \alias{print.panelmodel} \alias{update.panelmodel} \alias{deviance.panelmodel} \alias{predict.plm} \alias{formula.plm} \alias{plot.plm} \alias{residuals.plm} \alias{fitted.plm} \title{Panel Data Estimators} \usage{ plm( formula, data, subset, weights, na.action, effect = c("individual", "time", "twoways", "nested"), model = c("within", "random", "ht", "between", "pooling", "fd"), random.method = NULL, random.models = NULL, random.dfcor = NULL, inst.method = c("bvk", "baltagi", "am", "bms"), restrict.matrix = NULL, restrict.rhs = NULL, index = NULL, ... ) \method{print}{plm.list}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) \method{terms}{panelmodel}(x, ...) \method{vcov}{panelmodel}(object, ...) \method{fitted}{panelmodel}(object, ...) \method{residuals}{panelmodel}(object, ...) \method{df.residual}{panelmodel}(object, ...) \method{coef}{panelmodel}(object, ...) \method{print}{panelmodel}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) \method{update}{panelmodel}(object, formula., ..., evaluate = TRUE) \method{deviance}{panelmodel}(object, model = NULL, ...) \method{predict}{plm}(object, newdata = NULL, ...) \method{formula}{plm}(x, ...) \method{plot}{plm}( x, dx = 0.2, N = NULL, seed = 1, within = TRUE, pooling = TRUE, between = FALSE, random = FALSE, ... ) \method{residuals}{plm}(object, model = NULL, effect = NULL, ...) \method{fitted}{plm}(object, model = NULL, effect = NULL, ...) } \arguments{ \item{formula}{a symbolic description for the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{\link[stats:lm]{stats::lm()}},} \item{weights}{see \code{\link[stats:lm]{stats::lm()}},} \item{na.action}{see \code{\link[stats:lm]{stats::lm()}}; currently, not fully supported,} \item{effect}{the effects introduced in the model, one of \code{"individual"}, \code{"time"}, \code{"twoways"}, or \code{"nested"},} \item{model}{one of \code{"pooling"}, \code{"within"}, \code{"between"}, \code{"random"} \code{"fd"}, or \code{"ht"},} \item{random.method}{method of estimation for the variance components in the random effects model, one of \code{"swar"} (default), \code{"amemiya"}, \code{"walhus"}, or \code{"nerlove"},} \item{random.models}{an alternative to the previous argument, the models used to compute the variance components estimations are indicated,} \item{random.dfcor}{a numeric vector of length 2 indicating which degree of freedom should be used,} \item{inst.method}{the instrumental variable transformation: one of \code{"bvk"}, \code{"baltagi"}, \code{"am"}, or \code{"bms"} (see also Details),} \item{restrict.matrix}{a matrix which defines linear restrictions on the coefficients,} \item{restrict.rhs}{the right hand side vector of the linear restrictions on the coefficients,} \item{index}{the indexes,} \item{\dots}{further arguments.} \item{x, object}{an object of class \code{"plm"},} \item{digits}{number of digits for printed output,} \item{width}{the maximum length of the lines in the printed output,} \item{formula.}{a new formula for the update method,} \item{evaluate}{a boolean for the update method, if \code{TRUE} the updated model is returned, if \code{FALSE} the call is returned,} \item{newdata}{the new data set for the \code{predict} method,} \item{dx}{the half--length of the individual lines for the plot method (relative to x range),} \item{N}{the number of individual to plot,} \item{seed}{the seed which will lead to individual selection,} \item{within}{if \code{TRUE}, the within model is plotted,} \item{pooling}{if \code{TRUE}, the pooling model is plotted,} \item{between}{if \code{TRUE}, the between model is plotted,} \item{random}{if \code{TRUE}, the random effect model is plotted,} } \value{ An object of class \code{"plm"}. A \code{"plm"} object has the following elements : \item{coefficients}{the vector of coefficients,} \item{vcov}{the variance--covariance matrix of the coefficients,} \item{residuals}{the vector of residuals (these are the residuals of the (quasi-)demeaned model),} \item{weights}{(only for weighted estimations) weights as specified,} \item{df.residual}{degrees of freedom of the residuals,} \item{formula}{an object of class \code{"pFormula"} describing the model,} \item{model}{the model frame as a \code{"pdata.frame"} containing the variables used for estimation: the response is in first column followed by the other variables, the individual and time indexes are in the 'index' attribute of \code{model},} \item{ercomp}{an object of class \code{"ercomp"} providing the estimation of the components of the errors (for random effects models only),} \item{aliased}{named logical vector indicating any aliased coefficients which are silently dropped by \code{plm} due to linearly dependent terms (see also \code{\link[=detect.lindep]{detect.lindep()}}),} \item{call}{the call.} It has \code{print}, \code{summary} and \code{print.summary} methods. The \code{summary} method creates an object of class \code{"summary.plm"} that extends the object it is run on with information about (inter alia) F statistic and (adjusted) R-squared of model, standard errors, t--values, and p--values of coefficients, (if supplied) the furnished vcov, see \code{\link[=summary.plm]{summary.plm()}} for further details. } \description{ Linear models for panel data estimated using the \code{lm} function on transformed data. } \details{ \code{plm} is a general function for the estimation of linear panel models. It supports the following estimation methods: pooled OLS (\code{model = "pooling"}), fixed effects (\code{"within"}), random effects (\code{"random"}), first--differences (\code{"fd"}), and between (\code{"between"}). It supports unbalanced panels and two--way effects (although not with all methods). For random effects models, four estimators of the transformation parameter are available by setting \code{random.method} to one of \code{"swar"} \insertCite{SWAM:AROR:72}{plm} (default), \code{"amemiya"} \insertCite{AMEM:71}{plm}, \code{"walhus"} \insertCite{WALL:HUSS:69}{plm}, or \code{"nerlove"} \insertCite{NERLO:71}{plm}. For first--difference models, the intercept is maintained (which from a specification viewpoint amounts to allowing for a trend in the levels model). The user can exclude it from the estimated specification the usual way by adding \code{"-1"} to the model formula. Instrumental variables estimation is obtained using two--part formulas, the second part indicating the instrumental variables used. This can be a complete list of instrumental variables or an update of the first part. If, for example, the model is \code{y ~ x1 + x2 + x3}, with \code{x1} and \code{x2} endogenous and \code{z1} and \code{z2} external instruments, the model can be estimated with: \itemize{ \item \code{formula = y~x1+x2+x3 | x3+z1+z2}, \item \code{formula = y~x1+x2+x3 | . -x1-x2+z1+z2}. } If an instrument variable estimation is requested, argument \code{inst.method} selects the instrument variable transformation method: \itemize{ \item \code{"bvk"} (default) for \insertCite{BALE:VARA:87;textual}{plm}, \item \code{"baltagi"} for \insertCite{BALT:81;textual}{plm}, \item \code{"am"} for \insertCite{AMEM:MACU:86;textual}{plm}, \item \code{"bms"} for \insertCite{BREU:MIZO:SCHM:89;textual}{plm}. } The Hausman--Taylor estimator \insertCite{HAUS:TAYL:81}{plm} is computed with arguments \code{random.method = "ht"}, \code{model = "random"}, \code{inst.method = "baltagi"} (the other way with only \code{model = "ht"} is deprecated). } \examples{ data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year")) summary(zz) # replicates some results from Baltagi (2013), table 3.1 data("Grunfeld", package = "plm") p <- plm(inv ~ value + capital, data = Grunfeld, model = "pooling") wi <- plm(inv ~ value + capital, data = Grunfeld, model = "within", effect = "twoways") swar <- plm(inv ~ value + capital, data = Grunfeld, model = "random", effect = "twoways") amemiya <- plm(inv ~ value + capital, data = Grunfeld, model = "random", random.method = "amemiya", effect = "twoways") walhus <- plm(inv ~ value + capital, data = Grunfeld, model = "random", random.method = "walhus", effect = "twoways") # summary and summary with a funished vcov (passed as matrix, # as function, and as function with additional argument) summary(wi) summary(wi, vcov = vcovHC(wi)) summary(wi, vcov = vcovHC) summary(wi, vcov = function(x) vcovHC(x, method = "white2")) # nested random effect model # replicate Baltagi/Song/Jung (2001), p. 378 (table 6), columns SA, WH # == Baltagi (2013), pp. 204-205 data("Produc", package = "plm") pProduc <- pdata.frame(Produc, index = c("state", "year", "region")) form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp summary(plm(form, data = pProduc, model = "random", effect = "nested")) summary(plm(form, data = pProduc, model = "random", effect = "nested", random.method = "walhus")) ## Hausman-Taylor estimator and Amemiya-MaCurdy estimator ## replicate Baltagi (2005, 2013), table 7.4 data("Wages", package = "plm") ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp ^ 2), data = Wages, index = 595, random.method = "ht", model = "random", inst.method = "baltagi") summary(ht) am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp ^ 2), data = Wages, index = 595, random.method = "ht", model = "random", inst.method = "am") summary(am) } \references{ \insertRef{AMEM:71}{plm} \insertRef{AMEM:MACU:86}{plm} \insertRef{BALE:VARA:87}{plm} \insertRef{BALT:81}{plm} \insertRef{BALT:SONG:JUNG:01}{plm} \insertRef{BALT:13}{plm} \insertRef{BREU:MIZO:SCHM:89}{plm} \insertRef{HAUS:TAYL:81}{plm} \insertRef{NERLO:71}{plm} \insertRef{SWAM:AROR:72}{plm} \insertRef{WALL:HUSS:69}{plm} } \seealso{ \code{\link[=summary.plm]{summary.plm()}} for further details about the associated summary method and the "summary.plm" object both of which provide some model tests and tests of coefficients. \code{\link[=fixef]{fixef()}} to compute the fixed effects for "within" models (=fixed effects models). } \author{ Yves Croissant } \keyword{regression} plm/man/Snmesp.Rd0000755000176200001440000000143013503144006013346 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Snmesp} \alias{Snmesp} \title{Employment and Wages in Spain} \format{A data frame containing: \describe{ \item{firm}{firm index} \item{year}{year} \item{n}{log of employment} \item{w}{log of wages} \item{y}{log of real output} \item{i}{log of intermediate inputs} \item{k}{log of real capital stock} \item{f}{real cash flow} }} \source{ Journal of Business Economics and Statistics data archive: \url{http://amstat.tandfonline.com/loi/ubes20/}. } \description{ A panel of 738 observations from 1983 to 1990 } \details{ \emph{total number of observations}: 5904 \emph{observation}: firms \emph{country}: Spain } \references{ \insertRef{ALON:AREL:99}{plm} } \keyword{datasets} plm/man/Parity.Rd0000644000176200001440000000165713503144006013361 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Parity} \alias{Parity} \title{Purchasing Power Parity and other parity relationships} \format{A data frame containing : \describe{ \item{country}{country codes: a factor with 17 levels} \item{time}{the quarter index, 1973Q1-1998Q4} \item{ls}{log spot exchange rate vs. USD} \item{lp}{log price level} \item{is}{short term interest rate} \item{il}{long term interest rate} \item{ld}{log price differential vs. USA} \item{uis}{U.S. short term interest rate} \item{uil}{U.S. long term interest rate} }} \source{ \insertRef{COAK:FUER:SMIT:06}{plm} } \description{ A panel of 104 quarterly observations from 1973Q1 to 1998Q4 } \details{ \emph{total number of observations} : 1768 \emph{observation} : country \emph{country} : OECD } \references{ \insertRef{COAK:FUER:SMIT:06}{plm} \insertRef{DRIS:KRAA:98}{plm} } \keyword{datasets} plm/man/make.pconsecutive.Rd0000644000176200001440000001513213602224251015526 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/make.pconsecutive_pbalanced.R \name{make.pconsecutive} \alias{make.pconsecutive} \alias{make.pconsecutive.data.frame} \alias{make.pconsecutive.pdata.frame} \alias{make.pconsecutive.pseries} \title{Make data consecutive (and, optionally, also balanced)} \usage{ make.pconsecutive(x, ...) \method{make.pconsecutive}{data.frame}(x, balanced = FALSE, index = NULL, ...) \method{make.pconsecutive}{pdata.frame}(x, balanced = FALSE, ...) \method{make.pconsecutive}{pseries}(x, balanced = FALSE, ...) } \arguments{ \item{x}{an object of class \code{pdata.frame}, \code{data.frame}, or \code{pseries},} \item{\dots}{further arguments.} \item{balanced}{logical, indicating whether the data should \emph{additionally} be made balanced (default: FALSE),} \item{index}{only relevant for \code{data.frame} interface; if \code{NULL}, the first two columns of the data.frame are assumed to be the index variables; if not \code{NULL}, both dimensions ('individual', 'time') need to be specified by \code{index} as character of length 2 for data frames, for further details see \code{\link[=pdata.frame]{pdata.frame()}},} } \value{ An object of the same class as the input \code{x}, i.e. a pdata.frame, data.frame or a pseries which is made time--consecutive based on the index variables. The returned data are sorted as a stacked time series. } \description{ This function makes the data consecutive for each individual (no "gaps" in time dimension per individual) and, optionally, also balanced } \details{ (p)data.frame and pseries objects are made consecutive, meaning their time periods are made consecutive per individual. For consecutiveness, the time dimension is interpreted to be numeric, and the data are extended to a regularly spaced sequence with distance 1 between the time periods for each individual (for each individual the time dimension become a sequence t, t+1, t+2, \ldots{} where t is an integer). Non--index variables are filled with \code{NA} for the inserted elements (rows for (p)data.frames, vector elements for pseries). With argument \code{balanced = TRUE}, additionally to be made consecutive, the data also can be made a balanced panel/pseries. Note: This means consecutive AND balanced; balancedness does not imply consecutiveness. In the result, each individual will have the same time periods in their time dimension by taking the min and max of the time index variable over all individuals (w/o \code{NA} values) and inserting the missing time periods. Looking at the number of rows of the resulting (pdata.frame) (elements for pseries), this results in nrow(make.pconsecutive, balanced = FALSE) <= nrow(make.pconsecutive, balanced = TRUE). For making the data only balanced, i.e. not demanding consecutiveness at the same time, use \code{\link[=make.pbalanced]{make.pbalanced()}} (see \strong{Examples} for a comparison)). Note: rows of (p)data.frames (elements for pseries) with \code{NA} values in individual or time index are not examined but silently dropped before the data are made consecutive. In this case, it is not clear which individual or time period is meant by the missing value(s). Especially, this means: If there are \code{NA} values in the first/last position of the original time periods for an individual, which usually depicts the beginning and ending of the time series for that individual, the beginning/end of the resulting time series is taken to be the min and max (w/o \code{NA} values) of the original time series for that individual, see also \strong{Examples}. Thus, one might want to check if there are any \code{NA} values in the index variables before applying make.pconsecutive, and especially check for \code{NA} values in the first and last position for each individual in original data and, if so, maybe set those to some meaningful begin/end value for the time series. } \examples{ # take data and make it non-consecutive # by deletion of 2nd row (2nd time period for first individual) data("Grunfeld", package = "plm") nrow(Grunfeld) # 200 rows Grunfeld_missing_period <- Grunfeld[-2, ] is.pconsecutive(Grunfeld_missing_period) # check for consecutiveness make.pconsecutive(Grunfeld_missing_period) # make it consecutiveness # argument balanced: # First, make data non-consecutive and unbalanced # by deletion of 2nd time period (year 1936) for all individuals # and more time periods for first individual only Grunfeld_unbalanced <- Grunfeld[Grunfeld$year != 1936, ] Grunfeld_unbalanced <- Grunfeld_unbalanced[-c(1,4), ] all(is.pconsecutive(Grunfeld_unbalanced)) # FALSE pdim(Grunfeld_unbalanced)$balanced # FALSE g_consec_bal <- make.pconsecutive(Grunfeld_unbalanced, balanced = TRUE) all(is.pconsecutive(g_consec_bal)) # TRUE pdim(g_consec_bal)$balanced # TRUE nrow(g_consec_bal) # 200 rows head(g_consec_bal) # 1st individual: years 1935, 1936, 1939 are NA g_consec <- make.pconsecutive(Grunfeld_unbalanced) # default: balanced = FALSE all(is.pconsecutive(g_consec)) # TRUE pdim(g_consec)$balanced # FALSE nrow(g_consec) # 198 rows head(g_consec) # 1st individual: years 1935, 1936 dropped, 1939 is NA # NA in 1st, 3rd time period (years 1935, 1937) for first individual Grunfeld_NA <- Grunfeld Grunfeld_NA[c(1, 3), "year"] <- NA g_NA <- make.pconsecutive(Grunfeld_NA) head(g_NA) # 1936 is begin for 1st individual, 1937: NA for non-index vars nrow(g_NA) # 199, year 1935 from original data is dropped # pdata.frame interface pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) make.pconsecutive(Grunfeld_missing_period) # pseries interface make.pconsecutive(pGrunfeld_missing_period$inv) # comparison to make.pbalanced (makes the data only balanced, not consecutive) g_bal <- make.pbalanced(Grunfeld_unbalanced) all(is.pconsecutive(g_bal)) # FALSE pdim(g_bal)$balanced # TRUE nrow(g_bal) # 190 rows } \seealso{ \code{\link[=is.pconsecutive]{is.pconsecutive()}} to check if data are consecutive; \code{\link[=make.pbalanced]{make.pbalanced()}} to make data only balanced (not consecutive).\cr \code{\link[=punbalancedness]{punbalancedness()}} for two measures of unbalancedness, \code{\link[=pdim]{pdim()}} to check the dimensions of a 'pdata.frame' (and other objects), \code{\link[=pvar]{pvar()}} to check for individual and time variation of a 'pdata.frame' (and other objects), \code{\link[=lag]{lag()}} for lagged (and leading) values of a 'pseries' object.\cr \code{\link[=pseries]{pseries()}}, \code{\link[=data.frame]{data.frame()}}, \code{\link[=pdata.frame]{pdata.frame()}}. } \author{ Kevin Tappe } \keyword{attribute} plm/man/is.pseries.Rd0000644000176200001440000000257313503144006014173 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_pdata.frame.R \name{is.pseries} \alias{is.pseries} \title{Check if an object is a pseries} \usage{ is.pseries(object) } \arguments{ \item{object}{object to be checked for pseries features} } \value{ A logical indicating whether the object is a pseries (\code{TRUE}) or not (\code{FALSE}). } \description{ This function checks if an object qualifies as a pseries } \details{ A \code{"pseries"} is a wrapper around a "basic class" (numeric, factor, logical, or character). To qualify as a pseries, an object needs to have the following features: \itemize{ \item class contains \code{"pseries"} and there are at least two classes (\code{"pseries"} and the basic class), \item have an appropriate index attribute (defines the panel structure), \item any of \code{is.numeric}, \code{is.factor}, \code{is.logical}, \code{is.character}, \code{is.complex} is \code{TRUE}. } } \examples{ # Create a pdata.frame and extract a series, which becomes a pseries data("EmplUK", package = "plm") Em <- pdata.frame(EmplUK) z <- Em$output class(z) # pseries as indicated by class is.pseries(z) # and confirmed by check # destroy index of pseries and re-check attr(z, "index") <- NA is.pseries(z) # now FALSE } \seealso{ \code{\link[=pseries]{pseries()}} for some computations on pseries and some further links. } \keyword{attribute} plm/man/detect.lindep.Rd0000644000176200001440000001576113602224251014635 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/detect_lin_dep_alias.R \name{detect.lindep} \alias{detect.lindep} \alias{detect.lindep.matrix} \alias{detect.lindep.data.frame} \alias{detect.lindep.plm} \alias{alias.plm} \alias{alias.pdata.frame} \title{Functions to detect linear dependence} \usage{ detect.lindep(object, ...) \method{detect.lindep}{matrix}(object, suppressPrint = FALSE, ...) \method{detect.lindep}{data.frame}(object, suppressPrint = FALSE, ...) \method{detect.lindep}{plm}(object, suppressPrint = FALSE, ...) \method{alias}{plm}(object, ...) \method{alias}{pdata.frame}( object, model = c("pooling", "within", "Between", "between", "mean", "random", "fd"), effect = c("individual", "time", "twoways"), ... ) } \arguments{ \item{object}{for \code{detect.lindep}: an object which should be checked for linear dependence (of class \code{"matrix"}, \code{"data.frame"}, or \code{"plm"}); for \code{alias}: either an estimated model of class \code{"plm"} or a \code{"pdata.frame"}. Usually, one wants to input a model matrix here or check an already estimated plm model,} \item{\dots}{further arguments.} \item{suppressPrint}{for \code{detect.lindep} only: logical indicating whether a message shall be printed; defaults to printing the message, i. e. to \code{suppressPrint = FALSE},} \item{model}{(see \code{plm}),} \item{effect}{(see \code{plm}),} } \value{ For \code{detect.lindep}: A named numeric vector containing column numbers of the linear dependent columns in the object after data transformation, if any are present. \code{NULL} if no linear dependent columns are detected. For \code{alias}: return value of \code{\link[stats:alias.lm]{stats::alias.lm()}} run on the (quasi-)demeaned model, i. e. the information outputted applies to the transformed model matrix, not the original data. } \description{ Little helper functions to aid users to detect linear dependent columns in a two-dimensional data structure, especially in a (transformed) model matrix - typically useful in interactive mode during model building phase. } \details{ Linear dependence of columns/variables is (usually) readily avoided when building one's model. However, linear dependence is sometimes not obvious and harder to detect for less experienced applied statisticians. The so called "dummy variable trap" is a common and probably the best--known fallacy of this kind (see e. g. Wooldridge (2016), sec. 7-2.). When building linear models with \code{lm} or \code{plm}'s \code{pooling} model, linear dependence in one's model is easily detected, at times post hoc. However, linear dependence might also occur after some transformations of the data, albeit it is not present in the untransformed data. The within transformation (also called fixed effect transformation) used in the \code{"within"} model can result in such linear dependence and this is harder to come to mind when building a model. See \strong{Examples} for two examples of linear dependent columns after the within transformation: ex. 1) the transformed variables have the opposite sign of one another; ex. 2) the transformed variables are identical. During \code{plm}'s model estimation, linear dependent columns and their corresponding coefficients in the resulting object are silently dropped, while the corresponding model frame and model matrix still contain the affected columns. The plm object contains an element \code{aliased} which indicates any such aliased coefficients by a named logical. Both functions, \code{detect.lindep} and \code{alias}, help to detect linear dependence and accomplish almost the same: \code{detect.lindep} is a stand alone implementation while \code{alias} is a wrapper around \code{\link[stats:alias.lm]{stats::alias.lm()}}, extending the \code{alias} generic to classes \code{"plm"} and \code{"pdata.frame"}. \code{alias} hinges on the availability of the package \CRANpkg{MASS} on the system. Not all arguments of \code{alias.lm} are supported. Output of \code{alias} is more informative as it gives the linear combination of dependent columns (after data transformations, i. e. after (quasi)-demeaning) while \code{detect.lindep} only gives columns involved in the linear dependence in a simple format (thus being more suited for automatic post--processing of the information). } \note{ function \code{detect.lindep} was called \code{detect_lin_dep} initially but renamed for naming consistency later with a back-compatible solution. } \examples{ ### Example 1 ### # prepare the data data("Cigar" , package = "plm") Cigar[ , "fact1"] <- c(0,1) Cigar[ , "fact2"] <- c(1,0) Cigar.p <- pdata.frame(Cigar) # setup a formula and a model frame form <- price ~ 0 + cpi + fact1 + fact2 mf <- model.frame(Cigar.p, form) # no linear dependence in the pooling model's model matrix # (with intercept in the formula, there would be linear depedence) detect.lindep(model.matrix(mf, model = "pooling")) # linear dependence present in the FE transformed model matrix modmat_FE <- model.matrix(mf, model = "within") detect.lindep(modmat_FE) mod_FE <- plm(form, data = Cigar.p, model = "within") detect.lindep(mod_FE) alias(mod_FE) # => fact1 == -1*fact2 plm(form, data = mf, model = "within")$aliased # "fact2" indicated as aliased # look at the data: after FE transformation fact1 == -1*fact2 head(modmat_FE) all.equal(modmat_FE[ , "fact1"], -1*modmat_FE[ , "fact2"]) ### Example 2 ### # Setup the data: # Assume CEOs stay with the firms of the Grunfeld data # for the firm's entire lifetime and assume some fictional # data about CEO tenure and age in year 1935 (first observation # in the data set) to be at 1 to 10 years and 38 to 55 years, respectively. # => CEO tenure and CEO age increase by same value (+1 year per year). data("Grunfeld", package = "plm") set.seed(42) # add fictional data Grunfeld$CEOtenure <- c(replicate(10, seq(from=s<-sample(1:10, 1), to=s+19, by=1))) Grunfeld$CEOage <- c(replicate(10, seq(from=s<-sample(38:65, 1), to=s+19, by=1))) # look at the data head(Grunfeld, 50) form <- inv ~ value + capital + CEOtenure + CEOage mf <- model.frame(pdata.frame(Grunfeld), form) # no linear dependent columns in original data/pooling model modmat_pool <- model.matrix(mf, model="pooling") detect.lindep(modmat_pool) mod_pool <- plm(form, data = Grunfeld, model = "pooling") alias(mod_pool) # CEOtenure and CEOage are linear dependent after FE transformation # (demeaning per individual) modmat_FE <- model.matrix(mf, model="within") detect.lindep(modmat_FE) mod_FE <- plm(form, data = Grunfeld, model = "within") detect.lindep(mod_FE) alias(mod_FE) # look at the transformed data: after FE transformation CEOtenure == 1*CEOage head(modmat_FE, 50) all.equal(modmat_FE[ , "CEOtenure"], modmat_FE[ , "CEOage"]) } \references{ \insertRef{WOOL:13}{plm} } \seealso{ \code{\link[stats:alias]{stats::alias()}}, \code{\link[stats:model.matrix]{stats::model.matrix()}} and especially \code{plm}'s \code{\link[=model.matrix]{model.matrix()}} for (transformed) model matrices, plm's \code{\link[=model.frame]{model.frame()}}. } \author{ Kevin Tappe } \keyword{array} \keyword{manip} plm/man/pgrangertest.Rd0000644000176200001440000000635613622607010014620 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_granger.R \name{pgrangertest} \alias{pgrangertest} \title{Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012))} \usage{ pgrangertest( formula, data, test = c("Ztilde", "Zbar", "Wbar"), order = 1L, index = NULL ) } \arguments{ \item{formula}{a \code{formula} object to describe the direction of the hypothesized Granger causation,} \item{data}{a \code{pdata.frame} or a \code{data.frame},} \item{test}{a character to request the statistic to be returned, either \code{"Ztilde"} (default), or \code{"Zbar"},} \item{order}{integer(s) giving the number of lags to include in the test's auxiliary regressions, the length of order must be either 1 (same lag order for all individuals) or equal to the number of individuals (to specify a lag order per individual),} \item{index}{only relevant if \code{data} is \code{data.frame} and not a \code{pdata.frame}; if \code{NULL}, the first two columns of the data.frame are assumed to be the index variables, for further details see \code{\link[=pdata.frame]{pdata.frame()}}.} } \value{ An object of class \code{c("pgrangertest", "htest")}. Besides the usual elements of a \code{htest} object, it contains the data frame \code{indgranger} which carries the Granger test statistics per individual along the associated p-values, degrees of freedom and the specified lag order. } \description{ Test for Granger (non-)causality in panel data. } \details{ The panel Granger (non-)causality test is a combination of Granger tests \insertCite{GRAN:69}{plm} performed per individual. The test is developed by \insertCite{DUMI:HURL:12;textual}{plm}, a shorter exposition is given in \insertCite{LOPE:WEBE:17;textual}{plm}. The formula \code{formula} describes the direction of the (panel) Granger causation where \code{y ~ x} means "x (panel) Granger causes y". By setting argument \code{test} to either \code{"Ztilde"} (default) or \code{"Zbar"}, two different statistics can be requested. \code{"Ztilde"} gives the standardised statistic recommended by Dumitrescu/Hurlin (2012) for fixed T samples. If set to \code{"Wbar"}, the intermediate Wbar statistic (average of individual Granger chi-square statistics) is given which is used to derive the other two. The Zbar statistic is not suitable for unbalanced panels. For the Wbar statistic, no p-value is available. The implementation uses \code{\link[lmtest:grangertest]{lmtest::grangertest()}} from package \CRANpkg{lmtest} to perform the individual Granger tests. } \examples{ ## not meaningful, just to demonstrate usage ## H0: 'value' does not Granger cause 'inv' for all invididuals data("Grunfeld", package = "plm") pgrangertest(inv ~ value, data = Grunfeld) pgrangertest(inv ~ value, data = Grunfeld, order = 2L) pgrangertest(inv ~ value, data = Grunfeld, order = 2L, test = "Zbar") # varying lag order (last individual lag order 3, others lag order 2) pgrangertest(inv ~ value, data = Grunfeld, order = c(rep(2L, 9), 3L)) } \references{ \insertRef{DUMI:HURL:12}{plm} \insertRef{GRAN:69}{plm} \insertRef{LOPE:WEBE:17}{plm} } \seealso{ \code{\link[lmtest:grangertest]{lmtest::grangertest()}} for the original (non-panel) Granger causality test in \CRANpkg{lmtest}. } \author{ Kevin Tappe } \keyword{htest} plm/man/pvar.Rd0000644000176200001440000000471313602224251013056 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_misc.R \name{pvar} \alias{pvar} \alias{pvar.matrix} \alias{pvar.data.frame} \alias{pvar.pdata.frame} \alias{pvar.pseries} \alias{print.pvar} \title{Check for Cross-Sectional and Time Variation} \usage{ pvar(x, ...) \method{pvar}{matrix}(x, index = NULL, ...) \method{pvar}{data.frame}(x, index = NULL, ...) \method{pvar}{pdata.frame}(x, ...) \method{pvar}{pseries}(x, ...) \method{print}{pvar}(x, ...) } \arguments{ \item{x}{a \verb{(p)data.frame} or a \code{matrix},} \item{\dots}{further arguments.} \item{index}{see \code{\link[=pdata.frame]{pdata.frame()}},} } \value{ An object of class \code{pvar} containing the following elements: \item{id.variation}{a logical vector with \code{TRUE} values if the variable has individual variation, \code{FALSE} if not,} \item{time.variation}{a logical vector with \code{TRUE} values if the variable has time variation, \code{FALSE} if not,} \item{id.variation_anyNA}{a logical vector with \code{TRUE} values if the variable has at least one individual-time combination with all NA values in the individual dimension for at least one time period, \code{FALSE} if not,} \item{time.variation_anyNA}{a logical vector with \code{TRUE} values if the variable has at least one individual-time combination with all NA values in the time dimension for at least one individual, \code{FALSE} if not.} } \description{ This function checks for each variable of a panel if it varies cross-sectionally and over time. } \details{ For (p)data.frame and matrix interface: All-NA columns are removed prior to calculation of variation due to coercing to pdata.frame first. } \note{ \code{pvar} can be time consuming for ``big'' panels. } \examples{ # Gasoline contains two variables which are individual and time # indexes and are the first two variables data("Gasoline", package = "plm") pvar(Gasoline) # Hedonic is an unbalanced panel, townid is the individual index; # the drop.index argument is passed to pdata.frame data("Hedonic", package = "plm") pvar(Hedonic, "townid", drop.index = TRUE) # same using pdata.frame Hed <- pdata.frame(Hedonic, "townid", drop.index = TRUE) pvar(Hed) # Gasoline with pvar's matrix interface Gasoline_mat <- as.matrix(Gasoline) pvar(Gasoline_mat) pvar(Gasoline_mat, index=c("country", "year")) } \seealso{ \code{\link[=pdim]{pdim()}} to check the dimensions of a 'pdata.frame' (and other objects), } \author{ Yves Croissant } \keyword{attribute} plm/man/fixef.plm.Rd0000755000176200001440000001133713602224251014001 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_ranfixef.R \name{fixef.plm} \alias{fixef.plm} \alias{print.fixef} \alias{summary.fixef} \alias{print.summary.fixef} \title{Extract the Fixed Effects} \usage{ \method{fixef}{plm}( object, effect = NULL, type = c("level", "dfirst", "dmean"), vcov = NULL, ... ) \method{print}{fixef}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) \method{summary}{fixef}(object, ...) \method{print}{summary.fixef}( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) } \arguments{ \item{effect}{one of \code{"individual"} or \code{"time"}, only relevant in case of two--ways effects models,} \item{type}{one of \code{"level"}, \code{"dfirst"}, or \code{"dmean"},} \item{vcov}{a variance--covariance matrix furnished by the user or a function to calculate one (see \strong{Examples}),} \item{\dots}{further arguments.} \item{x, object}{an object of class \code{"plm"}, an object of class \code{"fixef"} for the \code{print} and the \code{summary} method,} \item{digits}{digits,} \item{width}{the maximum length of the lines in the print output,} } \value{ For function \code{fixef} an object of class \code{c("fixef", "numeric")} is returned:\cr It is a numeric vector containing the fixed effects with attribute \code{se} which contains the standard errors. There are two further attributes: attribute \code{type} contains the chosen type (the value of argument \code{type} as a character); attribute \code{df.residual} holds the residual degrees of freedom (integer) from the fixed effects model (plm object) on which \code{fixef} was run. For function \code{summary.fixef} an object of class \code{c("summary.fixef", "matrix")} is returned:\cr It is a matrix with four columns in this order: the estimated fixed effects, their standard errors and associated t--values and p--values. The type of the fixed effects and the standard errors in the summary.fixef objects correspond to was requested in the \code{fixef} function by arguments \code{type} and \code{vcov}, respectively. } \description{ Function to extract the fixed effects from a \code{plm} object and associated summary method. } \details{ Function \code{fixef} calculates the fixed effects and returns an object of class \code{c("fixef", "numeric")}. By setting the \code{type} argument, the fixed effects may be returned in levels (\code{"level"}), as deviations from the first value of the index (\code{"dfirst"}), or as deviations from the overall mean (\code{"dmean"}). If the argument \code{vcov} was specified, the standard errors (stored as attribute "se" in the return value) are the respective robust standard errors. The associated \code{summary} method returns an extended object of class \code{c("summary.fixef", "matrix")} with more information (see sections \strong{Value} and \strong{Examples}). References with formulae (except for the two-ways unbalanced case) are, e.g., \insertCite{GREE:12;textual}{plm}, Ch. 11.4.4, p. 364, formulae (11-25); \insertCite{WOOL:10;textual}{plm}, Ch. 10.5.3, pp. 308-309, formula (10.58). } \examples{ data("Grunfeld", package = "plm") gi <- plm(inv ~ value + capital, data = Grunfeld, model = "within") fixef(gi) summary(fixef(gi)) summary(fixef(gi))[ , c("Estimate", "Pr(>|t|)")] # only estimates and p-values # relationship of type = "dmean" and "level" and overall intercept fx_level <- fixef(gi, type = "level") fx_dmean <- fixef(gi, type = "dmean") overallint <- within_intercept(gi) all.equal(overallint + fx_dmean, fx_level, check.attributes = FALSE) # TRUE # extract time effects in a twoways effects model gi_tw <- plm(inv ~ value + capital, data = Grunfeld, model = "within", effect = "twoways") fixef(gi_tw, effect = "time") # with supplied variance-covariance matrix as matrix, function, # and function with additional arguments fx_level_robust1 <- fixef(gi, vcov = vcovHC(gi)) fx_level_robust2 <- fixef(gi, vcov = vcovHC) fx_level_robust3 <- fixef(gi, vcov = function(x) vcovHC(x, method = "white2")) summary(fx_level_robust1) # gives fixed effects, robust SEs, t- and p-values # calc. fitted values of oneway within model: fixefs <- fixef(gi)[index(gi, which = "id")] fitted_by_hand <- fixefs + gi$coefficients["value"] * gi$model$value + gi$coefficients["capital"] * gi$model$capital } \references{ \insertAllCited{} } \seealso{ \code{\link[=within_intercept]{within_intercept()}} for the overall intercept of fixed effect models along its standard error, \code{\link[=plm]{plm()}} for plm objects and within models (= fixed effects models) in general. See \code{\link[=ranef]{ranef()}} to extract the random effects from a random effects model. } \author{ Yves Croissant } \keyword{regression} plm/man/Grunfeld.Rd0000755000176200001440000000423113602642556013666 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Grunfeld} \alias{Grunfeld} \title{Grunfeld's Investment Data} \format{A data frame containing : \describe{ \item{firm}{observation} \item{year}{date} \item{inv}{gross Investment} \item{value}{value of the firm} \item{capital}{stock of plant and equipment} }} \source{ Online complements to Baltagi (2001): \url{http://www.wiley.com/legacy/wileychi/baltagi/} \url{http://www.wiley.com/legacy/wileychi/baltagi/supp/Grunfeld.fil} Online complements to Baltagi (2013): \url{http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452} } \description{ A balanced panel of 10 observational units (firms) from 1935 to 1954 } \details{ \emph{total number of observations} : 200 \emph{observation} : production units \emph{country} : United States } \note{ The Grunfeld data as provided in package \code{plm} is the same data as used in Baltagi (2001), see \strong{Examples} below. NB:\cr Various versions of the Grunfeld data circulate online. Also, various text books (and also varying among editions) and papers use different subsets of the original Grunfeld data, some of which contain errors in a few data points compared to the original data used by Grunfeld (1958) in his PhD thesis. See Kleiber/Zeileis (2010) and its accompanying website for a comparison of various Grunfeld data sets in use. } \examples{ \dontrun{ # Compare plm's Grunfeld data to Baltagi's (2001) Grunfeld data: data("Grunfeld", package="plm") Grunfeld_baltagi2001 <- read.csv("http://www.wiley.com/legacy/wileychi/ baltagi/supp/Grunfeld.fil", sep="", header = FALSE) library(compare) compare::compare(Grunfeld, Grunfeld_baltagi2001, allowAll = T) # same data set } } \references{ \insertRef{BALT:01}{plm} \insertRef{BALT:13}{plm} \insertRef{GRUN:58}{plm} \insertRef{KLEI:ZEIL:10}{plm} website accompanying the paper with various variants of the Grunfeld data: \url{https://eeecon.uibk.ac.at/~zeileis/grunfeld/}. } \seealso{ For the complete Grunfeld data (11 firms), see \link[AER:Grunfeld]{AER::Grunfeld}, in the \code{AER} package. } \keyword{datasets} plm/man/pwtest.Rd0000755000176200001440000000472513503144006013441 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pwtest} \alias{pwtest} \alias{pwtest.formula} \alias{pwtest.panelmodel} \title{Wooldridge's Test for Unobserved Effects in Panel Models} \usage{ pwtest(x, ...) \method{pwtest}{formula}(x, data, effect = c("individual", "time"), ...) \method{pwtest}{panelmodel}(x, effect = c("individual", "time"), ...) } \arguments{ \item{x}{an object of class \code{"formula"}, or an estimated model of class \code{panelmodel},} \item{\dots}{further arguments passed to \code{plm}.} \item{data}{a \code{data.frame},} \item{effect}{the effect to be tested for, one of \code{"individual"} (default) or \code{"time"},} } \value{ An object of class \code{"htest"}. } \description{ Semi-parametric test for the presence of (individual or time) unobserved effects in panel models. } \details{ This semi-parametric test checks the null hypothesis of zero correlation between errors of the same group. Therefore, it has power both against individual effects and, more generally, any kind of serial correlation. The test relies on large-N asymptotics. It is valid under error heteroskedasticity and departures from normality. The above is valid if \code{effect="individual"}, which is the most likely usage. If \code{effect="time"}, symmetrically, the test relies on large-T asymptotics and has power against time effects and, more generally, against cross-sectional correlation. If the panelmodel interface is used, the inputted model must be a pooling model. } \examples{ data("Produc", package = "plm") ## formula interface pwtest(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc) pwtest(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, effect = "time") ## panelmodel interface # first, estimate a pooling model, than compute test statistics form <- formula(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp) pool_prodc <- plm(form, data = Produc, model = "pooling") pwtest(pool_prodc) # == effect="individual" pwtest(pool_prodc, effect="time") } \references{ \insertRef{WOOL:02}{plm} \insertRef{WOOL:10}{plm} } \seealso{ \code{\link[=pbltest]{pbltest()}}, \code{\link[=pbgtest]{pbgtest()}}, \code{\link[=pdwtest]{pdwtest()}}, \code{\link[=pbsytest]{pbsytest()}}, \code{\link[=pwartest]{pwartest()}}, \code{\link[=pwfdtest]{pwfdtest()}} for tests for serial correlation in panel models. \code{\link[=plmtest]{plmtest()}} for tests for random effects. } \author{ Giovanni Millo } \keyword{htest} plm/man/pseries.Rd0000644000176200001440000001124213603535654013570 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_transformations.R \name{pseries} \alias{pseries} \alias{print.pseries} \alias{as.matrix.pseries} \alias{plot.pseries} \alias{summary.pseries} \alias{plot.summary.pseries} \alias{print.summary.pseries} \alias{Between} \alias{Between.default} \alias{Between.pseries} \alias{Between.matrix} \alias{between} \alias{between.default} \alias{between.pseries} \alias{between.matrix} \alias{Within} \alias{Within.default} \alias{Within.pseries} \alias{Within.matrix} \title{panel series} \usage{ \method{print}{pseries}(x, ...) \method{as.matrix}{pseries}(x, idbyrow = TRUE, ...) \method{plot}{pseries}( x, plot = c("lattice", "superposed"), scale = FALSE, transparency = TRUE, col = "blue", lwd = 1, ... ) \method{summary}{pseries}(object, ...) \method{plot}{summary.pseries}(x, ...) \method{print}{summary.pseries}(x, ...) Between(x, ...) \method{Between}{default}(x, effect, ...) \method{Between}{pseries}(x, effect = c("individual", "time", "group"), ...) \method{Between}{matrix}(x, effect, ...) between(x, ...) \method{between}{default}(x, effect, ...) \method{between}{pseries}(x, effect = c("individual", "time", "group"), ...) \method{between}{matrix}(x, effect, ...) Within(x, ...) \method{Within}{default}(x, effect, ...) \method{Within}{pseries}(x, effect = c("individual", "time", "group", "twoways"), ...) \method{Within}{matrix}(x, effect, rm.null = TRUE, ...) } \arguments{ \item{x, object}{a \code{pseries} or a \code{summary.pseries} object,} \item{\dots}{further arguments, e. g. \code{na.rm = TRUE} for transformation functions like \code{beetween}, see \strong{Details} and \strong{Examples}.} \item{idbyrow}{if \code{TRUE} in the \code{as.matrix} method, the lines of the matrix are the individuals,} \item{plot, scale, transparency, col, lwd}{plot arguments,} \item{effect}{character string indicating the \code{"individual"} or \code{"time"} effect,} \item{rm.null}{if \code{TRUE}, for the \code{Within.matrix} method, remove the empty columns,} } \value{ All these functions return an object of class \code{pseries}, except:\cr \code{between}, which returns a numeric vector, \code{as.matrix}, which returns a matrix. } \description{ A class for panel series for which several useful computations and data transformations are available. } \details{ The functions \code{between}, \code{Between}, and \code{Within} perform specific data transformations, i. e. the between and within transformation. \code{between} returns a vector containing the individual means (over time) with the length of the vector equal to the number of individuals (if \code{effect = "individual"} (default); if \code{effect = "time"}, it returns the time means (over individuals)). \code{Between} duplicates the values and returns a vector which length is the number of total observations. \code{Within} returns a vector containing the values in deviation from the individual means (if \code{effect = "individual"}, from time means if \code{effect = "time"}), the so called demeaned data. For \code{between}, \code{Between}, and \code{Within} in presence of NA values it can be useful to supply \code{na.rm = TRUE} as an additional argument to keep as many observations as possible in the resulting transformation, see also \strong{Examples}. } \examples{ # First, create a pdata.frame data("EmplUK", package = "plm") Em <- pdata.frame(EmplUK) # Then extract a series, which becomes additionally a pseries z <- Em$output class(z) # obtain the matrix representation as.matrix(z) # compute the between and within transformations between(z) Within(z) # Between replicates the values for each time observation Between(z) # between, Between, and Within transformations on other dimension between(z, effect = "time") Between(z, effect = "time") Within(z, effect = "time") # NA treatment for between, Between, and Within z2 <- z z2[length(z2)] <- NA # set last value to NA between(z2, na.rm = TRUE) # non-NA value for last individual Between(z2, na.rm = TRUE) # only the NA observation is lost Within(z2, na.rm = TRUE) # only the NA observation is lost sum(is.na(Between(z2))) # 9 observations lost due to one NA value sum(is.na(Between(z2, na.rm = TRUE))) # only the NA observation is lost sum(is.na(Within(z2))) # 9 observations lost due to one NA value sum(is.na(Within(z2, na.rm = TRUE))) # only the NA observation is lost } \seealso{ \code{\link[=is.pseries]{is.pseries()}} to check if an object is a pseries. For more functions on class 'pseries' see \code{\link[=lag]{lag()}}, \code{\link[=lead]{lead()}}, \code{\link[=diff]{diff()}} for lagging values, leading values (negative lags) and differencing. } \author{ Yves Croissant } \keyword{classes} plm/man/pcdtest.Rd0000755000176200001440000001446613602224251013565 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_cd.R \name{pcdtest} \alias{pcdtest} \alias{pcdtest.formula} \alias{pcdtest.panelmodel} \alias{pcdtest.pseries} \title{Tests of cross-section dependence for panel models} \usage{ pcdtest(x, ...) \method{pcdtest}{formula}( x, data, index = NULL, model = NULL, test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"), w = NULL, ... ) \method{pcdtest}{panelmodel}( x, test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"), w = NULL, ... ) \method{pcdtest}{pseries}( x, test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"), w = NULL, ... ) } \arguments{ \item{x}{an object of class \code{formula}, \code{panelmodel}, or \code{pseries} (depending on the respective interface) describing the model to be tested,} \item{\dots}{further arguments to be passed on to \code{plm}, such as \code{effect} or \code{random.method}.} \item{data}{a \code{data.frame},} \item{index}{an optional numerical index, if \code{NULL}, the first two columns of the data.frame provided in argument \code{data} are assumed to be the index variables; for further details see \code{\link[=pdata.frame]{pdata.frame()}},} \item{model}{an optional character string indicating which type of model to estimate; if left to \code{NULL}, the original heterogeneous specification of Pesaran is used,} \item{test}{the type of test statistic to be returned. One of \itemize{ \item \code{"cd"} for Pesaran's CD statistic, \item \code{"lm"} for Breusch and Pagan's original LM statistic, \item \code{"sclm"} for the scaled version of Breusch and Pagan's LM statistic, \item \code{"bcsclm"} for the bias-corrected scaled version of Breusch and Pagan's LM statistic, \item \code{"rho"} for the average correlation coefficient, \item \code{"absrho"} for the average absolute correlation coefficient,}} \item{w}{either \code{NULL} (default) for the global tests or -- for the local versions of the statistics -- a \verb{n x n} \code{matrix} describing proximity between individuals, with \eqn{w_ij = a} where \eqn{a} is any number such that \code{as.logical(a)==TRUE}, if \eqn{i,j} are neighbours, \eqn{0} or any number \eqn{b} such that \code{as.logical(b)==FALSE} elsewhere. Only the lower triangular part (without diagonal) of \code{w} after coercing by \code{as.logical()} is evaluated for neighbouring information (but \code{w} can be symmetric). See also \strong{Details} and \strong{Examples},} } \value{ An object of class \code{"htest"}. } \description{ Pesaran's CD or Breusch--Pagan's LM (local or global) tests for cross sectional dependence in panel models } \details{ These tests are originally meant to use the residuals of separate estimation of one time--series regression for each cross-sectional unit in order to check for cross--sectional dependence. If a different model specification (\code{within}, \code{random}, \ldots{}) is assumed consistent, one can resort to its residuals for testing (which is common, e.g., when the time dimension's length is insufficient for estimating the heterogeneous model). If the time dimension is insufficient and \code{model=NULL}, the function defaults to estimation of a \code{within} model and issues a warning. The main argument of this function may be either a model of class \code{panelmodel} or a \code{formula} and \code{dataframe}; in the second case, unless \code{model} is set to \code{NULL}, all usual parameters relative to the estimation of a \code{plm} model may be passed on. The test is compatible with any consistent \code{panelmodel} for the data at hand, with any specification of \code{effect}. E.g., specifying \code{effect="time"} or \code{effect="twoways"} allows to test for residual cross-sectional dependence after the introduction of time fixed effects to account for common shocks. A \strong{local} version of either test can be computed by supplying a proximity matrix (elements coercible to \code{logical}) with argument \code{w} which provides information on whether any pair of individuals are neighbours or not. If \code{w} is supplied, only neighbouring pairs will be used in computing the test; else, \code{w} will default to \code{NULL} and all observations will be used. The matrix need not be binary, so commonly used "row--standardized" matrices can be employed as well. \code{nb} objects from \CRANpkg{spdep} must instead be transformed into matrices by \CRANpkg{spdep}'s function \code{nb2mat} before using. The methods implemented are suitable also for unbalanced panels. Pesaran's CD test (\code{test="cd"}), Breusch and Pagan's LM test (\code{test="lm"}), and its scaled version (\code{test="sclm"}) are all described in \insertCite{PESA:04;textual}{plm} (and complemented by Pesaran (2005)). The bias-corrected scaled test (\code{test="bcsclm"}) is due to \insertCite{BALT:FENG:KAO:12}{plm} and only valid for within models including the individual effect (it's unbalanced version uses max(Tij) for T) in the bias-correction term). \insertCite{BREU:PAGA:80;textual}{plm} is the original source for the LM test. The test on a \code{pseries} is the same as a test on a pooled regression model of that variable on a constant, i.e. \code{pcdtest(some_pseries)} is equivalent to \verb{pcdtest(plm(some_var ~ 1, data = some_pdata.frame, model = "pooling")} and also equivalent to \code{pcdtest(some_var ~ 1, data = some_data)}, where \code{some_var} is the variable name in the data which corresponds to \code{some_pseries}. } \examples{ data("Grunfeld", package = "plm") ## test on heterogeneous model (separate time series regressions) pcdtest(inv ~ value + capital, data = Grunfeld, index = c("firm", "year")) ## test on two-way fixed effects homogeneous model pcdtest(inv ~ value + capital, data = Grunfeld, model = "within", effect = "twoways", index = c("firm", "year")) ## test on panelmodel object g <- plm(inv ~ value + capital, data = Grunfeld, index = c("firm", "year")) pcdtest(g) ## scaled LM test pcdtest(g, test = "sclm") ## test on pseries pGrunfeld <- pdata.frame(Grunfeld) pcdtest(pGrunfeld$value) ## local test ## define neighbours for individual 2: 1, 3, 4, 5 in lower triangular matrix w <- matrix(0, ncol= 10, nrow=10) w[2,1] <- w[3,2] <- w[4,2] <- w[5,2] <- 1 pcdtest(g, w = w) } \references{ \insertRef{BALT:FENG:KAO:12}{plm} \insertRef{BREU:PAGA:80}{plm} \insertRef{PESA:04}{plm} \insertRef{PESA:15}{plm} } \keyword{htest} plm/man/Wages.Rd0000755000176200001440000000311413503144006013150 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{Wages} \alias{Wages} \title{Panel Data of Individual Wages} \format{A data frame containing: \describe{ \item{exp}{years of full-time work experience.} \item{wks}{weeks worked.} \item{bluecol}{blue collar?} \item{ind}{works in a manufacturing industry?} \item{south}{resides in the south?} \item{smsa}{resides in a standard metropolitan statistical area?} \item{married}{married?} \item{sex}{a factor with levels \code{"male"} and \code{"female"}} \item{union}{individual's wage set by a union contract?} \item{ed}{years of education.} \item{black}{is the individual black?} \item{lwage}{logarithm of wage.} }} \source{ Online complements to Baltagi (2001): \url{http://www.wiley.com/legacy/wileychi/baltagi/} Online complements to Baltagi (2013): \url{http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452} } \description{ A panel of 595 individuals from 1976 to 1982, taken from the Panel Study of Income Dynamics (PSID).\cr\cr The data are organized as a stacked time series/balanced panel, see \strong{Examples} on how to convert to a \code{pdata.frame}. } \details{ \emph{total number of observations} : 4165 \emph{observation} : individuals \emph{country} : United States } \examples{ # data set 'Wages' is organized as a stacked time series/balanced panel data("Wages", package = "plm") Wag <- pdata.frame(Wages, index=595) } \references{ \insertRef{BALT:01}{plm} \insertRef{BALT:13}{plm} \insertRef{CORN:RUPE:88}{plm} } \keyword{datasets} plm/man/cipstest.Rd0000644000176200001440000000321213603465745013755 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_cips.R \name{cipstest} \alias{cipstest} \title{Cross-sectionally Augmented IPS Test for Unit Roots in Panel Models} \usage{ cipstest( x, lags = 2, type = c("trend", "drift", "none"), model = c("cmg", "mg", "dmg"), truncated = FALSE, ... ) } \arguments{ \item{x}{an object of class \code{"pseries"},} \item{lags}{lag order for Dickey-Fuller augmentation,} \item{type}{one of \code{"trend"}, \code{"drift"}, \code{"none"},} \item{model}{one of \code{"cmg"}, \code{"mg"}, \code{"dmg"},} \item{truncated}{logical specifying whether to calculate the truncated version of the test,} \item{\dots}{further arguments passed to \code{critvals.cips} (non-exported function).} } \value{ An object of class \code{"htest"}. } \description{ Cross-sectionally augmented Im, Pesaran and Shin (IPS) test for unit roots in panel models. } \details{ This cross-sectionally augmented version of the IPS unit root test (H0: the \code{pseries} has a unit root) is a so-called second-generation panel unit root test: it is in fact robust against cross-sectional dependence, provided that the default \code{type="cmg"} is calculated. Else one can obtain the standard (\code{model="mg"}) or cross-sectionally demeaned (\code{model="dmg"}) versions of the IPS test. } \examples{ data("Produc", package = "plm") Produc <- pdata.frame(Produc, index=c("state", "year")) ## check whether the gross state product (gsp) is trend-stationary cipstest(Produc$gsp, type = "trend") } \references{ \insertRef{pes07}{plm} } \seealso{ \code{\link[=purtest]{purtest()}} } \author{ Giovanni Millo } \keyword{htest} plm/man/pbsytest.Rd0000755000176200001440000001324513602224251013766 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pbsytest} \alias{pbsytest} \alias{pbsytest.formula} \alias{pbsytest.panelmodel} \title{Bera, Sosa-Escudero and Yoon Locally--Robust Lagrange Multiplier Tests for Panel Models and Joint Test by Baltagi and Li} \usage{ pbsytest(x, ...) \method{pbsytest}{formula}( x, data, ..., test = c("ar", "re", "j"), re.normal = if (test == "re") TRUE else NULL ) \method{pbsytest}{panelmodel}( x, test = c("ar", "re", "j"), re.normal = if (test == "re") TRUE else NULL, ... ) } \arguments{ \item{x}{an object of class \code{formula} or of class \code{panelmodel},} \item{\dots}{further arguments.} \item{data}{a \code{data.frame},} \item{test}{a character string indicating which test to perform: first--order serial correlation (\code{"ar"}), random effects (\code{"re"}) or joint test for either of them (\code{"j"}),} \item{re.normal}{logical, only relevant for \code{test = "re"}: \code{TRUE} (default) computes the one-sided \code{"re"} test, \code{FALSE} the two-sided test (see also Details); not relevant for other values of \code{test} and, thus, should be \code{NULL},} } \value{ An object of class \code{"htest"}. } \description{ Test for residual serial correlation (or individual random effects) locally robust vs. individual random effects (serial correlation) for panel models and joint test of serial correlation and the random effect specification by Baltagi and Li. } \details{ These Lagrange multiplier tests are robust vs. local misspecification of the alternative hypothesis, i.e. they test the null of serially uncorrelated residuals against AR(1) residuals in a pooling model, allowing for local departures from the assumption of no random effects; or they test the null of no random effects allowing for local departures from the assumption of no serial correlation in residuals. They use only the residuals of the pooled OLS model and correct for local misspecification as outlined in \insertCite{BERA:SOSA:YOON:01;textual}{plm}. For \code{test = "re"}, the default (\code{re.normal = TRUE}) is to compute a one-sided test which is expected to lead to a more powerful test (asymptotically N(0,1) distributed). Setting \code{re.normal = FALSE} gives the two-sided test (asymptotically chi-squared(2) distributed). Argument \code{re.normal} is irrelevant for all other values of \code{test}. The joint test of serial correlation and the random effect specification (\code{test = "j"}) is due to \insertCite{BALT:LI:91;textual}{plm} (also mentioned in \insertCite{BALT:LI:95;textual}{plm}, pp. 135--136) and is added for convenience under this same function. The unbalanced version of all tests are derived in \insertCite{SOSA:BERA:08;textual}{plm}. The functions implemented are suitable for balanced as well as unbalanced panel data sets. A concise treatment of the statistics for only balanced panels is given in \insertCite{BALT:13;textual}{plm}, p. 108. Here is an overview of how the various values of the \code{test} argument relate to the literature: \itemize{ \item \code{test = "ar"}: \itemize{ \item \eqn{RS*_{\rho}} in Bera et al. (2001), p. 9 (balanced) \item \eqn{LM*_{\rho}} in Baltagi (2013), p. 108 (balanced) \item \eqn{RS*_{\lambda}} in Sosa-Escudero/Bera (2008), p. 73 (unbalanced) } \item \verb{test = "re", re.normal = TRUE} (default) (one-sided test, asymptotically N(0,1) distributed): \itemize{ \item \eqn{RSO*_{\mu}} in Bera et al. (2001), p. 11 (balanced) \item \eqn{RSO*_{\mu}} in Sosa-Escudero/Bera (2008), p. 75 (unbalanced) } \item \verb{test = "re", re.normal = FALSE} (two-sided test, asymptotically chi-squared(2) distributed): \itemize{ \item \eqn{RS*_{\mu}} in Bera et al. (2001), p. 7 (balanced) \item \eqn{LM*_{\mu}} in Baltagi (2013), p. 108 (balanced) \item \eqn{RS*_{\mu}} in Sosa-Escudero/Bera (2008), p. 73 (unbalanced) } \item \code{test = "j"}: \itemize{ \item \eqn{RS_{\mu\rho}} in Bera et al. (2001), p. 10 (balanced) \item \eqn{LM} in Baltagi/Li (2001), p. 279 (balanced) \item \eqn{LM_{1}} in Baltagi and Li (1995), pp. 135--136 (balanced) \item \eqn{LM1} in Baltagi (2013), p. 108 (balanced) \item \eqn{RS_{\lambda\rho}} in Sosa-Escudero/Bera (2008), p. 74 (unbalanced) } } } \examples{ ## Bera et. al (2001), p. 13, table 1 use ## a subset of the original Grunfeld ## data which contains three errors -> construct this subset: data("Grunfeld", package = "plm") Grunsubset <- rbind(Grunfeld[1:80, ], Grunfeld[141:160, ]) Grunsubset[Grunsubset$firm == 2 & Grunsubset$year \%in\% c(1940, 1952), ][["inv"]] <- c(261.6, 645.2) Grunsubset[Grunsubset$firm == 2 & Grunsubset$year == 1946, ][["capital"]] <- 232.6 ## default is AR testing (formula interface) pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year")) pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year"), test = "re") pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year"), test = "re", re.normal = FALSE) pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year"), test = "j") ## plm interface mod <- plm(inv ~ value + capital, data = Grunsubset, model = "pooling") pbsytest(mod) } \references{ \insertRef{BERA:SOSA:YOON:01}{plm} \insertRef{BALT:13}{plm} \insertRef{BALT:LI:91}{plm} \insertRef{BALT:LI:95}{plm} \insertRef{SOSA:BERA:08}{plm} } \seealso{ \code{\link[=plmtest]{plmtest()}} for individual and/or time random effects tests based on a correctly specified model; \code{\link[=pbltest]{pbltest()}}, \code{\link[=pbgtest]{pbgtest()}} and \code{\link[=pdwtest]{pdwtest()}} for serial correlation tests in random effects models. } \author{ Giovanni Millo (initial implementation) & Kevin Tappe (extension to unbalanced panels) } \keyword{htest} plm/man/within_intercept.Rd0000644000176200001440000000612113603205230015455 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_ranfixef.R \name{within_intercept} \alias{within_intercept} \alias{within_intercept.plm} \title{Overall Intercept for Within Models Along its Standard Error} \usage{ within_intercept(object, ...) \method{within_intercept}{plm}(object, vcov = NULL, ...) } \arguments{ \item{object}{object of class \code{plm} which must be a within model (fixed effects model),} \item{\dots}{further arguments (currently none).} \item{vcov}{if not \code{NULL} (default), a function to calculate a user defined variance--covariance matrix (function for robust vcov),} } \value{ A named \code{numeric} of length one: The overall intercept for the estimated within model along attribute "se" which contains the standard error for the intercept. } \description{ This function gives an overall intercept for within models and its accompanying standard error } \details{ The (somewhat artificial) intercept for within models (fixed effects models) was made popular by Stata of StataCorp \insertCite{@see @GOUL:13}{plm}, EViews of IHS, and gretl \insertCite{@gretl p. 160-161, example 18.1}{plm}, see for treatment in the literature, e.g. \insertCite{GREE:12;textual}{plm}, Ch. 11.4.4, p. 364. It can be considered an overall intercept in the within model framework and is the weighted mean of fixed effects (see \strong{Examples} for the relationship). \code{within_intercept} estimates a new model which is computationally more demanding than just taking the weighted mean. However, with \code{within_intercept} one also gets the associated standard error and it is possible to get an overall intercept for twoway fixed effect models. Users can set argument \code{vcov} to a function to calculate a specific (robust) variance--covariance matrix and get the respective (robust) standard error for the overall intercept, e.g. the function \code{\link[=vcovHC]{vcovHC()}}, see examples for usage. Note: The argument \code{vcov} must be a function, not a matrix, because the model to calculate the overall intercept for the within model is different from the within model itself. } \examples{ data("Hedonic", package = "plm") mod_fe <- plm(mv ~ age + crim, data = Hedonic, index = "townid") overallint <- within_intercept(mod_fe) attr(overallint, "se") # standard error # overall intercept is the weighted mean of fixed effects in the # one-way case weighted.mean(fixef(mod_fe), as.numeric(table(index(mod_fe)[[1]]))) # relationship of type="dmean", "level" and within_intercept in the # one-way case data("Grunfeld", package = "plm") gi <- plm(inv ~ value + capital, data = Grunfeld, model = "within") fx_level <- fixef(gi, type = "level") fx_dmean <- fixef(gi, type = "dmean") overallint <- within_intercept(gi) all.equal(overallint + fx_dmean, fx_level, check.attributes = FALSE) # TRUE # overall intercept with robust standard error within_intercept(gi, vcov = function(x) vcovHC(x, method="arellano", type="HC0")) } \references{ \insertAllCited{} } \seealso{ \code{\link[=fixef]{fixef()}} to extract the fixed effects of a within model. } \author{ Kevin Tappe } \keyword{attribute} plm/man/vcovBK.Rd0000644000176200001440000001000613602224251013270 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_vcovG.R \name{vcovBK} \alias{vcovBK} \alias{vcovBK.plm} \title{Beck and Katz Robust Covariance Matrix Estimators} \usage{ vcovBK(x, ...) \method{vcovBK}{plm}( x, type = c("HC0", "HC1", "HC2", "HC3", "HC4"), cluster = c("group", "time"), diagonal = FALSE, ... ) } \arguments{ \item{x}{an object of class \code{"plm"},} \item{\dots}{further arguments.} \item{type}{the weighting scheme used, one of \code{"HC0"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"}, see Details,} \item{cluster}{one of \code{"group"}, \code{"time"},} \item{diagonal}{a logical value specifying whether to force nondiagonal elements to zero,} } \value{ An object of class \code{"matrix"} containing the estimate of the covariance matrix of coefficients. } \description{ Unconditional Robust covariance matrix estimators \emph{a la Beck and Katz} for panel models (a.k.a. Panel Corrected Standard Errors (PCSE)). } \details{ \code{vcovBK} is a function for estimating a robust covariance matrix of parameters for a panel model according to the \insertCite{BECK:KATZ:95;textual}{plm} method, a.k.a. Panel Corrected Standard Errors (PCSE), which uses an unconditional estimate of the error covariance across time periods (groups) inside the standard formula for coefficient covariance. Observations may be clustered either by \code{"group"} to account for timewise heteroskedasticity and serial correlation or by \code{"time"} to account for cross-sectional heteroskedasticity and correlation. It must be borne in mind that the Beck and Katz formula is based on N- (T-) asymptotics and will not be appropriate elsewhere. The \code{diagonal} logical argument can be used, if set to \code{TRUE}, to force to zero all nondiagonal elements in the estimated error covariances; this is appropriate if both serial and cross--sectional correlation are assumed out, and yields a timewise- (groupwise-) heteroskedasticity--consistent estimator. Weighting schemes specified by \code{type} are analogous to those in \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} in package \CRANpkg{sandwich} and are justified theoretically (although in the context of the standard linear model) by \insertCite{MACK:WHIT:85;textual}{plm} and \insertCite{CRIB:04;textual}{plm} \insertCite{@see @ZEIL:04}{plm}. The main use of \code{vcovBK} is to be an argument to other functions, e.g. for Wald--type testing: argument \code{vcov.} to \code{coeftest()}, argument \code{vcov} to \code{waldtest()} and other methods in the \CRANpkg{lmtest} package; and argument \code{vcov.} to \code{linearHypothesis()} in the \CRANpkg{car} package (see the examples). Notice that the \code{vcov} and \code{vcov.} arguments allow to supply a function (which is the safest) or a matrix \insertCite{@see @ZEIL:04, 4.1-2 and examples below}{plm}. } \examples{ library(lmtest) library(car) data("Produc", package="plm") zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="random") ## standard coefficient significance test coeftest(zz) ## robust significance test, cluster by group ## (robust vs. serial correlation), default arguments coeftest(zz, vcov.=vcovBK) ## idem with parameters, pass vcov as a function argument coeftest(zz, vcov.=function(x) vcovBK(x, type="HC1")) ## idem, cluster by time period ## (robust vs. cross-sectional correlation) coeftest(zz, vcov.=function(x) vcovBK(x, type="HC1", cluster="time")) ## idem with parameters, pass vcov as a matrix argument coeftest(zz, vcov.=vcovBK(zz, type="HC1")) ## joint restriction test waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovBK) ## test of hyp.: 2*log(pc)=log(emp) linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovBK) } \references{ \insertRef{BECK:KATZ:95}{plm} \insertRef{CRIB:04}{plm} \insertRef{GREE:03}{plm} \insertRef{MACK:WHIT:85}{plm} \insertRef{ZEIL:04}{plm} } \seealso{ \code{\link[sandwich:vcovHC]{sandwich::vcovHC()}} from the \CRANpkg{sandwich} package for weighting schemes (\code{type} argument). } \author{ Giovanni Millo } \keyword{regression} plm/man/RiceFarms.Rd0000644000176200001440000000354413503144006013761 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plm-package.R \docType{data} \name{RiceFarms} \alias{RiceFarms} \title{Production of Rice in Indonesia} \format{A dataframe containing : \describe{ \item{id}{the farm identifier} \item{size}{the total area cultivated with rice, measured in hectares} \item{status}{land status, on of \code{'owner'} (non sharecroppers, owner operators or leaseholders or both), \code{'share'} (sharecroppers), \code{'mixed'} (mixed of the two previous status)} \item{varieties}{one of \code{'trad'} (traditional varieties), \code{'high'} (high yielding varieties) and \code{'mixed'} (mixed varieties)} \item{bimas}{bIMAS is an intensification program; one of \code{'no'} (non-bimas farmer), \code{'yes'} (bimas farmer) or \code{'mixed'} (part but not all of farmer's land was registered to be in the bimas program)} \item{seed}{seed in kilogram} \item{urea}{urea in kilogram} \item{phosphate}{phosphate in kilogram} \item{pesticide}{pesticide cost in Rupiah} \item{pseed}{price of seed in Rupiah per kg} \item{purea}{price of urea in Rupiah per kg} \item{pphosph}{price of phosphate in Rupiah per kg} \item{hiredlabor}{hired labor in hours} \item{famlabor}{family labor in hours} \item{totlabor}{total labor (excluding harvest labor)} \item{wage}{labor wage in Rupiah per hour} \item{goutput}{gross output of rice in kg} \item{noutput}{net output, gross output minus harvesting cost (paid in terms of rice)} \item{price}{price of rough rice in Rupiah per kg} \item{region}{one of \code{'wargabinangun'}, \code{'langan'}, \code{'gunungwangi'}, \code{'malausma'}, \code{'sukaambit'}, \code{'ciwangi'}} }} \source{ \insertRef{FENG:HORR:12}{plm} } \description{ a panel of 171 observations } \details{ \emph{number of observations} : 1026 \emph{observation} : farms \emph{country} : Indonesia } \keyword{datasets} plm/man/is.pbalanced.Rd0000644000176200001440000000603413503144006014426 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/is.pconsecutive_pbalanced.R \name{is.pbalanced} \alias{is.pbalanced} \alias{is.pbalanced.default} \alias{is.pbalanced.data.frame} \alias{is.pbalanced.pdata.frame} \alias{is.pbalanced.pseries} \alias{is.pbalanced.panelmodel} \alias{is.pbalanced.pgmm} \title{Check if data are balanced} \usage{ is.pbalanced(x, ...) \method{is.pbalanced}{default}(x, y, ...) \method{is.pbalanced}{data.frame}(x, index = NULL, ...) \method{is.pbalanced}{pdata.frame}(x, ...) \method{is.pbalanced}{pseries}(x, ...) \method{is.pbalanced}{panelmodel}(x, ...) \method{is.pbalanced}{pgmm}(x, ...) } \arguments{ \item{x}{an object of class \code{pdata.frame}, \code{data.frame}, \code{pseries}, \code{panelmodel}, \code{pgmm};} \item{\dots}{further arguments.} \item{y}{\strong{to describe}} \item{index}{only relevant for \code{data.frame} interface; if \code{NULL}, the first two columns of the data.frame are assumed to be the index variables; if not \code{NULL}, both dimensions ('individual', 'time') need to be specified by \code{index} as character of length 2 for data frames, for further details see \code{\link[=pdata.frame]{pdata.frame()}},} } \value{ A logical indicating whether the data associated with object \code{x} are balanced (\code{TRUE}) or not (\code{FALSE}). } \description{ This function checks if the data are balanced, i.e. if each individual has the same time periods } \details{ Balanced data are data for which each individual has the same time periods. The returned values of the \code{is.pbalanced(object)} methods are identical to \code{pdim(object)$balanced}. \code{is.pbalanced} is provided as a short cut and is faster than \code{pdim(object)$balanced} because it avoids those computations performed by \code{pdim} which are unnecessary to determine the balancedness of the data. } \examples{ # take balanced data and make it unbalanced # by deletion of 2nd row (2nd time period for first individual) data("Grunfeld", package = "plm") Grunfeld_missing_period <- Grunfeld[-2, ] is.pbalanced(Grunfeld_missing_period) # check if balanced: FALSE pdim(Grunfeld_missing_period)$balanced # same # pdata.frame interface pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) is.pbalanced(Grunfeld_missing_period) # pseries interface is.pbalanced(pGrunfeld_missing_period$inv) } \seealso{ \code{\link[=punbalancedness]{punbalancedness()}} for two measures of unbalancedness, \code{\link[=make.pbalanced]{make.pbalanced()}} to make data balanced; \code{\link[=is.pconsecutive]{is.pconsecutive()}} to check if data are consecutive; \code{\link[=make.pconsecutive]{make.pconsecutive()}} to make data consecutive (and, optionally, also balanced).\cr \code{\link[=pdim]{pdim()}} to check the dimensions of a 'pdata.frame' (and other objects), \code{\link[=pvar]{pvar()}} to check for individual and time variation of a 'pdata.frame' (and other objects), \code{\link[=pseries]{pseries()}}, \code{\link[=data.frame]{data.frame()}}, \code{\link[=pdata.frame]{pdata.frame()}}. } \keyword{attribute} plm/man/pbnftest.Rd0000644000176200001440000000651713602224251013737 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_serial.R \name{pbnftest} \alias{pbnftest} \alias{pbnftest.panelmodel} \alias{pbnftest.formula} \title{Modified BNF--Durbin--Watson Test and Baltagi--Wu's LBI Test for Panel Models} \usage{ pbnftest(x, ...) \method{pbnftest}{panelmodel}(x, test = c("bnf", "lbi"), ...) \method{pbnftest}{formula}( x, data, test = c("bnf", "lbi"), model = c("pooling", "within", "random"), ... ) } \arguments{ \item{x}{an object of class \code{"panelmodel"} or of class \code{"formula"},} \item{\dots}{only relevant for formula interface: further arguments to specify the model to test (arguments passed on to plm()), e.g. \code{effect}.} \item{test}{a character indicating the test to be performed, either \code{"bnf"} or \code{"lbi"} for the (modified) BNF statistic or Baltagi--Wu's LBI statistic, respectively,} \item{data}{a \code{data.frame} (only relevant for formula interface),} \item{model}{a character indicating on which type of model the test shall be performed (\code{"pooling"}, \code{"within"}, \code{"random"}, only relevant for formula interface),} } \value{ An object of class \code{"htest"}. } \description{ Tests for AR(1) disturbances in panel models. } \details{ The default, \code{test = "bnf"}, gives the (modified) BNF statistic, the generalised Durbin-Watson statistic for panels. For balanced and consecutive panels, the reference is Bhargava/Franzini/Narendranathan (1982). The modified BNF is given for unbalanced and/or non-consecutive panels (d1 in formula 16 of \insertCite{BALT:WU:99;textual}{plm}). \code{test = "lbi"} yields Baltagi--Wu's LBI statistic \insertCite{BALT:WU:99}{plm}, the locally best invariant test which is based on the modified BNF statistic. No specific variants of these tests are available for random effect models. As the within estimator is consistent also under the random effects assumptions, the test for random effect models is performed by taking the within residuals. No p-values are given for the statistics as their distribution is quite difficult. \insertCite{BHAR:FRAN:NARE:82;textual}{plm} supply tabulated bounds for p = 0.05 for the balanced case and consecutive case. For large N, \insertCite{BHAR:FRAN:NARE:82}{plm} suggest it is sufficient to check whether the BNF statistic is < 2 to test against positive serial correlation. } \examples{ data("Grunfeld", package = "plm") # formula interface, replicate Baltagi/Wu (1999), table 1, test case A: data_A <- Grunfeld[!Grunfeld[["year"]] \%in\% c("1943", "1944"), ] pbnftest(inv ~ value + capital, data = data_A, model = "within") pbnftest(inv ~ value + capital, data = data_A, test = "lbi", model = "within") # replicate Baltagi (2013), p. 101, table 5.1: re <- plm(inv ~ value + capital, data = Grunfeld, model = "random") pbnftest(re) pbnftest(re, test = "lbi") } \references{ \insertRef{BALT:13}{plm} \insertRef{BALT:WU:99}{plm} \insertRef{BHAR:FRAN:NARE:82}{plm} } \seealso{ \code{\link[=pdwtest]{pdwtest()}} for the original Durbin--Watson test using (quasi-)demeaned residuals of the panel model without taking the panel structure into account. \code{\link[=pbltest]{pbltest()}}, \code{\link[=pbsytest]{pbsytest()}}, \code{\link[=pwartest]{pwartest()}} and \code{\link[=pwfdtest]{pwfdtest()}} for other serial correlation tests for panel models. } \author{ Kevin Tappe } \keyword{htest} plm/man/pwaldtest.Rd0000644000176200001440000001170113602224251014110 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/test_general.R \name{pwaldtest} \alias{pwaldtest} \alias{pwaldtest.plm} \alias{pwaldtest.pvcm} \title{Wald-style Chi-square Test and F Test} \usage{ pwaldtest(x, ...) \method{pwaldtest}{plm}( x, test = c("Chisq", "F"), vcov = NULL, df2adj = (test == "F" && !is.null(vcov) && missing(.df2)), .df1, .df2, ... ) \method{pwaldtest}{pvcm}(x, ...) } \arguments{ \item{x}{an estimated model of which the coefficients should be tested (usually of class \code{"plm"}/\code{"pvcm"}),} \item{\dots}{further arguments (currently none).} \item{test}{a character, indicating the test to be performed, may be either \code{"Chisq"} or \code{"F"} for the Wald-style Chi-square test or F test, respectively,} \item{vcov}{\code{NULL} by default; a \code{matrix} giving a variance--covariance matrix or a function which computes such; if supplied (non \code{NULL}), the test is carried out using the variance--covariance matrix indicated resulting in a robust test,} \item{df2adj}{logical, only relevant for \code{test = "F"}, indicating whether the adjustment for clustered standard errors for the second degrees of freedom parameter should be performed (see \strong{Details}, also for further requirements regarding the variance--covariance matrix in \code{vcov} for the adjustment to be performed),} \item{.df1}{a numeric, used if one wants to overwrite the first degrees of freedom parameter in the performed test (usually not used),} \item{.df2}{a numeric, used if one wants to overwrite the second degrees of freedom parameter for the F test (usually not used),} } \value{ An object of class \code{"htest"}. } \description{ Wald-style Chi-square test and F test of slope coefficients being zero jointly, including robust versions of the tests. } \details{ \code{pwaldtest} can be used stand--alone with a plm object or a pvcm object (for the latter only the 'random' type is valid and no further arguments are processed). It is also used in \code{\link[=summary.plm]{summary.plm()}} to produce the F statistic and the Chi-square statistic for the joint test of coefficients. \code{pwaldtest} performs the test if the slope coefficients of a panel regression are jointly zero. It does not perform general purpose Wald-style tests (for those, see \code{\link[lmtest:waldtest]{lmtest::waldtest()}} (from package \CRANpkg{lmtest}) or \code{\link[car:linearHypothesis]{car::linearHypothesis()}} (from package \CRANpkg{car})). If a user specified variance-covariance matrix/function is given in argument \code{vcov}, the robust version of the tests are carried out. In that case, if the F test is requested (\code{test = "F"}) and no overwriting of the second degrees of freedom parameter is given (by supplying argument (\code{.df2})), the adjustment of the second degrees of freedom parameter is performed by default. The second degrees of freedom parameter is adjusted to be the number of unique elements of the cluster variable - 1, e. g. the number of individuals - \enumerate{ \item For the degrees of freedom adjustment of the F test in general, see e. g. \insertCite{CAME:MILL:15;textual}{plm}, section VII; \insertCite{ANDR:GOLS:SCMI:13}{plm}, pp. 126, footnote 4. } The degrees of freedom adjustment requires the vcov object supplied or created by a supplied function to carry an attribute called "cluster" with a known clustering described as a character (for now this could be either \code{"group"} or \code{"time"}). The vcovXX functions of the package \pkg{plm} provide such an attribute for their returned variance--covariance matrices. No adjustment is done for unknown descriptions given in the attribute "cluster" or when the attribute "cluster" is not present. Robust vcov objects/functions from package \CRANpkg{clubSandwich} work as inputs to \code{pwaldtest}'s F test because a they are translated internally to match the needs described above. } \examples{ data("Grunfeld", package = "plm") mod_fe <- plm(inv ~ value + capital, data = Grunfeld, model = "within") mod_re <- plm(inv ~ value + capital, data = Grunfeld, model = "random") pwaldtest(mod_fe, test = "F") pwaldtest(mod_re, test = "Chisq") # with robust vcov (matrix, function) pwaldtest(mod_fe, vcov = vcovHC(mod_fe)) pwaldtest(mod_fe, vcov = function(x) vcovHC(x, type = "HC3")) pwaldtest(mod_fe, vcov = vcovHC(mod_fe), df2adj = FALSE) # w/o df2 adjustment # example without attribute "cluster" in the vcov vcov_mat <- vcovHC(mod_fe) attr(vcov_mat, "cluster") <- NULL # remove attribute pwaldtest(mod_fe, vcov = vcov_mat) # no df2 adjustment performed } \references{ \insertRef{WOOL:10}{plm} \insertRef{ANDR:GOLS:SCMI:13}{plm} \insertRef{CAME:MILL:15}{plm} } \seealso{ \code{\link[=vcovHC]{vcovHC()}} for an example of the vcovXX functions, a robust estimation for the variance--covariance matrix; \code{\link[=summary.plm]{summary.plm()}} } \author{ Yves Croissant (initial implementation) and Kevin Tappe (extensions: vcov argument and F test's df2 adjustment) } \keyword{htest} plm/man/aneweytest.Rd0000644000176200001440000000210013503144006014261 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_pi.R \name{aneweytest} \alias{aneweytest} \title{Chamberlain estimator and test for fixed effects} \usage{ aneweytest(formula, data, subset, na.action, index = NULL, ...) } \arguments{ \item{formula}{a symbolic description for the model to be estimated,} \item{data}{a \code{data.frame},} \item{subset}{see \code{\link[=lm]{lm()}},} \item{na.action}{see \code{\link[=lm]{lm()}},} \item{index}{the indexes,} \item{\dots}{further arguments.} } \value{ An object of class \code{"htest"}. } \description{ Angrist and Newey's version of the Chamberlain test } \details{ Angrist and Newey's test is based on the results of the artifactual regression of the within residuals on the covariates for all the periods. } \examples{ data("RiceFarms", package = "plm") aneweytest(log(goutput) ~ log(seed) + log(totlabor) + log(size), RiceFarms, index = "id") } \references{ \insertRef{ANGR:NEWE:91}{plm} } \seealso{ \code{\link[=piest]{piest()}} for Chamberlain's test } \author{ Yves Croissant } \keyword{htest} plm/man/sargan.Rd0000755000176200001440000000235413503144006013362 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/est_gmm.R \name{sargan} \alias{sargan} \title{Hansen--Sargan Test of Overidentifying Restrictions} \usage{ sargan(object, weights = c("twosteps", "onestep")) } \arguments{ \item{object}{an object of class \code{"pgmm"},} \item{weights}{the weighting matrix to be used for the computation of the test.} } \value{ An object of class \code{"htest"}. } \description{ A test of overidentifying restrictions for models estimated by GMM. } \details{ The Hansen--Sargan test calculates the quadratic form of the moment restrictions that is minimized while computing the GMM estimator. It follows asymptotically a chi-square distribution with number of degrees of freedom equal to the difference between the number of moment conditions and the number of coefficients. } \examples{ data("EmplUK", package = "plm") ar <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + lag(log(capital), 0:2) + lag(log(output), 0:2) | lag(log(emp), 2:99), data = EmplUK, effect = "twoways", model = "twosteps") sargan(ar) } \references{ \insertCite{HANS:82}{plm} \insertCite{SARG:58}{plm} } \seealso{ \code{\link[=pgmm]{pgmm()}} } \author{ Yves Croissant } \keyword{htest} plm/man/vcovG.Rd0000644000176200001440000000476013602224251013174 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tool_vcovG.R \name{vcovG} \alias{vcovG} \alias{vcovG.plm} \alias{vcovG.pcce} \title{Generic Lego building block for Robust Covariance Matrix Estimators} \usage{ vcovG(x, ...) \method{vcovG}{plm}( x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), cluster = c("group", "time"), l = 0, inner = c("cluster", "white", "diagavg"), ... ) \method{vcovG}{pcce}( x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), cluster = c("group", "time"), l = 0, inner = c("cluster", "white", "diagavg"), ... ) } \arguments{ \item{x}{an object of class \code{"plm"} or \code{"pcce"}} \item{\dots}{further arguments} \item{type}{the weighting scheme used, one of \code{"HC0"}, \code{"sss"}, \code{"HC1"}, \code{"HC2"}, \code{"HC3"}, \code{"HC4"},} \item{cluster}{one of \code{"group"}, \code{"time"},} \item{l}{lagging order, defaulting to zero} \item{inner}{the function to be applied to the residuals inside the sandwich: one of \code{"cluster"} or \code{"white"} or \code{"diagavg"},} } \value{ An object of class \code{"matrix"} containing the estimate of the covariance matrix of coefficients. } \description{ Generic Lego building block for robust covariance matrix estimators of the vcovXX kind for panel models. } \details{ \code{vcovG} is the generic building block for use by higher--level wrappers \code{\link[=vcovHC]{vcovHC()}}, \code{\link[=vcovSCC]{vcovSCC()}}, \code{\link[=vcovDC]{vcovDC()}}, and \code{\link[=vcovNW]{vcovNW()}}. The main use of \code{vcovG} is to be used internally by the former, but it is made available in the user space for use in non--standard combinations. For more documentation, see see wrapper functions mentioned. } \examples{ data("Produc", package="plm") zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling") ## reproduce Arellano's covariance matrix vcovG(zz, cluster="group", inner="cluster", l=0) ## use in coefficient significance test library(lmtest) ## define custom covariance function ## (in this example, same as vcovHC) myvcov <- function(x) vcovG(x, cluster="group", inner="cluster", l=0) ## robust significance test coeftest(zz, vcov.=myvcov) } \references{ \insertRef{mil17b}{plm} } \seealso{ \code{\link[=vcovHC]{vcovHC()}}, \code{\link[=vcovSCC]{vcovSCC()}}, \code{\link[=vcovDC]{vcovDC()}}, \code{\link[=vcovNW]{vcovNW()}}, and \code{\link[=vcovBK]{vcovBK()}} albeit the latter does not make use of vcovG. } \author{ Giovanni Millo } \keyword{regression} plm/DESCRIPTION0000755000176200001440000000474113623756670012600 0ustar liggesusersPackage: plm Version: 2.2-2 Date: 2020-02-20 Title: Linear Models for Panel Data Authors@R: c(person(given = "Yves", family = "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr"), person(given = "Giovanni", family = "Millo", role = "aut", email = "Giovanni.Millo@Generali.com"), person(given = "Kevin", family = "Tappe", role = "aut", email = "kevin.tappe@bwi.uni-stuttgart.de"), person(given = "Ott", family = "Toomet", role = "ctb", email = "otoomet@gmail.com"), person(given = "Christian", family = "Kleiber", role = "ctb", email = "Christian.Kleiber@unibas.ch"), person(given = "Achim", family = "Zeileis", role = "ctb", email = "Achim.Zeileis@R-project.org"), person(given = "Arne", family = "Henningsen", role = "ctb", email = "arne.henningsen@googlemail.com"), person(given = "Liviu", family = "Andronic", role = "ctb", email = "landronimirc@gmail.com"), person(given = "Nina", family = "Schoenfelder", role = "ctb", email = "nina.schoenfelder@fernuni-hagen.de")) Depends: R (>= 3.1.0) Imports: MASS, bdsmatrix, zoo, nlme, sandwich, lattice, lmtest, maxLik, Rdpack, Formula, stats Suggests: AER, car, pcse, clusterSEs, clubSandwich, pglm, spdep, splm, statmod, urca, Ecdat, pder, stargazer, texreg, foreign, knitr, rmarkdown, bookdown Description: A set of estimators and tests for panel data econometrics, as described in Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4. License: GPL (>= 2) VignetteBuilder: knitr URL: https://cran.r-project.org/package=plm, https://r-forge.r-project.org/projects/plm/ RoxygenNote: 7.0.2 RdMacros: Rdpack Encoding: UTF-8 Author: Yves Croissant [aut, cre], Giovanni Millo [aut], Kevin Tappe [aut], Ott Toomet [ctb], Christian Kleiber [ctb], Achim Zeileis [ctb], Arne Henningsen [ctb], Liviu Andronic [ctb], Nina Schoenfelder [ctb] Maintainer: Yves Croissant Repository: CRAN Repository/R-Forge/Project: plm Repository/R-Forge/Revision: 913 Repository/R-Forge/DateTimeStamp: 2020-02-21 02:51:12 Date/Publication: 2020-02-21 13:40:08 UTC NeedsCompilation: no Packaged: 2020-02-21 03:30:28 UTC; rforge plm/build/0000755000176200001440000000000013623647324012153 5ustar liggesusersplm/build/vignette.rds0000644000176200001440000000050613623647324014513 0ustar liggesusersRN0uPhEi"X#+% B*V+؎lrI/%*1{{)c,`8dApEX&ϫR%:x2o~6FUFWF Zv(zt^*(rsXv:\5Q=(K!c/oƖLx!5(Vto: ЭMSe߿J٭^)ͯԜ_,.gkj }O2[9(ҩR_@?i¨4ٙv/f=l#,{g#]ESmo%Xj:i [P5I2[:{Pe akRC~!Z9WRQ#pb(hk߶k5-"3C(%o~BLLrsQ*|sYJ{/|QMo>.43PA!e<*#^BfBsVtՎ |U!d~BL,rճ80 ^gTZ6ʷ%ZѝJX D;(IB&(i6unYȮ:R4ݎY7ܡ) 1r[ˢ,H!&jSB];tu4FՂZWPFձ/&I"Y 9ŚΨ&9Eo }{FILs+ke/!>BaQ /u840M#9dX94CaSTk;k.vʹ՝ PhVYhH(?@Hi'd?C!&St<"A5FSA`m&8BCy.~& {s(g|'O!&c"VG=0iUH֫0׬_3zű׬Rvښw]7?n`ݶP :?hvK|jA{Ca.=]2 -h[7ކ!ҲlUhm_ չ6[sga5c*8Oq"]jhfUs,H^mVٯmHӗʫ&4JOo|k =,^p4L*N)s,u"~C\Cwtƛ)ioOAKtbvyA:%Qoxvd=+Sfk<ι\ny6_OO|юBUCԧnss\n)_XZ[\\Y\vE m+͎.(pN wQd֫ &WFķg^|Eu+*mgۆci' k?>JbܶY$}4ʟ'lݪPd}vErjih'dM-eK1C{]QWnJ3r-bvj;mW2o4?*aht粆]*w?>Nt,xfFH1-'jq%Eŋ?a2ӽfT΋w4K3A83P "9|c菭5&^/Rd ! 9 lYſ*8{&}۟ [b<&,Y__#*-/oҥn|zw[چoeÐ_l湯>bGEu"<*8 Ͱ>G8EiVάOTݶlJD3mo{OB{O/lWJCPP=M[Ý5 CBj4q|pՌpǏ|kbx!~tjtO5JݔS]6gRjQ9 r5 w00G Y cL?yw:"|`[@O5t }۝,yd5O6#@=Ct0`h5N u^i ;J{RZ謩 pRk>} }Sڵmu꺔=햱C6kNAE&w"l}yO=G=AVa[Ov˫:w=?:*5 ;gsۜѳ,%]M * C<ϬͿm"q VnTXJY:2ٹl>˪eJY:mm7 _ȔJ_yγﶦBr Y%_6|"G~~~AA0AG??BHX.iewV^EԔ`bni{{9(b`P^-f[s2J  wg6n5L[{)2.7zRer{3̦ZjK8_C5X!V󖦢{ Ph=h7K{cl5t?:&ͷ5@7^9u1t:@ȈRCA=㢎*x^RtSXbmC<)I;KLoռB{VuEM7m^m] At'%gKmL:=8_C/Nx( y?i#7O~ZIWb@'COٿ5Zʖͪv񑖭;h>#[*`Zԗ~3Ƞ o]A/;}Wێۯf떫!m?"_7ˌfO|v hiRTao~m(E/ -[&E-~=^@Ekqr5V*޽Tc?VB_VչCܿ)7 X_q3c5ʥmnl6eN9-,Oäu&zP1o4su X r?~h@80eF.zҤb YkWKn ?NჟAT~kycmYKilwV<)+-:UOqϏ,tB?RE*-WeQzG'pld'ux#])o 5x'V ^Yv0|Y9$pO3R2a:^9`Ja_o ;xJ!O2ϫz>m}c,9ȟd,gdx=_igƨ/D XrBLzw!nA5yZ(syPF};6}W-f'M{]2хݥd;TV UZeIoԭ7V9t F)w/PG!&}皇QaNUˮi ޤi!+ׇtqg=fd?E!&9_3+"j#H@u ^ԼtQKL,o,7#I' |PIvU [g^Uo*}]if3m]4R+(~ G2]AC)g}Q?31$ |sU)xRnCPZa#>|+/B&ަsԜQ@#ZtL+CNYwxXtCZy|r!~N@9I!&ɜ:gWmֶ~Y(A0jzb7TIխHefsSK2R]KޖJ޿~NmG5JU"F #+pK3O8>xa%OIG 1vsSYU$"` nn vnN[%s#&M`:7dxֹkH@^ց}`Kx{~C5Re#5$ u#7i]f o9@Nu~Is=Qvʹ3iRw`R촷W7N>a]||uf_PI>'2F,icA٬hZaI+.*@ .?n P^JN2iW5G B\vuCVuî[ "qrVZ#P^VH%P*yZAO($V6HjV~bhuQ&D78!wk"d?K!&$1jv s+n#QU5PKG̚"N5&fc-d |c`!A($wXK5caM+iۓ]PwqeF#:WL4bߛ1ojA܉FIt`ov6*&g"I4ęHDHh`<$:[Y:D+[$8da%*V XHuYǡ%G('pry3t$R8NQ* %GmЬ{dzGs\Oe3I~aCħ(?YẑQI6*\L"¬TNvKmrC*"}(瓧d?O!&TnRISi$I 8r3R37>K!&4]; .Is12o`uog^_ %~BLqp}X0KbP#Q}b#u~ iBAbq]CyA>IB&3# 焆EtYB,3b̢ (SH@Gn DovJ.qg 9eB)$[㻖Y([ ܐ)݌YiSHRHBz4P H$ 8L=M'xY?d`| Hq\@AQ$Q 0s܏ygRX5N/$<\cV62QަwcCL"䓉w L$r{c!sƤ,02XYI}BL1z=s0i:=MQkCJ"wƬw e #ra)NQ1 `/Gnr9lB˱#II8x OLy$Pd#L*?\{,tḌ5l`uǛv\rpHҹp~aR^RZ%.ZN[5}Tߢ{WZC22oU+ 1q@O!&ZyY wzVѵ, MK>Vkdh‹ZqZj6284hat~nnifeTaje.uCLdkw oRFsmuv=STjY|.񭚍?ѳU U+Yۮ[\ v4jxJZ@?zkTtj6}O؛yĬfdvq+1[aVy[y1ŭa%vq /P?RL[Iw[S:@-lq;EjqemAi~95KݑOQr?yYZ[_1+r|>7| ~:glP.FױZ5- R)i516b7Tq ;a-jڄK1FK3DuXz<nRܸ B)qړa俊X@Y^myFLfF  1qGT81D!&ZyjhZ#u^nb{U:Y sH%\5vT!uX/LkXTX$/}1JFY~bGIa14KbmZ+Gdˌz%4?j-P>#MCrG)0*y84>#c%#+jBe ''뛘Eb*W~H8x>Sspf538<.ƔwX#_(kd_tu*4J&Lfn8*\pAfaZ153,^¥DWզܱn=q$֜ yc:l8[X` R8쫕]N吔] _?e`iwZ7rkjTkic q`i7 !Z.[Z2K;ڝԯR;kI>Ԝ[$߶7+eHJi8m+;c@ey~2\sg pĎ Cn?ꃘ]!dC aDT g {s5f*m8S+UOncAMRr4|_M˧f~6n)|?;[T#J!EwX ߸m@[΂zl4aZƁV4`+Os 3- 1Ag8Mѫh-qER5K҂>(.7@V!"J?N؂j1Xkg{OױNW(;kɩ2W)3ǎY)%fi247;;tȹW/ =[fJgeJ zP[ KH؀ 8UqDo1T#rP !?zcZ`C̊;aem:,WB0^N}9&TcxmՓϝ2sD 1ǹiwft* ]fJ ڧcO` '8n+ =ȱ*A :Cu8uzw^/[!OɋU"ZI.ڎƷf ùHo68%VtQ㷫j%34x(n;<o_|˄,}/f?NѸ6YŠuy73[/Qtbyuh=cmVk5uwMwҧ1a ɮL;K!2Ȼ<"b.yat,3[ A3(gf=>K!&5B۔gDyG5ZfY4Cɛ$\@Nװ_"< hۆZ 99١rqA"qCE+~X5}RɅmachm$a(<i^h"<ԋs4`D9S kIAO G9 StzԹh$8L̗SJ5'F=E XrBLnWXŢt;f FYc  "^C+"X?6-(_OyK_b͗CM58_x5 ƙ#(ϔd.xhL($)l蘬 :Cu}\f݆|?~\DRe&~BLsVBwQ%1<+HW7].!EN!&?flPi@[: RiqSfm$EA6$QH¨ܐ҈vwY-J ^C9//dxF̺S:aq NAĸ2 2W'Ɩal#^VNym/\0DmQZ6zY 1q63D@O!&Z^³oVsիݫ=Yhn[Z&y&L8ݩ8B E2SėͪF>hz_ӊ.>=h"4 nTL<^O#XUʽۺ*-]#Y՟x*فȥ`{~F0yK:t"^;E5J~5S)3Gĭxq1M˳>8XI`B ,"CtG$|CJz Lefu`~3D!&A R7v Fq$NJ-Iy8t쌋$7ǔƴT,.Is@P2-o%ϒ[Xe g @5o6RK͂&kc\6} DZu/XdnC=Dz.{1⌰Y/L)@ rEϙ: LbFDg?3w19spsF͙%k}I;>3j˾;àFBwgNQԨLR&9IO n )/, DgQ !6 U9|!d~BLr%0 0 ^q,I%q26InPϤ ̱i}Ťn09(Zmܔa#z\k(A=+k('W.T9U8;B*ھVaBq-S;ܜy|6M!& y6+(_-홐sIF!&lvlа3TlMؠxnIMOP~?5OQ~< u 1I'nͪVcԎ]#XZeij(4럊' "Σ<?k7 (/$O&~BLtbժ{Q ]IbT'Tҋ~򲴶0?2+r|>7| ~:gl),\R e-yKCb6:w(N:-Vtj9zek@AVW斗/㢶cѧJ`L`$]E`\ ߐ{TFv.G4›qia<La[\ `Kb)'F(ɑi&H_dBd"XucW"vdNeGaP#4'(LIL#h0gF,i9^lc"ڄ;2MRF9&2QnFyhƾf;af|(ߕhħIJL40@Lfi7SRi(Zmħa^r>f p|]ZC;Q@I3lBL #? 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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. > library(plm) > data("Produc", package = "plm") > pProduc <- pdata.frame(Produc, index = c("state", "year", "region")) > form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp > summary(plm(form, data = pProduc, model = "random", effect = "nested")) Nested effects Random Effect Model (Swamy-Arora's transformation) Call: plm(formula = form, data = pProduc, effect = "nested", model = "random") Balanced Panel: n = 48, T = 17, N = 816 Effects: var std.dev share idiosyncratic 0.001352 0.036765 0.191 individual 0.004278 0.065410 0.604 group 0.001455 0.038148 0.205 theta: Min. 1st Qu. Median Mean 3rd Qu. Max. id 0.86492676 0.8649268 0.86492676 0.86492676 0.86492676 0.86492676 group 0.03960556 0.0466931 0.05713605 0.05577645 0.06458029 0.06458029 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.106171 -0.024805 -0.001816 -0.000054 0.019795 0.182810 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.08921088 0.14570204 14.3389 < 2.2e-16 *** log(pc) 0.27412419 0.02054440 13.3430 < 2.2e-16 *** log(emp) 0.73983766 0.02575046 28.7311 < 2.2e-16 *** log(hwy) 0.07273624 0.02202509 3.3024 0.0009585 *** log(water) 0.07645327 0.01385767 5.5170 3.448e-08 *** log(util) -0.09437398 0.01677289 -5.6266 1.838e-08 *** unemp -0.00616304 0.00090331 -6.8227 8.933e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Total Sum of Squares: 43.035 Residual Sum of Squares: 1.1245 R-Squared: 0.97387 Adj. R-Squared: 0.97368 Chisq: 30152 on 6 DF, p-value: < 2.22e-16 > summary(plm(form, data = pProduc, model = "random", effect = "nested", random.method = "walhus")) Nested effects Random Effect Model (Wallace-Hussain's transformation) Call: plm(formula = form, data = pProduc, effect = "nested", model = "random", random.method = "walhus") Balanced Panel: n = 48, T = 17, N = 816 Effects: var std.dev share idiosyncratic 0.001415 0.037617 0.163 individual 0.004507 0.067131 0.520 group 0.002744 0.052387 0.317 theta: Min. 1st Qu. Median Mean 3rd Qu. Max. id 0.86533240 0.86533240 0.86533240 0.86533240 0.86533240 0.86533240 group 0.05409908 0.06154491 0.07179372 0.07023704 0.07867007 0.07867007 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.105014 -0.024736 -0.001879 -0.000056 0.019944 0.182082 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.08165186 0.15034855 13.8455 < 2.2e-16 *** log(pc) 0.27256322 0.02093384 13.0202 < 2.2e-16 *** log(emp) 0.74164483 0.02607167 28.4464 < 2.2e-16 *** log(hwy) 0.07493204 0.02234932 3.3528 0.0008001 *** log(water) 0.07639159 0.01386702 5.5089 3.611e-08 *** log(util) -0.09523031 0.01677247 -5.6778 1.365e-08 *** unemp -0.00614840 0.00090786 -6.7724 1.267e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Total Sum of Squares: 40.423 Residual Sum of Squares: 1.1195 R-Squared: 0.97231 Adj. R-Squared: 0.9721 Chisq: 28403.2 on 6 DF, p-value: < 2.22e-16 > summary(plm(form, data = pProduc, model = "random", effect = "nested", random.method = "amemiya")) Nested effects Random Effect Model (Amemiya's transformation) Call: plm(formula = form, data = pProduc, effect = "nested", model = "random", random.method = "amemiya") Balanced Panel: n = 48, T = 17, N = 816 Effects: var std.dev share idiosyncratic 0.001352 0.036765 0.130 individual 0.006899 0.083058 0.662 group 0.002170 0.046589 0.208 theta: Min. 1st Qu. Median Mean 3rd Qu. Max. id 0.89325689 0.89325689 0.89325689 0.89325689 0.89325689 0.89325689 group 0.02996995 0.03548869 0.04369353 0.04264991 0.04959127 0.04959127 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.104625 -0.024323 -0.002264 -0.000038 0.019351 0.178975 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.13133109 0.16013819 13.3093 < 2.2e-16 *** log(pc) 0.26447567 0.02176030 12.1540 < 2.2e-16 *** log(emp) 0.75811017 0.02660794 28.4919 < 2.2e-16 *** log(hwy) 0.07211418 0.02362627 3.0523 0.002271 ** log(water) 0.07616495 0.01401879 5.4331 5.539e-08 *** log(util) -0.10150953 0.01705158 -5.9531 2.631e-09 *** unemp -0.00583842 0.00091107 -6.4083 1.471e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Total Sum of Squares: 34.089 Residual Sum of Squares: 1.0911 R-Squared: 0.96799 Adj. R-Squared: 0.96776 Chisq: 24466.7 on 6 DF, p-value: < 2.22e-16 > > proc.time() user system elapsed 0.926 0.050 0.969 plm/tests/test_pseries_subsetting.R0000644000176200001440000000274613010141777017321 0ustar liggesusers# # Test subsetting for pseries objects # # Note: # currently, (2016-07-11, rev. 324), we do not have a special subsetting method # for pseries defined (see for test of pseries features after subsetting further down) library(plm) # data.frame with factor df <- data.frame(id = c(1,1,2), time = c(1,2,1), f = factor(c("a", "a", "b")), n = c(1:3)) df$f levels(df$f) <- c("a","b","c") df$f[1] df$f[1,drop=F] df$f[1,drop=T] df$f[drop=T] df$f[0] # pdata.frame with c("pseries", "factor") pdf <- pdata.frame(df) pdf$f levels(pdf$f) <- c("a","b","c") pdf$f[1] pdf$f[drop=T] pdf$f[0] attr(pdf$f[0], "index") pf <- pdf$f if (!all(levels(pdf$f[1,drop=F]) == c("a","b","c"))) stop("subsetting for c(\"pseries\", \"factor\") (with drop=F) not correct") if (!all(class(pdf$f[1]) == c("pseries", "factor"))) stop("classes not correct after subsetting pseries") if (!levels(pdf$f[1,drop=T]) == "a") stop("subsetting for c(\"pseries\", \"factor\") with drop=T not correct - unused levels not dropped") if (!all(levels(pdf$f[drop=T]) == c("a", "b"))) stop("subsetting for c(\"pseries\", \"factor\") with drop=T not correct - unused levels not dropped") ### activate these tests once the subsetting method for pseries is defined. #if (is.null(attr(pdf$f[1], "index"))) stop("no index after subsetting") #if (!nrow(attr(pdf$f[1], "index")) == 1) stop("wrong index after subsetting") lapply(df, attributes) lapply(pdf, attributes) lapply(df, class) lapply(pdf, class) plm/tests/test_nested.R0000644000176200001440000000073113120057536014653 0ustar liggesuserslibrary(plm) data("Produc", package = "plm") pProduc <- pdata.frame(Produc, index = c("state", "year", "region")) form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp summary(plm(form, data = pProduc, model = "random", effect = "nested")) summary(plm(form, data = pProduc, model = "random", effect = "nested", random.method = "walhus")) summary(plm(form, data = pProduc, model = "random", effect = "nested", random.method = "amemiya")) plm/tests/test_model.matrix_pmodel.response.R0000644000176200001440000001141013413473532021170 0ustar liggesusers## Tests for model.matrix[.pFormula|.plm] and pmodel.response.[pFormula|.plm|.data.frame] # commented lines do not run in v1.5-15 # 1) model.matrix[.pFormula|.plm] # 2) pmodel.response.[pFormula|.plm|.data.frame] library(plm) data("Grunfeld", package="plm") form <- formula(inv ~ value + capital) plm_pool <- plm(form, data=Grunfeld, model="pooling") plm_fe <- plm(form, data=Grunfeld, model="within") plm_re <- plm(form, data=Grunfeld, model="random") ########### 1) model.matrix[.pFormula|.plm] ########### # pooling and within models work pdata.frame [albeit one should input a model.frame of class pdata.frame] pGrunfeld <- pdata.frame(Grunfeld, index = c("firm", "year")) mf <- model.frame(pGrunfeld, form) #MM modmat_pFormula_pdf_pool <- plm:::model.matrix.pFormula(form, data=pGrunfeld, model="pooling") # works #MM modmat_pFormula_pdf_fe <- plm:::model.matrix.pFormula(form, data=pGrunfeld, model="within") # works modmat_pFormula_pdf_pool <- plm:::model.matrix.pdata.frame(mf, model="pooling") # works modmat_pFormula_pdf_fe <- plm:::model.matrix.pdata.frame(mf, model="within") # works #modmat_pFormula_re2 <- plm:::model.matrix.pFormula(form, data=pGrunfeld, model="random") # still fails in v1.5-15 # Error: # Error in plm:::model.matrix.pFormula(form, data = pGrunfeld, model = "random") : # dims [product 600] do not match the length of object [0] #### some sanity checks if various interfaces yield the same result ### modmat_plm_pool <- model.matrix(plm_pool) modmat_plm_fe <- model.matrix(plm_fe) modmat_plm_re <- model.matrix(plm_re) ##### interfaces: plm vs. pFormula with pdata.frame if(!isTRUE(all.equal(modmat_plm_pool, modmat_pFormula_pdf_pool, check.attributes = FALSE))) stop("model.matrix's are not the same") if(!isTRUE(all.equal(modmat_plm_fe, modmat_pFormula_pdf_fe, check.attributes = FALSE))) stop("model.matrix's are not the same") #if(!isTRUE(all.equal(modmat_plm_re, modmat_pFormula_pdf_re, check.attributes = FALSE))) stop("model.matrix's are not the same") ########### 2) pmodel.response.[pFormula|.plm|.data.frame] ########### # pooling and within models work on a pdata.frame [the plain pdata.frame is coerced to a model.frame # internally in pmodel.response.pFormula] #MM resp_pFormula_pool <- plm:::pmodel.response.formula(form, data = pGrunfeld, model = "pooling") #MM resp_pFormula_fe <- plm:::pmodel.response.formula(form, data = pGrunfeld, model = "within") resp_pFormula_pool <- plm:::pmodel.response.formula(form, data = pGrunfeld, model = "pooling") resp_pFormula_fe <- plm:::pmodel.response.formula(form, data = pGrunfeld, model = "within") # still fails # resp_pFormula_re <- plm:::pmodel.response.pFormula(form, data = pGrunfeld, model = "random") # # Error in model.matrix.pFormula(pFormula(formula), data = data, model = model, : # dims [product 200] do not match the length of object [0] ### pmodel.response.data.frame on data.frame/pdata.frame ## the 'data' data.frame for pmodel.response.data.frame must be a model.frame created by plm's model.frame ## it needs to be a model.frame because then it is ensured we find the response variable in the fist column #pGrunfeld_mf <- model.frame(pFormula(form), data = pGrunfeld) pGrunfeld_mf <- model.frame(pGrunfeld, form) resp_pdf_mf_pool <- plm:::pmodel.response.data.frame(pGrunfeld_mf, model = "pooling") # works resp_pdf_mf_fe <- plm:::pmodel.response.data.frame(pGrunfeld_mf, model = "within") # works #resp_pdf_mf_re <- plm:::pmodel.response.data.frame(pGrunfeld_mf, model = "random") # error, likely due to missing arguments ## these errored pre rev. 601 due to missing 'match.arg()' to set default value: #pmodel.response(pFormula(form), data = pGrunfeld) pmodel.response(form, data = pGrunfeld) pmodel.response(pGrunfeld_mf) #### some sanity checks if various interfaces yield the same result ### resp_plm_pool <- pmodel.response(plm_pool) resp_plm_fe <- pmodel.response(plm_fe) resp_plm_re <- pmodel.response(plm_re) # compare interface pFormula with plm if(!isTRUE(all.equal(resp_pFormula_pool, resp_plm_pool, check.attributes = FALSE))) stop("responses not equal") if(!isTRUE(all.equal(resp_pFormula_fe, resp_plm_fe, check.attributes = FALSE))) stop("responses not equal") #if(!isTRUE(all.equal(resp_pFormula_re, resp_plm_re, check.attributes = FALSE))) stop("responses not equal") # compare interface data.frame with model.frame with plm if(!isTRUE(all.equal(resp_pdf_mf_pool, resp_plm_pool, check.attributes = FALSE))) stop("responses not equal") if(!isTRUE(all.equal(resp_pdf_mf_fe, resp_plm_fe, check.attributes = FALSE))) stop("responses not equal") #if(!isTRUE(all.equal(resp_pdf_mf_re, resp_plm_re, check.attributes = FALSE))) stop("responses not equal") plm/tests/test_pgrangertest.Rout.save0000644000176200001440000000743513623642640017577 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. > library(plm) > data("Grunfeld", package = "plm") > > pgrangertest(inv ~ value, data = Grunfeld) Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) data: inv ~ value Ztilde = 3.2896, p-value = 0.001003 alternative hypothesis: Granger causality for at least one individual > pgrangertest(inv ~ value, data = Grunfeld, order = 2L) Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) data: inv ~ value Ztilde = 1.6832, p-value = 0.09234 alternative hypothesis: Granger causality for at least one individual > pgrangertest(inv ~ value, data = Grunfeld, order = 2L, test = "Zbar") Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) data: inv ~ value Zbar = 2.9657, p-value = 0.00302 alternative hypothesis: Granger causality for at least one individual > > > # unbalanced > unbal <- pgrangertest(inv ~ value, data = Grunfeld[1:199, ], order = 2L) > unbal$indgranger firm Chisq p-value df lag 1 1 1.8255237 0.401414049 2 2 2 2 4.3694800 0.112506980 2 2 3 3 0.7983334 0.670878856 2 2 4 4 3.3069760 0.191381208 2 2 5 5 11.0631807 0.003959687 2 2 6 6 10.8343468 0.004439678 2 2 7 7 1.3410752 0.511433558 2 2 8 8 0.2900525 0.864999607 2 2 9 9 4.4068769 0.110422824 2 2 10 10 0.2960011 0.862430626 2 2 > > # varying lag order > bal_varorder <- pgrangertest(inv ~ value, data = Grunfeld[1:199, ], order = c(rep(2L, 9), 3L)) > bal_varorder$indgranger firm Chisq p-value df lag 1 1 1.8255237 0.401414049 2 2 2 2 4.3694800 0.112506980 2 2 3 3 0.7983334 0.670878856 2 2 4 4 3.3069760 0.191381208 2 2 5 5 11.0631807 0.003959687 2 2 6 6 10.8343468 0.004439678 2 2 7 7 1.3410752 0.511433558 2 2 8 8 0.2900525 0.864999607 2 2 9 9 4.4068769 0.110422824 2 2 10 10 2.9874921 0.393557671 3 3 > unbal_varorder <- pgrangertest(inv ~ value, data = Grunfeld[1:199, ], order = c(rep(2L, 9), 3L)) > unbal_varorder$indgranger firm Chisq p-value df lag 1 1 1.8255237 0.401414049 2 2 2 2 4.3694800 0.112506980 2 2 3 3 0.7983334 0.670878856 2 2 4 4 3.3069760 0.191381208 2 2 5 5 11.0631807 0.003959687 2 2 6 6 10.8343468 0.004439678 2 2 7 7 1.3410752 0.511433558 2 2 8 8 0.2900525 0.864999607 2 2 9 9 4.4068769 0.110422824 2 2 10 10 2.9874921 0.393557671 3 3 > > > ## Demo data from Dumitrescu/Hurlin (2012) supplement: > ## http://www.runmycode.org/companion/view/42 > ## The data are in the following format: 20 x 20 > ## First 20 columns are the x series for the 10 individual > ## next 20 columns are the y series for the 10 individuals > ## -> need to convert to 'long' format first > > # demodat <- readxl::read_excel("data/Granger_Data_demo_long.xls") > # demodat <- data.frame(demodat) > # pdemodat <- pdata.frame(demodat) > > # pgrangertest(y ~ x, data = pdemodat, order = 1L) > # pgrangertest(y ~ x, data = pdemodat, order = 1L, test = "Zbar") > # > # pgrangertest(y ~ x, data = pdemodat, order = 2L) > # pgrangertest(y ~ x, data = pdemodat, order = 2L, test = "Zbar") > > proc.time() user system elapsed 1.154 0.067 1.209 plm/tests/test_phtest_Hausman_regression.Rout.save0000644000176200001440000002707413623642640022316 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. > #### Hausman test (original version and regression-based version) > ## > ## > ## (1) comparison to Baltagi (2013), sec. 4.3.1, example 1 (pp. 81-82) > ## (2) comparison to Baltagi (2013), sec. 4.3.2, example 2 (pp. 82-83) > ## (3) comparison to Stata > > > ################################## (1) ################################## > # Baltagi (2013), Econometric Analysis of Panel Data, 5th edition, Wiley & Sons > # Sec 4.3.1, p. 81 (example 1): > # > #### statistics are: 2.33 for original Hausman (m1) > # 2.131 for m2, m3 (for the Grunfeld data) > # > #### vcov within * 10^-3: > # > # 0.14058 -0.077468 > # 0.3011788 > # > #### vcov between * 10^-3: > # > # 0.82630142 -3.7002477 > # 36.4572431 > > options(digits = 10) > library(plm) > data("Grunfeld", package = "plm") > Grunfeldpdata <- pdata.frame(Grunfeld, index = c("firm", "year"), drop.index = FALSE, row.names = TRUE) > fe_grun <- plm(inv ~ value + capital, data=Grunfeldpdata, model="within") > be_grun <- plm(inv ~ value + capital, data=Grunfeldpdata, model="between") > re_grun <- plm(inv ~ value + capital, data=Grunfeldpdata, model="random") > pool_grun <- plm(inv ~ value + capital, data=Grunfeldpdata, model="pooling") > > # Hausman test > # m1, m2, m3 are all mathematically identical; however computer computation differs a little bit > > phtest(inv ~ value + capital, Grunfeldpdata) # replicates Baltagi's m1 = 2.33 Hausman Test data: inv ~ value + capital chisq = 2.3303669, df = 2, p-value = 0.3118654 alternative hypothesis: one model is inconsistent > phtest(fe_grun, re_grun) # same as above, replicates Baltagi's m1 = 2.33 Hausman Test data: inv ~ value + capital chisq = 2.3303669, df = 2, p-value = 0.3118654 alternative hypothesis: one model is inconsistent > phtest(re_grun, fe_grun) Hausman Test data: inv ~ value + capital chisq = 2.3303669, df = 2, p-value = 0.3118654 alternative hypothesis: one model is inconsistent > > phtest(be_grun, re_grun) # replicates Baltagi's m2 = 2.131 Hausman Test data: inv ~ value + capital chisq = 2.1313791, df = 2, p-value = 0.3444902 alternative hypothesis: one model is inconsistent > phtest(re_grun, be_grun) Hausman Test data: inv ~ value + capital chisq = 2.1313791, df = 2, p-value = 0.3444902 alternative hypothesis: one model is inconsistent > phtest(be_grun, fe_grun) # replicates Baltagi's m3 = 2.131 [values m2 and m3 coincide in this case] Hausman Test data: inv ~ value + capital chisq = 2.1313662, df = 2, p-value = 0.3444924 alternative hypothesis: one model is inconsistent > phtest(fe_grun, be_grun) Hausman Test data: inv ~ value + capital chisq = 2.1313662, df = 2, p-value = 0.3444924 alternative hypothesis: one model is inconsistent > > phtest(inv ~ value + capital, Grunfeldpdata, method="aux") # replicates m3 from above in regression test Regression-based Hausman test data: inv ~ value + capital chisq = 2.1313662, df = 2, p-value = 0.3444924 alternative hypothesis: one model is inconsistent > phtest(inv ~ value + capital, Grunfeldpdata, method="aux", vcov = vcovHC) # no comparison value given Regression-based Hausman test, vcov: vcovHC data: inv ~ value + capital chisq = 8.2998366, df = 2, p-value = 0.0157657 alternative hypothesis: one model is inconsistent > > # replicates variance-covariance matrices > vcov(fe_grun)*1000 value capital value 0.14058119769 -0.07746798877 capital -0.07746798877 0.30117876659 > vcov(be_grun)*1000 (Intercept) value capital (Intercept) 2257704.4692300 127.5372064329 -6060.336301170 value 127.5372064 0.8263014212 -3.700247744 capital -6060.3363012 -3.7002477442 36.457243151 > > > ################################## (2) ################################## > # Baltagi (2013), Econometric Analysis of Panel Data, 5th edition, Wiley & Sons > # Sec 4.3.2, p. 82-83 (example 2): > ### Baltagi's Gasoline example > data("Gasoline", package = "plm") > form <- lgaspcar ~ lincomep + lrpmg + lcarpcap > fe <- plm(form, data = Gasoline, model = "within") > be <- plm(form, data = Gasoline, model = "between") > re <- plm(form, data = Gasoline, model = "random") > > phtest(fe, re) # replicates Baltagi's m1 = 302.8 Hausman Test data: form chisq = 302.80375, df = 3, p-value < 2.2204e-16 alternative hypothesis: one model is inconsistent > phtest(form, data = Gasoline) # same as above (m1) Hausman Test data: form chisq = 302.80375, df = 3, p-value < 2.2204e-16 alternative hypothesis: one model is inconsistent > > phtest(be, re) # replicates Baltagi's m2 = 27.45 Hausman Test data: form chisq = 27.454835, df = 3, p-value = 4.72651e-06 alternative hypothesis: one model is inconsistent > phtest(be, fe) # replicates Baltagi's m3 = 26.507 almost Hausman Test data: form chisq = 26.495054, df = 3, p-value = 7.511821e-06 alternative hypothesis: one model is inconsistent > > phtest(form, data = Gasoline, method = "aux") # chisq = 26.495054, replicates _almost_ Baltagi's m3 = 26.507 Regression-based Hausman test data: form chisq = 26.495054, df = 3, p-value = 7.511821e-06 alternative hypothesis: one model is inconsistent > > # replicates variance-covariance matrices > # > # vcov in Baltagi within: > # 0.539 0.029 -0.205 > # 0.194 0.009 > # 0.088 > # > # vcov in Baltagi between: > # 2.422 -1.694 -1.056 > # 1.766 0.883 > # 0.680 > vcov(fe)*100 lincomep lrpmg lcarpcap lincomep 0.53855115445 0.02895845376 -0.20490968678 lrpmg 0.02895845376 0.19447441921 0.00886367791 lcarpcap -0.20490968678 0.00886367791 0.08808342018 > vcov(be)*100 (Intercept) lincomep lrpmg lcarpcap (Intercept) 27.7501849361 4.431994498 -1.4951182465 0.1224824001 lincomep 4.4319944982 2.423196927 -1.6955014702 -1.0571031309 lrpmg -1.4951182465 -1.695501470 1.7668108564 0.8836800189 lcarpcap 0.1224824001 -1.057103131 0.8836800189 0.6801996482 > > > ##### twoways case ### > fe2_grun <- plm(inv ~ value + capital, data=Grunfeldpdata, model="within", effect = "twoways") > # be_grun <- plm(inv ~ value + capital, data=Grunfeldpdata, model="between") > # RE gives warning due to neg. variance estimation > re2_grun <- plm(inv ~ value + capital, data=Grunfeldpdata, model="random", effect = "twoways") > > > phtest(fe2_grun, re2_grun) # 13.460, p = 0.00194496 [also given by EViews 9.5; Hausman Test data: inv ~ value + capital chisq = 13.460061, df = 2, p-value = 0.001194496 alternative hypothesis: one model is inconsistent > # Baltagi (2013), p. 85 has other values due to older/wrong version of EViews?] > > > phtest(inv ~ value + capital, data=Grunfeldpdata, effect = "twoways") Hausman Test data: inv ~ value + capital chisq = 13.460061, df = 2, p-value = 0.001194496 alternative hypothesis: one model is inconsistent > phtest(inv ~ value + capital, data=Grunfeldpdata, effect = "time") Hausman Test data: inv ~ value + capital chisq = 0.32309434, df = 2, p-value = 0.8508264 alternative hypothesis: one model is inconsistent > > # test to see of phtest(, method = "aux") respects argument effect > # (rev. 305a introduced a quick fix and extracted argument effect from dots in function signature) > # formal test (statistic is about 13 for twoways case and well below in one-way cases) > testobj <- phtest(inv ~ value + capital, data=Grunfeldpdata, effect = "twoways", method = "aux") > #YC if (round(testobj$statistic, digits = 0) != 13) stop("argument effect seems to be not respected with method = \"aux\"") > testobj2 <- phtest(inv ~ value + capital, data=Grunfeldpdata, effect = "twoways") # just to be sure: test for method="chisq" also... > #YC if (round(testobj2$statistic, digits = 0) != 13) stop("argument effect seems to be not respected with method = \"chisq\"") > > > > # test for class of statistic [was matrix pre rev. 305] > testobj1 <- phtest(inv ~ value + capital, data=Grunfeldpdata, effect = "twoways", method = "aux") > testobj2 <- phtest(fe2_grun, re2_grun) > testobj3 <- phtest(inv ~ value + capital, data=Grunfeldpdata, effect = "twoways") > if (class(testobj1$statistic) != "numeric") stop(paste0("class of statistic is not numeric, but ", class(testobj1$statistic))) > if (class(testobj2$statistic) != "numeric") stop(paste0("class of statistic is not numeric, but ", class(testobj2$statistic))) > if (class(testobj3$statistic) != "numeric") stop(paste0("class of statistic is not numeric, but ", class(testobj3$statistic))) > > > > > # Two-ways case with beetween model should result in informative errors. > # phtest(fe2_grun, be_grun) > # phtest(re2_grun, be_grun) > > > > > ################################## (3) ################################## > ### comparison to Stata: > # Hausman test with Stata example 2, pp. 5-6 in http://www.stata.com/manuals/xtxtregpostestimation.pdf > # > # Results of phtest differ, most likely because RE model differs slightly from Stata's RE model as the > # default RE model in Stata uses a slightly different implementation of Swamy-Arora method > # [see http://www.stata.com/manuals/xtxtreg.pdf] > # > # Stata: > # chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) > # = 149.43 > # Prob>chi2 = 0.0000 > > # library(haven) > # nlswork <- read_dta("http://www.stata-press.com/data/r14/nlswork.dta") # large file > # nlswork$race <- factor(nlswork$race) # convert > # nlswork$race2 <- factor(ifelse(nlswork$race == 2, 1, 0)) # need this variable for example > # nlswork$grade <- as.numeric(nlswork$grade) > # nlswork$age2 <- (nlswork$age)^2 > # nlswork$tenure2 <- (nlswork$tenure)^2 > # nlswork$ttl_exp2 <- (nlswork$ttl_exp)^2 > # > # pnlswork <- pdata.frame(nlswork, index=c("idcode", "year"), drop.index=F) > # > # form_nls_ex2 <- formula(ln_wage ~ grade + age + age2 + ttl_exp + ttl_exp2 + tenure + tenure2 + race2 + not_smsa + south) > # > # plm_fe_nlswork <- plm(form_nls_ex2, data = pnlswork, model = "within") > # plm_be_nlswork <- plm(form_nls_ex2, data = pnlswork, model = "between") > # plm_re_nlswork <- plm(form_nls_ex2, data = pnlswork, model = "random") > # > # summary(plm_re_nlswork) > # > # ### Stata: chi2(8) = 149.43 > # phtest(plm_fe_nlswork, plm_re_nlswork) # chisq = 176.39, df = 8, p-value < 2.2e-16 > # phtest(plm_be_nlswork, plm_re_nlswork) # chisq = 141.97, df = 10, p-value < 2.2e-16 > # phtest(form_nls_ex2, data = pnlswork, method="aux") # chisq = 627.46, df = 8, p-value < 2.2e-16 [this resulted in an error for SVN revisions 125 - 141] > # phtest(form_nls_ex2, data = nlswork, method="aux") # same on data.frame > # phtest(form_nls_ex2, data = pnlswork, method="aux", vcov = vcovHC) # chisq = 583.56, df = 8, p-value < 2.2e-16 > # # phtest(form_nls_ex2, data = pnlswork, method="aux", vcov = function(x) vcovHC(x, method="white2", type="HC3")) # computationally too heavy! > > > > proc.time() user system elapsed 1.216 0.102 1.308 plm/tests/test_pgmm.R0000644000176200001440000000331513245066260014334 0ustar liggesuserslibrary("plm") data("EmplUK", package = "plm") ## Arellano and Bond (1991), table 4 col. b z1 <- 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, effect = "twoways", model = "twosteps") summary(z1) z1col <- 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, effect = "twoways", model = "twosteps", collapse = TRUE) summary(z1col) z1ind <- 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, effect = "individual", model = "twosteps") summary(z1ind) z1indcol <- 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, effect = "individual", model = "twosteps") summary(z1indcol) ## Blundell and Bond (1998) table 4 (cf DPD for OX p.12 col.4) ## not quite... z2 <- pgmm(log(emp) ~ lag(log(emp), 1)+ lag(log(wage), 0:1) + lag(log(capital), 0:1) | lag(log(emp), 2:99) + lag(log(wage), 3:99) + lag(log(capital), 2:99), data = EmplUK, effect = "twoways", model = "onestep", transformation = "ld") summary(z2, robust = TRUE) z2b <- pgmm(log(emp) ~ lag(log(emp), 1)+ lag(log(wage), 0:1) + lag(log(capital), 0:1) | lag(log(emp), 2:99) + lag(log(wage), 3:99) + lag(log(capital), 2:99), data = EmplUK, effect = "individual", model = "onestep", transformation = "ld") summary(z2b, robust = TRUE) plm/tests/test_vcovG_lin_dep.R0000644000176200001440000000564113413473532016157 0ustar liggesusers# Currently (in at least rev. 195), plm() and summary() can deal with linear dependent # columns (by silently dropping them), but vcovG framework had a hiccup pre rev. 302 # see the example below library(plm) data("Cigar", package = "plm") Cigar.p <- pdata.frame(Cigar) Cigar.p[ , "fact1"] <- c(0,1) Cigar.p[ , "fact2"] <- c(1,0) # linear dependent columns are silently dropped in these functions, thus they work mod_fe_lin_dep <- plm(price ~ cpi + fact1 + fact2, data = Cigar.p, model = "within") # contains lin dep columns mod_fe_no_lin_dep <- plm(price ~ cpi + fact1, data = Cigar.p, model = "within") # does not contain lin dep columns summary(mod_fe_lin_dep) # works with linear dep columns summary(mod_fe_no_lin_dep) # detect linear dependence detect_lin_dep(model.matrix(mod_fe_lin_dep)) detect_lin_dep(model.matrix(mod_fe_no_lin_dep)) mod_fe_lin_dep$aliased mod_fe_no_lin_dep$aliased # failed in vcovG up to rev. 301; # fixed in rev. 302 by taking care of aliased coefficients # the linear dependent column is not dropped leading to failing function due # to the non-invertible matrix vcovHC(mod_fe_lin_dep) vcovHC(mod_fe_no_lin_dep) if (!identical(vcovHC(mod_fe_lin_dep), vcovHC(mod_fe_no_lin_dep))) { stop("vcov w/ linear dependent columns and the corresponding one w/o are not identical") } ## test for vcovBK because code is separate from the vcovG framework vcovBK(mod_fe_lin_dep) vcovBK(mod_fe_no_lin_dep) if (!identical(vcovBK(mod_fe_lin_dep), vcovBK(mod_fe_no_lin_dep))) { stop("vcov w/ linear dependent columns and the corresponding one w/o are not identical") } ## test for IV models with linear dependent columns data("Crime", package = "plm") cr <- plm(log(crmrte) ~ log(prbarr) + log(polpc) + log(prbconv) + I(2*log(prbconv)) | log(prbarr) + log(polpc) + log(taxpc) + log(mix), data = Crime, model = "pooling") head(model.matrix(cr$formula, cr$model, rhs = 1)) head(model.matrix(cr$formula, cr$model, rhs = 2)) detect_lin_dep(cr) vcovHC(cr) vcovBK(cr) ## linear dependence in instrument part cr2 <- plm(log(crmrte) ~ log(prbarr) + log(polpc) + log(prbconv) | log(prbarr) + log(polpc) + log(taxpc) + log(mix) + I(2*log(mix)), data = Crime, model = "pooling") detect_lin_dep(cr2) # does not inspect instrument matrix head(model.matrix(cr2$formula, cr2$model, rhs = 2)) detect_lin_dep(model.matrix(cr2$formula, cr2$model, rhs = 2)) vcovHC(cr2) vcovBK(cr2) # just run test for for pgmm models (as vcovXX.pgmm methods use vcovXX.plm) # (no linear dependence involved here) data("EmplUK", package="plm") ar <- pgmm(dynformula(log(emp) ~ log(wage) + log(capital) + log(output), list(2, 1, 2, 2)), data = EmplUK, effect = "twoways", model = "twosteps", gmm.inst = ~ log(emp), lag.gmm = list(c(2, 99))) vcovHC(ar) plm:::vcovHC.pgmm(ar) plm/tests/Examples/0000755000176200001440000000000013623646153013773 5ustar liggesusersplm/tests/Examples/plm-Ex.Rout.save0000644000176200001440000237416213623642640016761 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. 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Type 'q()' to quit R. > pkgname <- "plm" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('plm') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("Grunfeld") > ### * Grunfeld > > flush(stderr()); flush(stdout()) > > ### Name: Grunfeld > ### Title: Grunfeld's Investment Data > ### Aliases: Grunfeld > ### Keywords: datasets > > ### ** Examples > > > ## Not run: > ##D # Compare plm's Grunfeld data to Baltagi's (2001) Grunfeld data: > ##D data("Grunfeld", package="plm") > ##D Grunfeld_baltagi2001 <- read.csv("http://www.wiley.com/legacy/wileychi/ > ##D baltagi/supp/Grunfeld.fil", sep="", header = FALSE) > ##D library(compare) > ##D compare::compare(Grunfeld, Grunfeld_baltagi2001, allowAll = T) # same data set > ##D > ## End(Not run) > > > > > cleanEx() > nameEx("Wages") > ### * Wages > > flush(stderr()); flush(stdout()) > > ### Name: Wages > ### Title: Panel Data of Individual Wages > ### Aliases: Wages > ### Keywords: datasets > > ### ** Examples > > > # data set 'Wages' is organized as a stacked time series/balanced panel > data("Wages", package = "plm") > Wag <- pdata.frame(Wages, index=595) > > > > > cleanEx() > nameEx("aneweytest") > ### * aneweytest > > flush(stderr()); flush(stdout()) > > ### Name: aneweytest > ### Title: Chamberlain estimator and test for fixed effects > ### Aliases: aneweytest > ### Keywords: htest > > ### ** Examples > > > data("RiceFarms", package = "plm") > aneweytest(log(goutput) ~ log(seed) + log(totlabor) + log(size), RiceFarms, index = "id") Angrist and Newey's test of within model data: log(goutput) ~ log(seed) + log(totlabor) + log(size) chisq = 141.89, df = 87, p-value = 0.0001851 > > > > > cleanEx() > nameEx("cipstest") > ### * cipstest > > flush(stderr()); flush(stdout()) > > ### Name: cipstest > ### Title: Cross-sectionally Augmented IPS Test for Unit Roots in Panel > ### Models > ### Aliases: cipstest > ### Keywords: htest > > ### ** Examples > > > data("Produc", package = "plm") > Produc <- pdata.frame(Produc, index=c("state", "year")) > ## check whether the gross state product (gsp) is trend-stationary > cipstest(Produc$gsp, type = "trend") Warning in cipstest(Produc$gsp, type = "trend") : p-value greater than printed p-value Pesaran's CIPS test for unit roots data: Produc$gsp CIPS test = -0.58228, lag order = 2, p-value = 0.1 alternative hypothesis: Stationarity > > > > > cleanEx() > nameEx("detect.lindep") > ### * detect.lindep > > flush(stderr()); flush(stdout()) > > ### Name: detect.lindep > ### Title: Functions to detect linear dependence > ### Aliases: detect.lindep detect.lindep.matrix detect.lindep.data.frame > ### detect.lindep.plm alias.plm alias.pdata.frame > ### Keywords: array manip > > ### ** Examples > > > ### Example 1 ### > # prepare the data > data("Cigar" , package = "plm") > Cigar[ , "fact1"] <- c(0,1) > Cigar[ , "fact2"] <- c(1,0) > Cigar.p <- pdata.frame(Cigar) > > # setup a formula and a model frame > form <- price ~ 0 + cpi + fact1 + fact2 > mf <- model.frame(Cigar.p, form) > # no linear dependence in the pooling model's model matrix > # (with intercept in the formula, there would be linear depedence) > detect.lindep(model.matrix(mf, model = "pooling")) [1] "No linear dependent column(s) detected." > # linear dependence present in the FE transformed model matrix > modmat_FE <- model.matrix(mf, model = "within") > detect.lindep(modmat_FE) [1] "Suspiscious column number(s): 2, 3" [1] "Suspiscious column name(s): fact1, fact2" > mod_FE <- plm(form, data = Cigar.p, model = "within") > detect.lindep(mod_FE) [1] "Suspiscious column number(s): 2, 3" [1] "Suspiscious column name(s): fact1, fact2" > alias(mod_FE) # => fact1 == -1*fact2 Model : [1] "price ~ 0 + cpi + fact1 + fact2" Complete : cpi fact1 fact2 0 -1 > plm(form, data = mf, model = "within")$aliased # "fact2" indicated as aliased cpi fact1 fact2 FALSE FALSE TRUE > > # look at the data: after FE transformation fact1 == -1*fact2 > head(modmat_FE) cpi fact1 fact2 1 -42.99667 -0.5 0.5 2 -42.59667 0.5 -0.5 3 -42.09667 -0.5 0.5 4 -41.19667 0.5 -0.5 5 -40.19667 -0.5 0.5 6 -38.79667 0.5 -0.5 > all.equal(modmat_FE[ , "fact1"], -1*modmat_FE[ , "fact2"]) [1] TRUE > > ### Example 2 ### > # Setup the data: > # Assume CEOs stay with the firms of the Grunfeld data > # for the firm's entire lifetime and assume some fictional > # data about CEO tenure and age in year 1935 (first observation > # in the data set) to be at 1 to 10 years and 38 to 55 years, respectively. > # => CEO tenure and CEO age increase by same value (+1 year per year). > data("Grunfeld", package = "plm") > set.seed(42) > # add fictional data > Grunfeld$CEOtenure <- c(replicate(10, seq(from=s<-sample(1:10, 1), to=s+19, by=1))) > Grunfeld$CEOage <- c(replicate(10, seq(from=s<-sample(38:65, 1), to=s+19, by=1))) > > # look at the data > head(Grunfeld, 50) firm year inv value capital CEOtenure CEOage 1 1 1935 317.6 3078.5 2.8 1 44 2 1 1936 391.8 4661.7 52.6 2 45 3 1 1937 410.6 5387.1 156.9 3 46 4 1 1938 257.7 2792.2 209.2 4 47 5 1 1939 330.8 4313.2 203.4 5 48 6 1 1940 461.2 4643.9 207.2 6 49 7 1 1941 512.0 4551.2 255.2 7 50 8 1 1942 448.0 3244.1 303.7 8 51 9 1 1943 499.6 4053.7 264.1 9 52 10 1 1944 547.5 4379.3 201.6 10 53 11 1 1945 561.2 4840.9 265.0 11 54 12 1 1946 688.1 4900.9 402.2 12 55 13 1 1947 568.9 3526.5 761.5 13 56 14 1 1948 529.2 3254.7 922.4 14 57 15 1 1949 555.1 3700.2 1020.1 15 58 16 1 1950 642.9 3755.6 1099.0 16 59 17 1 1951 755.9 4833.0 1207.7 17 60 18 1 1952 891.2 4924.9 1430.5 18 61 19 1 1953 1304.4 6241.7 1777.3 19 62 20 1 1954 1486.7 5593.6 2226.3 20 63 21 2 1935 209.9 1362.4 53.8 5 41 22 2 1936 355.3 1807.1 50.5 6 42 23 2 1937 469.9 2676.3 118.1 7 43 24 2 1938 262.3 1801.9 260.2 8 44 25 2 1939 230.4 1957.3 312.7 9 45 26 2 1940 361.6 2202.9 254.2 10 46 27 2 1941 472.8 2380.5 261.4 11 47 28 2 1942 445.6 2168.6 298.7 12 48 29 2 1943 361.6 1985.1 301.8 13 49 30 2 1944 288.2 1813.9 279.1 14 50 31 2 1945 258.7 1850.2 213.8 15 51 32 2 1946 420.3 2067.7 132.6 16 52 33 2 1947 420.5 1796.7 264.8 17 53 34 2 1948 494.5 1625.8 306.9 18 54 35 2 1949 405.1 1667.0 351.1 19 55 36 2 1950 418.8 1677.4 357.8 20 56 37 2 1951 588.2 2289.5 342.1 21 57 38 2 1952 645.5 2159.4 444.2 22 58 39 2 1953 641.0 2031.3 623.6 23 59 40 2 1954 459.3 2115.5 669.7 24 60 41 3 1935 33.1 1170.6 97.8 1 62 42 3 1936 45.0 2015.8 104.4 2 63 43 3 1937 77.2 2803.3 118.0 3 64 44 3 1938 44.6 2039.7 156.2 4 65 45 3 1939 48.1 2256.2 172.6 5 66 46 3 1940 74.4 2132.2 186.6 6 67 47 3 1941 113.0 1834.1 220.9 7 68 48 3 1942 91.9 1588.0 287.8 8 69 49 3 1943 61.3 1749.4 319.9 9 70 50 3 1944 56.8 1687.2 321.3 10 71 > > form <- inv ~ value + capital + CEOtenure + CEOage > mf <- model.frame(pdata.frame(Grunfeld), form) > # no linear dependent columns in original data/pooling model > modmat_pool <- model.matrix(mf, model="pooling") > detect.lindep(modmat_pool) [1] "No linear dependent column(s) detected." > mod_pool <- plm(form, data = Grunfeld, model = "pooling") > alias(mod_pool) Model : [1] "inv ~ value + capital + CEOtenure + CEOage" > > # CEOtenure and CEOage are linear dependent after FE transformation > # (demeaning per individual) > modmat_FE <- model.matrix(mf, model="within") > detect.lindep(modmat_FE) [1] "Suspiscious column number(s): 3, 4" [1] "Suspiscious column name(s): CEOtenure, CEOage" > mod_FE <- plm(form, data = Grunfeld, model = "within") > detect.lindep(mod_FE) [1] "Suspiscious column number(s): 3, 4" [1] "Suspiscious column name(s): CEOtenure, CEOage" > alias(mod_FE) Model : [1] "inv ~ value + capital + CEOtenure + CEOage" Complete : value capital CEOtenure CEOage 0 0 1 > > # look at the transformed data: after FE transformation CEOtenure == 1*CEOage > head(modmat_FE, 50) value capital CEOtenure CEOage 1 -1255.345 -645.635 -9.5 -9.5 2 327.855 -595.835 -8.5 -8.5 3 1053.255 -491.535 -7.5 -7.5 4 -1541.645 -439.235 -6.5 -6.5 5 -20.645 -445.035 -5.5 -5.5 6 310.055 -441.235 -4.5 -4.5 7 217.355 -393.235 -3.5 -3.5 8 -1089.745 -344.735 -2.5 -2.5 9 -280.145 -384.335 -1.5 -1.5 10 45.455 -446.835 -0.5 -0.5 11 507.055 -383.435 0.5 0.5 12 567.055 -246.235 1.5 1.5 13 -807.345 113.065 2.5 2.5 14 -1079.145 273.965 3.5 3.5 15 -633.645 371.665 4.5 4.5 16 -578.245 450.565 5.5 5.5 17 499.155 559.265 6.5 6.5 18 591.055 782.065 7.5 7.5 19 1907.855 1128.865 8.5 8.5 20 1259.755 1577.865 9.5 9.5 21 -609.425 -241.055 -9.5 -9.5 22 -164.725 -244.355 -8.5 -8.5 23 704.475 -176.755 -7.5 -7.5 24 -169.925 -34.655 -6.5 -6.5 25 -14.525 17.845 -5.5 -5.5 26 231.075 -40.655 -4.5 -4.5 27 408.675 -33.455 -3.5 -3.5 28 196.775 3.845 -2.5 -2.5 29 13.275 6.945 -1.5 -1.5 30 -157.925 -15.755 -0.5 -0.5 31 -121.625 -81.055 0.5 0.5 32 95.875 -162.255 1.5 1.5 33 -175.125 -30.055 2.5 2.5 34 -346.025 12.045 3.5 3.5 35 -304.825 56.245 4.5 4.5 36 -294.425 62.945 5.5 5.5 37 317.675 47.245 6.5 6.5 38 187.575 149.345 7.5 7.5 39 59.475 328.745 8.5 8.5 40 143.675 374.845 9.5 9.5 41 -770.725 -302.360 -9.5 -9.5 42 74.475 -295.760 -8.5 -8.5 43 861.975 -282.160 -7.5 -7.5 44 98.375 -243.960 -6.5 -6.5 45 314.875 -227.560 -5.5 -5.5 46 190.875 -213.560 -4.5 -4.5 47 -107.225 -179.260 -3.5 -3.5 48 -353.325 -112.360 -2.5 -2.5 49 -191.925 -80.260 -1.5 -1.5 50 -254.125 -78.860 -0.5 -0.5 > all.equal(modmat_FE[ , "CEOtenure"], modmat_FE[ , "CEOage"]) [1] TRUE > > > > > cleanEx() > nameEx("ercomp") > ### * ercomp > > flush(stderr()); flush(stdout()) > > ### Name: ercomp > ### Title: Estimation of the error components > ### Aliases: ercomp ercomp.plm ercomp.pdata.frame ercomp.formula > ### print.ercomp > ### Keywords: regression > > ### ** Examples > > > data("Produc", package = "plm") > # an example of the formula method > ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, + method = "walhus", effect = "time") var std.dev share idiosyncratic 0.0075942 0.0871449 0.985 time 0.0001192 0.0109175 0.015 theta: 0.2448 > # same with the plm method > z <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, random.method = "walhus", + effect = "time", model = "random") > ercomp(z) var std.dev share idiosyncratic 0.0075942 0.0871449 0.985 time 0.0001192 0.0109175 0.015 theta: 0.2448 > # a two-ways model > ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, + method = "amemiya", effect = "twoways") var std.dev share idiosyncratic 0.0011695 0.0341975 0.046 individual 0.0238635 0.1544780 0.929 time 0.0006534 0.0255613 0.025 theta: 0.9464 (id) 0.8104 (time) 0.8084 (total) > > > > > cleanEx() > nameEx("fixef.plm") > ### * fixef.plm > > flush(stderr()); flush(stdout()) > > ### Name: fixef.plm > ### Title: Extract the Fixed Effects > ### Aliases: fixef.plm print.fixef summary.fixef print.summary.fixef > ### Keywords: regression > > ### ** Examples > > > data("Grunfeld", package = "plm") > gi <- plm(inv ~ value + capital, data = Grunfeld, model = "within") > fixef(gi) 1 2 3 4 5 6 7 8 -70.2967 101.9058 -235.5718 -27.8093 -114.6168 -23.1613 -66.5535 -57.5457 9 10 -87.2223 -6.5678 > summary(fixef(gi)) Estimate Std. Error t-value Pr(>|t|) 1 -70.2967 49.7080 -1.4142 0.15896 2 101.9058 24.9383 4.0863 6.485e-05 *** 3 -235.5718 24.4316 -9.6421 < 2.2e-16 *** 4 -27.8093 14.0778 -1.9754 0.04969 * 5 -114.6168 14.1654 -8.0913 7.141e-14 *** 6 -23.1613 12.6687 -1.8282 0.06910 . 7 -66.5535 12.8430 -5.1821 5.629e-07 *** 8 -57.5457 13.9931 -4.1124 5.848e-05 *** 9 -87.2223 12.8919 -6.7657 1.635e-10 *** 10 -6.5678 11.8269 -0.5553 0.57933 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(fixef(gi))[ , c("Estimate", "Pr(>|t|)")] # only estimates and p-values Estimate Pr(>|t|) 1 -70.296717 1.589588e-01 2 101.905814 6.485244e-05 3 -235.571841 3.951420e-18 4 -27.809295 4.968535e-02 5 -114.616813 7.141096e-14 6 -23.161295 6.910077e-02 7 -66.553474 5.629040e-07 8 -57.545657 5.847710e-05 9 -87.222272 1.634506e-10 10 -6.567844 5.793282e-01 > > # relationship of type = "dmean" and "level" and overall intercept > fx_level <- fixef(gi, type = "level") > fx_dmean <- fixef(gi, type = "dmean") > overallint <- within_intercept(gi) > all.equal(overallint + fx_dmean, fx_level, check.attributes = FALSE) # TRUE [1] TRUE > > # extract time effects in a twoways effects model > gi_tw <- plm(inv ~ value + capital, data = Grunfeld, + model = "within", effect = "twoways") > fixef(gi_tw, effect = "time") 1935 1936 1937 1938 1939 1940 1941 1942 -32.836 -52.034 -73.526 -72.063 -102.307 -77.071 -51.641 -53.976 1943 1944 1945 1946 1947 1948 1949 1950 -75.814 -75.935 -88.519 -64.006 -72.229 -76.553 -106.331 -108.732 1951 1952 1953 1954 -95.317 -97.469 -100.554 -126.363 > > # with supplied variance-covariance matrix as matrix, function, > # and function with additional arguments > fx_level_robust1 <- fixef(gi, vcov = vcovHC(gi)) > fx_level_robust2 <- fixef(gi, vcov = vcovHC) > fx_level_robust3 <- fixef(gi, vcov = function(x) vcovHC(x, method = "white2")) > summary(fx_level_robust1) # gives fixed effects, robust SEs, t- and p-values Estimate Std. Error t-value Pr(>|t|) 1 -70.2967 85.9735 -0.8177 0.4145887 2 101.9058 40.4965 2.5164 0.0126921 * 3 -235.5718 44.2724 -5.3210 2.917e-07 *** 4 -27.8093 18.5658 -1.4979 0.1358420 5 -114.6168 28.8403 -3.9742 0.0001006 *** 6 -23.1613 15.4598 -1.4982 0.1357680 7 -66.5535 20.7159 -3.2127 0.0015474 ** 8 -57.5457 17.2617 -3.3337 0.0010321 ** 9 -87.2223 21.5669 -4.0443 7.654e-05 *** 10 -6.5678 11.8616 -0.5537 0.5804368 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > # calc. fitted values of oneway within model: > fixefs <- fixef(gi)[index(gi, which = "id")] > fitted_by_hand <- fixefs + gi$coefficients["value"] * gi$model$value + + gi$coefficients["capital"] * gi$model$capital > > > > > cleanEx() > nameEx("index.plm") > ### * index.plm > > flush(stderr()); flush(stdout()) > > ### Name: index.plm > ### Title: Extract the indexes of panel data > ### Aliases: index.plm index.pindex index.pdata.frame index.pseries > ### index.panelmodel > ### Keywords: attribute > > ### ** Examples > > > data("Grunfeld", package = "plm") > Gr <- pdata.frame(Grunfeld, index = c("firm", "year")) > m <- plm(inv ~ value + capital, data = Gr) > index(Gr, "firm") [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 [26] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 [51] 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [76] 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [101] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 [126] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 [151] 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 [176] 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Levels: 1 2 3 4 5 6 7 8 9 10 > index(Gr, "time") [1] 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 [16] 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 [31] 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939 [46] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 [61] 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 [76] 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 [91] 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939 [106] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 [121] 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 [136] 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 [151] 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939 [166] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 [181] 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 [196] 1950 1951 1952 1953 1954 20 Levels: 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 ... 1954 > index(Gr$inv, c(2, 1)) year firm 1 1935 1 2 1936 1 3 1937 1 4 1938 1 5 1939 1 6 1940 1 7 1941 1 8 1942 1 9 1943 1 10 1944 1 11 1945 1 12 1946 1 13 1947 1 14 1948 1 15 1949 1 16 1950 1 17 1951 1 18 1952 1 19 1953 1 20 1954 1 21 1935 2 22 1936 2 23 1937 2 24 1938 2 25 1939 2 26 1940 2 27 1941 2 28 1942 2 29 1943 2 30 1944 2 31 1945 2 32 1946 2 33 1947 2 34 1948 2 35 1949 2 36 1950 2 37 1951 2 38 1952 2 39 1953 2 40 1954 2 41 1935 3 42 1936 3 43 1937 3 44 1938 3 45 1939 3 46 1940 3 47 1941 3 48 1942 3 49 1943 3 50 1944 3 51 1945 3 52 1946 3 53 1947 3 54 1948 3 55 1949 3 56 1950 3 57 1951 3 58 1952 3 59 1953 3 60 1954 3 61 1935 4 62 1936 4 63 1937 4 64 1938 4 65 1939 4 66 1940 4 67 1941 4 68 1942 4 69 1943 4 70 1944 4 71 1945 4 72 1946 4 73 1947 4 74 1948 4 75 1949 4 76 1950 4 77 1951 4 78 1952 4 79 1953 4 80 1954 4 81 1935 5 82 1936 5 83 1937 5 84 1938 5 85 1939 5 86 1940 5 87 1941 5 88 1942 5 89 1943 5 90 1944 5 91 1945 5 92 1946 5 93 1947 5 94 1948 5 95 1949 5 96 1950 5 97 1951 5 98 1952 5 99 1953 5 100 1954 5 101 1935 6 102 1936 6 103 1937 6 104 1938 6 105 1939 6 106 1940 6 107 1941 6 108 1942 6 109 1943 6 110 1944 6 111 1945 6 112 1946 6 113 1947 6 114 1948 6 115 1949 6 116 1950 6 117 1951 6 118 1952 6 119 1953 6 120 1954 6 121 1935 7 122 1936 7 123 1937 7 124 1938 7 125 1939 7 126 1940 7 127 1941 7 128 1942 7 129 1943 7 130 1944 7 131 1945 7 132 1946 7 133 1947 7 134 1948 7 135 1949 7 136 1950 7 137 1951 7 138 1952 7 139 1953 7 140 1954 7 141 1935 8 142 1936 8 143 1937 8 144 1938 8 145 1939 8 146 1940 8 147 1941 8 148 1942 8 149 1943 8 150 1944 8 151 1945 8 152 1946 8 153 1947 8 154 1948 8 155 1949 8 156 1950 8 157 1951 8 158 1952 8 159 1953 8 160 1954 8 161 1935 9 162 1936 9 163 1937 9 164 1938 9 165 1939 9 166 1940 9 167 1941 9 168 1942 9 169 1943 9 170 1944 9 171 1945 9 172 1946 9 173 1947 9 174 1948 9 175 1949 9 176 1950 9 177 1951 9 178 1952 9 179 1953 9 180 1954 9 181 1935 10 182 1936 10 183 1937 10 184 1938 10 185 1939 10 186 1940 10 187 1941 10 188 1942 10 189 1943 10 190 1944 10 191 1945 10 192 1946 10 193 1947 10 194 1948 10 195 1949 10 196 1950 10 197 1951 10 198 1952 10 199 1953 10 200 1954 10 > index(m, "id") [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 [26] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 [51] 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [76] 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [101] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 [126] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 [151] 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 [176] 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Levels: 1 2 3 4 5 6 7 8 9 10 > > # with additional group index > data("Produc", package = "plm") > pProduc <- pdata.frame(Produc, index = c("state", "year", "region")) > index(pProduc, 3) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [149] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [223] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [260] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [297] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [334] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 [371] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [408] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [445] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [482] 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [519] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [556] 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 [593] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 [630] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [667] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [704] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [741] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 [778] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 [815] 9 9 Levels: 1 2 3 4 5 6 7 8 9 > index(pProduc, "region") [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [149] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [223] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [260] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [297] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [334] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 [371] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [408] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [445] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [482] 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [519] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [556] 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 [593] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 [630] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [667] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [704] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [741] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 [778] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 [815] 9 9 Levels: 1 2 3 4 5 6 7 8 9 > index(pProduc, "group") [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [149] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [223] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [260] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [297] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 [334] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 [371] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [408] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [445] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 [482] 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [519] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [556] 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 [593] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 [630] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [667] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [704] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 [741] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 [778] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 [815] 9 9 Levels: 1 2 3 4 5 6 7 8 9 > > > > > cleanEx() > nameEx("is.pbalanced") > ### * is.pbalanced > > flush(stderr()); flush(stdout()) > > ### Name: is.pbalanced > ### Title: Check if data are balanced > ### Aliases: is.pbalanced is.pbalanced.default is.pbalanced.data.frame > ### is.pbalanced.pdata.frame is.pbalanced.pseries is.pbalanced.panelmodel > ### is.pbalanced.pgmm > ### Keywords: attribute > > ### ** Examples > > > # take balanced data and make it unbalanced > # by deletion of 2nd row (2nd time period for first individual) > data("Grunfeld", package = "plm") > Grunfeld_missing_period <- Grunfeld[-2, ] > is.pbalanced(Grunfeld_missing_period) # check if balanced: FALSE [1] FALSE > pdim(Grunfeld_missing_period)$balanced # same [1] FALSE > > # pdata.frame interface > pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) > is.pbalanced(Grunfeld_missing_period) [1] FALSE > > # pseries interface > is.pbalanced(pGrunfeld_missing_period$inv) [1] FALSE > > > > > cleanEx() > nameEx("is.pconsecutive") > ### * is.pconsecutive > > flush(stderr()); flush(stdout()) > > ### Name: is.pconsecutive > ### Title: Check if time periods are consecutive > ### Aliases: is.pconsecutive is.pconsecutive.default > ### is.pconsecutive.data.frame is.pconsecutive.pseries > ### is.pconsecutive.pdata.frame is.pconsecutive.panelmodel > ### Keywords: attribute > > ### ** Examples > > > data("Grunfeld", package = "plm") > is.pconsecutive(Grunfeld) 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > is.pconsecutive(Grunfeld, index=c("firm", "year")) 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > # delete 2nd row (2nd time period for first individual) > # -> non consecutive > Grunfeld_missing_period <- Grunfeld[-2, ] > is.pconsecutive(Grunfeld_missing_period) 1 2 3 4 5 6 7 8 9 10 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > all(is.pconsecutive(Grunfeld_missing_period)) # FALSE [1] FALSE > > # delete rows 1 and 2 (1st and 2nd time period for first individual) > # -> consecutive > Grunfeld_missing_period_other <- Grunfeld[-c(1,2), ] > is.pconsecutive(Grunfeld_missing_period_other) # all TRUE 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > # delete year 1937 (3rd period) for _all_ individuals > Grunfeld_wo_1937 <- Grunfeld[Grunfeld$year != 1937, ] > is.pconsecutive(Grunfeld_wo_1937) # all FALSE 1 2 3 4 5 6 7 8 9 10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE > > # pdata.frame interface > pGrunfeld <- pdata.frame(Grunfeld) > pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) > is.pconsecutive(pGrunfeld) # all TRUE 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > is.pconsecutive(pGrunfeld_missing_period) # first FALSE, others TRUE 1 2 3 4 5 6 7 8 9 10 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > > # panelmodel interface (first, estimate some models) > mod_pGrunfeld <- plm(inv ~ value + capital, data = Grunfeld) > mod_pGrunfeld_missing_period <- plm(inv ~ value + capital, data = Grunfeld_missing_period) > > is.pconsecutive(mod_pGrunfeld) 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > is.pconsecutive(mod_pGrunfeld_missing_period) 1 2 3 4 5 6 7 8 9 10 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > nobs(mod_pGrunfeld) # 200 [1] 200 > nobs(mod_pGrunfeld_missing_period) # 199 [1] 199 > > > # pseries interface > pinv <- pGrunfeld$inv > pinv_missing_period <- pGrunfeld_missing_period$inv > > is.pconsecutive(pinv) 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > is.pconsecutive(pinv_missing_period) 1 2 3 4 5 6 7 8 9 10 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > # default method for arbitrary vectors or NULL > inv <- Grunfeld$inv > inv_missing_period <- Grunfeld_missing_period$inv > is.pconsecutive(inv, id = Grunfeld$firm, time = Grunfeld$year) 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > is.pconsecutive(inv_missing_period, id = Grunfeld_missing_period$firm, + time = Grunfeld_missing_period$year) 1 2 3 4 5 6 7 8 9 10 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > # (not run) demonstrate mismatch lengths of x, id, time > # is.pconsecutive(x = inv_missing_period, id = Grunfeld$firm, time = Grunfeld$year) > > # only id and time are needed for evaluation > is.pconsecutive(NULL, id = Grunfeld$firm, time = Grunfeld$year) 1 2 3 4 5 6 7 8 9 10 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > > > > cleanEx() > nameEx("is.pseries") > ### * is.pseries > > flush(stderr()); flush(stdout()) > > ### Name: is.pseries > ### Title: Check if an object is a pseries > ### Aliases: is.pseries > ### Keywords: attribute > > ### ** Examples > > > # Create a pdata.frame and extract a series, which becomes a pseries > data("EmplUK", package = "plm") > Em <- pdata.frame(EmplUK) > z <- Em$output > > class(z) # pseries as indicated by class [1] "pseries" "numeric" > is.pseries(z) # and confirmed by check [1] TRUE > > # destroy index of pseries and re-check > attr(z, "index") <- NA > is.pseries(z) # now FALSE [1] FALSE > > > > > cleanEx() > nameEx("lag.plm") > ### * lag.plm > > flush(stderr()); flush(stdout()) > > ### Name: lag.plm > ### Title: lag, lead, and diff for panel data > ### Aliases: lag.plm lead lag.pseries lead.pseries diff.pseries > ### Keywords: classes > > ### ** Examples > > > # First, create a pdata.frame > data("EmplUK", package = "plm") > Em <- pdata.frame(EmplUK) > > # Then extract a series, which becomes additionally a pseries > z <- Em$output > class(z) [1] "pseries" "numeric" > > # compute the first and third lag, and the difference lagged twice > lag(z) 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 1-1983 2-1977 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 NA 2-1978 2-1979 2-1980 2-1981 2-1982 2-1983 3-1977 3-1978 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 NA 95.7072 3-1979 3-1980 3-1981 3-1982 3-1983 4-1977 4-1978 4-1979 97.3569 99.6083 100.5501 99.5581 98.6151 NA 118.2223 120.1551 4-1980 4-1981 4-1982 4-1983 5-1976 5-1977 5-1978 5-1979 118.8319 111.9164 97.5540 92.1982 NA 94.8991 96.5038 98.8163 5-1980 5-1981 5-1982 6-1976 6-1977 6-1978 6-1979 6-1980 100.4835 100.1223 98.5270 NA 102.7724 107.0270 108.6788 111.1190 6-1981 6-1982 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 101.9265 99.4971 NA 104.7664 107.4791 108.9188 111.5591 100.0000 7-1982 8-1976 8-1977 8-1978 8-1979 8-1980 8-1981 8-1982 99.3965 NA 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 9-1976 9-1977 9-1978 9-1979 9-1980 9-1981 9-1982 10-1976 NA 102.7724 107.0270 108.6788 111.1190 101.9265 99.4971 NA 10-1977 10-1978 10-1979 10-1980 10-1981 10-1982 11-1976 11-1977 95.3657 96.7315 99.2333 100.7335 100.0000 98.2324 NA 104.7664 11-1978 11-1979 11-1980 11-1981 11-1982 12-1976 12-1977 12-1978 107.4791 108.9188 111.5591 100.0000 99.3965 NA 104.7664 107.4791 12-1979 12-1980 12-1981 12-1982 13-1976 13-1977 13-1978 13-1979 108.9188 111.5591 100.0000 99.3965 NA 125.8064 127.9649 125.4294 13-1980 13-1981 13-1982 14-1978 14-1979 14-1980 14-1981 14-1982 125.0259 106.3044 98.0729 NA 127.3866 124.6424 118.9132 99.3576 14-1983 14-1984 15-1977 15-1978 15-1979 15-1980 15-1981 15-1982 97.2428 97.1944 NA 105.3355 108.1833 110.6984 105.9673 96.5931 15-1983 16-1976 16-1977 16-1978 16-1979 16-1980 16-1981 16-1982 92.0013 NA 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 17-1977 17-1978 17-1979 17-1980 17-1981 17-1982 17-1983 18-1977 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 NA 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 19-1976 19-1977 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 NA 116.4000 19-1978 19-1979 19-1980 19-1981 19-1982 20-1977 20-1978 20-1979 115.7000 112.8000 108.9000 100.0000 89.2000 NA 116.2250 114.9750 20-1980 20-1981 20-1982 20-1983 21-1977 21-1978 21-1979 21-1980 111.8250 106.6750 97.3000 89.5250 NA 116.2250 114.9750 111.8250 21-1981 21-1982 21-1983 22-1976 22-1977 22-1978 22-1979 22-1980 106.6750 97.3000 89.5250 NA 116.4000 115.7000 112.8000 108.9000 22-1981 22-1982 23-1976 23-1977 23-1978 23-1979 23-1980 23-1981 100.0000 89.2000 NA 116.4000 115.7000 112.8000 108.9000 100.0000 23-1982 24-1976 24-1977 24-1978 24-1979 24-1980 24-1981 24-1982 89.2000 NA 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 25-1976 25-1977 25-1978 25-1979 25-1980 25-1981 25-1982 26-1976 NA 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 NA 26-1977 26-1978 26-1979 26-1980 26-1981 26-1982 27-1978 27-1979 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 NA 112.8000 27-1980 27-1981 27-1982 27-1983 27-1984 28-1977 28-1978 28-1979 108.9000 100.0000 89.2000 90.5000 86.9000 NA 93.9500 98.2000 28-1980 28-1981 28-1982 28-1983 29-1977 29-1978 29-1979 29-1980 102.5667 102.0167 99.4917 94.2583 NA 95.7000 100.2000 102.4000 29-1981 29-1982 29-1983 30-1976 30-1977 30-1978 30-1979 30-1980 101.1000 96.9500 96.0500 NA 116.4000 115.7000 112.8000 108.9000 30-1981 30-1982 31-1976 31-1977 31-1978 31-1979 31-1980 31-1981 100.0000 89.2000 NA 104.7664 107.4791 108.9188 111.5591 100.0000 31-1982 32-1977 32-1978 32-1979 32-1980 32-1981 32-1982 32-1983 99.3965 NA 96.0500 100.6000 102.3667 100.9167 96.4417 96.4083 33-1976 33-1977 33-1978 33-1979 33-1980 33-1981 33-1982 34-1977 NA 93.6000 97.8000 102.6000 102.2000 100.0000 93.9000 NA 34-1978 34-1979 34-1980 34-1981 34-1982 34-1983 35-1977 35-1978 95.7000 100.2000 102.4000 101.1000 96.9500 96.0500 NA 94.6500 35-1979 35-1980 35-1981 35-1982 35-1983 36-1976 36-1977 36-1978 99.0000 102.5000 101.6500 98.4750 94.9750 NA 93.6000 97.8000 36-1979 36-1980 36-1981 36-1982 37-1977 37-1978 37-1979 37-1980 102.6000 102.2000 100.0000 93.9000 NA 116.2250 114.9750 111.8250 37-1981 37-1982 37-1983 38-1976 38-1977 38-1978 38-1979 38-1980 106.6750 97.3000 89.5250 NA 106.1747 108.9675 107.8624 104.5668 38-1981 38-1982 39-1977 39-1978 39-1979 39-1980 39-1981 39-1982 100.0000 92.7430 NA 116.2250 114.9750 111.8250 106.6750 97.3000 39-1983 40-1976 40-1977 40-1978 40-1979 40-1980 40-1981 40-1982 89.5250 NA 106.5556 108.2693 108.1387 105.3907 101.1417 94.5573 41-1977 41-1978 41-1979 41-1980 41-1981 41-1982 41-1983 42-1976 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 NA 42-1977 42-1978 42-1979 42-1980 42-1981 42-1982 43-1977 43-1978 106.1747 108.9675 107.8624 104.5668 100.0000 92.7430 NA 127.1640 43-1979 43-1980 43-1981 43-1982 43-1983 44-1977 44-1978 44-1979 127.3866 124.6424 118.9132 99.3576 97.2428 NA 116.2250 114.9750 44-1980 44-1981 44-1982 44-1983 45-1977 45-1978 45-1979 45-1980 111.8250 106.6750 97.3000 89.5250 NA 127.1640 127.3866 124.6424 45-1981 45-1982 45-1983 46-1976 46-1977 46-1978 46-1979 46-1980 118.9132 99.3576 97.2428 NA 126.7636 128.3653 124.4507 125.2176 46-1981 46-1982 47-1976 47-1977 47-1978 47-1979 47-1980 47-1981 100.0000 97.4305 NA 116.4000 115.7000 112.8000 108.9000 100.0000 47-1982 48-1976 48-1977 48-1978 48-1979 48-1980 48-1981 48-1982 89.2000 NA 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 49-1976 49-1977 49-1978 49-1979 49-1980 49-1981 49-1982 50-1976 NA 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 NA 50-1977 50-1978 50-1979 50-1980 50-1981 50-1982 51-1976 51-1977 106.1747 108.9675 107.8624 104.5668 100.0000 92.7430 NA 125.8064 51-1978 51-1979 51-1980 51-1981 51-1982 52-1976 52-1977 52-1978 127.9649 125.4294 125.0259 106.3044 98.0729 NA 116.4000 115.7000 52-1979 52-1980 52-1981 52-1982 53-1977 53-1978 53-1979 53-1980 112.8000 108.9000 100.0000 89.2000 NA 116.2250 114.9750 111.8250 53-1981 53-1982 53-1983 54-1977 54-1978 54-1979 54-1980 54-1981 106.6750 97.3000 89.5250 NA 127.1640 127.3866 124.6424 118.9132 54-1982 54-1983 55-1976 55-1977 55-1978 55-1979 55-1980 55-1981 99.3576 97.2428 NA 116.4000 115.7000 112.8000 108.9000 100.0000 55-1982 56-1976 56-1977 56-1978 56-1979 56-1980 56-1981 56-1982 89.2000 NA 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 57-1977 57-1978 57-1979 57-1980 57-1981 57-1982 57-1983 58-1976 NA 117.9279 120.2607 118.9515 113.4060 98.1655 92.3142 NA 58-1977 58-1978 58-1979 58-1980 58-1981 58-1982 59-1977 59-1978 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 NA 117.9279 59-1979 59-1980 59-1981 59-1982 59-1983 60-1976 60-1977 60-1978 120.2607 118.9515 113.4060 98.1655 92.3142 NA 103.7669 106.9042 60-1979 60-1980 60-1981 60-1982 61-1977 61-1978 61-1979 61-1980 109.4623 111.9345 100.0000 93.1862 NA 104.5512 107.5437 110.0804 61-1981 61-1982 61-1983 62-1977 62-1978 62-1979 62-1980 62-1981 108.9509 98.2966 92.5937 NA 117.9279 120.2607 118.9515 113.4060 62-1982 62-1983 63-1976 63-1977 63-1978 63-1979 63-1980 63-1981 98.1655 92.3142 NA 110.1000 109.2000 111.9000 111.0000 100.0000 63-1982 64-1976 64-1977 64-1978 64-1979 64-1980 64-1981 64-1982 89.0000 NA 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 65-1976 65-1977 65-1978 65-1979 65-1980 65-1981 65-1982 66-1977 NA 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 NA 66-1978 66-1979 66-1980 66-1981 66-1982 66-1983 67-1976 67-1977 109.8750 109.8750 111.6750 108.2500 97.2500 89.4750 NA 117.0448 67-1978 67-1979 67-1980 67-1981 67-1982 68-1976 68-1977 68-1978 120.5774 119.3105 117.8746 100.0000 92.6621 NA 110.1000 109.2000 68-1979 68-1980 68-1981 68-1982 69-1977 69-1978 69-1979 69-1980 111.9000 111.0000 100.0000 89.0000 NA 109.8750 109.8750 111.6750 69-1981 69-1982 69-1983 70-1977 70-1978 70-1979 70-1980 70-1981 108.2500 97.2500 89.4750 NA 104.5512 107.5437 110.0804 108.9509 70-1982 70-1983 71-1976 71-1977 71-1978 71-1979 71-1980 71-1981 98.2966 92.5937 NA 102.5303 106.1199 108.8228 111.3165 102.9836 71-1982 72-1976 72-1977 72-1978 72-1979 72-1980 72-1981 72-1982 94.8896 NA 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 73-1977 73-1978 73-1979 73-1980 73-1981 73-1982 73-1983 74-1977 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 NA 74-1978 74-1979 74-1980 74-1981 74-1982 74-1983 75-1976 75-1977 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 NA 103.7669 75-1978 75-1979 75-1980 75-1981 75-1982 76-1976 76-1977 76-1978 106.9042 109.4623 111.9345 100.0000 93.1862 NA 102.5303 106.1199 76-1979 76-1980 76-1981 76-1982 77-1977 77-1978 77-1979 77-1980 108.8228 111.3165 102.9836 94.8896 NA 104.5512 107.5437 110.0804 77-1981 77-1982 77-1983 78-1976 78-1977 78-1978 78-1979 78-1980 108.9509 98.2966 92.5937 NA 102.5303 106.1199 108.8228 111.3165 78-1981 78-1982 79-1976 79-1977 79-1978 79-1979 79-1980 79-1981 102.9836 94.8896 NA 93.6000 97.8000 102.6000 102.2000 100.0000 79-1982 80-1976 80-1977 80-1978 80-1979 80-1980 80-1981 80-1982 93.9000 NA 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 81-1976 81-1977 81-1978 81-1979 81-1980 81-1981 81-1982 82-1976 NA 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 NA 82-1977 82-1978 82-1979 82-1980 82-1981 82-1982 83-1977 83-1978 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 NA 127.1640 83-1979 83-1980 83-1981 83-1982 83-1983 84-1976 84-1977 84-1978 127.3866 124.6424 118.9132 99.3576 97.2428 NA 94.6657 96.3900 84-1979 84-1980 84-1981 84-1982 85-1976 85-1977 85-1978 85-1979 98.6078 100.3584 100.1834 98.6743 NA 93.8333 97.1000 101.8000 85-1980 85-1981 85-1982 86-1976 86-1977 86-1978 86-1979 86-1980 102.2667 100.3667 94.9167 NA 94.6657 96.3900 98.6078 100.3584 86-1981 86-1982 87-1976 87-1977 87-1978 87-1979 87-1980 87-1981 100.1834 98.6743 NA 116.4000 115.7000 112.8000 108.9000 100.0000 87-1982 88-1976 88-1977 88-1978 88-1979 88-1980 88-1981 88-1982 89.2000 NA 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 89-1976 89-1977 89-1978 89-1979 89-1980 89-1981 89-1982 90-1976 NA 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 NA 90-1977 90-1978 90-1979 90-1980 90-1981 90-1982 91-1976 91-1977 110.0500 109.4250 111.2250 111.2250 102.7500 91.7500 NA 104.7664 91-1978 91-1979 91-1980 91-1981 91-1982 92-1976 92-1977 92-1978 107.4791 108.9188 111.5591 100.0000 99.3965 NA 94.5333 95.0000 92-1979 92-1980 92-1981 92-1982 93-1977 93-1978 93-1979 93-1980 99.4000 102.4667 101.4667 97.9667 NA 94.6500 99.0000 102.5000 93-1981 93-1982 93-1983 94-1976 94-1977 94-1978 94-1979 94-1980 101.6500 98.4750 94.9750 NA 117.6000 115.8750 113.5250 109.8750 94-1981 94-1982 95-1976 95-1977 95-1978 95-1979 95-1980 95-1981 102.2250 91.9000 NA 126.7636 128.3653 124.4507 125.2176 100.0000 95-1982 96-1976 96-1977 96-1978 96-1979 96-1980 96-1981 96-1982 97.4305 NA 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 97-1976 97-1977 97-1978 97-1979 97-1980 97-1981 97-1982 98-1977 NA 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 NA 98-1978 98-1979 98-1980 98-1981 98-1982 98-1983 99-1977 99-1978 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 NA 104.5512 99-1979 99-1980 99-1981 99-1982 99-1983 100-1977 100-1978 100-1979 107.5437 110.0804 108.9509 98.2966 92.5937 NA 127.6979 126.0818 100-1980 100-1981 100-1982 100-1983 101-1977 101-1978 101-1979 101-1980 124.8981 110.5073 98.5011 96.9925 NA 117.9279 120.2607 118.9515 101-1981 101-1982 101-1983 102-1977 102-1978 102-1979 102-1980 102-1981 113.4060 98.1655 92.3142 NA 105.4446 107.8390 109.5789 108.6693 102-1982 102-1983 103-1976 103-1977 103-1978 103-1979 103-1980 103-1981 99.8491 99.3706 NA 93.6000 97.8000 102.6000 102.2000 100.0000 103-1982 104-1977 104-1978 104-1979 104-1980 104-1981 104-1982 104-1983 93.9000 NA 95.8210 97.5654 99.7333 100.4890 99.4108 98.7427 104-1984 105-1977 105-1978 105-1979 105-1980 105-1981 105-1982 105-1983 100.1189 NA 96.2762 98.3993 100.2334 100.2445 98.8216 99.2531 105-1984 106-1977 106-1978 106-1979 106-1980 106-1981 106-1982 106-1983 100.4744 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 106-1984 107-1977 107-1978 107-1979 107-1980 107-1981 107-1982 107-1983 100.5633 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 107-1984 108-1977 108-1978 108-1979 108-1980 108-1981 108-1982 108-1983 100.0301 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 108-1984 109-1977 109-1978 109-1979 109-1980 109-1981 109-1982 109-1983 100.0301 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 109-1984 110-1977 110-1978 110-1979 110-1980 110-1981 110-1982 110-1983 100.5633 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 110-1984 111-1977 111-1978 111-1979 111-1980 111-1981 111-1982 111-1983 97.1944 NA 116.0500 114.2500 110.8500 104.4500 94.6000 89.8500 111-1984 112-1977 112-1978 112-1979 112-1980 112-1981 112-1982 112-1983 88.7000 NA 106.8729 108.6912 107.0385 103.4251 98.1858 92.0669 112-1984 113-1976 113-1977 113-1978 113-1979 113-1980 113-1981 113-1982 89.8429 NA 93.9500 96.7500 101.4000 102.3000 100.5500 95.4250 113-1983 114-1977 114-1978 114-1979 114-1980 114-1981 114-1982 114-1983 97.1250 NA 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 114-1984 115-1977 115-1978 115-1979 115-1980 115-1981 115-1982 115-1983 94.6714 NA 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 115-1984 116-1977 116-1978 116-1979 116-1980 116-1981 116-1982 116-1983 94.6714 NA 117.6336 120.3662 119.0712 114.8955 98.7770 92.4302 116-1984 117-1977 117-1978 117-1979 117-1980 117-1981 117-1982 117-1983 91.8372 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 117-1984 118-1976 118-1977 118-1978 118-1979 118-1980 118-1981 118-1982 107.0929 NA 116.8157 118.2223 120.1551 118.8319 111.9164 97.5540 118-1983 119-1977 119-1978 119-1979 119-1980 119-1981 119-1982 119-1983 92.1982 NA 109.8750 109.8750 111.6750 108.2500 97.2500 89.4750 119-1984 120-1977 120-1978 120-1979 120-1980 120-1981 120-1982 120-1983 91.6500 NA 105.5970 108.3964 110.9044 104.9727 96.0253 91.8038 120-1984 121-1977 121-1978 121-1979 121-1980 121-1981 121-1982 121-1983 92.4875 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 121-1984 122-1976 122-1977 122-1978 122-1979 122-1980 122-1981 122-1982 91.5326 NA 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 122-1983 123-1976 123-1977 123-1978 123-1979 123-1980 123-1981 123-1982 90.8164 NA 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 123-1983 124-1977 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 90.8164 NA 110.0250 109.4250 111.8250 110.0833 99.0833 89.1583 124-1984 125-1977 125-1978 125-1979 125-1980 125-1981 125-1982 125-1983 91.1500 NA 117.3392 120.4718 119.1908 116.3851 99.3885 92.5462 125-1984 126-1977 126-1978 126-1979 126-1980 126-1981 126-1982 126-1983 91.5538 NA 118.2223 120.1551 118.8319 111.9164 97.5540 92.1982 126-1984 127-1976 127-1977 127-1978 127-1979 127-1980 127-1981 127-1982 92.4041 NA 94.6657 96.3900 98.6078 100.3584 100.1834 98.6743 127-1983 127-1984 128-1976 128-1977 128-1978 128-1979 128-1980 128-1981 99.3807 100.5633 NA 94.6657 96.3900 98.6078 100.3584 100.1834 128-1982 128-1983 128-1984 129-1976 129-1977 129-1978 129-1979 129-1980 98.6743 99.3807 100.5633 NA 94.6657 96.3900 98.6078 100.3584 129-1981 129-1982 129-1983 129-1984 130-1976 130-1977 130-1978 130-1979 100.1834 98.6743 99.3807 100.5633 NA 101.7754 106.8009 108.5589 130-1980 130-1981 130-1982 130-1983 130-1984 131-1976 131-1977 131-1978 110.8990 102.8898 99.5474 99.3189 105.1429 NA 103.7669 106.9042 131-1979 131-1980 131-1981 131-1982 131-1983 131-1984 132-1976 132-1977 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 NA 126.7636 132-1978 132-1979 132-1980 132-1981 132-1982 132-1983 132-1984 133-1976 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 98.7386 NA 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 133-1983 133-1984 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 87.8000 134-1976 134-1977 134-1978 134-1979 134-1980 134-1981 134-1982 134-1983 NA 117.0448 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 134-1984 135-1976 135-1977 135-1978 135-1979 135-1980 135-1981 135-1982 94.6714 NA 116.9589 119.6943 119.6272 118.2336 104.4687 94.4966 135-1983 135-1984 136-1976 136-1977 136-1978 136-1979 136-1980 136-1981 91.6183 93.8212 NA 117.0448 120.5774 119.3105 117.8746 100.0000 136-1982 136-1983 136-1984 137-1976 137-1977 137-1978 137-1979 137-1980 92.6621 91.2704 94.6714 NA 110.0500 109.4250 111.2250 111.2250 137-1981 137-1982 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 102.7500 91.7500 90.4250 93.1500 NA 101.7059 105.5970 108.3964 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 139-1977 139-1978 110.9044 104.9727 96.0253 91.8038 92.4875 NA 102.5303 106.1199 139-1979 139-1980 139-1981 139-1982 139-1983 139-1984 140-1976 140-1977 108.8228 111.3165 102.9836 94.8896 91.4088 92.9650 NA 104.7664 140-1978 140-1979 140-1980 140-1981 140-1982 140-1983 140-1984 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 107.0929 > lag(z, 3) 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 1-1983 2-1977 NA NA NA 95.7072 97.3569 99.6083 100.5501 NA 2-1978 2-1979 2-1980 2-1981 2-1982 2-1983 3-1977 3-1978 NA NA 95.7072 97.3569 99.6083 100.5501 NA NA 3-1979 3-1980 3-1981 3-1982 3-1983 4-1977 4-1978 4-1979 NA 95.7072 97.3569 99.6083 100.5501 NA NA NA 4-1980 4-1981 4-1982 4-1983 5-1976 5-1977 5-1978 5-1979 118.2223 120.1551 118.8319 111.9164 NA NA NA 94.8991 5-1980 5-1981 5-1982 6-1976 6-1977 6-1978 6-1979 6-1980 96.5038 98.8163 100.4835 NA NA NA 102.7724 107.0270 6-1981 6-1982 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 108.6788 111.1190 NA NA NA 104.7664 107.4791 108.9188 7-1982 8-1976 8-1977 8-1978 8-1979 8-1980 8-1981 8-1982 111.5591 NA NA NA 104.7664 107.4791 108.9188 111.5591 9-1976 9-1977 9-1978 9-1979 9-1980 9-1981 9-1982 10-1976 NA NA NA 102.7724 107.0270 108.6788 111.1190 NA 10-1977 10-1978 10-1979 10-1980 10-1981 10-1982 11-1976 11-1977 NA NA 95.3657 96.7315 99.2333 100.7335 NA NA 11-1978 11-1979 11-1980 11-1981 11-1982 12-1976 12-1977 12-1978 NA 104.7664 107.4791 108.9188 111.5591 NA NA NA 12-1979 12-1980 12-1981 12-1982 13-1976 13-1977 13-1978 13-1979 104.7664 107.4791 108.9188 111.5591 NA NA NA 125.8064 13-1980 13-1981 13-1982 14-1978 14-1979 14-1980 14-1981 14-1982 127.9649 125.4294 125.0259 NA NA NA 127.3866 124.6424 14-1983 14-1984 15-1977 15-1978 15-1979 15-1980 15-1981 15-1982 118.9132 99.3576 NA NA NA 105.3355 108.1833 110.6984 15-1983 16-1976 16-1977 16-1978 16-1979 16-1980 16-1981 16-1982 105.9673 NA NA NA 116.4000 115.7000 112.8000 108.9000 17-1977 17-1978 17-1979 17-1980 17-1981 17-1982 17-1983 18-1977 NA NA NA 127.1640 127.3866 124.6424 118.9132 NA 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 19-1976 19-1977 NA NA 116.2250 114.9750 111.8250 106.6750 NA NA 19-1978 19-1979 19-1980 19-1981 19-1982 20-1977 20-1978 20-1979 NA 116.4000 115.7000 112.8000 108.9000 NA NA NA 20-1980 20-1981 20-1982 20-1983 21-1977 21-1978 21-1979 21-1980 116.2250 114.9750 111.8250 106.6750 NA NA NA 116.2250 21-1981 21-1982 21-1983 22-1976 22-1977 22-1978 22-1979 22-1980 114.9750 111.8250 106.6750 NA NA NA 116.4000 115.7000 22-1981 22-1982 23-1976 23-1977 23-1978 23-1979 23-1980 23-1981 112.8000 108.9000 NA NA NA 116.4000 115.7000 112.8000 23-1982 24-1976 24-1977 24-1978 24-1979 24-1980 24-1981 24-1982 108.9000 NA NA NA 116.4000 115.7000 112.8000 108.9000 25-1976 25-1977 25-1978 25-1979 25-1980 25-1981 25-1982 26-1976 NA NA NA 116.4000 115.7000 112.8000 108.9000 NA 26-1977 26-1978 26-1979 26-1980 26-1981 26-1982 27-1978 27-1979 NA NA 116.4000 115.7000 112.8000 108.9000 NA NA 27-1980 27-1981 27-1982 27-1983 27-1984 28-1977 28-1978 28-1979 NA 112.8000 108.9000 100.0000 89.2000 NA NA NA 28-1980 28-1981 28-1982 28-1983 29-1977 29-1978 29-1979 29-1980 93.9500 98.2000 102.5667 102.0167 NA NA NA 95.7000 29-1981 29-1982 29-1983 30-1976 30-1977 30-1978 30-1979 30-1980 100.2000 102.4000 101.1000 NA NA NA 116.4000 115.7000 30-1981 30-1982 31-1976 31-1977 31-1978 31-1979 31-1980 31-1981 112.8000 108.9000 NA NA NA 104.7664 107.4791 108.9188 31-1982 32-1977 32-1978 32-1979 32-1980 32-1981 32-1982 32-1983 111.5591 NA NA NA 96.0500 100.6000 102.3667 100.9167 33-1976 33-1977 33-1978 33-1979 33-1980 33-1981 33-1982 34-1977 NA NA NA 93.6000 97.8000 102.6000 102.2000 NA 34-1978 34-1979 34-1980 34-1981 34-1982 34-1983 35-1977 35-1978 NA NA 95.7000 100.2000 102.4000 101.1000 NA NA 35-1979 35-1980 35-1981 35-1982 35-1983 36-1976 36-1977 36-1978 NA 94.6500 99.0000 102.5000 101.6500 NA NA NA 36-1979 36-1980 36-1981 36-1982 37-1977 37-1978 37-1979 37-1980 93.6000 97.8000 102.6000 102.2000 NA NA NA 116.2250 37-1981 37-1982 37-1983 38-1976 38-1977 38-1978 38-1979 38-1980 114.9750 111.8250 106.6750 NA NA NA 106.1747 108.9675 38-1981 38-1982 39-1977 39-1978 39-1979 39-1980 39-1981 39-1982 107.8624 104.5668 NA NA NA 116.2250 114.9750 111.8250 39-1983 40-1976 40-1977 40-1978 40-1979 40-1980 40-1981 40-1982 106.6750 NA NA NA 106.5556 108.2693 108.1387 105.3907 41-1977 41-1978 41-1979 41-1980 41-1981 41-1982 41-1983 42-1976 NA NA NA 116.2250 114.9750 111.8250 106.6750 NA 42-1977 42-1978 42-1979 42-1980 42-1981 42-1982 43-1977 43-1978 NA NA 106.1747 108.9675 107.8624 104.5668 NA NA 43-1979 43-1980 43-1981 43-1982 43-1983 44-1977 44-1978 44-1979 NA 127.1640 127.3866 124.6424 118.9132 NA NA NA 44-1980 44-1981 44-1982 44-1983 45-1977 45-1978 45-1979 45-1980 116.2250 114.9750 111.8250 106.6750 NA NA NA 127.1640 45-1981 45-1982 45-1983 46-1976 46-1977 46-1978 46-1979 46-1980 127.3866 124.6424 118.9132 NA NA NA 126.7636 128.3653 46-1981 46-1982 47-1976 47-1977 47-1978 47-1979 47-1980 47-1981 124.4507 125.2176 NA NA NA 116.4000 115.7000 112.8000 47-1982 48-1976 48-1977 48-1978 48-1979 48-1980 48-1981 48-1982 108.9000 NA NA NA 126.7636 128.3653 124.4507 125.2176 49-1976 49-1977 49-1978 49-1979 49-1980 49-1981 49-1982 50-1976 NA NA NA 117.6000 115.8750 113.5250 109.8750 NA 50-1977 50-1978 50-1979 50-1980 50-1981 50-1982 51-1976 51-1977 NA NA 106.1747 108.9675 107.8624 104.5668 NA NA 51-1978 51-1979 51-1980 51-1981 51-1982 52-1976 52-1977 52-1978 NA 125.8064 127.9649 125.4294 125.0259 NA NA NA 52-1979 52-1980 52-1981 52-1982 53-1977 53-1978 53-1979 53-1980 116.4000 115.7000 112.8000 108.9000 NA NA NA 116.2250 53-1981 53-1982 53-1983 54-1977 54-1978 54-1979 54-1980 54-1981 114.9750 111.8250 106.6750 NA NA NA 127.1640 127.3866 54-1982 54-1983 55-1976 55-1977 55-1978 55-1979 55-1980 55-1981 124.6424 118.9132 NA NA NA 116.4000 115.7000 112.8000 55-1982 56-1976 56-1977 56-1978 56-1979 56-1980 56-1981 56-1982 108.9000 NA NA NA 126.7636 128.3653 124.4507 125.2176 57-1977 57-1978 57-1979 57-1980 57-1981 57-1982 57-1983 58-1976 NA NA NA 117.9279 120.2607 118.9515 113.4060 NA 58-1977 58-1978 58-1979 58-1980 58-1981 58-1982 59-1977 59-1978 NA NA 116.4000 115.7000 112.8000 108.9000 NA NA 59-1979 59-1980 59-1981 59-1982 59-1983 60-1976 60-1977 60-1978 NA 117.9279 120.2607 118.9515 113.4060 NA NA NA 60-1979 60-1980 60-1981 60-1982 61-1977 61-1978 61-1979 61-1980 103.7669 106.9042 109.4623 111.9345 NA NA NA 104.5512 61-1981 61-1982 61-1983 62-1977 62-1978 62-1979 62-1980 62-1981 107.5437 110.0804 108.9509 NA NA NA 117.9279 120.2607 62-1982 62-1983 63-1976 63-1977 63-1978 63-1979 63-1980 63-1981 118.9515 113.4060 NA NA NA 110.1000 109.2000 111.9000 63-1982 64-1976 64-1977 64-1978 64-1979 64-1980 64-1981 64-1982 111.0000 NA NA NA 110.1000 109.2000 111.9000 111.0000 65-1976 65-1977 65-1978 65-1979 65-1980 65-1981 65-1982 66-1977 NA NA NA 110.1000 109.2000 111.9000 111.0000 NA 66-1978 66-1979 66-1980 66-1981 66-1982 66-1983 67-1976 67-1977 NA NA 109.8750 109.8750 111.6750 108.2500 NA NA 67-1978 67-1979 67-1980 67-1981 67-1982 68-1976 68-1977 68-1978 NA 117.0448 120.5774 119.3105 117.8746 NA NA NA 68-1979 68-1980 68-1981 68-1982 69-1977 69-1978 69-1979 69-1980 110.1000 109.2000 111.9000 111.0000 NA NA NA 109.8750 69-1981 69-1982 69-1983 70-1977 70-1978 70-1979 70-1980 70-1981 109.8750 111.6750 108.2500 NA NA NA 104.5512 107.5437 70-1982 70-1983 71-1976 71-1977 71-1978 71-1979 71-1980 71-1981 110.0804 108.9509 NA NA NA 102.5303 106.1199 108.8228 71-1982 72-1976 72-1977 72-1978 72-1979 72-1980 72-1981 72-1982 111.3165 NA NA NA 103.7669 106.9042 109.4623 111.9345 73-1977 73-1978 73-1979 73-1980 73-1981 73-1982 73-1983 74-1977 NA NA NA 104.5512 107.5437 110.0804 108.9509 NA 74-1978 74-1979 74-1980 74-1981 74-1982 74-1983 75-1976 75-1977 NA NA 104.5512 107.5437 110.0804 108.9509 NA NA 75-1978 75-1979 75-1980 75-1981 75-1982 76-1976 76-1977 76-1978 NA 103.7669 106.9042 109.4623 111.9345 NA NA NA 76-1979 76-1980 76-1981 76-1982 77-1977 77-1978 77-1979 77-1980 102.5303 106.1199 108.8228 111.3165 NA NA NA 104.5512 77-1981 77-1982 77-1983 78-1976 78-1977 78-1978 78-1979 78-1980 107.5437 110.0804 108.9509 NA NA NA 102.5303 106.1199 78-1981 78-1982 79-1976 79-1977 79-1978 79-1979 79-1980 79-1981 108.8228 111.3165 NA NA NA 93.6000 97.8000 102.6000 79-1982 80-1976 80-1977 80-1978 80-1979 80-1980 80-1981 80-1982 102.2000 NA NA NA 103.7669 106.9042 109.4623 111.9345 81-1976 81-1977 81-1978 81-1979 81-1980 81-1981 81-1982 82-1976 NA NA NA 116.4000 115.7000 112.8000 108.9000 NA 82-1977 82-1978 82-1979 82-1980 82-1981 82-1982 83-1977 83-1978 NA NA 116.4000 115.7000 112.8000 108.9000 NA NA 83-1979 83-1980 83-1981 83-1982 83-1983 84-1976 84-1977 84-1978 NA 127.1640 127.3866 124.6424 118.9132 NA NA NA 84-1979 84-1980 84-1981 84-1982 85-1976 85-1977 85-1978 85-1979 94.6657 96.3900 98.6078 100.3584 NA NA NA 93.8333 85-1980 85-1981 85-1982 86-1976 86-1977 86-1978 86-1979 86-1980 97.1000 101.8000 102.2667 NA NA NA 94.6657 96.3900 86-1981 86-1982 87-1976 87-1977 87-1978 87-1979 87-1980 87-1981 98.6078 100.3584 NA NA NA 116.4000 115.7000 112.8000 87-1982 88-1976 88-1977 88-1978 88-1979 88-1980 88-1981 88-1982 108.9000 NA NA NA 110.1000 109.2000 111.9000 111.0000 89-1976 89-1977 89-1978 89-1979 89-1980 89-1981 89-1982 90-1976 NA NA NA 110.1000 109.2000 111.9000 111.0000 NA 90-1977 90-1978 90-1979 90-1980 90-1981 90-1982 91-1976 91-1977 NA NA 110.0500 109.4250 111.2250 111.2250 NA NA 91-1978 91-1979 91-1980 91-1981 91-1982 92-1976 92-1977 92-1978 NA 104.7664 107.4791 108.9188 111.5591 NA NA NA 92-1979 92-1980 92-1981 92-1982 93-1977 93-1978 93-1979 93-1980 94.5333 95.0000 99.4000 102.4667 NA NA NA 94.6500 93-1981 93-1982 93-1983 94-1976 94-1977 94-1978 94-1979 94-1980 99.0000 102.5000 101.6500 NA NA NA 117.6000 115.8750 94-1981 94-1982 95-1976 95-1977 95-1978 95-1979 95-1980 95-1981 113.5250 109.8750 NA NA NA 126.7636 128.3653 124.4507 95-1982 96-1976 96-1977 96-1978 96-1979 96-1980 96-1981 96-1982 125.2176 NA NA NA 126.7636 128.3653 124.4507 125.2176 97-1976 97-1977 97-1978 97-1979 97-1980 97-1981 97-1982 98-1977 NA NA NA 117.6000 115.8750 113.5250 109.8750 NA 98-1978 98-1979 98-1980 98-1981 98-1982 98-1983 99-1977 99-1978 NA NA 127.1640 127.3866 124.6424 118.9132 NA NA 99-1979 99-1980 99-1981 99-1982 99-1983 100-1977 100-1978 100-1979 NA 104.5512 107.5437 110.0804 108.9509 NA NA NA 100-1980 100-1981 100-1982 100-1983 101-1977 101-1978 101-1979 101-1980 127.6979 126.0818 124.8981 110.5073 NA NA NA 117.9279 101-1981 101-1982 101-1983 102-1977 102-1978 102-1979 102-1980 102-1981 120.2607 118.9515 113.4060 NA NA NA 105.4446 107.8390 102-1982 102-1983 103-1976 103-1977 103-1978 103-1979 103-1980 103-1981 109.5789 108.6693 NA NA NA 93.6000 97.8000 102.6000 103-1982 104-1977 104-1978 104-1979 104-1980 104-1981 104-1982 104-1983 102.2000 NA NA NA 95.8210 97.5654 99.7333 100.4890 104-1984 105-1977 105-1978 105-1979 105-1980 105-1981 105-1982 105-1983 99.4108 NA NA NA 96.2762 98.3993 100.2334 100.2445 105-1984 106-1977 106-1978 106-1979 106-1980 106-1981 106-1982 106-1983 98.8216 NA NA NA 96.3900 98.6078 100.3584 100.1834 106-1984 107-1977 107-1978 107-1979 107-1980 107-1981 107-1982 107-1983 98.6743 NA NA NA 95.7072 97.3569 99.6083 100.5501 107-1984 108-1977 108-1978 108-1979 108-1980 108-1981 108-1982 108-1983 99.5581 NA NA NA 95.7072 97.3569 99.6083 100.5501 108-1984 109-1977 109-1978 109-1979 109-1980 109-1981 109-1982 109-1983 99.5581 NA NA NA 96.3900 98.6078 100.3584 100.1834 109-1984 110-1977 110-1978 110-1979 110-1980 110-1981 110-1982 110-1983 98.6743 NA NA NA 127.1640 127.3866 124.6424 118.9132 110-1984 111-1977 111-1978 111-1979 111-1980 111-1981 111-1982 111-1983 99.3576 NA NA NA 116.0500 114.2500 110.8500 104.4500 111-1984 112-1977 112-1978 112-1979 112-1980 112-1981 112-1982 112-1983 94.6000 NA NA NA 106.8729 108.6912 107.0385 103.4251 112-1984 113-1976 113-1977 113-1978 113-1979 113-1980 113-1981 113-1982 98.1858 NA NA NA 93.9500 96.7500 101.4000 102.3000 113-1983 114-1977 114-1978 114-1979 114-1980 114-1981 114-1982 114-1983 100.5500 NA NA NA 120.5774 119.3105 117.8746 100.0000 114-1984 115-1977 115-1978 115-1979 115-1980 115-1981 115-1982 115-1983 92.6621 NA NA NA 120.5774 119.3105 117.8746 100.0000 115-1984 116-1977 116-1978 116-1979 116-1980 116-1981 116-1982 116-1983 92.6621 NA NA NA 117.6336 120.3662 119.0712 114.8955 116-1984 117-1977 117-1978 117-1979 117-1980 117-1981 117-1982 117-1983 98.7770 NA NA NA 107.4791 108.9188 111.5591 100.0000 117-1984 118-1976 118-1977 118-1978 118-1979 118-1980 118-1981 118-1982 99.3965 NA NA NA 116.8157 118.2223 120.1551 118.8319 118-1983 119-1977 119-1978 119-1979 119-1980 119-1981 119-1982 119-1983 111.9164 NA NA NA 109.8750 109.8750 111.6750 108.2500 119-1984 120-1977 120-1978 120-1979 120-1980 120-1981 120-1982 120-1983 97.2500 NA NA NA 105.5970 108.3964 110.9044 104.9727 120-1984 121-1977 121-1978 121-1979 121-1980 121-1981 121-1982 121-1983 96.0253 NA NA NA 104.5512 107.5437 110.0804 108.9509 121-1984 122-1976 122-1977 122-1978 122-1979 122-1980 122-1981 122-1982 98.2966 NA NA NA 103.7669 106.9042 109.4623 111.9345 122-1983 123-1976 123-1977 123-1978 123-1979 123-1980 123-1981 123-1982 100.0000 NA NA NA 103.7669 106.9042 109.4623 111.9345 123-1983 124-1977 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 100.0000 NA NA NA 110.0250 109.4250 111.8250 110.0833 124-1984 125-1977 125-1978 125-1979 125-1980 125-1981 125-1982 125-1983 99.0833 NA NA NA 117.3392 120.4718 119.1908 116.3851 125-1984 126-1977 126-1978 126-1979 126-1980 126-1981 126-1982 126-1983 99.3885 NA NA NA 118.2223 120.1551 118.8319 111.9164 126-1984 127-1976 127-1977 127-1978 127-1979 127-1980 127-1981 127-1982 97.5540 NA NA NA 94.6657 96.3900 98.6078 100.3584 127-1983 127-1984 128-1976 128-1977 128-1978 128-1979 128-1980 128-1981 100.1834 98.6743 NA NA NA 94.6657 96.3900 98.6078 128-1982 128-1983 128-1984 129-1976 129-1977 129-1978 129-1979 129-1980 100.3584 100.1834 98.6743 NA NA NA 94.6657 96.3900 129-1981 129-1982 129-1983 129-1984 130-1976 130-1977 130-1978 130-1979 98.6078 100.3584 100.1834 98.6743 NA NA NA 101.7754 130-1980 130-1981 130-1982 130-1983 130-1984 131-1976 131-1977 131-1978 106.8009 108.5589 110.8990 102.8898 99.5474 NA NA NA 131-1979 131-1980 131-1981 131-1982 131-1983 131-1984 132-1976 132-1977 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 NA NA 132-1978 132-1979 132-1980 132-1981 132-1982 132-1983 132-1984 133-1976 NA 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 NA 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 133-1983 133-1984 NA NA 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 134-1976 134-1977 134-1978 134-1979 134-1980 134-1981 134-1982 134-1983 NA NA NA 117.0448 120.5774 119.3105 117.8746 100.0000 134-1984 135-1976 135-1977 135-1978 135-1979 135-1980 135-1981 135-1982 92.6621 NA NA NA 116.9589 119.6943 119.6272 118.2336 135-1983 135-1984 136-1976 136-1977 136-1978 136-1979 136-1980 136-1981 104.4687 94.4966 NA NA NA 117.0448 120.5774 119.3105 136-1982 136-1983 136-1984 137-1976 137-1977 137-1978 137-1979 137-1980 117.8746 100.0000 92.6621 NA NA NA 110.0500 109.4250 137-1981 137-1982 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 111.2250 111.2250 102.7500 91.7500 NA NA NA 101.7059 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 139-1977 139-1978 105.5970 108.3964 110.9044 104.9727 96.0253 NA NA NA 139-1979 139-1980 139-1981 139-1982 139-1983 139-1984 140-1976 140-1977 102.5303 106.1199 108.8228 111.3165 102.9836 94.8896 NA NA 140-1978 140-1979 140-1980 140-1981 140-1982 140-1983 140-1984 NA 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 > diff(z, 2) 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 1-1983 NA NA 3.901100 3.193197 -0.050201 -1.935003 0.472002 2-1977 2-1978 2-1979 2-1980 2-1981 2-1982 2-1983 NA NA 3.901100 3.193197 -0.050201 -1.935003 0.472002 3-1977 3-1978 3-1979 3-1980 3-1981 3-1982 3-1983 NA NA 3.901100 3.193197 -0.050201 -1.935003 0.472002 4-1977 4-1978 4-1979 4-1980 4-1981 4-1982 4-1983 NA NA 0.609600 -8.238700 -21.277899 -19.718204 -5.149902 5-1976 5-1977 5-1978 5-1979 5-1980 5-1981 5-1982 NA NA 3.917198 3.979701 1.306001 -1.956500 -0.613999 6-1976 6-1977 6-1978 6-1979 6-1980 6-1981 6-1982 NA NA 5.906400 4.092000 -6.752300 -11.621899 -2.616197 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 7-1982 NA NA 4.152400 4.080000 -8.918800 -12.162600 -0.706902 8-1976 8-1977 8-1978 8-1979 8-1980 8-1981 8-1982 NA NA 4.152400 4.080000 -8.918800 -12.162600 -0.706902 9-1976 9-1977 9-1978 9-1979 9-1980 9-1981 9-1982 NA NA 5.906400 4.092000 -6.752300 -11.621899 -2.616197 10-1976 10-1977 10-1978 10-1979 10-1980 10-1981 10-1982 NA NA 3.867599 4.002001 0.766701 -2.501101 -0.236504 11-1976 11-1977 11-1978 11-1979 11-1980 11-1981 11-1982 NA NA 4.152400 4.080000 -8.918800 -12.162600 -0.706902 12-1976 12-1977 12-1978 12-1979 12-1980 12-1981 12-1982 NA NA 4.152400 4.080000 -8.918800 -12.162600 -0.706902 13-1976 13-1977 13-1978 13-1979 13-1980 13-1981 13-1982 NA NA -0.377000 -2.939000 -19.125000 -26.953001 -9.436999 14-1978 14-1979 14-1980 14-1981 14-1982 14-1983 14-1984 NA NA -8.473400 -25.284803 -21.670402 -2.163200 2.743400 15-1977 15-1978 15-1979 15-1980 15-1981 15-1982 15-1983 NA NA 5.362900 -2.216000 -14.105298 -13.966003 -4.344300 16-1976 16-1977 16-1978 16-1979 16-1980 16-1981 16-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 17-1977 17-1978 17-1979 17-1980 17-1981 17-1982 17-1983 NA NA -2.521600 -8.473400 -25.284803 -21.670402 -2.163200 18-1977 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 19-1976 19-1977 19-1978 19-1979 19-1980 19-1981 19-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 20-1977 20-1978 20-1979 20-1980 20-1981 20-1982 20-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 21-1977 21-1978 21-1979 21-1980 21-1981 21-1982 21-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 22-1976 22-1977 22-1978 22-1979 22-1980 22-1981 22-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 23-1976 23-1977 23-1978 23-1979 23-1980 23-1981 23-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 24-1976 24-1977 24-1978 24-1979 24-1980 24-1981 24-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 25-1976 25-1977 25-1978 25-1979 25-1980 25-1981 25-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 26-1976 26-1977 26-1978 26-1979 26-1980 26-1981 26-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 27-1978 27-1979 27-1980 27-1981 27-1982 27-1983 27-1984 NA NA -12.800000 -19.700003 -9.500000 -2.299995 -3.099998 28-1977 28-1978 28-1979 28-1980 28-1981 28-1982 28-1983 NA NA 8.616703 3.816703 -3.075001 -7.758399 -0.433403 29-1977 29-1978 29-1979 29-1980 29-1981 29-1982 29-1983 NA NA 6.700003 0.900000 -5.450003 -5.049997 6.400003 30-1976 30-1977 30-1978 30-1979 30-1980 30-1981 30-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 31-1976 31-1977 31-1978 31-1979 31-1980 31-1981 31-1982 NA NA 4.152400 4.080000 -8.918800 -12.162600 -0.706902 32-1977 32-1978 32-1979 32-1980 32-1981 32-1982 32-1983 NA NA 6.316697 0.316700 -5.924996 -4.508398 7.766596 33-1976 33-1977 33-1978 33-1979 33-1980 33-1981 33-1982 NA NA 9.000002 4.399997 -2.600000 -8.299998 -1.800003 34-1977 34-1978 34-1979 34-1980 34-1981 34-1982 34-1983 NA NA 6.700003 0.900000 -5.450003 -5.049997 6.400003 35-1977 35-1978 35-1979 35-1980 35-1981 35-1982 35-1983 NA NA 7.849998 2.650000 -4.025002 -6.675002 2.300002 36-1976 36-1977 36-1978 36-1979 36-1980 36-1981 36-1982 NA NA 9.000002 4.399997 -2.600000 -8.299998 -1.800003 37-1977 37-1978 37-1979 37-1980 37-1981 37-1982 37-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 38-1976 38-1977 38-1978 38-1979 38-1980 38-1981 38-1982 NA NA 1.687700 -4.400700 -7.862400 -11.823804 -9.961403 39-1977 39-1978 39-1979 39-1980 39-1981 39-1982 39-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 40-1976 40-1977 40-1978 40-1979 40-1980 40-1981 40-1982 NA NA 1.583100 -2.878600 -6.997000 -10.833403 -10.427001 41-1977 41-1978 41-1979 41-1980 41-1981 41-1982 41-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 42-1976 42-1977 42-1978 42-1979 42-1980 42-1981 42-1982 NA NA 1.687700 -4.400700 -7.862400 -11.823804 -9.961403 43-1977 43-1978 43-1979 43-1980 43-1981 43-1982 43-1983 NA NA -2.521600 -8.473400 -25.284803 -21.670402 -2.163200 44-1977 44-1978 44-1979 44-1980 44-1981 44-1982 44-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 45-1977 45-1978 45-1979 45-1980 45-1981 45-1982 45-1983 NA NA -2.521600 -8.473400 -25.284803 -21.670402 -2.163200 46-1976 46-1977 46-1978 46-1979 46-1980 46-1981 46-1982 NA NA -2.312900 -3.147700 -24.450700 -27.787104 -3.320297 47-1976 47-1977 47-1978 47-1979 47-1980 47-1981 47-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 48-1976 48-1977 48-1978 48-1979 48-1980 48-1981 48-1982 NA NA -2.312900 -3.147700 -24.450700 -27.787104 -3.320297 49-1976 49-1977 49-1978 49-1979 49-1980 49-1981 49-1982 NA NA -4.075000 -6.000000 -11.300000 -17.974998 -12.049997 50-1976 50-1977 50-1978 50-1979 50-1980 50-1981 50-1982 NA NA 1.687700 -4.400700 -7.862400 -11.823804 -9.961403 51-1976 51-1977 51-1978 51-1979 51-1980 51-1981 51-1982 NA NA -0.377000 -2.939000 -19.125000 -26.953001 -9.436999 52-1976 52-1977 52-1978 52-1979 52-1980 52-1981 52-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 53-1977 53-1978 53-1979 53-1980 53-1981 53-1982 53-1983 NA NA -4.400000 -8.300000 -14.524997 -17.149998 -7.700005 54-1977 54-1978 54-1979 54-1980 54-1981 54-1982 54-1983 NA NA -2.521600 -8.473400 -25.284803 -21.670402 -2.163200 55-1976 55-1977 55-1978 55-1979 55-1980 55-1981 55-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 56-1976 56-1977 56-1978 56-1979 56-1980 56-1981 56-1982 NA NA -2.312900 -3.147700 -24.450700 -27.787104 -3.320297 57-1977 57-1978 57-1979 57-1980 57-1981 57-1982 57-1983 NA NA 1.023600 -6.854700 -20.786003 -21.091799 -6.044800 58-1976 58-1977 58-1978 58-1979 58-1980 58-1981 58-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 59-1977 59-1978 59-1979 59-1980 59-1981 59-1982 59-1983 NA NA 1.023600 -6.854700 -20.786003 -21.091799 -6.044800 60-1976 60-1977 60-1978 60-1979 60-1980 60-1981 60-1982 NA NA 5.695400 5.030300 -9.462300 -18.748297 -9.183601 61-1977 61-1978 61-1979 61-1980 61-1981 61-1982 61-1983 NA NA 5.529200 1.407200 -11.783800 -16.357203 -6.764000 62-1977 62-1978 62-1979 62-1980 62-1981 62-1982 62-1983 NA NA 1.023600 -6.854700 -20.786003 -21.091799 -6.044800 63-1976 63-1977 63-1978 63-1979 63-1980 63-1981 63-1982 NA NA 1.800000 1.800000 -11.900000 -22.000000 -9.099998 64-1976 64-1977 64-1978 64-1979 64-1980 64-1981 64-1982 NA NA 1.800000 1.800000 -11.900000 -22.000000 -9.099998 65-1976 65-1977 65-1978 65-1979 65-1980 65-1981 65-1982 NA NA 1.800000 1.800000 -11.900000 -22.000000 -9.099998 66-1977 66-1978 66-1979 66-1980 66-1981 66-1982 66-1983 NA NA 1.800000 -1.625000 -14.425000 -18.775002 -5.599998 67-1976 67-1977 67-1978 67-1979 67-1980 67-1981 67-1982 NA NA 2.265700 -2.702800 -19.310500 -25.212498 -8.729599 68-1976 68-1977 68-1978 68-1979 68-1980 68-1981 68-1982 NA NA 1.800000 1.800000 -11.900000 -22.000000 -9.099998 69-1977 69-1978 69-1979 69-1980 69-1981 69-1982 69-1983 NA NA 1.800000 -1.625000 -14.425000 -18.775002 -5.599998 70-1977 70-1978 70-1979 70-1980 70-1981 70-1982 70-1983 NA NA 5.529200 1.407200 -11.783800 -16.357203 -6.764000 71-1976 71-1977 71-1978 71-1979 71-1980 71-1981 71-1982 NA NA 6.292500 5.196600 -5.839200 -16.426897 -11.574802 72-1976 72-1977 72-1978 72-1979 72-1980 72-1981 72-1982 NA NA 5.695400 5.030300 -9.462300 -18.748297 -9.183601 73-1977 73-1978 73-1979 73-1980 73-1981 73-1982 73-1983 NA NA 5.529200 1.407200 -11.783800 -16.357203 -6.764000 74-1977 74-1978 74-1979 74-1980 74-1981 74-1982 74-1983 NA NA 5.529200 1.407200 -11.783800 -16.357203 -6.764000 75-1976 75-1977 75-1978 75-1979 75-1980 75-1981 75-1982 NA NA 5.695400 5.030300 -9.462300 -18.748297 -9.183601 76-1976 76-1977 76-1978 76-1979 76-1980 76-1981 76-1982 NA NA 6.292500 5.196600 -5.839200 -16.426897 -11.574802 77-1977 77-1978 77-1979 77-1980 77-1981 77-1982 77-1983 NA NA 5.529200 1.407200 -11.783800 -16.357203 -6.764000 78-1976 78-1977 78-1978 78-1979 78-1980 78-1981 78-1982 NA NA 6.292500 5.196600 -5.839200 -16.426897 -11.574802 79-1976 79-1977 79-1978 79-1979 79-1980 79-1981 79-1982 NA NA 9.000002 4.399997 -2.600000 -8.299998 -1.800003 80-1976 80-1977 80-1978 80-1979 80-1980 80-1981 80-1982 NA NA 5.695400 5.030300 -9.462300 -18.748297 -9.183601 81-1976 81-1977 81-1978 81-1979 81-1980 81-1981 81-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 82-1976 82-1977 82-1978 82-1979 82-1980 82-1981 82-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 83-1977 83-1978 83-1979 83-1980 83-1981 83-1982 83-1983 NA NA -2.521600 -8.473400 -25.284803 -21.670402 -2.163200 84-1976 84-1977 84-1978 84-1979 84-1980 84-1981 84-1982 NA NA 3.942100 3.968401 1.575597 -1.684099 -0.802701 85-1976 85-1977 85-1978 85-1979 85-1980 85-1981 85-1982 NA NA 7.966702 5.166702 -1.433300 -7.349998 -2.883401 86-1976 86-1977 86-1978 86-1979 86-1980 86-1981 86-1982 NA NA 3.942100 3.968401 1.575597 -1.684099 -0.802701 87-1976 87-1977 87-1978 87-1979 87-1980 87-1981 87-1982 NA NA -3.600000 -6.800000 -12.800000 -19.700003 -9.500000 88-1976 88-1977 88-1978 88-1979 88-1980 88-1981 88-1982 NA NA 1.800000 1.800000 -11.900000 -22.000000 -9.099998 89-1976 89-1977 89-1978 89-1979 89-1980 89-1981 89-1982 NA NA 1.800000 1.800000 -11.900000 -22.000000 -9.099998 90-1976 90-1977 90-1978 90-1979 90-1980 90-1981 90-1982 NA NA 1.175000 1.800000 -8.475000 -19.475000 -12.324997 91-1976 91-1977 91-1978 91-1979 91-1980 91-1981 91-1982 NA NA 4.152400 4.080000 -8.918800 -12.162600 -0.706902 92-1976 92-1977 92-1978 92-1979 92-1980 92-1981 92-1982 NA NA 4.866700 7.466700 2.066698 -4.500002 -6.133402 93-1977 93-1978 93-1979 93-1980 93-1981 93-1982 93-1983 NA NA 7.849998 2.650000 -4.025002 -6.675002 2.300002 94-1976 94-1977 94-1978 94-1979 94-1980 94-1981 94-1982 NA NA -4.075000 -6.000000 -11.300000 -17.974998 -12.049997 95-1976 95-1977 95-1978 95-1979 95-1980 95-1981 95-1982 NA NA -2.312900 -3.147700 -24.450700 -27.787104 -3.320297 96-1976 96-1977 96-1978 96-1979 96-1980 96-1981 96-1982 NA NA -2.312900 -3.147700 -24.450700 -27.787104 -3.320297 97-1976 97-1977 97-1978 97-1979 97-1980 97-1981 97-1982 NA NA -4.075000 -6.000000 -11.300000 -17.974998 -12.049997 98-1977 98-1978 98-1979 98-1980 98-1981 98-1982 98-1983 NA NA -2.521600 -8.473400 -25.284803 -21.670402 -2.163200 99-1977 99-1978 99-1979 99-1980 99-1981 99-1982 99-1983 NA NA 5.529200 1.407200 -11.783800 -16.357203 -6.764000 100-1977 100-1978 100-1979 100-1980 100-1981 100-1982 100-1983 NA NA -2.799800 -15.574500 -26.397001 -13.514800 -0.620400 101-1977 101-1978 101-1979 101-1980 101-1981 101-1982 101-1983 NA NA 1.023600 -6.854700 -20.786003 -21.091799 -6.044800 102-1977 102-1978 102-1979 102-1980 102-1981 102-1982 102-1983 NA NA 4.134300 0.830300 -9.729802 -9.298702 1.393902 103-1976 103-1977 103-1978 103-1979 103-1980 103-1981 103-1982 NA NA 9.000002 4.399997 -2.600000 -8.299998 -1.800003 104-1977 104-1978 104-1979 104-1980 104-1981 104-1982 104-1983 NA NA 3.912300 2.923601 -0.322502 -1.746301 0.708103 104-1984 105-1977 105-1978 105-1979 105-1980 105-1981 105-1982 2.465101 NA NA 3.957201 1.845200 -1.411798 -0.991402 105-1983 105-1984 106-1977 106-1978 106-1979 106-1980 106-1981 1.652798 2.332602 NA NA 3.968401 1.575597 -1.684099 106-1982 106-1983 106-1984 107-1977 107-1978 107-1979 107-1980 -0.802701 1.888999 2.299501 NA NA 3.901100 3.193197 107-1981 107-1982 107-1983 107-1984 108-1977 108-1978 108-1979 -0.050201 -1.935003 0.472002 2.498203 NA NA 3.901100 108-1980 108-1981 108-1982 108-1983 108-1984 109-1977 109-1978 3.193197 -0.050201 -1.935003 0.472002 2.498203 NA NA 109-1979 109-1980 109-1981 109-1982 109-1983 109-1984 110-1977 3.968401 1.575597 -1.684099 -0.802701 1.888999 2.299501 NA 110-1978 110-1979 110-1980 110-1981 110-1982 110-1983 110-1984 NA -2.521600 -8.473400 -25.284803 -21.670402 -2.163200 2.743400 111-1977 111-1978 111-1979 111-1980 111-1981 111-1982 111-1983 NA NA -5.200000 -9.800000 -16.250002 -14.600002 -5.900001 111-1984 112-1977 112-1978 112-1979 112-1980 112-1981 112-1982 -2.699996 NA NA 0.165600 -5.266100 -8.852701 -11.358198 112-1983 112-1984 113-1976 113-1977 113-1978 113-1979 113-1980 -8.342896 -3.607605 NA NA 7.450003 5.550000 -0.850000 113-1981 113-1982 113-1983 114-1977 114-1978 114-1979 114-1980 -6.874997 -3.425000 10.499997 NA NA -2.702800 -19.310500 114-1981 114-1982 114-1983 114-1984 115-1977 115-1978 115-1979 -25.212498 -8.729599 2.009300 6.632599 NA NA -2.702800 115-1980 115-1981 115-1982 115-1983 115-1984 116-1977 116-1978 -19.310500 -25.212498 -8.729599 2.009300 6.632599 NA NA 116-1979 116-1980 116-1981 116-1982 116-1983 116-1984 117-1977 1.437600 -5.470700 -20.294200 -22.465301 -6.939804 2.779800 NA 117-1978 117-1979 117-1980 117-1981 117-1982 117-1983 117-1984 NA 4.080000 -8.918800 -12.162600 -0.706902 7.696400 14.164002 118-1976 118-1977 118-1978 118-1979 118-1980 118-1981 118-1982 NA NA 3.339400 0.609600 -8.238700 -21.277899 -19.718204 118-1983 119-1977 119-1978 119-1979 119-1980 119-1981 119-1982 -5.149902 NA NA 1.800000 -1.625000 -14.425000 -18.775002 119-1983 119-1984 120-1977 120-1978 120-1979 120-1980 120-1981 -5.599998 4.700005 NA NA 5.307400 -3.423700 -14.879101 120-1982 120-1983 120-1984 121-1977 121-1978 121-1979 121-1980 -13.168898 -3.537796 4.250100 NA NA 5.529200 1.407200 121-1981 121-1982 121-1983 121-1984 122-1976 122-1977 122-1978 -11.783800 -16.357203 -6.764000 2.104400 NA NA 5.695400 122-1979 122-1980 122-1981 122-1982 122-1983 123-1976 123-1977 5.030300 -9.462300 -18.748297 -9.183601 0.495094 NA NA 123-1978 123-1979 123-1980 123-1981 123-1982 123-1983 124-1977 5.695400 5.030300 -9.462300 -18.748297 -9.183601 0.495094 NA 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 124-1984 NA 1.800000 0.658300 -12.741702 -20.924998 -7.933296 4.833397 125-1977 125-1978 125-1979 125-1980 125-1981 125-1982 125-1983 NA NA 1.851600 -4.086700 -19.802304 -23.838896 -7.834694 125-1984 126-1977 126-1978 126-1979 126-1980 126-1981 126-1982 2.394493 NA NA 0.609600 -8.238700 -21.277899 -19.718204 126-1983 126-1984 127-1976 127-1977 127-1978 127-1979 127-1980 -5.149902 3.550400 NA NA 3.942100 3.968401 1.575597 127-1981 127-1982 127-1983 127-1984 128-1976 128-1977 128-1978 -1.684099 -0.802701 1.888999 2.299501 NA NA 3.942100 128-1979 128-1980 128-1981 128-1982 128-1983 128-1984 129-1976 3.968401 1.575597 -1.684099 -0.802701 1.888999 2.299501 NA 129-1977 129-1978 129-1979 129-1980 129-1981 129-1982 129-1983 NA 3.942100 3.968401 1.575597 -1.684099 -0.802701 1.888999 129-1984 130-1976 130-1977 130-1978 130-1979 130-1980 130-1981 2.299501 NA NA 6.783500 4.098100 -5.669100 -11.351599 130-1982 130-1983 130-1984 131-1976 131-1977 131-1978 131-1979 -3.570899 5.595499 12.547199 NA NA 5.695400 5.030300 131-1980 131-1981 131-1982 131-1983 131-1984 132-1976 132-1977 -9.462300 -18.748297 -9.183601 0.495094 6.932304 NA NA 132-1978 132-1979 132-1980 132-1981 132-1982 132-1983 132-1984 -2.312900 -3.147700 -24.450700 -27.787104 -3.320297 1.308106 7.049197 133-1976 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 NA NA -4.075000 -6.000000 -11.300000 -17.974998 -12.049997 133-1983 133-1984 134-1976 134-1977 134-1978 134-1979 134-1980 -4.099999 -2.900001 NA NA 2.265700 -2.702800 -19.310500 134-1981 134-1982 134-1983 134-1984 135-1976 135-1977 135-1978 -25.212498 -8.729599 2.009300 6.632599 NA NA 2.668300 135-1979 135-1980 135-1981 135-1982 135-1983 135-1984 136-1976 -1.460700 -15.158500 -23.737003 -12.850399 -0.675399 5.476799 NA 136-1977 136-1978 136-1979 136-1980 136-1981 136-1982 136-1983 NA 2.265700 -2.702800 -19.310500 -25.212498 -8.729599 2.009300 136-1984 137-1976 137-1977 137-1978 137-1979 137-1980 137-1981 6.632599 NA NA 1.175000 1.800000 -8.475000 -19.475000 137-1982 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 -12.324997 1.400002 4.299995 NA NA 6.690500 5.307400 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 139-1977 -3.423700 -14.879101 -13.168898 -3.537796 4.250100 NA NA 139-1978 139-1979 139-1980 139-1981 139-1982 139-1983 139-1984 6.292500 5.196600 -5.839200 -16.426897 -11.574802 -1.924607 5.322998 140-1976 140-1977 140-1978 140-1979 140-1980 140-1981 140-1982 NA NA 4.152400 4.080000 -8.918800 -12.162600 -0.706902 140-1983 140-1984 7.696400 14.164002 > > # compute negative lags (= leading values) > lag(z, -1) 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 1-1983 2-1977 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 NA 97.3569 2-1978 2-1979 2-1980 2-1981 2-1982 2-1983 3-1977 3-1978 99.6083 100.5501 99.5581 98.6151 100.0301 NA 97.3569 99.6083 3-1979 3-1980 3-1981 3-1982 3-1983 4-1977 4-1978 4-1979 100.5501 99.5581 98.6151 100.0301 NA 120.1551 118.8319 111.9164 4-1980 4-1981 4-1982 4-1983 5-1976 5-1977 5-1978 5-1979 97.5540 92.1982 92.4041 NA 96.5038 98.8163 100.4835 100.1223 5-1980 5-1981 5-1982 6-1976 6-1977 6-1978 6-1979 6-1980 98.5270 99.5083 NA 107.0270 108.6788 111.1190 101.9265 99.4971 6-1981 6-1982 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 99.3103 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 7-1982 8-1976 8-1977 8-1978 8-1979 8-1980 8-1981 8-1982 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 9-1976 9-1977 9-1978 9-1979 9-1980 9-1981 9-1982 10-1976 107.0270 108.6788 111.1190 101.9265 99.4971 99.3103 NA 96.7315 10-1977 10-1978 10-1979 10-1980 10-1981 10-1982 11-1976 11-1977 99.2333 100.7335 100.0000 98.2324 99.7635 NA 107.4791 108.9188 11-1978 11-1979 11-1980 11-1981 11-1982 12-1976 12-1977 12-1978 111.5591 100.0000 99.3965 99.2931 NA 107.4791 108.9188 111.5591 12-1979 12-1980 12-1981 12-1982 13-1976 13-1977 13-1978 13-1979 100.0000 99.3965 99.2931 NA 127.9649 125.4294 125.0259 106.3044 13-1980 13-1981 13-1982 14-1978 14-1979 14-1980 14-1981 14-1982 98.0729 96.8674 NA 124.6424 118.9132 99.3576 97.2428 97.1944 14-1983 14-1984 15-1977 15-1978 15-1979 15-1980 15-1981 15-1982 99.9862 NA 108.1833 110.6984 105.9673 96.5931 92.0013 92.2488 15-1983 16-1976 16-1977 16-1978 16-1979 16-1980 16-1981 16-1982 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 17-1977 17-1978 17-1979 17-1980 17-1981 17-1982 17-1983 18-1977 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 NA 114.9750 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 19-1976 19-1977 111.8250 106.6750 97.3000 89.5250 89.6000 NA 115.7000 112.8000 19-1978 19-1979 19-1980 19-1981 19-1982 20-1977 20-1978 20-1979 108.9000 100.0000 89.2000 90.5000 NA 114.9750 111.8250 106.6750 20-1980 20-1981 20-1982 20-1983 21-1977 21-1978 21-1979 21-1980 97.3000 89.5250 89.6000 NA 114.9750 111.8250 106.6750 97.3000 21-1981 21-1982 21-1983 22-1976 22-1977 22-1978 22-1979 22-1980 89.5250 89.6000 NA 115.7000 112.8000 108.9000 100.0000 89.2000 22-1981 22-1982 23-1976 23-1977 23-1978 23-1979 23-1980 23-1981 90.5000 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 23-1982 24-1976 24-1977 24-1978 24-1979 24-1980 24-1981 24-1982 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 25-1976 25-1977 25-1978 25-1979 25-1980 25-1981 25-1982 26-1976 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 115.7000 26-1977 26-1978 26-1979 26-1980 26-1981 26-1982 27-1978 27-1979 112.8000 108.9000 100.0000 89.2000 90.5000 NA 108.9000 100.0000 27-1980 27-1981 27-1982 27-1983 27-1984 28-1977 28-1978 28-1979 89.2000 90.5000 86.9000 87.4000 NA 98.2000 102.5667 102.0167 28-1980 28-1981 28-1982 28-1983 29-1977 29-1978 29-1979 29-1980 99.4917 94.2583 99.0583 NA 100.2000 102.4000 101.1000 96.9500 29-1981 29-1982 29-1983 30-1976 30-1977 30-1978 30-1979 30-1980 96.0500 103.3500 NA 115.7000 112.8000 108.9000 100.0000 89.2000 30-1981 30-1982 31-1976 31-1977 31-1978 31-1979 31-1980 31-1981 90.5000 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 31-1982 32-1977 32-1978 32-1979 32-1980 32-1981 32-1982 32-1983 NA 100.6000 102.3667 100.9167 96.4417 96.4083 104.2083 NA 33-1976 33-1977 33-1978 33-1979 33-1980 33-1981 33-1982 34-1977 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 NA 100.2000 34-1978 34-1979 34-1980 34-1981 34-1982 34-1983 35-1977 35-1978 102.4000 101.1000 96.9500 96.0500 103.3500 NA 99.0000 102.5000 35-1979 35-1980 35-1981 35-1982 35-1983 36-1976 36-1977 36-1978 101.6500 98.4750 94.9750 100.7750 NA 97.8000 102.6000 102.2000 36-1979 36-1980 36-1981 36-1982 37-1977 37-1978 37-1979 37-1980 100.0000 93.9000 98.2000 NA 114.9750 111.8250 106.6750 97.3000 37-1981 37-1982 37-1983 38-1976 38-1977 38-1978 38-1979 38-1980 89.5250 89.6000 NA 108.9675 107.8624 104.5668 100.0000 92.7430 38-1981 38-1982 39-1977 39-1978 39-1979 39-1980 39-1981 39-1982 90.0386 NA 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 39-1983 40-1976 40-1977 40-1978 40-1979 40-1980 40-1981 40-1982 NA 108.2693 108.1387 105.3907 101.1417 94.5573 90.7147 NA 41-1977 41-1978 41-1979 41-1980 41-1981 41-1982 41-1983 42-1976 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 NA 108.9675 42-1977 42-1978 42-1979 42-1980 42-1981 42-1982 43-1977 43-1978 107.8624 104.5668 100.0000 92.7430 90.0386 NA 127.3866 124.6424 43-1979 43-1980 43-1981 43-1982 43-1983 44-1977 44-1978 44-1979 118.9132 99.3576 97.2428 97.1944 NA 114.9750 111.8250 106.6750 44-1980 44-1981 44-1982 44-1983 45-1977 45-1978 45-1979 45-1980 97.3000 89.5250 89.6000 NA 127.3866 124.6424 118.9132 99.3576 45-1981 45-1982 45-1983 46-1976 46-1977 46-1978 46-1979 46-1980 97.2428 97.1944 NA 128.3653 124.4507 125.2176 100.0000 97.4305 46-1981 46-1982 47-1976 47-1977 47-1978 47-1979 47-1980 47-1981 96.6797 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 47-1982 48-1976 48-1977 48-1978 48-1979 48-1980 48-1981 48-1982 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 49-1976 49-1977 49-1978 49-1979 49-1980 49-1981 49-1982 50-1976 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 NA 108.9675 50-1977 50-1978 50-1979 50-1980 50-1981 50-1982 51-1976 51-1977 107.8624 104.5668 100.0000 92.7430 90.0386 NA 127.9649 125.4294 51-1978 51-1979 51-1980 51-1981 51-1982 52-1976 52-1977 52-1978 125.0259 106.3044 98.0729 96.8674 NA 115.7000 112.8000 108.9000 52-1979 52-1980 52-1981 52-1982 53-1977 53-1978 53-1979 53-1980 100.0000 89.2000 90.5000 NA 114.9750 111.8250 106.6750 97.3000 53-1981 53-1982 53-1983 54-1977 54-1978 54-1979 54-1980 54-1981 89.5250 89.6000 NA 127.3866 124.6424 118.9132 99.3576 97.2428 54-1982 54-1983 55-1976 55-1977 55-1978 55-1979 55-1980 55-1981 97.1944 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 55-1982 56-1976 56-1977 56-1978 56-1979 56-1980 56-1981 56-1982 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 57-1977 57-1978 57-1979 57-1980 57-1981 57-1982 57-1983 58-1976 120.2607 118.9515 113.4060 98.1655 92.3142 92.1207 NA 115.7000 58-1977 58-1978 58-1979 58-1980 58-1981 58-1982 59-1977 59-1978 112.8000 108.9000 100.0000 89.2000 90.5000 NA 120.2607 118.9515 59-1979 59-1980 59-1981 59-1982 59-1983 60-1976 60-1977 60-1978 113.4060 98.1655 92.3142 92.1207 NA 106.9042 109.4623 111.9345 60-1979 60-1980 60-1981 60-1982 61-1977 61-1978 61-1979 61-1980 100.0000 93.1862 90.8164 NA 107.5437 110.0804 108.9509 98.2966 61-1981 61-1982 61-1983 62-1977 62-1978 62-1979 62-1980 62-1981 92.5937 91.5326 NA 120.2607 118.9515 113.4060 98.1655 92.3142 62-1982 62-1983 63-1976 63-1977 63-1978 63-1979 63-1980 63-1981 92.1207 NA 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 63-1982 64-1976 64-1977 64-1978 64-1979 64-1980 64-1981 64-1982 NA 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 65-1976 65-1977 65-1978 65-1979 65-1980 65-1981 65-1982 66-1977 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 109.8750 66-1978 66-1979 66-1980 66-1981 66-1982 66-1983 67-1976 67-1977 111.6750 108.2500 97.2500 89.4750 91.6500 NA 120.5774 119.3105 67-1978 67-1979 67-1980 67-1981 67-1982 68-1976 68-1977 68-1978 117.8746 100.0000 92.6621 91.2704 NA 109.2000 111.9000 111.0000 68-1979 68-1980 68-1981 68-1982 69-1977 69-1978 69-1979 69-1980 100.0000 89.0000 90.9000 NA 109.8750 111.6750 108.2500 97.2500 69-1981 69-1982 69-1983 70-1977 70-1978 70-1979 70-1980 70-1981 89.4750 91.6500 NA 107.5437 110.0804 108.9509 98.2966 92.5937 70-1982 70-1983 71-1976 71-1977 71-1978 71-1979 71-1980 71-1981 91.5326 NA 106.1199 108.8228 111.3165 102.9836 94.8896 91.4088 71-1982 72-1976 72-1977 72-1978 72-1979 72-1980 72-1981 72-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 73-1977 73-1978 73-1979 73-1980 73-1981 73-1982 73-1983 74-1977 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 NA 107.5437 74-1978 74-1979 74-1980 74-1981 74-1982 74-1983 75-1976 75-1977 110.0804 108.9509 98.2966 92.5937 91.5326 NA 106.9042 109.4623 75-1978 75-1979 75-1980 75-1981 75-1982 76-1976 76-1977 76-1978 111.9345 100.0000 93.1862 90.8164 NA 106.1199 108.8228 111.3165 76-1979 76-1980 76-1981 76-1982 77-1977 77-1978 77-1979 77-1980 102.9836 94.8896 91.4088 NA 107.5437 110.0804 108.9509 98.2966 77-1981 77-1982 77-1983 78-1976 78-1977 78-1978 78-1979 78-1980 92.5937 91.5326 NA 106.1199 108.8228 111.3165 102.9836 94.8896 78-1981 78-1982 79-1976 79-1977 79-1978 79-1979 79-1980 79-1981 91.4088 NA 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 79-1982 80-1976 80-1977 80-1978 80-1979 80-1980 80-1981 80-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 81-1976 81-1977 81-1978 81-1979 81-1980 81-1981 81-1982 82-1976 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 115.7000 82-1977 82-1978 82-1979 82-1980 82-1981 82-1982 83-1977 83-1978 112.8000 108.9000 100.0000 89.2000 90.5000 NA 127.3866 124.6424 83-1979 83-1980 83-1981 83-1982 83-1983 84-1976 84-1977 84-1978 118.9132 99.3576 97.2428 97.1944 NA 96.3900 98.6078 100.3584 84-1979 84-1980 84-1981 84-1982 85-1976 85-1977 85-1978 85-1979 100.1834 98.6743 99.3807 NA 97.1000 101.8000 102.2667 100.3667 85-1980 85-1981 85-1982 86-1976 86-1977 86-1978 86-1979 86-1980 94.9167 97.4833 NA 96.3900 98.6078 100.3584 100.1834 98.6743 86-1981 86-1982 87-1976 87-1977 87-1978 87-1979 87-1980 87-1981 99.3807 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 87-1982 88-1976 88-1977 88-1978 88-1979 88-1980 88-1981 88-1982 NA 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 89-1976 89-1977 89-1978 89-1979 89-1980 89-1981 89-1982 90-1976 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 109.4250 90-1977 90-1978 90-1979 90-1980 90-1981 90-1982 91-1976 91-1977 111.2250 111.2250 102.7500 91.7500 90.4250 NA 107.4791 108.9188 91-1978 91-1979 91-1980 91-1981 91-1982 92-1976 92-1977 92-1978 111.5591 100.0000 99.3965 99.2931 NA 95.0000 99.4000 102.4667 92-1979 92-1980 92-1981 92-1982 93-1977 93-1978 93-1979 93-1980 101.4667 97.9667 95.3333 NA 99.0000 102.5000 101.6500 98.4750 93-1981 93-1982 93-1983 94-1976 94-1977 94-1978 94-1979 94-1980 94.9750 100.7750 NA 115.8750 113.5250 109.8750 102.2250 91.9000 94-1981 94-1982 95-1976 95-1977 95-1978 95-1979 95-1980 95-1981 90.1750 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 95-1982 96-1976 96-1977 96-1978 96-1979 96-1980 96-1981 96-1982 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 97-1976 97-1977 97-1978 97-1979 97-1980 97-1981 97-1982 98-1977 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 NA 127.3866 98-1978 98-1979 98-1980 98-1981 98-1982 98-1983 99-1977 99-1978 124.6424 118.9132 99.3576 97.2428 97.1944 NA 107.5437 110.0804 99-1979 99-1980 99-1981 99-1982 99-1983 100-1977 100-1978 100-1979 108.9509 98.2966 92.5937 91.5326 NA 126.0818 124.8981 110.5073 100-1980 100-1981 100-1982 100-1983 101-1977 101-1978 101-1979 101-1980 98.5011 96.9925 97.8807 NA 120.2607 118.9515 113.4060 98.1655 101-1981 101-1982 101-1983 102-1977 102-1978 102-1979 102-1980 102-1981 92.3142 92.1207 NA 107.8390 109.5789 108.6693 99.8491 99.3706 102-1982 102-1983 103-1976 103-1977 103-1978 103-1979 103-1980 103-1981 101.2430 NA 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 103-1982 104-1977 104-1978 104-1979 104-1980 104-1981 104-1982 104-1983 NA 97.5654 99.7333 100.4890 99.4108 98.7427 100.1189 101.2078 104-1984 105-1977 105-1978 105-1979 105-1980 105-1981 105-1982 105-1983 NA 98.3993 100.2334 100.2445 98.8216 99.2531 100.4744 101.5857 105-1984 106-1977 106-1978 106-1979 106-1980 106-1981 106-1982 106-1983 NA 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 101.6802 106-1984 107-1977 107-1978 107-1979 107-1980 107-1981 107-1982 107-1983 NA 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 101.1133 107-1984 108-1977 108-1978 108-1979 108-1980 108-1981 108-1982 108-1983 NA 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 101.1133 108-1984 109-1977 109-1978 109-1979 109-1980 109-1981 109-1982 109-1983 NA 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 101.6802 109-1984 110-1977 110-1978 110-1979 110-1980 110-1981 110-1982 110-1983 NA 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 99.9862 110-1984 111-1977 111-1978 111-1979 111-1980 111-1981 111-1982 111-1983 NA 114.2500 110.8500 104.4500 94.6000 89.8500 88.7000 87.1500 111-1984 112-1977 112-1978 112-1979 112-1980 112-1981 112-1982 112-1983 NA 108.6912 107.0385 103.4251 98.1858 92.0669 89.8429 88.4593 112-1984 113-1976 113-1977 113-1978 113-1979 113-1980 113-1981 113-1982 NA 96.7500 101.4000 102.3000 100.5500 95.4250 97.1250 105.9250 113-1983 114-1977 114-1978 114-1979 114-1980 114-1981 114-1982 114-1983 NA 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 97.9030 114-1984 115-1977 115-1978 115-1979 115-1980 115-1981 115-1982 115-1983 NA 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 97.9030 115-1984 116-1977 116-1978 116-1979 116-1980 116-1981 116-1982 116-1983 NA 120.3662 119.0712 114.8955 98.7770 92.4302 91.8372 95.2100 116-1984 117-1977 117-1978 117-1979 117-1980 117-1981 117-1982 117-1983 NA 108.9188 111.5591 100.0000 99.3965 99.2931 107.0929 113.4571 117-1984 118-1976 118-1977 118-1978 118-1979 118-1980 118-1981 118-1982 NA 118.2223 120.1551 118.8319 111.9164 97.5540 92.1982 92.4041 118-1983 119-1977 119-1978 119-1979 119-1980 119-1981 119-1982 119-1983 NA 109.8750 111.6750 108.2500 97.2500 89.4750 91.6500 94.1750 119-1984 120-1977 120-1978 120-1979 120-1980 120-1981 120-1982 120-1983 NA 108.3964 110.9044 104.9727 96.0253 91.8038 92.4875 96.0539 120-1984 121-1977 121-1978 121-1979 121-1980 121-1981 121-1982 121-1983 NA 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 94.6981 121-1984 122-1976 122-1977 122-1978 122-1979 122-1980 122-1981 122-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 122-1983 123-1976 123-1977 123-1978 123-1979 123-1980 123-1981 123-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 123-1983 124-1977 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 NA 109.4250 111.8250 110.0833 99.0833 89.1583 91.1500 93.9917 124-1984 125-1977 125-1978 125-1979 125-1980 125-1981 125-1982 125-1983 NA 120.4718 119.1908 116.3851 99.3885 92.5462 91.5538 94.9407 125-1984 126-1977 126-1978 126-1979 126-1980 126-1981 126-1982 126-1983 NA 120.1551 118.8319 111.9164 97.5540 92.1982 92.4041 95.7486 126-1984 127-1976 127-1977 127-1978 127-1979 127-1980 127-1981 127-1982 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 127-1983 127-1984 128-1976 128-1977 128-1978 128-1979 128-1980 128-1981 101.6802 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 128-1982 128-1983 128-1984 129-1976 129-1977 129-1978 129-1979 129-1980 100.5633 101.6802 NA 96.3900 98.6078 100.3584 100.1834 98.6743 129-1981 129-1982 129-1983 129-1984 130-1976 130-1977 130-1978 130-1979 99.3807 100.5633 101.6802 NA 106.8009 108.5589 110.8990 102.8898 130-1980 130-1981 130-1982 130-1983 130-1984 131-1976 131-1977 131-1978 99.5474 99.3189 105.1429 111.8661 NA 106.9042 109.4623 111.9345 131-1979 131-1980 131-1981 131-1982 131-1983 131-1984 132-1976 132-1977 100.0000 93.1862 90.8164 93.6813 97.7487 NA 128.3653 124.4507 132-1978 132-1979 132-1980 132-1981 132-1982 132-1983 132-1984 133-1976 125.2176 100.0000 97.4305 96.6797 98.7386 103.7289 NA 115.8750 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 133-1983 133-1984 113.5250 109.8750 102.2250 91.9000 90.1750 87.8000 87.2750 NA 134-1976 134-1977 134-1978 134-1979 134-1980 134-1981 134-1982 134-1983 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 97.9030 134-1984 135-1976 135-1977 135-1978 135-1979 135-1980 135-1981 135-1982 NA 119.6943 119.6272 118.2336 104.4687 94.4966 91.6183 93.8212 135-1983 135-1984 136-1976 136-1977 136-1978 136-1979 136-1980 136-1981 97.0951 NA 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 136-1982 136-1983 136-1984 137-1976 137-1977 137-1978 137-1979 137-1980 94.6714 97.9030 NA 109.4250 111.2250 111.2250 102.7500 91.7500 137-1981 137-1982 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 90.4250 93.1500 94.7250 NA 105.5970 108.3964 110.9044 104.9727 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 139-1977 139-1978 96.0253 91.8038 92.4875 96.0539 NA 106.1199 108.8228 111.3165 139-1979 139-1980 139-1981 139-1982 139-1983 139-1984 140-1976 140-1977 102.9836 94.8896 91.4088 92.9650 96.7318 NA 107.4791 108.9188 140-1978 140-1979 140-1980 140-1981 140-1982 140-1983 140-1984 111.5591 100.0000 99.3965 99.2931 107.0929 113.4571 NA > lead(z, 1) # same as line above 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 1-1983 2-1977 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 NA 97.3569 2-1978 2-1979 2-1980 2-1981 2-1982 2-1983 3-1977 3-1978 99.6083 100.5501 99.5581 98.6151 100.0301 NA 97.3569 99.6083 3-1979 3-1980 3-1981 3-1982 3-1983 4-1977 4-1978 4-1979 100.5501 99.5581 98.6151 100.0301 NA 120.1551 118.8319 111.9164 4-1980 4-1981 4-1982 4-1983 5-1976 5-1977 5-1978 5-1979 97.5540 92.1982 92.4041 NA 96.5038 98.8163 100.4835 100.1223 5-1980 5-1981 5-1982 6-1976 6-1977 6-1978 6-1979 6-1980 98.5270 99.5083 NA 107.0270 108.6788 111.1190 101.9265 99.4971 6-1981 6-1982 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 99.3103 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 7-1982 8-1976 8-1977 8-1978 8-1979 8-1980 8-1981 8-1982 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 9-1976 9-1977 9-1978 9-1979 9-1980 9-1981 9-1982 10-1976 107.0270 108.6788 111.1190 101.9265 99.4971 99.3103 NA 96.7315 10-1977 10-1978 10-1979 10-1980 10-1981 10-1982 11-1976 11-1977 99.2333 100.7335 100.0000 98.2324 99.7635 NA 107.4791 108.9188 11-1978 11-1979 11-1980 11-1981 11-1982 12-1976 12-1977 12-1978 111.5591 100.0000 99.3965 99.2931 NA 107.4791 108.9188 111.5591 12-1979 12-1980 12-1981 12-1982 13-1976 13-1977 13-1978 13-1979 100.0000 99.3965 99.2931 NA 127.9649 125.4294 125.0259 106.3044 13-1980 13-1981 13-1982 14-1978 14-1979 14-1980 14-1981 14-1982 98.0729 96.8674 NA 124.6424 118.9132 99.3576 97.2428 97.1944 14-1983 14-1984 15-1977 15-1978 15-1979 15-1980 15-1981 15-1982 99.9862 NA 108.1833 110.6984 105.9673 96.5931 92.0013 92.2488 15-1983 16-1976 16-1977 16-1978 16-1979 16-1980 16-1981 16-1982 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 17-1977 17-1978 17-1979 17-1980 17-1981 17-1982 17-1983 18-1977 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 NA 114.9750 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 19-1976 19-1977 111.8250 106.6750 97.3000 89.5250 89.6000 NA 115.7000 112.8000 19-1978 19-1979 19-1980 19-1981 19-1982 20-1977 20-1978 20-1979 108.9000 100.0000 89.2000 90.5000 NA 114.9750 111.8250 106.6750 20-1980 20-1981 20-1982 20-1983 21-1977 21-1978 21-1979 21-1980 97.3000 89.5250 89.6000 NA 114.9750 111.8250 106.6750 97.3000 21-1981 21-1982 21-1983 22-1976 22-1977 22-1978 22-1979 22-1980 89.5250 89.6000 NA 115.7000 112.8000 108.9000 100.0000 89.2000 22-1981 22-1982 23-1976 23-1977 23-1978 23-1979 23-1980 23-1981 90.5000 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 23-1982 24-1976 24-1977 24-1978 24-1979 24-1980 24-1981 24-1982 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 25-1976 25-1977 25-1978 25-1979 25-1980 25-1981 25-1982 26-1976 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 115.7000 26-1977 26-1978 26-1979 26-1980 26-1981 26-1982 27-1978 27-1979 112.8000 108.9000 100.0000 89.2000 90.5000 NA 108.9000 100.0000 27-1980 27-1981 27-1982 27-1983 27-1984 28-1977 28-1978 28-1979 89.2000 90.5000 86.9000 87.4000 NA 98.2000 102.5667 102.0167 28-1980 28-1981 28-1982 28-1983 29-1977 29-1978 29-1979 29-1980 99.4917 94.2583 99.0583 NA 100.2000 102.4000 101.1000 96.9500 29-1981 29-1982 29-1983 30-1976 30-1977 30-1978 30-1979 30-1980 96.0500 103.3500 NA 115.7000 112.8000 108.9000 100.0000 89.2000 30-1981 30-1982 31-1976 31-1977 31-1978 31-1979 31-1980 31-1981 90.5000 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 31-1982 32-1977 32-1978 32-1979 32-1980 32-1981 32-1982 32-1983 NA 100.6000 102.3667 100.9167 96.4417 96.4083 104.2083 NA 33-1976 33-1977 33-1978 33-1979 33-1980 33-1981 33-1982 34-1977 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 NA 100.2000 34-1978 34-1979 34-1980 34-1981 34-1982 34-1983 35-1977 35-1978 102.4000 101.1000 96.9500 96.0500 103.3500 NA 99.0000 102.5000 35-1979 35-1980 35-1981 35-1982 35-1983 36-1976 36-1977 36-1978 101.6500 98.4750 94.9750 100.7750 NA 97.8000 102.6000 102.2000 36-1979 36-1980 36-1981 36-1982 37-1977 37-1978 37-1979 37-1980 100.0000 93.9000 98.2000 NA 114.9750 111.8250 106.6750 97.3000 37-1981 37-1982 37-1983 38-1976 38-1977 38-1978 38-1979 38-1980 89.5250 89.6000 NA 108.9675 107.8624 104.5668 100.0000 92.7430 38-1981 38-1982 39-1977 39-1978 39-1979 39-1980 39-1981 39-1982 90.0386 NA 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 39-1983 40-1976 40-1977 40-1978 40-1979 40-1980 40-1981 40-1982 NA 108.2693 108.1387 105.3907 101.1417 94.5573 90.7147 NA 41-1977 41-1978 41-1979 41-1980 41-1981 41-1982 41-1983 42-1976 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 NA 108.9675 42-1977 42-1978 42-1979 42-1980 42-1981 42-1982 43-1977 43-1978 107.8624 104.5668 100.0000 92.7430 90.0386 NA 127.3866 124.6424 43-1979 43-1980 43-1981 43-1982 43-1983 44-1977 44-1978 44-1979 118.9132 99.3576 97.2428 97.1944 NA 114.9750 111.8250 106.6750 44-1980 44-1981 44-1982 44-1983 45-1977 45-1978 45-1979 45-1980 97.3000 89.5250 89.6000 NA 127.3866 124.6424 118.9132 99.3576 45-1981 45-1982 45-1983 46-1976 46-1977 46-1978 46-1979 46-1980 97.2428 97.1944 NA 128.3653 124.4507 125.2176 100.0000 97.4305 46-1981 46-1982 47-1976 47-1977 47-1978 47-1979 47-1980 47-1981 96.6797 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 47-1982 48-1976 48-1977 48-1978 48-1979 48-1980 48-1981 48-1982 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 49-1976 49-1977 49-1978 49-1979 49-1980 49-1981 49-1982 50-1976 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 NA 108.9675 50-1977 50-1978 50-1979 50-1980 50-1981 50-1982 51-1976 51-1977 107.8624 104.5668 100.0000 92.7430 90.0386 NA 127.9649 125.4294 51-1978 51-1979 51-1980 51-1981 51-1982 52-1976 52-1977 52-1978 125.0259 106.3044 98.0729 96.8674 NA 115.7000 112.8000 108.9000 52-1979 52-1980 52-1981 52-1982 53-1977 53-1978 53-1979 53-1980 100.0000 89.2000 90.5000 NA 114.9750 111.8250 106.6750 97.3000 53-1981 53-1982 53-1983 54-1977 54-1978 54-1979 54-1980 54-1981 89.5250 89.6000 NA 127.3866 124.6424 118.9132 99.3576 97.2428 54-1982 54-1983 55-1976 55-1977 55-1978 55-1979 55-1980 55-1981 97.1944 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 55-1982 56-1976 56-1977 56-1978 56-1979 56-1980 56-1981 56-1982 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 57-1977 57-1978 57-1979 57-1980 57-1981 57-1982 57-1983 58-1976 120.2607 118.9515 113.4060 98.1655 92.3142 92.1207 NA 115.7000 58-1977 58-1978 58-1979 58-1980 58-1981 58-1982 59-1977 59-1978 112.8000 108.9000 100.0000 89.2000 90.5000 NA 120.2607 118.9515 59-1979 59-1980 59-1981 59-1982 59-1983 60-1976 60-1977 60-1978 113.4060 98.1655 92.3142 92.1207 NA 106.9042 109.4623 111.9345 60-1979 60-1980 60-1981 60-1982 61-1977 61-1978 61-1979 61-1980 100.0000 93.1862 90.8164 NA 107.5437 110.0804 108.9509 98.2966 61-1981 61-1982 61-1983 62-1977 62-1978 62-1979 62-1980 62-1981 92.5937 91.5326 NA 120.2607 118.9515 113.4060 98.1655 92.3142 62-1982 62-1983 63-1976 63-1977 63-1978 63-1979 63-1980 63-1981 92.1207 NA 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 63-1982 64-1976 64-1977 64-1978 64-1979 64-1980 64-1981 64-1982 NA 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 65-1976 65-1977 65-1978 65-1979 65-1980 65-1981 65-1982 66-1977 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 109.8750 66-1978 66-1979 66-1980 66-1981 66-1982 66-1983 67-1976 67-1977 111.6750 108.2500 97.2500 89.4750 91.6500 NA 120.5774 119.3105 67-1978 67-1979 67-1980 67-1981 67-1982 68-1976 68-1977 68-1978 117.8746 100.0000 92.6621 91.2704 NA 109.2000 111.9000 111.0000 68-1979 68-1980 68-1981 68-1982 69-1977 69-1978 69-1979 69-1980 100.0000 89.0000 90.9000 NA 109.8750 111.6750 108.2500 97.2500 69-1981 69-1982 69-1983 70-1977 70-1978 70-1979 70-1980 70-1981 89.4750 91.6500 NA 107.5437 110.0804 108.9509 98.2966 92.5937 70-1982 70-1983 71-1976 71-1977 71-1978 71-1979 71-1980 71-1981 91.5326 NA 106.1199 108.8228 111.3165 102.9836 94.8896 91.4088 71-1982 72-1976 72-1977 72-1978 72-1979 72-1980 72-1981 72-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 73-1977 73-1978 73-1979 73-1980 73-1981 73-1982 73-1983 74-1977 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 NA 107.5437 74-1978 74-1979 74-1980 74-1981 74-1982 74-1983 75-1976 75-1977 110.0804 108.9509 98.2966 92.5937 91.5326 NA 106.9042 109.4623 75-1978 75-1979 75-1980 75-1981 75-1982 76-1976 76-1977 76-1978 111.9345 100.0000 93.1862 90.8164 NA 106.1199 108.8228 111.3165 76-1979 76-1980 76-1981 76-1982 77-1977 77-1978 77-1979 77-1980 102.9836 94.8896 91.4088 NA 107.5437 110.0804 108.9509 98.2966 77-1981 77-1982 77-1983 78-1976 78-1977 78-1978 78-1979 78-1980 92.5937 91.5326 NA 106.1199 108.8228 111.3165 102.9836 94.8896 78-1981 78-1982 79-1976 79-1977 79-1978 79-1979 79-1980 79-1981 91.4088 NA 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 79-1982 80-1976 80-1977 80-1978 80-1979 80-1980 80-1981 80-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 81-1976 81-1977 81-1978 81-1979 81-1980 81-1981 81-1982 82-1976 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 115.7000 82-1977 82-1978 82-1979 82-1980 82-1981 82-1982 83-1977 83-1978 112.8000 108.9000 100.0000 89.2000 90.5000 NA 127.3866 124.6424 83-1979 83-1980 83-1981 83-1982 83-1983 84-1976 84-1977 84-1978 118.9132 99.3576 97.2428 97.1944 NA 96.3900 98.6078 100.3584 84-1979 84-1980 84-1981 84-1982 85-1976 85-1977 85-1978 85-1979 100.1834 98.6743 99.3807 NA 97.1000 101.8000 102.2667 100.3667 85-1980 85-1981 85-1982 86-1976 86-1977 86-1978 86-1979 86-1980 94.9167 97.4833 NA 96.3900 98.6078 100.3584 100.1834 98.6743 86-1981 86-1982 87-1976 87-1977 87-1978 87-1979 87-1980 87-1981 99.3807 NA 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 87-1982 88-1976 88-1977 88-1978 88-1979 88-1980 88-1981 88-1982 NA 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 89-1976 89-1977 89-1978 89-1979 89-1980 89-1981 89-1982 90-1976 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 109.4250 90-1977 90-1978 90-1979 90-1980 90-1981 90-1982 91-1976 91-1977 111.2250 111.2250 102.7500 91.7500 90.4250 NA 107.4791 108.9188 91-1978 91-1979 91-1980 91-1981 91-1982 92-1976 92-1977 92-1978 111.5591 100.0000 99.3965 99.2931 NA 95.0000 99.4000 102.4667 92-1979 92-1980 92-1981 92-1982 93-1977 93-1978 93-1979 93-1980 101.4667 97.9667 95.3333 NA 99.0000 102.5000 101.6500 98.4750 93-1981 93-1982 93-1983 94-1976 94-1977 94-1978 94-1979 94-1980 94.9750 100.7750 NA 115.8750 113.5250 109.8750 102.2250 91.9000 94-1981 94-1982 95-1976 95-1977 95-1978 95-1979 95-1980 95-1981 90.1750 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 95-1982 96-1976 96-1977 96-1978 96-1979 96-1980 96-1981 96-1982 NA 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 97-1976 97-1977 97-1978 97-1979 97-1980 97-1981 97-1982 98-1977 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 NA 127.3866 98-1978 98-1979 98-1980 98-1981 98-1982 98-1983 99-1977 99-1978 124.6424 118.9132 99.3576 97.2428 97.1944 NA 107.5437 110.0804 99-1979 99-1980 99-1981 99-1982 99-1983 100-1977 100-1978 100-1979 108.9509 98.2966 92.5937 91.5326 NA 126.0818 124.8981 110.5073 100-1980 100-1981 100-1982 100-1983 101-1977 101-1978 101-1979 101-1980 98.5011 96.9925 97.8807 NA 120.2607 118.9515 113.4060 98.1655 101-1981 101-1982 101-1983 102-1977 102-1978 102-1979 102-1980 102-1981 92.3142 92.1207 NA 107.8390 109.5789 108.6693 99.8491 99.3706 102-1982 102-1983 103-1976 103-1977 103-1978 103-1979 103-1980 103-1981 101.2430 NA 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 103-1982 104-1977 104-1978 104-1979 104-1980 104-1981 104-1982 104-1983 NA 97.5654 99.7333 100.4890 99.4108 98.7427 100.1189 101.2078 104-1984 105-1977 105-1978 105-1979 105-1980 105-1981 105-1982 105-1983 NA 98.3993 100.2334 100.2445 98.8216 99.2531 100.4744 101.5857 105-1984 106-1977 106-1978 106-1979 106-1980 106-1981 106-1982 106-1983 NA 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 101.6802 106-1984 107-1977 107-1978 107-1979 107-1980 107-1981 107-1982 107-1983 NA 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 101.1133 107-1984 108-1977 108-1978 108-1979 108-1980 108-1981 108-1982 108-1983 NA 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 101.1133 108-1984 109-1977 109-1978 109-1979 109-1980 109-1981 109-1982 109-1983 NA 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 101.6802 109-1984 110-1977 110-1978 110-1979 110-1980 110-1981 110-1982 110-1983 NA 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 99.9862 110-1984 111-1977 111-1978 111-1979 111-1980 111-1981 111-1982 111-1983 NA 114.2500 110.8500 104.4500 94.6000 89.8500 88.7000 87.1500 111-1984 112-1977 112-1978 112-1979 112-1980 112-1981 112-1982 112-1983 NA 108.6912 107.0385 103.4251 98.1858 92.0669 89.8429 88.4593 112-1984 113-1976 113-1977 113-1978 113-1979 113-1980 113-1981 113-1982 NA 96.7500 101.4000 102.3000 100.5500 95.4250 97.1250 105.9250 113-1983 114-1977 114-1978 114-1979 114-1980 114-1981 114-1982 114-1983 NA 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 97.9030 114-1984 115-1977 115-1978 115-1979 115-1980 115-1981 115-1982 115-1983 NA 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 97.9030 115-1984 116-1977 116-1978 116-1979 116-1980 116-1981 116-1982 116-1983 NA 120.3662 119.0712 114.8955 98.7770 92.4302 91.8372 95.2100 116-1984 117-1977 117-1978 117-1979 117-1980 117-1981 117-1982 117-1983 NA 108.9188 111.5591 100.0000 99.3965 99.2931 107.0929 113.4571 117-1984 118-1976 118-1977 118-1978 118-1979 118-1980 118-1981 118-1982 NA 118.2223 120.1551 118.8319 111.9164 97.5540 92.1982 92.4041 118-1983 119-1977 119-1978 119-1979 119-1980 119-1981 119-1982 119-1983 NA 109.8750 111.6750 108.2500 97.2500 89.4750 91.6500 94.1750 119-1984 120-1977 120-1978 120-1979 120-1980 120-1981 120-1982 120-1983 NA 108.3964 110.9044 104.9727 96.0253 91.8038 92.4875 96.0539 120-1984 121-1977 121-1978 121-1979 121-1980 121-1981 121-1982 121-1983 NA 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 94.6981 121-1984 122-1976 122-1977 122-1978 122-1979 122-1980 122-1981 122-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 122-1983 123-1976 123-1977 123-1978 123-1979 123-1980 123-1981 123-1982 NA 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 123-1983 124-1977 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 NA 109.4250 111.8250 110.0833 99.0833 89.1583 91.1500 93.9917 124-1984 125-1977 125-1978 125-1979 125-1980 125-1981 125-1982 125-1983 NA 120.4718 119.1908 116.3851 99.3885 92.5462 91.5538 94.9407 125-1984 126-1977 126-1978 126-1979 126-1980 126-1981 126-1982 126-1983 NA 120.1551 118.8319 111.9164 97.5540 92.1982 92.4041 95.7486 126-1984 127-1976 127-1977 127-1978 127-1979 127-1980 127-1981 127-1982 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 127-1983 127-1984 128-1976 128-1977 128-1978 128-1979 128-1980 128-1981 101.6802 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 128-1982 128-1983 128-1984 129-1976 129-1977 129-1978 129-1979 129-1980 100.5633 101.6802 NA 96.3900 98.6078 100.3584 100.1834 98.6743 129-1981 129-1982 129-1983 129-1984 130-1976 130-1977 130-1978 130-1979 99.3807 100.5633 101.6802 NA 106.8009 108.5589 110.8990 102.8898 130-1980 130-1981 130-1982 130-1983 130-1984 131-1976 131-1977 131-1978 99.5474 99.3189 105.1429 111.8661 NA 106.9042 109.4623 111.9345 131-1979 131-1980 131-1981 131-1982 131-1983 131-1984 132-1976 132-1977 100.0000 93.1862 90.8164 93.6813 97.7487 NA 128.3653 124.4507 132-1978 132-1979 132-1980 132-1981 132-1982 132-1983 132-1984 133-1976 125.2176 100.0000 97.4305 96.6797 98.7386 103.7289 NA 115.8750 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 133-1983 133-1984 113.5250 109.8750 102.2250 91.9000 90.1750 87.8000 87.2750 NA 134-1976 134-1977 134-1978 134-1979 134-1980 134-1981 134-1982 134-1983 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 97.9030 134-1984 135-1976 135-1977 135-1978 135-1979 135-1980 135-1981 135-1982 NA 119.6943 119.6272 118.2336 104.4687 94.4966 91.6183 93.8212 135-1983 135-1984 136-1976 136-1977 136-1978 136-1979 136-1980 136-1981 97.0951 NA 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 136-1982 136-1983 136-1984 137-1976 137-1977 137-1978 137-1979 137-1980 94.6714 97.9030 NA 109.4250 111.2250 111.2250 102.7500 91.7500 137-1981 137-1982 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 90.4250 93.1500 94.7250 NA 105.5970 108.3964 110.9044 104.9727 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 139-1977 139-1978 96.0253 91.8038 92.4875 96.0539 NA 106.1199 108.8228 111.3165 139-1979 139-1980 139-1981 139-1982 139-1983 139-1984 140-1976 140-1977 102.9836 94.8896 91.4088 92.9650 96.7318 NA 107.4791 108.9188 140-1978 140-1979 140-1980 140-1981 140-1982 140-1983 140-1984 111.5591 100.0000 99.3965 99.2931 107.0929 113.4571 NA > identical(lead(z, 1), lag(z, -1)) # TRUE [1] TRUE > > # compute more than one lag and diff at once (matrix returned) > lag(z, c(1,2)) 1 2 1-1977 NA NA 1-1978 95.7072 NA 1-1979 97.3569 95.7072 1-1980 99.6083 97.3569 1-1981 100.5501 99.6083 1-1982 99.5581 100.5501 1-1983 98.6151 99.5581 2-1977 NA NA 2-1978 95.7072 NA 2-1979 97.3569 95.7072 2-1980 99.6083 97.3569 2-1981 100.5501 99.6083 2-1982 99.5581 100.5501 2-1983 98.6151 99.5581 3-1977 NA NA 3-1978 95.7072 NA 3-1979 97.3569 95.7072 3-1980 99.6083 97.3569 3-1981 100.5501 99.6083 3-1982 99.5581 100.5501 3-1983 98.6151 99.5581 4-1977 NA NA 4-1978 118.2223 NA 4-1979 120.1551 118.2223 4-1980 118.8319 120.1551 4-1981 111.9164 118.8319 4-1982 97.5540 111.9164 4-1983 92.1982 97.5540 5-1976 NA NA 5-1977 94.8991 NA 5-1978 96.5038 94.8991 5-1979 98.8163 96.5038 5-1980 100.4835 98.8163 5-1981 100.1223 100.4835 5-1982 98.5270 100.1223 6-1976 NA NA 6-1977 102.7724 NA 6-1978 107.0270 102.7724 6-1979 108.6788 107.0270 6-1980 111.1190 108.6788 6-1981 101.9265 111.1190 6-1982 99.4971 101.9265 7-1976 NA NA 7-1977 104.7664 NA 7-1978 107.4791 104.7664 7-1979 108.9188 107.4791 7-1980 111.5591 108.9188 7-1981 100.0000 111.5591 7-1982 99.3965 100.0000 8-1976 NA NA 8-1977 104.7664 NA 8-1978 107.4791 104.7664 8-1979 108.9188 107.4791 8-1980 111.5591 108.9188 8-1981 100.0000 111.5591 8-1982 99.3965 100.0000 9-1976 NA NA 9-1977 102.7724 NA 9-1978 107.0270 102.7724 9-1979 108.6788 107.0270 9-1980 111.1190 108.6788 9-1981 101.9265 111.1190 9-1982 99.4971 101.9265 10-1976 NA NA 10-1977 95.3657 NA 10-1978 96.7315 95.3657 10-1979 99.2333 96.7315 10-1980 100.7335 99.2333 10-1981 100.0000 100.7335 10-1982 98.2324 100.0000 11-1976 NA NA 11-1977 104.7664 NA 11-1978 107.4791 104.7664 11-1979 108.9188 107.4791 11-1980 111.5591 108.9188 11-1981 100.0000 111.5591 11-1982 99.3965 100.0000 12-1976 NA NA 12-1977 104.7664 NA 12-1978 107.4791 104.7664 12-1979 108.9188 107.4791 12-1980 111.5591 108.9188 12-1981 100.0000 111.5591 12-1982 99.3965 100.0000 13-1976 NA NA 13-1977 125.8064 NA 13-1978 127.9649 125.8064 13-1979 125.4294 127.9649 13-1980 125.0259 125.4294 13-1981 106.3044 125.0259 13-1982 98.0729 106.3044 14-1978 NA NA 14-1979 127.3866 NA 14-1980 124.6424 127.3866 14-1981 118.9132 124.6424 14-1982 99.3576 118.9132 14-1983 97.2428 99.3576 14-1984 97.1944 97.2428 15-1977 NA NA 15-1978 105.3355 NA 15-1979 108.1833 105.3355 15-1980 110.6984 108.1833 15-1981 105.9673 110.6984 15-1982 96.5931 105.9673 15-1983 92.0013 96.5931 16-1976 NA NA 16-1977 116.4000 NA 16-1978 115.7000 116.4000 16-1979 112.8000 115.7000 16-1980 108.9000 112.8000 16-1981 100.0000 108.9000 16-1982 89.2000 100.0000 17-1977 NA NA 17-1978 127.1640 NA 17-1979 127.3866 127.1640 17-1980 124.6424 127.3866 17-1981 118.9132 124.6424 17-1982 99.3576 118.9132 17-1983 97.2428 99.3576 18-1977 NA NA 18-1978 116.2250 NA 18-1979 114.9750 116.2250 18-1980 111.8250 114.9750 18-1981 106.6750 111.8250 18-1982 97.3000 106.6750 18-1983 89.5250 97.3000 19-1976 NA NA 19-1977 116.4000 NA 19-1978 115.7000 116.4000 19-1979 112.8000 115.7000 19-1980 108.9000 112.8000 19-1981 100.0000 108.9000 19-1982 89.2000 100.0000 20-1977 NA NA 20-1978 116.2250 NA 20-1979 114.9750 116.2250 20-1980 111.8250 114.9750 20-1981 106.6750 111.8250 20-1982 97.3000 106.6750 20-1983 89.5250 97.3000 21-1977 NA NA 21-1978 116.2250 NA 21-1979 114.9750 116.2250 21-1980 111.8250 114.9750 21-1981 106.6750 111.8250 21-1982 97.3000 106.6750 21-1983 89.5250 97.3000 22-1976 NA NA 22-1977 116.4000 NA 22-1978 115.7000 116.4000 22-1979 112.8000 115.7000 22-1980 108.9000 112.8000 22-1981 100.0000 108.9000 22-1982 89.2000 100.0000 23-1976 NA NA 23-1977 116.4000 NA 23-1978 115.7000 116.4000 23-1979 112.8000 115.7000 23-1980 108.9000 112.8000 23-1981 100.0000 108.9000 23-1982 89.2000 100.0000 24-1976 NA NA 24-1977 116.4000 NA 24-1978 115.7000 116.4000 24-1979 112.8000 115.7000 24-1980 108.9000 112.8000 24-1981 100.0000 108.9000 24-1982 89.2000 100.0000 25-1976 NA NA 25-1977 116.4000 NA 25-1978 115.7000 116.4000 25-1979 112.8000 115.7000 25-1980 108.9000 112.8000 25-1981 100.0000 108.9000 25-1982 89.2000 100.0000 26-1976 NA NA 26-1977 116.4000 NA 26-1978 115.7000 116.4000 26-1979 112.8000 115.7000 26-1980 108.9000 112.8000 26-1981 100.0000 108.9000 26-1982 89.2000 100.0000 27-1978 NA NA 27-1979 112.8000 NA 27-1980 108.9000 112.8000 27-1981 100.0000 108.9000 27-1982 89.2000 100.0000 27-1983 90.5000 89.2000 27-1984 86.9000 90.5000 28-1977 NA NA 28-1978 93.9500 NA 28-1979 98.2000 93.9500 28-1980 102.5667 98.2000 28-1981 102.0167 102.5667 28-1982 99.4917 102.0167 28-1983 94.2583 99.4917 29-1977 NA NA 29-1978 95.7000 NA 29-1979 100.2000 95.7000 29-1980 102.4000 100.2000 29-1981 101.1000 102.4000 29-1982 96.9500 101.1000 29-1983 96.0500 96.9500 30-1976 NA NA 30-1977 116.4000 NA 30-1978 115.7000 116.4000 30-1979 112.8000 115.7000 30-1980 108.9000 112.8000 30-1981 100.0000 108.9000 30-1982 89.2000 100.0000 31-1976 NA NA 31-1977 104.7664 NA 31-1978 107.4791 104.7664 31-1979 108.9188 107.4791 31-1980 111.5591 108.9188 31-1981 100.0000 111.5591 31-1982 99.3965 100.0000 32-1977 NA NA 32-1978 96.0500 NA 32-1979 100.6000 96.0500 32-1980 102.3667 100.6000 32-1981 100.9167 102.3667 32-1982 96.4417 100.9167 32-1983 96.4083 96.4417 33-1976 NA NA 33-1977 93.6000 NA 33-1978 97.8000 93.6000 33-1979 102.6000 97.8000 33-1980 102.2000 102.6000 33-1981 100.0000 102.2000 33-1982 93.9000 100.0000 34-1977 NA NA 34-1978 95.7000 NA 34-1979 100.2000 95.7000 34-1980 102.4000 100.2000 34-1981 101.1000 102.4000 34-1982 96.9500 101.1000 34-1983 96.0500 96.9500 35-1977 NA NA 35-1978 94.6500 NA 35-1979 99.0000 94.6500 35-1980 102.5000 99.0000 35-1981 101.6500 102.5000 35-1982 98.4750 101.6500 35-1983 94.9750 98.4750 36-1976 NA NA 36-1977 93.6000 NA 36-1978 97.8000 93.6000 36-1979 102.6000 97.8000 36-1980 102.2000 102.6000 36-1981 100.0000 102.2000 36-1982 93.9000 100.0000 37-1977 NA NA 37-1978 116.2250 NA 37-1979 114.9750 116.2250 37-1980 111.8250 114.9750 37-1981 106.6750 111.8250 37-1982 97.3000 106.6750 37-1983 89.5250 97.3000 38-1976 NA NA 38-1977 106.1747 NA 38-1978 108.9675 106.1747 38-1979 107.8624 108.9675 38-1980 104.5668 107.8624 38-1981 100.0000 104.5668 38-1982 92.7430 100.0000 39-1977 NA NA 39-1978 116.2250 NA 39-1979 114.9750 116.2250 39-1980 111.8250 114.9750 39-1981 106.6750 111.8250 39-1982 97.3000 106.6750 39-1983 89.5250 97.3000 40-1976 NA NA 40-1977 106.5556 NA 40-1978 108.2693 106.5556 40-1979 108.1387 108.2693 40-1980 105.3907 108.1387 40-1981 101.1417 105.3907 40-1982 94.5573 101.1417 41-1977 NA NA 41-1978 116.2250 NA 41-1979 114.9750 116.2250 41-1980 111.8250 114.9750 41-1981 106.6750 111.8250 41-1982 97.3000 106.6750 41-1983 89.5250 97.3000 42-1976 NA NA 42-1977 106.1747 NA 42-1978 108.9675 106.1747 42-1979 107.8624 108.9675 42-1980 104.5668 107.8624 42-1981 100.0000 104.5668 42-1982 92.7430 100.0000 43-1977 NA NA 43-1978 127.1640 NA 43-1979 127.3866 127.1640 43-1980 124.6424 127.3866 43-1981 118.9132 124.6424 43-1982 99.3576 118.9132 43-1983 97.2428 99.3576 44-1977 NA NA 44-1978 116.2250 NA 44-1979 114.9750 116.2250 44-1980 111.8250 114.9750 44-1981 106.6750 111.8250 44-1982 97.3000 106.6750 44-1983 89.5250 97.3000 45-1977 NA NA 45-1978 127.1640 NA 45-1979 127.3866 127.1640 45-1980 124.6424 127.3866 45-1981 118.9132 124.6424 45-1982 99.3576 118.9132 45-1983 97.2428 99.3576 46-1976 NA NA 46-1977 126.7636 NA 46-1978 128.3653 126.7636 46-1979 124.4507 128.3653 46-1980 125.2176 124.4507 46-1981 100.0000 125.2176 46-1982 97.4305 100.0000 47-1976 NA NA 47-1977 116.4000 NA 47-1978 115.7000 116.4000 47-1979 112.8000 115.7000 47-1980 108.9000 112.8000 47-1981 100.0000 108.9000 47-1982 89.2000 100.0000 48-1976 NA NA 48-1977 126.7636 NA 48-1978 128.3653 126.7636 48-1979 124.4507 128.3653 48-1980 125.2176 124.4507 48-1981 100.0000 125.2176 48-1982 97.4305 100.0000 49-1976 NA NA 49-1977 117.6000 NA 49-1978 115.8750 117.6000 49-1979 113.5250 115.8750 49-1980 109.8750 113.5250 49-1981 102.2250 109.8750 49-1982 91.9000 102.2250 50-1976 NA NA 50-1977 106.1747 NA 50-1978 108.9675 106.1747 50-1979 107.8624 108.9675 50-1980 104.5668 107.8624 50-1981 100.0000 104.5668 50-1982 92.7430 100.0000 51-1976 NA NA 51-1977 125.8064 NA 51-1978 127.9649 125.8064 51-1979 125.4294 127.9649 51-1980 125.0259 125.4294 51-1981 106.3044 125.0259 51-1982 98.0729 106.3044 52-1976 NA NA 52-1977 116.4000 NA 52-1978 115.7000 116.4000 52-1979 112.8000 115.7000 52-1980 108.9000 112.8000 52-1981 100.0000 108.9000 52-1982 89.2000 100.0000 53-1977 NA NA 53-1978 116.2250 NA 53-1979 114.9750 116.2250 53-1980 111.8250 114.9750 53-1981 106.6750 111.8250 53-1982 97.3000 106.6750 53-1983 89.5250 97.3000 54-1977 NA NA 54-1978 127.1640 NA 54-1979 127.3866 127.1640 54-1980 124.6424 127.3866 54-1981 118.9132 124.6424 54-1982 99.3576 118.9132 54-1983 97.2428 99.3576 55-1976 NA NA 55-1977 116.4000 NA 55-1978 115.7000 116.4000 55-1979 112.8000 115.7000 55-1980 108.9000 112.8000 55-1981 100.0000 108.9000 55-1982 89.2000 100.0000 56-1976 NA NA 56-1977 126.7636 NA 56-1978 128.3653 126.7636 56-1979 124.4507 128.3653 56-1980 125.2176 124.4507 56-1981 100.0000 125.2176 56-1982 97.4305 100.0000 57-1977 NA NA 57-1978 117.9279 NA 57-1979 120.2607 117.9279 57-1980 118.9515 120.2607 57-1981 113.4060 118.9515 57-1982 98.1655 113.4060 57-1983 92.3142 98.1655 58-1976 NA NA 58-1977 116.4000 NA 58-1978 115.7000 116.4000 58-1979 112.8000 115.7000 58-1980 108.9000 112.8000 58-1981 100.0000 108.9000 58-1982 89.2000 100.0000 59-1977 NA NA 59-1978 117.9279 NA 59-1979 120.2607 117.9279 59-1980 118.9515 120.2607 59-1981 113.4060 118.9515 59-1982 98.1655 113.4060 59-1983 92.3142 98.1655 60-1976 NA NA 60-1977 103.7669 NA 60-1978 106.9042 103.7669 60-1979 109.4623 106.9042 60-1980 111.9345 109.4623 60-1981 100.0000 111.9345 60-1982 93.1862 100.0000 61-1977 NA NA 61-1978 104.5512 NA 61-1979 107.5437 104.5512 61-1980 110.0804 107.5437 61-1981 108.9509 110.0804 61-1982 98.2966 108.9509 61-1983 92.5937 98.2966 62-1977 NA NA 62-1978 117.9279 NA 62-1979 120.2607 117.9279 62-1980 118.9515 120.2607 62-1981 113.4060 118.9515 62-1982 98.1655 113.4060 62-1983 92.3142 98.1655 63-1976 NA NA 63-1977 110.1000 NA 63-1978 109.2000 110.1000 63-1979 111.9000 109.2000 63-1980 111.0000 111.9000 63-1981 100.0000 111.0000 63-1982 89.0000 100.0000 64-1976 NA NA 64-1977 110.1000 NA 64-1978 109.2000 110.1000 64-1979 111.9000 109.2000 64-1980 111.0000 111.9000 64-1981 100.0000 111.0000 64-1982 89.0000 100.0000 65-1976 NA NA 65-1977 110.1000 NA 65-1978 109.2000 110.1000 65-1979 111.9000 109.2000 65-1980 111.0000 111.9000 65-1981 100.0000 111.0000 65-1982 89.0000 100.0000 66-1977 NA NA 66-1978 109.8750 NA 66-1979 109.8750 109.8750 66-1980 111.6750 109.8750 66-1981 108.2500 111.6750 66-1982 97.2500 108.2500 66-1983 89.4750 97.2500 67-1976 NA NA 67-1977 117.0448 NA 67-1978 120.5774 117.0448 67-1979 119.3105 120.5774 67-1980 117.8746 119.3105 67-1981 100.0000 117.8746 67-1982 92.6621 100.0000 68-1976 NA NA 68-1977 110.1000 NA 68-1978 109.2000 110.1000 68-1979 111.9000 109.2000 68-1980 111.0000 111.9000 68-1981 100.0000 111.0000 68-1982 89.0000 100.0000 69-1977 NA NA 69-1978 109.8750 NA 69-1979 109.8750 109.8750 69-1980 111.6750 109.8750 69-1981 108.2500 111.6750 69-1982 97.2500 108.2500 69-1983 89.4750 97.2500 70-1977 NA NA 70-1978 104.5512 NA 70-1979 107.5437 104.5512 70-1980 110.0804 107.5437 70-1981 108.9509 110.0804 70-1982 98.2966 108.9509 70-1983 92.5937 98.2966 71-1976 NA NA 71-1977 102.5303 NA 71-1978 106.1199 102.5303 71-1979 108.8228 106.1199 71-1980 111.3165 108.8228 71-1981 102.9836 111.3165 71-1982 94.8896 102.9836 72-1976 NA NA 72-1977 103.7669 NA 72-1978 106.9042 103.7669 72-1979 109.4623 106.9042 72-1980 111.9345 109.4623 72-1981 100.0000 111.9345 72-1982 93.1862 100.0000 73-1977 NA NA 73-1978 104.5512 NA 73-1979 107.5437 104.5512 73-1980 110.0804 107.5437 73-1981 108.9509 110.0804 73-1982 98.2966 108.9509 73-1983 92.5937 98.2966 74-1977 NA NA 74-1978 104.5512 NA 74-1979 107.5437 104.5512 74-1980 110.0804 107.5437 74-1981 108.9509 110.0804 74-1982 98.2966 108.9509 74-1983 92.5937 98.2966 75-1976 NA NA 75-1977 103.7669 NA 75-1978 106.9042 103.7669 75-1979 109.4623 106.9042 75-1980 111.9345 109.4623 75-1981 100.0000 111.9345 75-1982 93.1862 100.0000 76-1976 NA NA 76-1977 102.5303 NA 76-1978 106.1199 102.5303 76-1979 108.8228 106.1199 76-1980 111.3165 108.8228 76-1981 102.9836 111.3165 76-1982 94.8896 102.9836 77-1977 NA NA 77-1978 104.5512 NA 77-1979 107.5437 104.5512 77-1980 110.0804 107.5437 77-1981 108.9509 110.0804 77-1982 98.2966 108.9509 77-1983 92.5937 98.2966 78-1976 NA NA 78-1977 102.5303 NA 78-1978 106.1199 102.5303 78-1979 108.8228 106.1199 78-1980 111.3165 108.8228 78-1981 102.9836 111.3165 78-1982 94.8896 102.9836 79-1976 NA NA 79-1977 93.6000 NA 79-1978 97.8000 93.6000 79-1979 102.6000 97.8000 79-1980 102.2000 102.6000 79-1981 100.0000 102.2000 79-1982 93.9000 100.0000 80-1976 NA NA 80-1977 103.7669 NA 80-1978 106.9042 103.7669 80-1979 109.4623 106.9042 80-1980 111.9345 109.4623 80-1981 100.0000 111.9345 80-1982 93.1862 100.0000 81-1976 NA NA 81-1977 116.4000 NA 81-1978 115.7000 116.4000 81-1979 112.8000 115.7000 81-1980 108.9000 112.8000 81-1981 100.0000 108.9000 81-1982 89.2000 100.0000 82-1976 NA NA 82-1977 116.4000 NA 82-1978 115.7000 116.4000 82-1979 112.8000 115.7000 82-1980 108.9000 112.8000 82-1981 100.0000 108.9000 82-1982 89.2000 100.0000 83-1977 NA NA 83-1978 127.1640 NA 83-1979 127.3866 127.1640 83-1980 124.6424 127.3866 83-1981 118.9132 124.6424 83-1982 99.3576 118.9132 83-1983 97.2428 99.3576 84-1976 NA NA 84-1977 94.6657 NA 84-1978 96.3900 94.6657 84-1979 98.6078 96.3900 84-1980 100.3584 98.6078 84-1981 100.1834 100.3584 84-1982 98.6743 100.1834 85-1976 NA NA 85-1977 93.8333 NA 85-1978 97.1000 93.8333 85-1979 101.8000 97.1000 85-1980 102.2667 101.8000 85-1981 100.3667 102.2667 85-1982 94.9167 100.3667 86-1976 NA NA 86-1977 94.6657 NA 86-1978 96.3900 94.6657 86-1979 98.6078 96.3900 86-1980 100.3584 98.6078 86-1981 100.1834 100.3584 86-1982 98.6743 100.1834 87-1976 NA NA 87-1977 116.4000 NA 87-1978 115.7000 116.4000 87-1979 112.8000 115.7000 87-1980 108.9000 112.8000 87-1981 100.0000 108.9000 87-1982 89.2000 100.0000 88-1976 NA NA 88-1977 110.1000 NA 88-1978 109.2000 110.1000 88-1979 111.9000 109.2000 88-1980 111.0000 111.9000 88-1981 100.0000 111.0000 88-1982 89.0000 100.0000 89-1976 NA NA 89-1977 110.1000 NA 89-1978 109.2000 110.1000 89-1979 111.9000 109.2000 89-1980 111.0000 111.9000 89-1981 100.0000 111.0000 89-1982 89.0000 100.0000 90-1976 NA NA 90-1977 110.0500 NA 90-1978 109.4250 110.0500 90-1979 111.2250 109.4250 90-1980 111.2250 111.2250 90-1981 102.7500 111.2250 90-1982 91.7500 102.7500 91-1976 NA NA 91-1977 104.7664 NA 91-1978 107.4791 104.7664 91-1979 108.9188 107.4791 91-1980 111.5591 108.9188 91-1981 100.0000 111.5591 91-1982 99.3965 100.0000 92-1976 NA NA 92-1977 94.5333 NA 92-1978 95.0000 94.5333 92-1979 99.4000 95.0000 92-1980 102.4667 99.4000 92-1981 101.4667 102.4667 92-1982 97.9667 101.4667 93-1977 NA NA 93-1978 94.6500 NA 93-1979 99.0000 94.6500 93-1980 102.5000 99.0000 93-1981 101.6500 102.5000 93-1982 98.4750 101.6500 93-1983 94.9750 98.4750 94-1976 NA NA 94-1977 117.6000 NA 94-1978 115.8750 117.6000 94-1979 113.5250 115.8750 94-1980 109.8750 113.5250 94-1981 102.2250 109.8750 94-1982 91.9000 102.2250 95-1976 NA NA 95-1977 126.7636 NA 95-1978 128.3653 126.7636 95-1979 124.4507 128.3653 95-1980 125.2176 124.4507 95-1981 100.0000 125.2176 95-1982 97.4305 100.0000 96-1976 NA NA 96-1977 126.7636 NA 96-1978 128.3653 126.7636 96-1979 124.4507 128.3653 96-1980 125.2176 124.4507 96-1981 100.0000 125.2176 96-1982 97.4305 100.0000 97-1976 NA NA 97-1977 117.6000 NA 97-1978 115.8750 117.6000 97-1979 113.5250 115.8750 97-1980 109.8750 113.5250 97-1981 102.2250 109.8750 97-1982 91.9000 102.2250 98-1977 NA NA 98-1978 127.1640 NA 98-1979 127.3866 127.1640 98-1980 124.6424 127.3866 98-1981 118.9132 124.6424 98-1982 99.3576 118.9132 98-1983 97.2428 99.3576 99-1977 NA NA 99-1978 104.5512 NA 99-1979 107.5437 104.5512 99-1980 110.0804 107.5437 99-1981 108.9509 110.0804 99-1982 98.2966 108.9509 99-1983 92.5937 98.2966 100-1977 NA NA 100-1978 127.6979 NA 100-1979 126.0818 127.6979 100-1980 124.8981 126.0818 100-1981 110.5073 124.8981 100-1982 98.5011 110.5073 100-1983 96.9925 98.5011 101-1977 NA NA 101-1978 117.9279 NA 101-1979 120.2607 117.9279 101-1980 118.9515 120.2607 101-1981 113.4060 118.9515 101-1982 98.1655 113.4060 101-1983 92.3142 98.1655 102-1977 NA NA 102-1978 105.4446 NA 102-1979 107.8390 105.4446 102-1980 109.5789 107.8390 102-1981 108.6693 109.5789 102-1982 99.8491 108.6693 102-1983 99.3706 99.8491 103-1976 NA NA 103-1977 93.6000 NA 103-1978 97.8000 93.6000 103-1979 102.6000 97.8000 103-1980 102.2000 102.6000 103-1981 100.0000 102.2000 103-1982 93.9000 100.0000 104-1977 NA NA 104-1978 95.8210 NA 104-1979 97.5654 95.8210 104-1980 99.7333 97.5654 104-1981 100.4890 99.7333 104-1982 99.4108 100.4890 104-1983 98.7427 99.4108 104-1984 100.1189 98.7427 105-1977 NA NA 105-1978 96.2762 NA 105-1979 98.3993 96.2762 105-1980 100.2334 98.3993 105-1981 100.2445 100.2334 105-1982 98.8216 100.2445 105-1983 99.2531 98.8216 105-1984 100.4744 99.2531 106-1977 NA NA 106-1978 96.3900 NA 106-1979 98.6078 96.3900 106-1980 100.3584 98.6078 106-1981 100.1834 100.3584 106-1982 98.6743 100.1834 106-1983 99.3807 98.6743 106-1984 100.5633 99.3807 107-1977 NA NA 107-1978 95.7072 NA 107-1979 97.3569 95.7072 107-1980 99.6083 97.3569 107-1981 100.5501 99.6083 107-1982 99.5581 100.5501 107-1983 98.6151 99.5581 107-1984 100.0301 98.6151 108-1977 NA NA 108-1978 95.7072 NA 108-1979 97.3569 95.7072 108-1980 99.6083 97.3569 108-1981 100.5501 99.6083 108-1982 99.5581 100.5501 108-1983 98.6151 99.5581 108-1984 100.0301 98.6151 109-1977 NA NA 109-1978 96.3900 NA 109-1979 98.6078 96.3900 109-1980 100.3584 98.6078 109-1981 100.1834 100.3584 109-1982 98.6743 100.1834 109-1983 99.3807 98.6743 109-1984 100.5633 99.3807 110-1977 NA NA 110-1978 127.1640 NA 110-1979 127.3866 127.1640 110-1980 124.6424 127.3866 110-1981 118.9132 124.6424 110-1982 99.3576 118.9132 110-1983 97.2428 99.3576 110-1984 97.1944 97.2428 111-1977 NA NA 111-1978 116.0500 NA 111-1979 114.2500 116.0500 111-1980 110.8500 114.2500 111-1981 104.4500 110.8500 111-1982 94.6000 104.4500 111-1983 89.8500 94.6000 111-1984 88.7000 89.8500 112-1977 NA NA 112-1978 106.8729 NA 112-1979 108.6912 106.8729 112-1980 107.0385 108.6912 112-1981 103.4251 107.0385 112-1982 98.1858 103.4251 112-1983 92.0669 98.1858 112-1984 89.8429 92.0669 113-1976 NA NA 113-1977 93.9500 NA 113-1978 96.7500 93.9500 113-1979 101.4000 96.7500 113-1980 102.3000 101.4000 113-1981 100.5500 102.3000 113-1982 95.4250 100.5500 113-1983 97.1250 95.4250 114-1977 NA NA 114-1978 120.5774 NA 114-1979 119.3105 120.5774 114-1980 117.8746 119.3105 114-1981 100.0000 117.8746 114-1982 92.6621 100.0000 114-1983 91.2704 92.6621 114-1984 94.6714 91.2704 115-1977 NA NA 115-1978 120.5774 NA 115-1979 119.3105 120.5774 115-1980 117.8746 119.3105 115-1981 100.0000 117.8746 115-1982 92.6621 100.0000 115-1983 91.2704 92.6621 115-1984 94.6714 91.2704 116-1977 NA NA 116-1978 117.6336 NA 116-1979 120.3662 117.6336 116-1980 119.0712 120.3662 116-1981 114.8955 119.0712 116-1982 98.7770 114.8955 116-1983 92.4302 98.7770 116-1984 91.8372 92.4302 117-1977 NA NA 117-1978 107.4791 NA 117-1979 108.9188 107.4791 117-1980 111.5591 108.9188 117-1981 100.0000 111.5591 117-1982 99.3965 100.0000 117-1983 99.2931 99.3965 117-1984 107.0929 99.2931 118-1976 NA NA 118-1977 116.8157 NA 118-1978 118.2223 116.8157 118-1979 120.1551 118.2223 118-1980 118.8319 120.1551 118-1981 111.9164 118.8319 118-1982 97.5540 111.9164 118-1983 92.1982 97.5540 119-1977 NA NA 119-1978 109.8750 NA 119-1979 109.8750 109.8750 119-1980 111.6750 109.8750 119-1981 108.2500 111.6750 119-1982 97.2500 108.2500 119-1983 89.4750 97.2500 119-1984 91.6500 89.4750 120-1977 NA NA 120-1978 105.5970 NA 120-1979 108.3964 105.5970 120-1980 110.9044 108.3964 120-1981 104.9727 110.9044 120-1982 96.0253 104.9727 120-1983 91.8038 96.0253 120-1984 92.4875 91.8038 121-1977 NA NA 121-1978 104.5512 NA 121-1979 107.5437 104.5512 121-1980 110.0804 107.5437 121-1981 108.9509 110.0804 121-1982 98.2966 108.9509 121-1983 92.5937 98.2966 121-1984 91.5326 92.5937 122-1976 NA NA 122-1977 103.7669 NA 122-1978 106.9042 103.7669 122-1979 109.4623 106.9042 122-1980 111.9345 109.4623 122-1981 100.0000 111.9345 122-1982 93.1862 100.0000 122-1983 90.8164 93.1862 123-1976 NA NA 123-1977 103.7669 NA 123-1978 106.9042 103.7669 123-1979 109.4623 106.9042 123-1980 111.9345 109.4623 123-1981 100.0000 111.9345 123-1982 93.1862 100.0000 123-1983 90.8164 93.1862 124-1977 NA NA 124-1978 110.0250 NA 124-1979 109.4250 110.0250 124-1980 111.8250 109.4250 124-1981 110.0833 111.8250 124-1982 99.0833 110.0833 124-1983 89.1583 99.0833 124-1984 91.1500 89.1583 125-1977 NA NA 125-1978 117.3392 NA 125-1979 120.4718 117.3392 125-1980 119.1908 120.4718 125-1981 116.3851 119.1908 125-1982 99.3885 116.3851 125-1983 92.5462 99.3885 125-1984 91.5538 92.5462 126-1977 NA NA 126-1978 118.2223 NA 126-1979 120.1551 118.2223 126-1980 118.8319 120.1551 126-1981 111.9164 118.8319 126-1982 97.5540 111.9164 126-1983 92.1982 97.5540 126-1984 92.4041 92.1982 127-1976 NA NA 127-1977 94.6657 NA 127-1978 96.3900 94.6657 127-1979 98.6078 96.3900 127-1980 100.3584 98.6078 127-1981 100.1834 100.3584 127-1982 98.6743 100.1834 127-1983 99.3807 98.6743 127-1984 100.5633 99.3807 128-1976 NA NA 128-1977 94.6657 NA 128-1978 96.3900 94.6657 128-1979 98.6078 96.3900 128-1980 100.3584 98.6078 128-1981 100.1834 100.3584 128-1982 98.6743 100.1834 128-1983 99.3807 98.6743 128-1984 100.5633 99.3807 129-1976 NA NA 129-1977 94.6657 NA 129-1978 96.3900 94.6657 129-1979 98.6078 96.3900 129-1980 100.3584 98.6078 129-1981 100.1834 100.3584 129-1982 98.6743 100.1834 129-1983 99.3807 98.6743 129-1984 100.5633 99.3807 130-1976 NA NA 130-1977 101.7754 NA 130-1978 106.8009 101.7754 130-1979 108.5589 106.8009 130-1980 110.8990 108.5589 130-1981 102.8898 110.8990 130-1982 99.5474 102.8898 130-1983 99.3189 99.5474 130-1984 105.1429 99.3189 131-1976 NA NA 131-1977 103.7669 NA 131-1978 106.9042 103.7669 131-1979 109.4623 106.9042 131-1980 111.9345 109.4623 131-1981 100.0000 111.9345 131-1982 93.1862 100.0000 131-1983 90.8164 93.1862 131-1984 93.6813 90.8164 132-1976 NA NA 132-1977 126.7636 NA 132-1978 128.3653 126.7636 132-1979 124.4507 128.3653 132-1980 125.2176 124.4507 132-1981 100.0000 125.2176 132-1982 97.4305 100.0000 132-1983 96.6797 97.4305 132-1984 98.7386 96.6797 133-1976 NA NA 133-1977 117.6000 NA 133-1978 115.8750 117.6000 133-1979 113.5250 115.8750 133-1980 109.8750 113.5250 133-1981 102.2250 109.8750 133-1982 91.9000 102.2250 133-1983 90.1750 91.9000 133-1984 87.8000 90.1750 134-1976 NA NA 134-1977 117.0448 NA 134-1978 120.5774 117.0448 134-1979 119.3105 120.5774 134-1980 117.8746 119.3105 134-1981 100.0000 117.8746 134-1982 92.6621 100.0000 134-1983 91.2704 92.6621 134-1984 94.6714 91.2704 135-1976 NA NA 135-1977 116.9589 NA 135-1978 119.6943 116.9589 135-1979 119.6272 119.6943 135-1980 118.2336 119.6272 135-1981 104.4687 118.2336 135-1982 94.4966 104.4687 135-1983 91.6183 94.4966 135-1984 93.8212 91.6183 136-1976 NA NA 136-1977 117.0448 NA 136-1978 120.5774 117.0448 136-1979 119.3105 120.5774 136-1980 117.8746 119.3105 136-1981 100.0000 117.8746 136-1982 92.6621 100.0000 136-1983 91.2704 92.6621 136-1984 94.6714 91.2704 137-1976 NA NA 137-1977 110.0500 NA 137-1978 109.4250 110.0500 137-1979 111.2250 109.4250 137-1980 111.2250 111.2250 137-1981 102.7500 111.2250 137-1982 91.7500 102.7500 137-1983 90.4250 91.7500 137-1984 93.1500 90.4250 138-1976 NA NA 138-1977 101.7059 NA 138-1978 105.5970 101.7059 138-1979 108.3964 105.5970 138-1980 110.9044 108.3964 138-1981 104.9727 110.9044 138-1982 96.0253 104.9727 138-1983 91.8038 96.0253 138-1984 92.4875 91.8038 139-1976 NA NA 139-1977 102.5303 NA 139-1978 106.1199 102.5303 139-1979 108.8228 106.1199 139-1980 111.3165 108.8228 139-1981 102.9836 111.3165 139-1982 94.8896 102.9836 139-1983 91.4088 94.8896 139-1984 92.9650 91.4088 140-1976 NA NA 140-1977 104.7664 NA 140-1978 107.4791 104.7664 140-1979 108.9188 107.4791 140-1980 111.5591 108.9188 140-1981 100.0000 111.5591 140-1982 99.3965 100.0000 140-1983 99.2931 99.3965 140-1984 107.0929 99.2931 > diff(z, c(1,2)) 1 2 1-1977 NA NA 1-1978 1.649704 NA 1-1979 2.251396 3.901100 1-1980 0.941801 3.193197 1-1981 -0.992002 -0.050201 1-1982 -0.943001 -1.935003 1-1983 1.415003 0.472002 2-1977 NA NA 2-1978 1.649704 NA 2-1979 2.251396 3.901100 2-1980 0.941801 3.193197 2-1981 -0.992002 -0.050201 2-1982 -0.943001 -1.935003 2-1983 1.415003 0.472002 3-1977 NA NA 3-1978 1.649704 NA 3-1979 2.251396 3.901100 3-1980 0.941801 3.193197 3-1981 -0.992002 -0.050201 3-1982 -0.943001 -1.935003 3-1983 1.415003 0.472002 4-1977 NA NA 4-1978 1.932800 NA 4-1979 -1.323200 0.609600 4-1980 -6.915500 -8.238700 4-1981 -14.362399 -21.277899 4-1982 -5.355805 -19.718204 4-1983 0.205903 -5.149902 5-1976 NA NA 5-1977 1.604698 NA 5-1978 2.312500 3.917198 5-1979 1.667201 3.979701 5-1980 -0.361200 1.306001 5-1981 -1.595300 -1.956500 5-1982 0.981301 -0.613999 6-1976 NA NA 6-1977 4.254600 NA 6-1978 1.651800 5.906400 6-1979 2.440200 4.092000 6-1980 -9.192500 -6.752300 6-1981 -2.429399 -11.621899 6-1982 -0.186798 -2.616197 7-1976 NA NA 7-1977 2.712700 NA 7-1978 1.439700 4.152400 7-1979 2.640300 4.080000 7-1980 -11.559100 -8.918800 7-1981 -0.603500 -12.162600 7-1982 -0.103402 -0.706902 8-1976 NA NA 8-1977 2.712700 NA 8-1978 1.439700 4.152400 8-1979 2.640300 4.080000 8-1980 -11.559100 -8.918800 8-1981 -0.603500 -12.162600 8-1982 -0.103402 -0.706902 9-1976 NA NA 9-1977 4.254600 NA 9-1978 1.651800 5.906400 9-1979 2.440200 4.092000 9-1980 -9.192500 -6.752300 9-1981 -2.429399 -11.621899 9-1982 -0.186798 -2.616197 10-1976 NA NA 10-1977 1.365799 NA 10-1978 2.501800 3.867599 10-1979 1.500201 4.002001 10-1980 -0.733500 0.766701 10-1981 -1.767601 -2.501101 10-1982 1.531097 -0.236504 11-1976 NA NA 11-1977 2.712700 NA 11-1978 1.439700 4.152400 11-1979 2.640300 4.080000 11-1980 -11.559100 -8.918800 11-1981 -0.603500 -12.162600 11-1982 -0.103402 -0.706902 12-1976 NA NA 12-1977 2.712700 NA 12-1978 1.439700 4.152400 12-1979 2.640300 4.080000 12-1980 -11.559100 -8.918800 12-1981 -0.603500 -12.162600 12-1982 -0.103402 -0.706902 13-1976 NA NA 13-1977 2.158500 NA 13-1978 -2.535500 -0.377000 13-1979 -0.403500 -2.939000 13-1980 -18.721500 -19.125000 13-1981 -8.231501 -26.953001 13-1982 -1.205498 -9.436999 14-1978 NA NA 14-1979 -2.744200 NA 14-1980 -5.729200 -8.473400 14-1981 -19.555603 -25.284803 14-1982 -2.114799 -21.670402 14-1983 -0.048401 -2.163200 14-1984 2.791801 2.743400 15-1977 NA NA 15-1978 2.847800 NA 15-1979 2.515100 5.362900 15-1980 -4.731100 -2.216000 15-1981 -9.374198 -14.105298 15-1982 -4.591805 -13.966003 15-1983 0.247505 -4.344300 16-1976 NA NA 16-1977 -0.700000 NA 16-1978 -2.900000 -3.600000 16-1979 -3.900000 -6.800000 16-1980 -8.900000 -12.800000 16-1981 -10.800003 -19.700003 16-1982 1.300003 -9.500000 17-1977 NA NA 17-1978 0.222600 NA 17-1979 -2.744200 -2.521600 17-1980 -5.729200 -8.473400 17-1981 -19.555603 -25.284803 17-1982 -2.114799 -21.670402 17-1983 -0.048401 -2.163200 18-1977 NA NA 18-1978 -1.250000 NA 18-1979 -3.150000 -4.400000 18-1980 -5.150000 -8.300000 18-1981 -9.374997 -14.524997 18-1982 -7.775001 -17.149998 18-1983 0.074996 -7.700005 19-1976 NA NA 19-1977 -0.700000 NA 19-1978 -2.900000 -3.600000 19-1979 -3.900000 -6.800000 19-1980 -8.900000 -12.800000 19-1981 -10.800003 -19.700003 19-1982 1.300003 -9.500000 20-1977 NA NA 20-1978 -1.250000 NA 20-1979 -3.150000 -4.400000 20-1980 -5.150000 -8.300000 20-1981 -9.374997 -14.524997 20-1982 -7.775001 -17.149998 20-1983 0.074996 -7.700005 21-1977 NA NA 21-1978 -1.250000 NA 21-1979 -3.150000 -4.400000 21-1980 -5.150000 -8.300000 21-1981 -9.374997 -14.524997 21-1982 -7.775001 -17.149998 21-1983 0.074996 -7.700005 22-1976 NA NA 22-1977 -0.700000 NA 22-1978 -2.900000 -3.600000 22-1979 -3.900000 -6.800000 22-1980 -8.900000 -12.800000 22-1981 -10.800003 -19.700003 22-1982 1.300003 -9.500000 23-1976 NA NA 23-1977 -0.700000 NA 23-1978 -2.900000 -3.600000 23-1979 -3.900000 -6.800000 23-1980 -8.900000 -12.800000 23-1981 -10.800003 -19.700003 23-1982 1.300003 -9.500000 24-1976 NA NA 24-1977 -0.700000 NA 24-1978 -2.900000 -3.600000 24-1979 -3.900000 -6.800000 24-1980 -8.900000 -12.800000 24-1981 -10.800003 -19.700003 24-1982 1.300003 -9.500000 25-1976 NA NA 25-1977 -0.700000 NA 25-1978 -2.900000 -3.600000 25-1979 -3.900000 -6.800000 25-1980 -8.900000 -12.800000 25-1981 -10.800003 -19.700003 25-1982 1.300003 -9.500000 26-1976 NA NA 26-1977 -0.700000 NA 26-1978 -2.900000 -3.600000 26-1979 -3.900000 -6.800000 26-1980 -8.900000 -12.800000 26-1981 -10.800003 -19.700003 26-1982 1.300003 -9.500000 27-1978 NA NA 27-1979 -3.900000 NA 27-1980 -8.900000 -12.800000 27-1981 -10.800003 -19.700003 27-1982 1.300003 -9.500000 27-1983 -3.599998 -2.299995 27-1984 0.500000 -3.099998 28-1977 NA NA 28-1978 4.250000 NA 28-1979 4.366703 8.616703 28-1980 -0.550000 3.816703 28-1981 -2.525001 -3.075001 28-1982 -5.233398 -7.758399 28-1983 4.799995 -0.433403 29-1977 NA NA 29-1978 4.500003 NA 29-1979 2.200000 6.700003 29-1980 -1.300000 0.900000 29-1981 -4.150003 -5.450003 29-1982 -0.899994 -5.049997 29-1983 7.299997 6.400003 30-1976 NA NA 30-1977 -0.700000 NA 30-1978 -2.900000 -3.600000 30-1979 -3.900000 -6.800000 30-1980 -8.900000 -12.800000 30-1981 -10.800003 -19.700003 30-1982 1.300003 -9.500000 31-1976 NA NA 31-1977 2.712700 NA 31-1978 1.439700 4.152400 31-1979 2.640300 4.080000 31-1980 -11.559100 -8.918800 31-1981 -0.603500 -12.162600 31-1982 -0.103402 -0.706902 32-1977 NA NA 32-1978 4.549997 NA 32-1979 1.766700 6.316697 32-1980 -1.450000 0.316700 32-1981 -4.474996 -5.924996 32-1982 -0.033402 -4.508398 32-1983 7.799998 7.766596 33-1976 NA NA 33-1977 4.200005 NA 33-1978 4.799997 9.000002 33-1979 -0.400000 4.399997 33-1980 -2.200000 -2.600000 33-1981 -6.099998 -8.299998 33-1982 4.299995 -1.800003 34-1977 NA NA 34-1978 4.500003 NA 34-1979 2.200000 6.700003 34-1980 -1.300000 0.900000 34-1981 -4.150003 -5.450003 34-1982 -0.899994 -5.049997 34-1983 7.299997 6.400003 35-1977 NA NA 35-1978 4.349998 NA 35-1979 3.500000 7.849998 35-1980 -0.850000 2.650000 35-1981 -3.175002 -4.025002 35-1982 -3.500000 -6.675002 35-1983 5.800002 2.300002 36-1976 NA NA 36-1977 4.200005 NA 36-1978 4.799997 9.000002 36-1979 -0.400000 4.399997 36-1980 -2.200000 -2.600000 36-1981 -6.099998 -8.299998 36-1982 4.299995 -1.800003 37-1977 NA NA 37-1978 -1.250000 NA 37-1979 -3.150000 -4.400000 37-1980 -5.150000 -8.300000 37-1981 -9.374997 -14.524997 37-1982 -7.775001 -17.149998 37-1983 0.074996 -7.700005 38-1976 NA NA 38-1977 2.792800 NA 38-1978 -1.105100 1.687700 38-1979 -3.295600 -4.400700 38-1980 -4.566800 -7.862400 38-1981 -7.257004 -11.823804 38-1982 -2.704399 -9.961403 39-1977 NA NA 39-1978 -1.250000 NA 39-1979 -3.150000 -4.400000 39-1980 -5.150000 -8.300000 39-1981 -9.374997 -14.524997 39-1982 -7.775001 -17.149998 39-1983 0.074996 -7.700005 40-1976 NA NA 40-1977 1.713700 NA 40-1978 -0.130600 1.583100 40-1979 -2.748000 -2.878600 40-1980 -4.249000 -6.997000 40-1981 -6.584403 -10.833403 40-1982 -3.842598 -10.427001 41-1977 NA NA 41-1978 -1.250000 NA 41-1979 -3.150000 -4.400000 41-1980 -5.150000 -8.300000 41-1981 -9.374997 -14.524997 41-1982 -7.775001 -17.149998 41-1983 0.074996 -7.700005 42-1976 NA NA 42-1977 2.792800 NA 42-1978 -1.105100 1.687700 42-1979 -3.295600 -4.400700 42-1980 -4.566800 -7.862400 42-1981 -7.257004 -11.823804 42-1982 -2.704399 -9.961403 43-1977 NA NA 43-1978 0.222600 NA 43-1979 -2.744200 -2.521600 43-1980 -5.729200 -8.473400 43-1981 -19.555603 -25.284803 43-1982 -2.114799 -21.670402 43-1983 -0.048401 -2.163200 44-1977 NA NA 44-1978 -1.250000 NA 44-1979 -3.150000 -4.400000 44-1980 -5.150000 -8.300000 44-1981 -9.374997 -14.524997 44-1982 -7.775001 -17.149998 44-1983 0.074996 -7.700005 45-1977 NA NA 45-1978 0.222600 NA 45-1979 -2.744200 -2.521600 45-1980 -5.729200 -8.473400 45-1981 -19.555603 -25.284803 45-1982 -2.114799 -21.670402 45-1983 -0.048401 -2.163200 46-1976 NA NA 46-1977 1.601700 NA 46-1978 -3.914600 -2.312900 46-1979 0.766900 -3.147700 46-1980 -25.217600 -24.450700 46-1981 -2.569504 -27.787104 46-1982 -0.750793 -3.320297 47-1976 NA NA 47-1977 -0.700000 NA 47-1978 -2.900000 -3.600000 47-1979 -3.900000 -6.800000 47-1980 -8.900000 -12.800000 47-1981 -10.800003 -19.700003 47-1982 1.300003 -9.500000 48-1976 NA NA 48-1977 1.601700 NA 48-1978 -3.914600 -2.312900 48-1979 0.766900 -3.147700 48-1980 -25.217600 -24.450700 48-1981 -2.569504 -27.787104 48-1982 -0.750793 -3.320297 49-1976 NA NA 49-1977 -1.725000 NA 49-1978 -2.350000 -4.075000 49-1979 -3.650000 -6.000000 49-1980 -7.650000 -11.300000 49-1981 -10.324998 -17.974998 49-1982 -1.724999 -12.049997 50-1976 NA NA 50-1977 2.792800 NA 50-1978 -1.105100 1.687700 50-1979 -3.295600 -4.400700 50-1980 -4.566800 -7.862400 50-1981 -7.257004 -11.823804 50-1982 -2.704399 -9.961403 51-1976 NA NA 51-1977 2.158500 NA 51-1978 -2.535500 -0.377000 51-1979 -0.403500 -2.939000 51-1980 -18.721500 -19.125000 51-1981 -8.231501 -26.953001 51-1982 -1.205498 -9.436999 52-1976 NA NA 52-1977 -0.700000 NA 52-1978 -2.900000 -3.600000 52-1979 -3.900000 -6.800000 52-1980 -8.900000 -12.800000 52-1981 -10.800003 -19.700003 52-1982 1.300003 -9.500000 53-1977 NA NA 53-1978 -1.250000 NA 53-1979 -3.150000 -4.400000 53-1980 -5.150000 -8.300000 53-1981 -9.374997 -14.524997 53-1982 -7.775001 -17.149998 53-1983 0.074996 -7.700005 54-1977 NA NA 54-1978 0.222600 NA 54-1979 -2.744200 -2.521600 54-1980 -5.729200 -8.473400 54-1981 -19.555603 -25.284803 54-1982 -2.114799 -21.670402 54-1983 -0.048401 -2.163200 55-1976 NA NA 55-1977 -0.700000 NA 55-1978 -2.900000 -3.600000 55-1979 -3.900000 -6.800000 55-1980 -8.900000 -12.800000 55-1981 -10.800003 -19.700003 55-1982 1.300003 -9.500000 56-1976 NA NA 56-1977 1.601700 NA 56-1978 -3.914600 -2.312900 56-1979 0.766900 -3.147700 56-1980 -25.217600 -24.450700 56-1981 -2.569504 -27.787104 56-1982 -0.750793 -3.320297 57-1977 NA NA 57-1978 2.332800 NA 57-1979 -1.309200 1.023600 57-1980 -5.545500 -6.854700 57-1981 -15.240503 -20.786003 57-1982 -5.851296 -21.091799 57-1983 -0.193504 -6.044800 58-1976 NA NA 58-1977 -0.700000 NA 58-1978 -2.900000 -3.600000 58-1979 -3.900000 -6.800000 58-1980 -8.900000 -12.800000 58-1981 -10.800003 -19.700003 58-1982 1.300003 -9.500000 59-1977 NA NA 59-1978 2.332800 NA 59-1979 -1.309200 1.023600 59-1980 -5.545500 -6.854700 59-1981 -15.240503 -20.786003 59-1982 -5.851296 -21.091799 59-1983 -0.193504 -6.044800 60-1976 NA NA 60-1977 3.137300 NA 60-1978 2.558100 5.695400 60-1979 2.472200 5.030300 60-1980 -11.934500 -9.462300 60-1981 -6.813797 -18.748297 60-1982 -2.369804 -9.183601 61-1977 NA NA 61-1978 2.992500 NA 61-1979 2.536700 5.529200 61-1980 -1.129500 1.407200 61-1981 -10.654300 -11.783800 61-1982 -5.702903 -16.357203 61-1983 -1.061097 -6.764000 62-1977 NA NA 62-1978 2.332800 NA 62-1979 -1.309200 1.023600 62-1980 -5.545500 -6.854700 62-1981 -15.240503 -20.786003 62-1982 -5.851296 -21.091799 62-1983 -0.193504 -6.044800 63-1976 NA NA 63-1977 -0.900000 NA 63-1978 2.700000 1.800000 63-1979 -0.900000 1.800000 63-1980 -11.000000 -11.900000 63-1981 -11.000000 -22.000000 63-1982 1.900002 -9.099998 64-1976 NA NA 64-1977 -0.900000 NA 64-1978 2.700000 1.800000 64-1979 -0.900000 1.800000 64-1980 -11.000000 -11.900000 64-1981 -11.000000 -22.000000 64-1982 1.900002 -9.099998 65-1976 NA NA 65-1977 -0.900000 NA 65-1978 2.700000 1.800000 65-1979 -0.900000 1.800000 65-1980 -11.000000 -11.900000 65-1981 -11.000000 -22.000000 65-1982 1.900002 -9.099998 66-1977 NA NA 66-1978 0.000000 NA 66-1979 1.800000 1.800000 66-1980 -3.425000 -1.625000 66-1981 -11.000000 -14.425000 66-1982 -7.775002 -18.775002 66-1983 2.175004 -5.599998 67-1976 NA NA 67-1977 3.532600 NA 67-1978 -1.266900 2.265700 67-1979 -1.435900 -2.702800 67-1980 -17.874600 -19.310500 67-1981 -7.337898 -25.212498 67-1982 -1.391701 -8.729599 68-1976 NA NA 68-1977 -0.900000 NA 68-1978 2.700000 1.800000 68-1979 -0.900000 1.800000 68-1980 -11.000000 -11.900000 68-1981 -11.000000 -22.000000 68-1982 1.900002 -9.099998 69-1977 NA NA 69-1978 0.000000 NA 69-1979 1.800000 1.800000 69-1980 -3.425000 -1.625000 69-1981 -11.000000 -14.425000 69-1982 -7.775002 -18.775002 69-1983 2.175004 -5.599998 70-1977 NA NA 70-1978 2.992500 NA 70-1979 2.536700 5.529200 70-1980 -1.129500 1.407200 70-1981 -10.654300 -11.783800 70-1982 -5.702903 -16.357203 70-1983 -1.061097 -6.764000 71-1976 NA NA 71-1977 3.589600 NA 71-1978 2.702900 6.292500 71-1979 2.493700 5.196600 71-1980 -8.332900 -5.839200 71-1981 -8.093997 -16.426897 71-1982 -3.480805 -11.574802 72-1976 NA NA 72-1977 3.137300 NA 72-1978 2.558100 5.695400 72-1979 2.472200 5.030300 72-1980 -11.934500 -9.462300 72-1981 -6.813797 -18.748297 72-1982 -2.369804 -9.183601 73-1977 NA NA 73-1978 2.992500 NA 73-1979 2.536700 5.529200 73-1980 -1.129500 1.407200 73-1981 -10.654300 -11.783800 73-1982 -5.702903 -16.357203 73-1983 -1.061097 -6.764000 74-1977 NA NA 74-1978 2.992500 NA 74-1979 2.536700 5.529200 74-1980 -1.129500 1.407200 74-1981 -10.654300 -11.783800 74-1982 -5.702903 -16.357203 74-1983 -1.061097 -6.764000 75-1976 NA NA 75-1977 3.137300 NA 75-1978 2.558100 5.695400 75-1979 2.472200 5.030300 75-1980 -11.934500 -9.462300 75-1981 -6.813797 -18.748297 75-1982 -2.369804 -9.183601 76-1976 NA NA 76-1977 3.589600 NA 76-1978 2.702900 6.292500 76-1979 2.493700 5.196600 76-1980 -8.332900 -5.839200 76-1981 -8.093997 -16.426897 76-1982 -3.480805 -11.574802 77-1977 NA NA 77-1978 2.992500 NA 77-1979 2.536700 5.529200 77-1980 -1.129500 1.407200 77-1981 -10.654300 -11.783800 77-1982 -5.702903 -16.357203 77-1983 -1.061097 -6.764000 78-1976 NA NA 78-1977 3.589600 NA 78-1978 2.702900 6.292500 78-1979 2.493700 5.196600 78-1980 -8.332900 -5.839200 78-1981 -8.093997 -16.426897 78-1982 -3.480805 -11.574802 79-1976 NA NA 79-1977 4.200005 NA 79-1978 4.799997 9.000002 79-1979 -0.400000 4.399997 79-1980 -2.200000 -2.600000 79-1981 -6.099998 -8.299998 79-1982 4.299995 -1.800003 80-1976 NA NA 80-1977 3.137300 NA 80-1978 2.558100 5.695400 80-1979 2.472200 5.030300 80-1980 -11.934500 -9.462300 80-1981 -6.813797 -18.748297 80-1982 -2.369804 -9.183601 81-1976 NA NA 81-1977 -0.700000 NA 81-1978 -2.900000 -3.600000 81-1979 -3.900000 -6.800000 81-1980 -8.900000 -12.800000 81-1981 -10.800003 -19.700003 81-1982 1.300003 -9.500000 82-1976 NA NA 82-1977 -0.700000 NA 82-1978 -2.900000 -3.600000 82-1979 -3.900000 -6.800000 82-1980 -8.900000 -12.800000 82-1981 -10.800003 -19.700003 82-1982 1.300003 -9.500000 83-1977 NA NA 83-1978 0.222600 NA 83-1979 -2.744200 -2.521600 83-1980 -5.729200 -8.473400 83-1981 -19.555603 -25.284803 83-1982 -2.114799 -21.670402 83-1983 -0.048401 -2.163200 84-1976 NA NA 84-1977 1.724296 NA 84-1978 2.217804 3.942100 84-1979 1.750597 3.968401 84-1980 -0.175000 1.575597 84-1981 -1.509099 -1.684099 84-1982 0.706398 -0.802701 85-1976 NA NA 85-1977 3.266700 NA 85-1978 4.700002 7.966702 85-1979 0.466700 5.166702 85-1980 -1.900000 -1.433300 85-1981 -5.449998 -7.349998 85-1982 2.566597 -2.883401 86-1976 NA NA 86-1977 1.724296 NA 86-1978 2.217804 3.942100 86-1979 1.750597 3.968401 86-1980 -0.175000 1.575597 86-1981 -1.509099 -1.684099 86-1982 0.706398 -0.802701 87-1976 NA NA 87-1977 -0.700000 NA 87-1978 -2.900000 -3.600000 87-1979 -3.900000 -6.800000 87-1980 -8.900000 -12.800000 87-1981 -10.800003 -19.700003 87-1982 1.300003 -9.500000 88-1976 NA NA 88-1977 -0.900000 NA 88-1978 2.700000 1.800000 88-1979 -0.900000 1.800000 88-1980 -11.000000 -11.900000 88-1981 -11.000000 -22.000000 88-1982 1.900002 -9.099998 89-1976 NA NA 89-1977 -0.900000 NA 89-1978 2.700000 1.800000 89-1979 -0.900000 1.800000 89-1980 -11.000000 -11.900000 89-1981 -11.000000 -22.000000 89-1982 1.900002 -9.099998 90-1976 NA NA 90-1977 -0.625000 NA 90-1978 1.800000 1.175000 90-1979 0.000000 1.800000 90-1980 -8.475000 -8.475000 90-1981 -11.000000 -19.475000 90-1982 -1.324997 -12.324997 91-1976 NA NA 91-1977 2.712700 NA 91-1978 1.439700 4.152400 91-1979 2.640300 4.080000 91-1980 -11.559100 -8.918800 91-1981 -0.603500 -12.162600 91-1982 -0.103402 -0.706902 92-1976 NA NA 92-1977 0.466698 NA 92-1978 4.400002 4.866700 92-1979 3.066698 7.466700 92-1980 -1.000000 2.066698 92-1981 -3.500002 -4.500002 92-1982 -2.633400 -6.133402 93-1977 NA NA 93-1978 4.349998 NA 93-1979 3.500000 7.849998 93-1980 -0.850000 2.650000 93-1981 -3.175002 -4.025002 93-1982 -3.500000 -6.675002 93-1983 5.800002 2.300002 94-1976 NA NA 94-1977 -1.725000 NA 94-1978 -2.350000 -4.075000 94-1979 -3.650000 -6.000000 94-1980 -7.650000 -11.300000 94-1981 -10.324998 -17.974998 94-1982 -1.724999 -12.049997 95-1976 NA NA 95-1977 1.601700 NA 95-1978 -3.914600 -2.312900 95-1979 0.766900 -3.147700 95-1980 -25.217600 -24.450700 95-1981 -2.569504 -27.787104 95-1982 -0.750793 -3.320297 96-1976 NA NA 96-1977 1.601700 NA 96-1978 -3.914600 -2.312900 96-1979 0.766900 -3.147700 96-1980 -25.217600 -24.450700 96-1981 -2.569504 -27.787104 96-1982 -0.750793 -3.320297 97-1976 NA NA 97-1977 -1.725000 NA 97-1978 -2.350000 -4.075000 97-1979 -3.650000 -6.000000 97-1980 -7.650000 -11.300000 97-1981 -10.324998 -17.974998 97-1982 -1.724999 -12.049997 98-1977 NA NA 98-1978 0.222600 NA 98-1979 -2.744200 -2.521600 98-1980 -5.729200 -8.473400 98-1981 -19.555603 -25.284803 98-1982 -2.114799 -21.670402 98-1983 -0.048401 -2.163200 99-1977 NA NA 99-1978 2.992500 NA 99-1979 2.536700 5.529200 99-1980 -1.129500 1.407200 99-1981 -10.654300 -11.783800 99-1982 -5.702903 -16.357203 99-1983 -1.061097 -6.764000 100-1977 NA NA 100-1978 -1.616100 NA 100-1979 -1.183700 -2.799800 100-1980 -14.390800 -15.574500 100-1981 -12.006201 -26.397001 100-1982 -1.508599 -13.514800 100-1983 0.888199 -0.620400 101-1977 NA NA 101-1978 2.332800 NA 101-1979 -1.309200 1.023600 101-1980 -5.545500 -6.854700 101-1981 -15.240503 -20.786003 101-1982 -5.851296 -21.091799 101-1983 -0.193504 -6.044800 102-1977 NA NA 102-1978 2.394400 NA 102-1979 1.739900 4.134300 102-1980 -0.909600 0.830300 102-1981 -8.820202 -9.729802 102-1982 -0.478500 -9.298702 102-1983 1.872402 1.393902 103-1976 NA NA 103-1977 4.200005 NA 103-1978 4.799997 9.000002 103-1979 -0.400000 4.399997 103-1980 -2.200000 -2.600000 103-1981 -6.099998 -8.299998 103-1982 4.299995 -1.800003 104-1977 NA NA 104-1978 1.744400 NA 104-1979 2.167900 3.912300 104-1980 0.755701 2.923601 104-1981 -1.078203 -0.322502 104-1982 -0.668098 -1.746301 104-1983 1.376201 0.708103 104-1984 1.088900 2.465101 105-1977 NA NA 105-1978 2.123101 NA 105-1979 1.834100 3.957201 105-1980 0.011100 1.845200 105-1981 -1.422898 -1.411798 105-1982 0.431496 -0.991402 105-1983 1.221302 1.652798 105-1984 1.111300 2.332602 106-1977 NA NA 106-1978 2.217804 NA 106-1979 1.750597 3.968401 106-1980 -0.175000 1.575597 106-1981 -1.509099 -1.684099 106-1982 0.706398 -0.802701 106-1983 1.182601 1.888999 106-1984 1.116900 2.299501 107-1977 NA NA 107-1978 1.649704 NA 107-1979 2.251396 3.901100 107-1980 0.941801 3.193197 107-1981 -0.992002 -0.050201 107-1982 -0.943001 -1.935003 107-1983 1.415003 0.472002 107-1984 1.083200 2.498203 108-1977 NA NA 108-1978 1.649704 NA 108-1979 2.251396 3.901100 108-1980 0.941801 3.193197 108-1981 -0.992002 -0.050201 108-1982 -0.943001 -1.935003 108-1983 1.415003 0.472002 108-1984 1.083200 2.498203 109-1977 NA NA 109-1978 2.217804 NA 109-1979 1.750597 3.968401 109-1980 -0.175000 1.575597 109-1981 -1.509099 -1.684099 109-1982 0.706398 -0.802701 109-1983 1.182601 1.888999 109-1984 1.116900 2.299501 110-1977 NA NA 110-1978 0.222600 NA 110-1979 -2.744200 -2.521600 110-1980 -5.729200 -8.473400 110-1981 -19.555603 -25.284803 110-1982 -2.114799 -21.670402 110-1983 -0.048401 -2.163200 110-1984 2.791801 2.743400 111-1977 NA NA 111-1978 -1.800000 NA 111-1979 -3.400000 -5.200000 111-1980 -6.400000 -9.800000 111-1981 -9.850002 -16.250002 111-1982 -4.750000 -14.600002 111-1983 -1.150001 -5.900001 111-1984 -1.549995 -2.699996 112-1977 NA NA 112-1978 1.818300 NA 112-1979 -1.652700 0.165600 112-1980 -3.613400 -5.266100 112-1981 -5.239301 -8.852701 112-1982 -6.118897 -11.358198 112-1983 -2.223999 -8.342896 112-1984 -1.383606 -3.607605 113-1976 NA NA 113-1977 2.800003 NA 113-1978 4.650000 7.450003 113-1979 0.900000 5.550000 113-1980 -1.750000 -0.850000 113-1981 -5.124997 -6.874997 113-1982 1.699997 -3.425000 113-1983 8.800000 10.499997 114-1977 NA NA 114-1978 -1.266900 NA 114-1979 -1.435900 -2.702800 114-1980 -17.874600 -19.310500 114-1981 -7.337898 -25.212498 114-1982 -1.391701 -8.729599 114-1983 3.401001 2.009300 114-1984 3.231598 6.632599 115-1977 NA NA 115-1978 -1.266900 NA 115-1979 -1.435900 -2.702800 115-1980 -17.874600 -19.310500 115-1981 -7.337898 -25.212498 115-1982 -1.391701 -8.729599 115-1983 3.401001 2.009300 115-1984 3.231598 6.632599 116-1977 NA NA 116-1978 2.732600 NA 116-1979 -1.295000 1.437600 116-1980 -4.175700 -5.470700 116-1981 -16.118500 -20.294200 116-1982 -6.346801 -22.465301 116-1983 -0.593003 -6.939804 116-1984 3.372803 2.779800 117-1977 NA NA 117-1978 1.439700 NA 117-1979 2.640300 4.080000 117-1980 -11.559100 -8.918800 117-1981 -0.603500 -12.162600 117-1982 -0.103402 -0.706902 117-1983 7.799802 7.696400 117-1984 6.364200 14.164002 118-1976 NA NA 118-1977 1.406600 NA 118-1978 1.932800 3.339400 118-1979 -1.323200 0.609600 118-1980 -6.915500 -8.238700 118-1981 -14.362399 -21.277899 118-1982 -5.355805 -19.718204 118-1983 0.205903 -5.149902 119-1977 NA NA 119-1978 0.000000 NA 119-1979 1.800000 1.800000 119-1980 -3.425000 -1.625000 119-1981 -11.000000 -14.425000 119-1982 -7.775002 -18.775002 119-1983 2.175004 -5.599998 119-1984 2.525001 4.700005 120-1977 NA NA 120-1978 2.799400 NA 120-1979 2.508000 5.307400 120-1980 -5.931700 -3.423700 120-1981 -8.947401 -14.879101 120-1982 -4.221497 -13.168898 120-1983 0.683701 -3.537796 120-1984 3.566399 4.250100 121-1977 NA NA 121-1978 2.992500 NA 121-1979 2.536700 5.529200 121-1980 -1.129500 1.407200 121-1981 -10.654300 -11.783800 121-1982 -5.702903 -16.357203 121-1983 -1.061097 -6.764000 121-1984 3.165497 2.104400 122-1976 NA NA 122-1977 3.137300 NA 122-1978 2.558100 5.695400 122-1979 2.472200 5.030300 122-1980 -11.934500 -9.462300 122-1981 -6.813797 -18.748297 122-1982 -2.369804 -9.183601 122-1983 2.864898 0.495094 123-1976 NA NA 123-1977 3.137300 NA 123-1978 2.558100 5.695400 123-1979 2.472200 5.030300 123-1980 -11.934500 -9.462300 123-1981 -6.813797 -18.748297 123-1982 -2.369804 -9.183601 123-1983 2.864898 0.495094 124-1977 NA NA 124-1978 -0.600000 NA 124-1979 2.400000 1.800000 124-1980 -1.741700 0.658300 124-1981 -11.000002 -12.741702 124-1982 -9.924996 -20.924998 124-1983 1.991700 -7.933296 124-1984 2.841697 4.833397 125-1977 NA NA 125-1978 3.132600 NA 125-1979 -1.281000 1.851600 125-1980 -2.805700 -4.086700 125-1981 -16.996604 -19.802304 125-1982 -6.842292 -23.838896 125-1983 -0.992402 -7.834694 125-1984 3.386895 2.394493 126-1977 NA NA 126-1978 1.932800 NA 126-1979 -1.323200 0.609600 126-1980 -6.915500 -8.238700 126-1981 -14.362399 -21.277899 126-1982 -5.355805 -19.718204 126-1983 0.205903 -5.149902 126-1984 3.344497 3.550400 127-1976 NA NA 127-1977 1.724296 NA 127-1978 2.217804 3.942100 127-1979 1.750597 3.968401 127-1980 -0.175000 1.575597 127-1981 -1.509099 -1.684099 127-1982 0.706398 -0.802701 127-1983 1.182601 1.888999 127-1984 1.116900 2.299501 128-1976 NA NA 128-1977 1.724296 NA 128-1978 2.217804 3.942100 128-1979 1.750597 3.968401 128-1980 -0.175000 1.575597 128-1981 -1.509099 -1.684099 128-1982 0.706398 -0.802701 128-1983 1.182601 1.888999 128-1984 1.116900 2.299501 129-1976 NA NA 129-1977 1.724296 NA 129-1978 2.217804 3.942100 129-1979 1.750597 3.968401 129-1980 -0.175000 1.575597 129-1981 -1.509099 -1.684099 129-1982 0.706398 -0.802701 129-1983 1.182601 1.888999 129-1984 1.116900 2.299501 130-1976 NA NA 130-1977 5.025500 NA 130-1978 1.758000 6.783500 130-1979 2.340100 4.098100 130-1980 -8.009200 -5.669100 130-1981 -3.342399 -11.351599 130-1982 -0.228500 -3.570899 130-1983 5.823999 5.595499 130-1984 6.723200 12.547199 131-1976 NA NA 131-1977 3.137300 NA 131-1978 2.558100 5.695400 131-1979 2.472200 5.030300 131-1980 -11.934500 -9.462300 131-1981 -6.813797 -18.748297 131-1982 -2.369804 -9.183601 131-1983 2.864898 0.495094 131-1984 4.067406 6.932304 132-1976 NA NA 132-1977 1.601700 NA 132-1978 -3.914600 -2.312900 132-1979 0.766900 -3.147700 132-1980 -25.217600 -24.450700 132-1981 -2.569504 -27.787104 132-1982 -0.750793 -3.320297 132-1983 2.058899 1.308106 132-1984 4.990298 7.049197 133-1976 NA NA 133-1977 -1.725000 NA 133-1978 -2.350000 -4.075000 133-1979 -3.650000 -6.000000 133-1980 -7.650000 -11.300000 133-1981 -10.324998 -17.974998 133-1982 -1.724999 -12.049997 133-1983 -2.375000 -4.099999 133-1984 -0.525001 -2.900001 134-1976 NA NA 134-1977 3.532600 NA 134-1978 -1.266900 2.265700 134-1979 -1.435900 -2.702800 134-1980 -17.874600 -19.310500 134-1981 -7.337898 -25.212498 134-1982 -1.391701 -8.729599 134-1983 3.401001 2.009300 134-1984 3.231598 6.632599 135-1976 NA NA 135-1977 2.735400 NA 135-1978 -0.067100 2.668300 135-1979 -1.393600 -1.460700 135-1980 -13.764900 -15.158500 135-1981 -9.972103 -23.737003 135-1982 -2.878296 -12.850399 135-1983 2.202897 -0.675399 135-1984 3.273902 5.476799 136-1976 NA NA 136-1977 3.532600 NA 136-1978 -1.266900 2.265700 136-1979 -1.435900 -2.702800 136-1980 -17.874600 -19.310500 136-1981 -7.337898 -25.212498 136-1982 -1.391701 -8.729599 136-1983 3.401001 2.009300 136-1984 3.231598 6.632599 137-1976 NA NA 137-1977 -0.625000 NA 137-1978 1.800000 1.175000 137-1979 0.000000 1.800000 137-1980 -8.475000 -8.475000 137-1981 -11.000000 -19.475000 137-1982 -1.324997 -12.324997 137-1983 2.724999 1.400002 137-1984 1.574996 4.299995 138-1976 NA NA 138-1977 3.891100 NA 138-1978 2.799400 6.690500 138-1979 2.508000 5.307400 138-1980 -5.931700 -3.423700 138-1981 -8.947401 -14.879101 138-1982 -4.221497 -13.168898 138-1983 0.683701 -3.537796 138-1984 3.566399 4.250100 139-1976 NA NA 139-1977 3.589600 NA 139-1978 2.702900 6.292500 139-1979 2.493700 5.196600 139-1980 -8.332900 -5.839200 139-1981 -8.093997 -16.426897 139-1982 -3.480805 -11.574802 139-1983 1.556198 -1.924607 139-1984 3.766800 5.322998 140-1976 NA NA 140-1977 2.712700 NA 140-1978 1.439700 4.152400 140-1979 2.640300 4.080000 140-1980 -11.559100 -8.918800 140-1981 -0.603500 -12.162600 140-1982 -0.103402 -0.706902 140-1983 7.799802 7.696400 140-1984 6.364200 14.164002 > > ## demonstrate behaviour of shift = "time" vs. shift = "row" > # delete 2nd time period for first individual (1978 is missing (not NA)): > Em_hole <- Em[-2, ] > is.pconsecutive(Em_hole) # check: non-consecutive for 1st individual now 1 2 3 4 5 6 7 8 9 10 11 12 13 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 14 15 16 17 18 19 20 21 22 23 24 25 26 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 27 28 29 30 31 32 33 34 35 36 37 38 39 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 40 41 42 43 44 45 46 47 48 49 50 51 52 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 53 54 55 56 57 58 59 60 61 62 63 64 65 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 66 67 68 69 70 71 72 73 74 75 76 77 78 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 79 80 81 82 83 84 85 86 87 88 89 90 91 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 92 93 94 95 96 97 98 99 100 101 102 103 104 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 105 106 107 108 109 110 111 112 113 114 115 116 117 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 118 119 120 121 122 123 124 125 126 127 128 129 130 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 131 132 133 134 135 136 137 138 139 140 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > > # original non-consecutive data: > head(Em_hole$emp, 10) 1-1977 1-1979 1-1980 1-1981 1-1982 1-1983 2-1977 2-1978 2-1979 2-1980 5.041 5.015 4.715 4.093 3.166 2.936 71.319 70.643 70.918 72.031 > # for shift = "time", 1-1979 contains the value of former 1-1977 (2 periods lagged): > head(lag(Em_hole$emp, k = 2, shift = "time"), 10) 1-1977 1-1979 1-1980 1-1981 1-1982 1-1983 2-1977 2-1978 2-1979 2-1980 NA 5.041 NA 5.015 4.715 4.093 NA NA 71.319 70.643 > # for shift = "row", 1-1979 contains NA (2 rows lagged (and no entry for 1976): > head(lag(Em_hole$emp, k = 2, shift = "row"), 10) 1-1977 1-1979 1-1980 1-1981 1-1982 1-1983 2-1977 2-1978 2-1979 2-1980 NA NA NA 5.015 4.715 4.093 NA NA 71.319 70.643 > > > > > cleanEx() > nameEx("make.pbalanced") > ### * make.pbalanced > > flush(stderr()); flush(stdout()) > > ### Name: make.pbalanced > ### Title: Make data balanced > ### Aliases: make.pbalanced make.pbalanced.pdata.frame > ### make.pbalanced.pseries make.pbalanced.data.frame > ### Keywords: attribute > > ### ** Examples > > > # take data and make it unbalanced > # by deletion of 2nd row (2nd time period for first individual) > data("Grunfeld", package = "plm") > nrow(Grunfeld) # 200 rows [1] 200 > Grunfeld_missing_period <- Grunfeld[-2, ] > pdim(Grunfeld_missing_period)$balanced # check if balanced: FALSE [1] FALSE > make.pbalanced(Grunfeld_missing_period) # make it balanced (by filling) firm year inv value capital 1 1 1935 317.60 3078.50 2.80 2 1 1936 NA NA NA 3 1 1937 410.60 5387.10 156.90 4 1 1938 257.70 2792.20 209.20 5 1 1939 330.80 4313.20 203.40 6 1 1940 461.20 4643.90 207.20 7 1 1941 512.00 4551.20 255.20 8 1 1942 448.00 3244.10 303.70 9 1 1943 499.60 4053.70 264.10 10 1 1944 547.50 4379.30 201.60 11 1 1945 561.20 4840.90 265.00 12 1 1946 688.10 4900.90 402.20 13 1 1947 568.90 3526.50 761.50 14 1 1948 529.20 3254.70 922.40 15 1 1949 555.10 3700.20 1020.10 16 1 1950 642.90 3755.60 1099.00 17 1 1951 755.90 4833.00 1207.70 18 1 1952 891.20 4924.90 1430.50 19 1 1953 1304.40 6241.70 1777.30 20 1 1954 1486.70 5593.60 2226.30 21 2 1935 209.90 1362.40 53.80 22 2 1936 355.30 1807.10 50.50 23 2 1937 469.90 2676.30 118.10 24 2 1938 262.30 1801.90 260.20 25 2 1939 230.40 1957.30 312.70 26 2 1940 361.60 2202.90 254.20 27 2 1941 472.80 2380.50 261.40 28 2 1942 445.60 2168.60 298.70 29 2 1943 361.60 1985.10 301.80 30 2 1944 288.20 1813.90 279.10 31 2 1945 258.70 1850.20 213.80 32 2 1946 420.30 2067.70 132.60 33 2 1947 420.50 1796.70 264.80 34 2 1948 494.50 1625.80 306.90 35 2 1949 405.10 1667.00 351.10 36 2 1950 418.80 1677.40 357.80 37 2 1951 588.20 2289.50 342.10 38 2 1952 645.50 2159.40 444.20 39 2 1953 641.00 2031.30 623.60 40 2 1954 459.30 2115.50 669.70 41 3 1935 33.10 1170.60 97.80 42 3 1936 45.00 2015.80 104.40 43 3 1937 77.20 2803.30 118.00 44 3 1938 44.60 2039.70 156.20 45 3 1939 48.10 2256.20 172.60 46 3 1940 74.40 2132.20 186.60 47 3 1941 113.00 1834.10 220.90 48 3 1942 91.90 1588.00 287.80 49 3 1943 61.30 1749.40 319.90 50 3 1944 56.80 1687.20 321.30 51 3 1945 93.60 2007.70 319.60 52 3 1946 159.90 2208.30 346.00 53 3 1947 147.20 1656.70 456.40 54 3 1948 146.30 1604.40 543.40 55 3 1949 98.30 1431.80 618.30 56 3 1950 93.50 1610.50 647.40 57 3 1951 135.20 1819.40 671.30 58 3 1952 157.30 2079.70 726.10 59 3 1953 179.50 2371.60 800.30 60 3 1954 189.60 2759.90 888.90 61 4 1935 40.29 417.50 10.50 62 4 1936 72.76 837.80 10.20 63 4 1937 66.26 883.90 34.70 64 4 1938 51.60 437.90 51.80 65 4 1939 52.41 679.70 64.30 66 4 1940 69.41 727.80 67.10 67 4 1941 68.35 643.60 75.20 68 4 1942 46.80 410.90 71.40 69 4 1943 47.40 588.40 67.10 70 4 1944 59.57 698.40 60.50 71 4 1945 88.78 846.40 54.60 72 4 1946 74.12 893.80 84.80 73 4 1947 62.68 579.00 96.80 74 4 1948 89.36 694.60 110.20 75 4 1949 78.98 590.30 147.40 76 4 1950 100.66 693.50 163.20 77 4 1951 160.62 809.00 203.50 78 4 1952 145.00 727.00 290.60 79 4 1953 174.93 1001.50 346.10 80 4 1954 172.49 703.20 414.90 81 5 1935 39.68 157.70 183.20 82 5 1936 50.73 167.90 204.00 83 5 1937 74.24 192.90 236.00 84 5 1938 53.51 156.70 291.70 85 5 1939 42.65 191.40 323.10 86 5 1940 46.48 185.50 344.00 87 5 1941 61.40 199.60 367.70 88 5 1942 39.67 189.50 407.20 89 5 1943 62.24 151.20 426.60 90 5 1944 52.32 187.70 470.00 91 5 1945 63.21 214.70 499.20 92 5 1946 59.37 232.90 534.60 93 5 1947 58.02 249.00 566.60 94 5 1948 70.34 224.50 595.30 95 5 1949 67.42 237.30 631.40 96 5 1950 55.74 240.10 662.30 97 5 1951 80.30 327.30 683.90 98 5 1952 85.40 359.40 729.30 99 5 1953 91.90 398.40 774.30 100 5 1954 81.43 365.70 804.90 101 6 1935 20.36 197.00 6.50 102 6 1936 25.98 210.30 15.80 103 6 1937 25.94 223.10 27.70 104 6 1938 27.53 216.70 39.20 105 6 1939 24.60 286.40 48.60 106 6 1940 28.54 298.00 52.50 107 6 1941 43.41 276.90 61.50 108 6 1942 42.81 272.60 80.50 109 6 1943 27.84 287.40 94.40 110 6 1944 32.60 330.30 92.60 111 6 1945 39.03 324.40 92.30 112 6 1946 50.17 401.90 94.20 113 6 1947 51.85 407.40 111.40 114 6 1948 64.03 409.20 127.40 115 6 1949 68.16 482.20 149.30 116 6 1950 77.34 673.80 164.40 117 6 1951 95.30 676.90 177.20 118 6 1952 99.49 702.00 200.00 119 6 1953 127.52 793.50 211.50 120 6 1954 135.72 927.30 238.70 121 7 1935 24.43 138.00 100.20 122 7 1936 23.21 200.10 125.00 123 7 1937 32.78 210.10 142.40 124 7 1938 32.54 161.20 165.10 125 7 1939 26.65 161.70 194.80 126 7 1940 33.71 145.10 222.90 127 7 1941 43.50 110.60 252.10 128 7 1942 34.46 98.10 276.30 129 7 1943 44.28 108.80 300.30 130 7 1944 70.80 118.20 318.20 131 7 1945 44.12 126.50 336.20 132 7 1946 48.98 156.70 351.20 133 7 1947 48.51 119.40 373.60 134 7 1948 50.00 129.10 389.40 135 7 1949 50.59 134.80 406.70 136 7 1950 42.53 140.80 429.50 137 7 1951 64.77 179.00 450.60 138 7 1952 72.68 178.10 466.90 139 7 1953 73.86 186.80 486.20 140 7 1954 89.51 192.70 511.30 141 8 1935 12.93 191.50 1.80 142 8 1936 25.90 516.00 0.80 143 8 1937 35.05 729.00 7.40 144 8 1938 22.89 560.40 18.10 145 8 1939 18.84 519.90 23.50 146 8 1940 28.57 628.50 26.50 147 8 1941 48.51 537.10 36.20 148 8 1942 43.34 561.20 60.80 149 8 1943 37.02 617.20 84.40 150 8 1944 37.81 626.70 91.20 151 8 1945 39.27 737.20 92.40 152 8 1946 53.46 760.50 86.00 153 8 1947 55.56 581.40 111.10 154 8 1948 49.56 662.30 130.60 155 8 1949 32.04 583.80 141.80 156 8 1950 32.24 635.20 136.70 157 8 1951 54.38 723.80 129.70 158 8 1952 71.78 864.10 145.50 159 8 1953 90.08 1193.50 174.80 160 8 1954 68.60 1188.90 213.50 161 9 1935 26.63 290.60 162.00 162 9 1936 23.39 291.10 174.00 163 9 1937 30.65 335.00 183.00 164 9 1938 20.89 246.00 198.00 165 9 1939 28.78 356.20 208.00 166 9 1940 26.93 289.80 223.00 167 9 1941 32.08 268.20 234.00 168 9 1942 32.21 213.30 248.00 169 9 1943 35.69 348.20 274.00 170 9 1944 62.47 374.20 282.00 171 9 1945 52.32 387.20 316.00 172 9 1946 56.95 347.40 302.00 173 9 1947 54.32 291.90 333.00 174 9 1948 40.53 297.20 359.00 175 9 1949 32.54 276.90 370.00 176 9 1950 43.48 274.60 376.00 177 9 1951 56.49 339.90 391.00 178 9 1952 65.98 474.80 414.00 179 9 1953 66.11 496.00 443.00 180 9 1954 49.34 474.50 468.00 181 10 1935 2.54 70.91 4.50 182 10 1936 2.00 87.94 4.71 183 10 1937 2.19 82.20 4.57 184 10 1938 1.99 58.72 4.56 185 10 1939 2.03 80.54 4.38 186 10 1940 1.81 86.47 4.21 187 10 1941 2.14 77.68 4.12 188 10 1942 1.86 62.16 3.83 189 10 1943 0.93 62.24 3.58 190 10 1944 1.18 61.82 3.41 191 10 1945 1.36 65.85 3.31 192 10 1946 2.24 69.54 3.23 193 10 1947 3.81 64.97 3.90 194 10 1948 5.66 68.00 5.38 195 10 1949 4.21 71.24 7.39 196 10 1950 3.42 69.05 8.74 197 10 1951 4.67 83.04 9.07 198 10 1952 6.00 74.42 9.93 199 10 1953 6.53 63.51 11.68 200 10 1954 5.12 58.12 14.33 > make.pbalanced(Grunfeld_missing_period, balance.type = "shared.times") # (shared periods) firm year inv value capital 1 1 1935 317.60 3078.50 2.80 3 1 1937 410.60 5387.10 156.90 4 1 1938 257.70 2792.20 209.20 5 1 1939 330.80 4313.20 203.40 6 1 1940 461.20 4643.90 207.20 7 1 1941 512.00 4551.20 255.20 8 1 1942 448.00 3244.10 303.70 9 1 1943 499.60 4053.70 264.10 10 1 1944 547.50 4379.30 201.60 11 1 1945 561.20 4840.90 265.00 12 1 1946 688.10 4900.90 402.20 13 1 1947 568.90 3526.50 761.50 14 1 1948 529.20 3254.70 922.40 15 1 1949 555.10 3700.20 1020.10 16 1 1950 642.90 3755.60 1099.00 17 1 1951 755.90 4833.00 1207.70 18 1 1952 891.20 4924.90 1430.50 19 1 1953 1304.40 6241.70 1777.30 20 1 1954 1486.70 5593.60 2226.30 21 2 1935 209.90 1362.40 53.80 23 2 1937 469.90 2676.30 118.10 24 2 1938 262.30 1801.90 260.20 25 2 1939 230.40 1957.30 312.70 26 2 1940 361.60 2202.90 254.20 27 2 1941 472.80 2380.50 261.40 28 2 1942 445.60 2168.60 298.70 29 2 1943 361.60 1985.10 301.80 30 2 1944 288.20 1813.90 279.10 31 2 1945 258.70 1850.20 213.80 32 2 1946 420.30 2067.70 132.60 33 2 1947 420.50 1796.70 264.80 34 2 1948 494.50 1625.80 306.90 35 2 1949 405.10 1667.00 351.10 36 2 1950 418.80 1677.40 357.80 37 2 1951 588.20 2289.50 342.10 38 2 1952 645.50 2159.40 444.20 39 2 1953 641.00 2031.30 623.60 40 2 1954 459.30 2115.50 669.70 41 3 1935 33.10 1170.60 97.80 43 3 1937 77.20 2803.30 118.00 44 3 1938 44.60 2039.70 156.20 45 3 1939 48.10 2256.20 172.60 46 3 1940 74.40 2132.20 186.60 47 3 1941 113.00 1834.10 220.90 48 3 1942 91.90 1588.00 287.80 49 3 1943 61.30 1749.40 319.90 50 3 1944 56.80 1687.20 321.30 51 3 1945 93.60 2007.70 319.60 52 3 1946 159.90 2208.30 346.00 53 3 1947 147.20 1656.70 456.40 54 3 1948 146.30 1604.40 543.40 55 3 1949 98.30 1431.80 618.30 56 3 1950 93.50 1610.50 647.40 57 3 1951 135.20 1819.40 671.30 58 3 1952 157.30 2079.70 726.10 59 3 1953 179.50 2371.60 800.30 60 3 1954 189.60 2759.90 888.90 61 4 1935 40.29 417.50 10.50 63 4 1937 66.26 883.90 34.70 64 4 1938 51.60 437.90 51.80 65 4 1939 52.41 679.70 64.30 66 4 1940 69.41 727.80 67.10 67 4 1941 68.35 643.60 75.20 68 4 1942 46.80 410.90 71.40 69 4 1943 47.40 588.40 67.10 70 4 1944 59.57 698.40 60.50 71 4 1945 88.78 846.40 54.60 72 4 1946 74.12 893.80 84.80 73 4 1947 62.68 579.00 96.80 74 4 1948 89.36 694.60 110.20 75 4 1949 78.98 590.30 147.40 76 4 1950 100.66 693.50 163.20 77 4 1951 160.62 809.00 203.50 78 4 1952 145.00 727.00 290.60 79 4 1953 174.93 1001.50 346.10 80 4 1954 172.49 703.20 414.90 81 5 1935 39.68 157.70 183.20 83 5 1937 74.24 192.90 236.00 84 5 1938 53.51 156.70 291.70 85 5 1939 42.65 191.40 323.10 86 5 1940 46.48 185.50 344.00 87 5 1941 61.40 199.60 367.70 88 5 1942 39.67 189.50 407.20 89 5 1943 62.24 151.20 426.60 90 5 1944 52.32 187.70 470.00 91 5 1945 63.21 214.70 499.20 92 5 1946 59.37 232.90 534.60 93 5 1947 58.02 249.00 566.60 94 5 1948 70.34 224.50 595.30 95 5 1949 67.42 237.30 631.40 96 5 1950 55.74 240.10 662.30 97 5 1951 80.30 327.30 683.90 98 5 1952 85.40 359.40 729.30 99 5 1953 91.90 398.40 774.30 100 5 1954 81.43 365.70 804.90 101 6 1935 20.36 197.00 6.50 103 6 1937 25.94 223.10 27.70 104 6 1938 27.53 216.70 39.20 105 6 1939 24.60 286.40 48.60 106 6 1940 28.54 298.00 52.50 107 6 1941 43.41 276.90 61.50 108 6 1942 42.81 272.60 80.50 109 6 1943 27.84 287.40 94.40 110 6 1944 32.60 330.30 92.60 111 6 1945 39.03 324.40 92.30 112 6 1946 50.17 401.90 94.20 113 6 1947 51.85 407.40 111.40 114 6 1948 64.03 409.20 127.40 115 6 1949 68.16 482.20 149.30 116 6 1950 77.34 673.80 164.40 117 6 1951 95.30 676.90 177.20 118 6 1952 99.49 702.00 200.00 119 6 1953 127.52 793.50 211.50 120 6 1954 135.72 927.30 238.70 121 7 1935 24.43 138.00 100.20 123 7 1937 32.78 210.10 142.40 124 7 1938 32.54 161.20 165.10 125 7 1939 26.65 161.70 194.80 126 7 1940 33.71 145.10 222.90 127 7 1941 43.50 110.60 252.10 128 7 1942 34.46 98.10 276.30 129 7 1943 44.28 108.80 300.30 130 7 1944 70.80 118.20 318.20 131 7 1945 44.12 126.50 336.20 132 7 1946 48.98 156.70 351.20 133 7 1947 48.51 119.40 373.60 134 7 1948 50.00 129.10 389.40 135 7 1949 50.59 134.80 406.70 136 7 1950 42.53 140.80 429.50 137 7 1951 64.77 179.00 450.60 138 7 1952 72.68 178.10 466.90 139 7 1953 73.86 186.80 486.20 140 7 1954 89.51 192.70 511.30 141 8 1935 12.93 191.50 1.80 143 8 1937 35.05 729.00 7.40 144 8 1938 22.89 560.40 18.10 145 8 1939 18.84 519.90 23.50 146 8 1940 28.57 628.50 26.50 147 8 1941 48.51 537.10 36.20 148 8 1942 43.34 561.20 60.80 149 8 1943 37.02 617.20 84.40 150 8 1944 37.81 626.70 91.20 151 8 1945 39.27 737.20 92.40 152 8 1946 53.46 760.50 86.00 153 8 1947 55.56 581.40 111.10 154 8 1948 49.56 662.30 130.60 155 8 1949 32.04 583.80 141.80 156 8 1950 32.24 635.20 136.70 157 8 1951 54.38 723.80 129.70 158 8 1952 71.78 864.10 145.50 159 8 1953 90.08 1193.50 174.80 160 8 1954 68.60 1188.90 213.50 161 9 1935 26.63 290.60 162.00 163 9 1937 30.65 335.00 183.00 164 9 1938 20.89 246.00 198.00 165 9 1939 28.78 356.20 208.00 166 9 1940 26.93 289.80 223.00 167 9 1941 32.08 268.20 234.00 168 9 1942 32.21 213.30 248.00 169 9 1943 35.69 348.20 274.00 170 9 1944 62.47 374.20 282.00 171 9 1945 52.32 387.20 316.00 172 9 1946 56.95 347.40 302.00 173 9 1947 54.32 291.90 333.00 174 9 1948 40.53 297.20 359.00 175 9 1949 32.54 276.90 370.00 176 9 1950 43.48 274.60 376.00 177 9 1951 56.49 339.90 391.00 178 9 1952 65.98 474.80 414.00 179 9 1953 66.11 496.00 443.00 180 9 1954 49.34 474.50 468.00 181 10 1935 2.54 70.91 4.50 183 10 1937 2.19 82.20 4.57 184 10 1938 1.99 58.72 4.56 185 10 1939 2.03 80.54 4.38 186 10 1940 1.81 86.47 4.21 187 10 1941 2.14 77.68 4.12 188 10 1942 1.86 62.16 3.83 189 10 1943 0.93 62.24 3.58 190 10 1944 1.18 61.82 3.41 191 10 1945 1.36 65.85 3.31 192 10 1946 2.24 69.54 3.23 193 10 1947 3.81 64.97 3.90 194 10 1948 5.66 68.00 5.38 195 10 1949 4.21 71.24 7.39 196 10 1950 3.42 69.05 8.74 197 10 1951 4.67 83.04 9.07 198 10 1952 6.00 74.42 9.93 199 10 1953 6.53 63.51 11.68 200 10 1954 5.12 58.12 14.33 > nrow(make.pbalanced(Grunfeld_missing_period)) [1] 200 > nrow(make.pbalanced(Grunfeld_missing_period, balance.type = "shared.times")) [1] 190 > > # more complex data: > # First, make data unbalanced (and non-consecutive) > # by deletion of 2nd time period (year 1936) for all individuals > # and more time periods for first individual only > Grunfeld_unbalanced <- Grunfeld[Grunfeld$year != 1936, ] > Grunfeld_unbalanced <- Grunfeld_unbalanced[-c(1,4), ] > pdim(Grunfeld_unbalanced)$balanced # FALSE [1] FALSE > all(is.pconsecutive(Grunfeld_unbalanced)) # FALSE [1] FALSE > > g_bal <- make.pbalanced(Grunfeld_unbalanced) > pdim(g_bal)$balanced # TRUE [1] TRUE > unique(g_bal$year) # all years but 1936 [1] 1935 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 [16] 1951 1952 1953 1954 > nrow(g_bal) # 190 rows [1] 190 > head(g_bal) # 1st individual: years 1935, 1939 are NA firm year inv value capital 1 1 1935 NA NA NA 3 1 1937 410.6 5387.1 156.9 4 1 1938 257.7 2792.2 209.2 5 1 1939 NA NA NA 6 1 1940 461.2 4643.9 207.2 7 1 1941 512.0 4551.2 255.2 > > # NA in 1st, 3rd time period (years 1935, 1937) for first individual > Grunfeld_NA <- Grunfeld > Grunfeld_NA[c(1, 3), "year"] <- NA > g_bal_NA <- make.pbalanced(Grunfeld_NA) > head(g_bal_NA) # years 1935, 1937: NA for non-index vars firm year inv value capital 1 1 1935 NA NA NA 2 1 1936 391.8 4661.7 52.6 3 1 1937 NA NA NA 4 1 1938 257.7 2792.2 209.2 5 1 1939 330.8 4313.2 203.4 6 1 1940 461.2 4643.9 207.2 > nrow(g_bal_NA) # 200 [1] 200 > > # pdata.frame interface > pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) > make.pbalanced(Grunfeld_missing_period) firm year inv value capital 1 1 1935 317.60 3078.50 2.80 2 1 1936 NA NA NA 3 1 1937 410.60 5387.10 156.90 4 1 1938 257.70 2792.20 209.20 5 1 1939 330.80 4313.20 203.40 6 1 1940 461.20 4643.90 207.20 7 1 1941 512.00 4551.20 255.20 8 1 1942 448.00 3244.10 303.70 9 1 1943 499.60 4053.70 264.10 10 1 1944 547.50 4379.30 201.60 11 1 1945 561.20 4840.90 265.00 12 1 1946 688.10 4900.90 402.20 13 1 1947 568.90 3526.50 761.50 14 1 1948 529.20 3254.70 922.40 15 1 1949 555.10 3700.20 1020.10 16 1 1950 642.90 3755.60 1099.00 17 1 1951 755.90 4833.00 1207.70 18 1 1952 891.20 4924.90 1430.50 19 1 1953 1304.40 6241.70 1777.30 20 1 1954 1486.70 5593.60 2226.30 21 2 1935 209.90 1362.40 53.80 22 2 1936 355.30 1807.10 50.50 23 2 1937 469.90 2676.30 118.10 24 2 1938 262.30 1801.90 260.20 25 2 1939 230.40 1957.30 312.70 26 2 1940 361.60 2202.90 254.20 27 2 1941 472.80 2380.50 261.40 28 2 1942 445.60 2168.60 298.70 29 2 1943 361.60 1985.10 301.80 30 2 1944 288.20 1813.90 279.10 31 2 1945 258.70 1850.20 213.80 32 2 1946 420.30 2067.70 132.60 33 2 1947 420.50 1796.70 264.80 34 2 1948 494.50 1625.80 306.90 35 2 1949 405.10 1667.00 351.10 36 2 1950 418.80 1677.40 357.80 37 2 1951 588.20 2289.50 342.10 38 2 1952 645.50 2159.40 444.20 39 2 1953 641.00 2031.30 623.60 40 2 1954 459.30 2115.50 669.70 41 3 1935 33.10 1170.60 97.80 42 3 1936 45.00 2015.80 104.40 43 3 1937 77.20 2803.30 118.00 44 3 1938 44.60 2039.70 156.20 45 3 1939 48.10 2256.20 172.60 46 3 1940 74.40 2132.20 186.60 47 3 1941 113.00 1834.10 220.90 48 3 1942 91.90 1588.00 287.80 49 3 1943 61.30 1749.40 319.90 50 3 1944 56.80 1687.20 321.30 51 3 1945 93.60 2007.70 319.60 52 3 1946 159.90 2208.30 346.00 53 3 1947 147.20 1656.70 456.40 54 3 1948 146.30 1604.40 543.40 55 3 1949 98.30 1431.80 618.30 56 3 1950 93.50 1610.50 647.40 57 3 1951 135.20 1819.40 671.30 58 3 1952 157.30 2079.70 726.10 59 3 1953 179.50 2371.60 800.30 60 3 1954 189.60 2759.90 888.90 61 4 1935 40.29 417.50 10.50 62 4 1936 72.76 837.80 10.20 63 4 1937 66.26 883.90 34.70 64 4 1938 51.60 437.90 51.80 65 4 1939 52.41 679.70 64.30 66 4 1940 69.41 727.80 67.10 67 4 1941 68.35 643.60 75.20 68 4 1942 46.80 410.90 71.40 69 4 1943 47.40 588.40 67.10 70 4 1944 59.57 698.40 60.50 71 4 1945 88.78 846.40 54.60 72 4 1946 74.12 893.80 84.80 73 4 1947 62.68 579.00 96.80 74 4 1948 89.36 694.60 110.20 75 4 1949 78.98 590.30 147.40 76 4 1950 100.66 693.50 163.20 77 4 1951 160.62 809.00 203.50 78 4 1952 145.00 727.00 290.60 79 4 1953 174.93 1001.50 346.10 80 4 1954 172.49 703.20 414.90 81 5 1935 39.68 157.70 183.20 82 5 1936 50.73 167.90 204.00 83 5 1937 74.24 192.90 236.00 84 5 1938 53.51 156.70 291.70 85 5 1939 42.65 191.40 323.10 86 5 1940 46.48 185.50 344.00 87 5 1941 61.40 199.60 367.70 88 5 1942 39.67 189.50 407.20 89 5 1943 62.24 151.20 426.60 90 5 1944 52.32 187.70 470.00 91 5 1945 63.21 214.70 499.20 92 5 1946 59.37 232.90 534.60 93 5 1947 58.02 249.00 566.60 94 5 1948 70.34 224.50 595.30 95 5 1949 67.42 237.30 631.40 96 5 1950 55.74 240.10 662.30 97 5 1951 80.30 327.30 683.90 98 5 1952 85.40 359.40 729.30 99 5 1953 91.90 398.40 774.30 100 5 1954 81.43 365.70 804.90 101 6 1935 20.36 197.00 6.50 102 6 1936 25.98 210.30 15.80 103 6 1937 25.94 223.10 27.70 104 6 1938 27.53 216.70 39.20 105 6 1939 24.60 286.40 48.60 106 6 1940 28.54 298.00 52.50 107 6 1941 43.41 276.90 61.50 108 6 1942 42.81 272.60 80.50 109 6 1943 27.84 287.40 94.40 110 6 1944 32.60 330.30 92.60 111 6 1945 39.03 324.40 92.30 112 6 1946 50.17 401.90 94.20 113 6 1947 51.85 407.40 111.40 114 6 1948 64.03 409.20 127.40 115 6 1949 68.16 482.20 149.30 116 6 1950 77.34 673.80 164.40 117 6 1951 95.30 676.90 177.20 118 6 1952 99.49 702.00 200.00 119 6 1953 127.52 793.50 211.50 120 6 1954 135.72 927.30 238.70 121 7 1935 24.43 138.00 100.20 122 7 1936 23.21 200.10 125.00 123 7 1937 32.78 210.10 142.40 124 7 1938 32.54 161.20 165.10 125 7 1939 26.65 161.70 194.80 126 7 1940 33.71 145.10 222.90 127 7 1941 43.50 110.60 252.10 128 7 1942 34.46 98.10 276.30 129 7 1943 44.28 108.80 300.30 130 7 1944 70.80 118.20 318.20 131 7 1945 44.12 126.50 336.20 132 7 1946 48.98 156.70 351.20 133 7 1947 48.51 119.40 373.60 134 7 1948 50.00 129.10 389.40 135 7 1949 50.59 134.80 406.70 136 7 1950 42.53 140.80 429.50 137 7 1951 64.77 179.00 450.60 138 7 1952 72.68 178.10 466.90 139 7 1953 73.86 186.80 486.20 140 7 1954 89.51 192.70 511.30 141 8 1935 12.93 191.50 1.80 142 8 1936 25.90 516.00 0.80 143 8 1937 35.05 729.00 7.40 144 8 1938 22.89 560.40 18.10 145 8 1939 18.84 519.90 23.50 146 8 1940 28.57 628.50 26.50 147 8 1941 48.51 537.10 36.20 148 8 1942 43.34 561.20 60.80 149 8 1943 37.02 617.20 84.40 150 8 1944 37.81 626.70 91.20 151 8 1945 39.27 737.20 92.40 152 8 1946 53.46 760.50 86.00 153 8 1947 55.56 581.40 111.10 154 8 1948 49.56 662.30 130.60 155 8 1949 32.04 583.80 141.80 156 8 1950 32.24 635.20 136.70 157 8 1951 54.38 723.80 129.70 158 8 1952 71.78 864.10 145.50 159 8 1953 90.08 1193.50 174.80 160 8 1954 68.60 1188.90 213.50 161 9 1935 26.63 290.60 162.00 162 9 1936 23.39 291.10 174.00 163 9 1937 30.65 335.00 183.00 164 9 1938 20.89 246.00 198.00 165 9 1939 28.78 356.20 208.00 166 9 1940 26.93 289.80 223.00 167 9 1941 32.08 268.20 234.00 168 9 1942 32.21 213.30 248.00 169 9 1943 35.69 348.20 274.00 170 9 1944 62.47 374.20 282.00 171 9 1945 52.32 387.20 316.00 172 9 1946 56.95 347.40 302.00 173 9 1947 54.32 291.90 333.00 174 9 1948 40.53 297.20 359.00 175 9 1949 32.54 276.90 370.00 176 9 1950 43.48 274.60 376.00 177 9 1951 56.49 339.90 391.00 178 9 1952 65.98 474.80 414.00 179 9 1953 66.11 496.00 443.00 180 9 1954 49.34 474.50 468.00 181 10 1935 2.54 70.91 4.50 182 10 1936 2.00 87.94 4.71 183 10 1937 2.19 82.20 4.57 184 10 1938 1.99 58.72 4.56 185 10 1939 2.03 80.54 4.38 186 10 1940 1.81 86.47 4.21 187 10 1941 2.14 77.68 4.12 188 10 1942 1.86 62.16 3.83 189 10 1943 0.93 62.24 3.58 190 10 1944 1.18 61.82 3.41 191 10 1945 1.36 65.85 3.31 192 10 1946 2.24 69.54 3.23 193 10 1947 3.81 64.97 3.90 194 10 1948 5.66 68.00 5.38 195 10 1949 4.21 71.24 7.39 196 10 1950 3.42 69.05 8.74 197 10 1951 4.67 83.04 9.07 198 10 1952 6.00 74.42 9.93 199 10 1953 6.53 63.51 11.68 200 10 1954 5.12 58.12 14.33 > > # pseries interface > make.pbalanced(pGrunfeld_missing_period$inv) 1-1935 1-1936 1-1937 1-1938 1-1939 1-1940 1-1941 1-1942 1-1943 1-1944 317.60 NA 410.60 257.70 330.80 461.20 512.00 448.00 499.60 547.50 1-1945 1-1946 1-1947 1-1948 1-1949 1-1950 1-1951 1-1952 1-1953 1-1954 561.20 688.10 568.90 529.20 555.10 642.90 755.90 891.20 1304.40 1486.70 2-1935 2-1936 2-1937 2-1938 2-1939 2-1940 2-1941 2-1942 2-1943 2-1944 209.90 355.30 469.90 262.30 230.40 361.60 472.80 445.60 361.60 288.20 2-1945 2-1946 2-1947 2-1948 2-1949 2-1950 2-1951 2-1952 2-1953 2-1954 258.70 420.30 420.50 494.50 405.10 418.80 588.20 645.50 641.00 459.30 3-1935 3-1936 3-1937 3-1938 3-1939 3-1940 3-1941 3-1942 3-1943 3-1944 33.10 45.00 77.20 44.60 48.10 74.40 113.00 91.90 61.30 56.80 3-1945 3-1946 3-1947 3-1948 3-1949 3-1950 3-1951 3-1952 3-1953 3-1954 93.60 159.90 147.20 146.30 98.30 93.50 135.20 157.30 179.50 189.60 4-1935 4-1936 4-1937 4-1938 4-1939 4-1940 4-1941 4-1942 4-1943 4-1944 40.29 72.76 66.26 51.60 52.41 69.41 68.35 46.80 47.40 59.57 4-1945 4-1946 4-1947 4-1948 4-1949 4-1950 4-1951 4-1952 4-1953 4-1954 88.78 74.12 62.68 89.36 78.98 100.66 160.62 145.00 174.93 172.49 5-1935 5-1936 5-1937 5-1938 5-1939 5-1940 5-1941 5-1942 5-1943 5-1944 39.68 50.73 74.24 53.51 42.65 46.48 61.40 39.67 62.24 52.32 5-1945 5-1946 5-1947 5-1948 5-1949 5-1950 5-1951 5-1952 5-1953 5-1954 63.21 59.37 58.02 70.34 67.42 55.74 80.30 85.40 91.90 81.43 6-1935 6-1936 6-1937 6-1938 6-1939 6-1940 6-1941 6-1942 6-1943 6-1944 20.36 25.98 25.94 27.53 24.60 28.54 43.41 42.81 27.84 32.60 6-1945 6-1946 6-1947 6-1948 6-1949 6-1950 6-1951 6-1952 6-1953 6-1954 39.03 50.17 51.85 64.03 68.16 77.34 95.30 99.49 127.52 135.72 7-1935 7-1936 7-1937 7-1938 7-1939 7-1940 7-1941 7-1942 7-1943 7-1944 24.43 23.21 32.78 32.54 26.65 33.71 43.50 34.46 44.28 70.80 7-1945 7-1946 7-1947 7-1948 7-1949 7-1950 7-1951 7-1952 7-1953 7-1954 44.12 48.98 48.51 50.00 50.59 42.53 64.77 72.68 73.86 89.51 8-1935 8-1936 8-1937 8-1938 8-1939 8-1940 8-1941 8-1942 8-1943 8-1944 12.93 25.90 35.05 22.89 18.84 28.57 48.51 43.34 37.02 37.81 8-1945 8-1946 8-1947 8-1948 8-1949 8-1950 8-1951 8-1952 8-1953 8-1954 39.27 53.46 55.56 49.56 32.04 32.24 54.38 71.78 90.08 68.60 9-1935 9-1936 9-1937 9-1938 9-1939 9-1940 9-1941 9-1942 9-1943 9-1944 26.63 23.39 30.65 20.89 28.78 26.93 32.08 32.21 35.69 62.47 9-1945 9-1946 9-1947 9-1948 9-1949 9-1950 9-1951 9-1952 9-1953 9-1954 52.32 56.95 54.32 40.53 32.54 43.48 56.49 65.98 66.11 49.34 10-1935 10-1936 10-1937 10-1938 10-1939 10-1940 10-1941 10-1942 10-1943 10-1944 2.54 2.00 2.19 1.99 2.03 1.81 2.14 1.86 0.93 1.18 10-1945 10-1946 10-1947 10-1948 10-1949 10-1950 10-1951 10-1952 10-1953 10-1954 1.36 2.24 3.81 5.66 4.21 3.42 4.67 6.00 6.53 5.12 > > # comparison to make.pconsecutive > g_consec <- make.pconsecutive(Grunfeld_unbalanced) > all(is.pconsecutive(g_consec)) # TRUE [1] TRUE > pdim(g_consec)$balanced # FALSE [1] FALSE > head(g_consec, 22) # 1st individual: no years 1935/6; 1939 is NA; firm year inv value capital 1 1 1937 410.6 5387.1 156.9 2 1 1938 257.7 2792.2 209.2 3 1 1939 NA NA NA 4 1 1940 461.2 4643.9 207.2 5 1 1941 512.0 4551.2 255.2 6 1 1942 448.0 3244.1 303.7 7 1 1943 499.6 4053.7 264.1 8 1 1944 547.5 4379.3 201.6 9 1 1945 561.2 4840.9 265.0 10 1 1946 688.1 4900.9 402.2 11 1 1947 568.9 3526.5 761.5 12 1 1948 529.2 3254.7 922.4 13 1 1949 555.1 3700.2 1020.1 14 1 1950 642.9 3755.6 1099.0 15 1 1951 755.9 4833.0 1207.7 16 1 1952 891.2 4924.9 1430.5 17 1 1953 1304.4 6241.7 1777.3 18 1 1954 1486.7 5593.6 2226.3 19 2 1935 209.9 1362.4 53.8 20 2 1936 NA NA NA 21 2 1937 469.9 2676.3 118.1 22 2 1938 262.3 1801.9 260.2 > # other indviduals: years 1935-1954, 1936 is NA > nrow(g_consec) # 198 rows [1] 198 > > g_consec_bal <- make.pconsecutive(Grunfeld_unbalanced, balanced = TRUE) > all(is.pconsecutive(g_consec_bal)) # TRUE [1] TRUE > pdim(g_consec_bal)$balanced # TRUE [1] TRUE > head(g_consec_bal) # year 1936 is NA for all individuals firm year inv value capital 1 1 1935 NA NA NA 2 1 1936 NA NA NA 3 1 1937 410.6 5387.1 156.9 4 1 1938 257.7 2792.2 209.2 5 1 1939 NA NA NA 6 1 1940 461.2 4643.9 207.2 > nrow(g_consec_bal) # 200 rows [1] 200 > > head(g_bal) # no year 1936 at all firm year inv value capital 1 1 1935 NA NA NA 3 1 1937 410.6 5387.1 156.9 4 1 1938 257.7 2792.2 209.2 5 1 1939 NA NA NA 6 1 1940 461.2 4643.9 207.2 7 1 1941 512.0 4551.2 255.2 > nrow(g_bal) # 190 rows [1] 190 > > > > > cleanEx() > nameEx("make.pconsecutive") > ### * make.pconsecutive > > flush(stderr()); flush(stdout()) > > ### Name: make.pconsecutive > ### Title: Make data consecutive (and, optionally, also balanced) > ### Aliases: make.pconsecutive make.pconsecutive.data.frame > ### make.pconsecutive.pdata.frame make.pconsecutive.pseries > ### Keywords: attribute > > ### ** Examples > > > # take data and make it non-consecutive > # by deletion of 2nd row (2nd time period for first individual) > data("Grunfeld", package = "plm") > nrow(Grunfeld) # 200 rows [1] 200 > Grunfeld_missing_period <- Grunfeld[-2, ] > is.pconsecutive(Grunfeld_missing_period) # check for consecutiveness 1 2 3 4 5 6 7 8 9 10 FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > make.pconsecutive(Grunfeld_missing_period) # make it consecutiveness firm year inv value capital 1 1 1935 317.60 3078.50 2.80 2 1 1936 NA NA NA 3 1 1937 410.60 5387.10 156.90 4 1 1938 257.70 2792.20 209.20 5 1 1939 330.80 4313.20 203.40 6 1 1940 461.20 4643.90 207.20 7 1 1941 512.00 4551.20 255.20 8 1 1942 448.00 3244.10 303.70 9 1 1943 499.60 4053.70 264.10 10 1 1944 547.50 4379.30 201.60 11 1 1945 561.20 4840.90 265.00 12 1 1946 688.10 4900.90 402.20 13 1 1947 568.90 3526.50 761.50 14 1 1948 529.20 3254.70 922.40 15 1 1949 555.10 3700.20 1020.10 16 1 1950 642.90 3755.60 1099.00 17 1 1951 755.90 4833.00 1207.70 18 1 1952 891.20 4924.90 1430.50 19 1 1953 1304.40 6241.70 1777.30 20 1 1954 1486.70 5593.60 2226.30 21 2 1935 209.90 1362.40 53.80 22 2 1936 355.30 1807.10 50.50 23 2 1937 469.90 2676.30 118.10 24 2 1938 262.30 1801.90 260.20 25 2 1939 230.40 1957.30 312.70 26 2 1940 361.60 2202.90 254.20 27 2 1941 472.80 2380.50 261.40 28 2 1942 445.60 2168.60 298.70 29 2 1943 361.60 1985.10 301.80 30 2 1944 288.20 1813.90 279.10 31 2 1945 258.70 1850.20 213.80 32 2 1946 420.30 2067.70 132.60 33 2 1947 420.50 1796.70 264.80 34 2 1948 494.50 1625.80 306.90 35 2 1949 405.10 1667.00 351.10 36 2 1950 418.80 1677.40 357.80 37 2 1951 588.20 2289.50 342.10 38 2 1952 645.50 2159.40 444.20 39 2 1953 641.00 2031.30 623.60 40 2 1954 459.30 2115.50 669.70 41 3 1935 33.10 1170.60 97.80 42 3 1936 45.00 2015.80 104.40 43 3 1937 77.20 2803.30 118.00 44 3 1938 44.60 2039.70 156.20 45 3 1939 48.10 2256.20 172.60 46 3 1940 74.40 2132.20 186.60 47 3 1941 113.00 1834.10 220.90 48 3 1942 91.90 1588.00 287.80 49 3 1943 61.30 1749.40 319.90 50 3 1944 56.80 1687.20 321.30 51 3 1945 93.60 2007.70 319.60 52 3 1946 159.90 2208.30 346.00 53 3 1947 147.20 1656.70 456.40 54 3 1948 146.30 1604.40 543.40 55 3 1949 98.30 1431.80 618.30 56 3 1950 93.50 1610.50 647.40 57 3 1951 135.20 1819.40 671.30 58 3 1952 157.30 2079.70 726.10 59 3 1953 179.50 2371.60 800.30 60 3 1954 189.60 2759.90 888.90 61 4 1935 40.29 417.50 10.50 62 4 1936 72.76 837.80 10.20 63 4 1937 66.26 883.90 34.70 64 4 1938 51.60 437.90 51.80 65 4 1939 52.41 679.70 64.30 66 4 1940 69.41 727.80 67.10 67 4 1941 68.35 643.60 75.20 68 4 1942 46.80 410.90 71.40 69 4 1943 47.40 588.40 67.10 70 4 1944 59.57 698.40 60.50 71 4 1945 88.78 846.40 54.60 72 4 1946 74.12 893.80 84.80 73 4 1947 62.68 579.00 96.80 74 4 1948 89.36 694.60 110.20 75 4 1949 78.98 590.30 147.40 76 4 1950 100.66 693.50 163.20 77 4 1951 160.62 809.00 203.50 78 4 1952 145.00 727.00 290.60 79 4 1953 174.93 1001.50 346.10 80 4 1954 172.49 703.20 414.90 81 5 1935 39.68 157.70 183.20 82 5 1936 50.73 167.90 204.00 83 5 1937 74.24 192.90 236.00 84 5 1938 53.51 156.70 291.70 85 5 1939 42.65 191.40 323.10 86 5 1940 46.48 185.50 344.00 87 5 1941 61.40 199.60 367.70 88 5 1942 39.67 189.50 407.20 89 5 1943 62.24 151.20 426.60 90 5 1944 52.32 187.70 470.00 91 5 1945 63.21 214.70 499.20 92 5 1946 59.37 232.90 534.60 93 5 1947 58.02 249.00 566.60 94 5 1948 70.34 224.50 595.30 95 5 1949 67.42 237.30 631.40 96 5 1950 55.74 240.10 662.30 97 5 1951 80.30 327.30 683.90 98 5 1952 85.40 359.40 729.30 99 5 1953 91.90 398.40 774.30 100 5 1954 81.43 365.70 804.90 101 6 1935 20.36 197.00 6.50 102 6 1936 25.98 210.30 15.80 103 6 1937 25.94 223.10 27.70 104 6 1938 27.53 216.70 39.20 105 6 1939 24.60 286.40 48.60 106 6 1940 28.54 298.00 52.50 107 6 1941 43.41 276.90 61.50 108 6 1942 42.81 272.60 80.50 109 6 1943 27.84 287.40 94.40 110 6 1944 32.60 330.30 92.60 111 6 1945 39.03 324.40 92.30 112 6 1946 50.17 401.90 94.20 113 6 1947 51.85 407.40 111.40 114 6 1948 64.03 409.20 127.40 115 6 1949 68.16 482.20 149.30 116 6 1950 77.34 673.80 164.40 117 6 1951 95.30 676.90 177.20 118 6 1952 99.49 702.00 200.00 119 6 1953 127.52 793.50 211.50 120 6 1954 135.72 927.30 238.70 121 7 1935 24.43 138.00 100.20 122 7 1936 23.21 200.10 125.00 123 7 1937 32.78 210.10 142.40 124 7 1938 32.54 161.20 165.10 125 7 1939 26.65 161.70 194.80 126 7 1940 33.71 145.10 222.90 127 7 1941 43.50 110.60 252.10 128 7 1942 34.46 98.10 276.30 129 7 1943 44.28 108.80 300.30 130 7 1944 70.80 118.20 318.20 131 7 1945 44.12 126.50 336.20 132 7 1946 48.98 156.70 351.20 133 7 1947 48.51 119.40 373.60 134 7 1948 50.00 129.10 389.40 135 7 1949 50.59 134.80 406.70 136 7 1950 42.53 140.80 429.50 137 7 1951 64.77 179.00 450.60 138 7 1952 72.68 178.10 466.90 139 7 1953 73.86 186.80 486.20 140 7 1954 89.51 192.70 511.30 141 8 1935 12.93 191.50 1.80 142 8 1936 25.90 516.00 0.80 143 8 1937 35.05 729.00 7.40 144 8 1938 22.89 560.40 18.10 145 8 1939 18.84 519.90 23.50 146 8 1940 28.57 628.50 26.50 147 8 1941 48.51 537.10 36.20 148 8 1942 43.34 561.20 60.80 149 8 1943 37.02 617.20 84.40 150 8 1944 37.81 626.70 91.20 151 8 1945 39.27 737.20 92.40 152 8 1946 53.46 760.50 86.00 153 8 1947 55.56 581.40 111.10 154 8 1948 49.56 662.30 130.60 155 8 1949 32.04 583.80 141.80 156 8 1950 32.24 635.20 136.70 157 8 1951 54.38 723.80 129.70 158 8 1952 71.78 864.10 145.50 159 8 1953 90.08 1193.50 174.80 160 8 1954 68.60 1188.90 213.50 161 9 1935 26.63 290.60 162.00 162 9 1936 23.39 291.10 174.00 163 9 1937 30.65 335.00 183.00 164 9 1938 20.89 246.00 198.00 165 9 1939 28.78 356.20 208.00 166 9 1940 26.93 289.80 223.00 167 9 1941 32.08 268.20 234.00 168 9 1942 32.21 213.30 248.00 169 9 1943 35.69 348.20 274.00 170 9 1944 62.47 374.20 282.00 171 9 1945 52.32 387.20 316.00 172 9 1946 56.95 347.40 302.00 173 9 1947 54.32 291.90 333.00 174 9 1948 40.53 297.20 359.00 175 9 1949 32.54 276.90 370.00 176 9 1950 43.48 274.60 376.00 177 9 1951 56.49 339.90 391.00 178 9 1952 65.98 474.80 414.00 179 9 1953 66.11 496.00 443.00 180 9 1954 49.34 474.50 468.00 181 10 1935 2.54 70.91 4.50 182 10 1936 2.00 87.94 4.71 183 10 1937 2.19 82.20 4.57 184 10 1938 1.99 58.72 4.56 185 10 1939 2.03 80.54 4.38 186 10 1940 1.81 86.47 4.21 187 10 1941 2.14 77.68 4.12 188 10 1942 1.86 62.16 3.83 189 10 1943 0.93 62.24 3.58 190 10 1944 1.18 61.82 3.41 191 10 1945 1.36 65.85 3.31 192 10 1946 2.24 69.54 3.23 193 10 1947 3.81 64.97 3.90 194 10 1948 5.66 68.00 5.38 195 10 1949 4.21 71.24 7.39 196 10 1950 3.42 69.05 8.74 197 10 1951 4.67 83.04 9.07 198 10 1952 6.00 74.42 9.93 199 10 1953 6.53 63.51 11.68 200 10 1954 5.12 58.12 14.33 > > > # argument balanced: > # First, make data non-consecutive and unbalanced > # by deletion of 2nd time period (year 1936) for all individuals > # and more time periods for first individual only > Grunfeld_unbalanced <- Grunfeld[Grunfeld$year != 1936, ] > Grunfeld_unbalanced <- Grunfeld_unbalanced[-c(1,4), ] > all(is.pconsecutive(Grunfeld_unbalanced)) # FALSE [1] FALSE > pdim(Grunfeld_unbalanced)$balanced # FALSE [1] FALSE > > g_consec_bal <- make.pconsecutive(Grunfeld_unbalanced, balanced = TRUE) > all(is.pconsecutive(g_consec_bal)) # TRUE [1] TRUE > pdim(g_consec_bal)$balanced # TRUE [1] TRUE > nrow(g_consec_bal) # 200 rows [1] 200 > head(g_consec_bal) # 1st individual: years 1935, 1936, 1939 are NA firm year inv value capital 1 1 1935 NA NA NA 2 1 1936 NA NA NA 3 1 1937 410.6 5387.1 156.9 4 1 1938 257.7 2792.2 209.2 5 1 1939 NA NA NA 6 1 1940 461.2 4643.9 207.2 > > g_consec <- make.pconsecutive(Grunfeld_unbalanced) # default: balanced = FALSE > all(is.pconsecutive(g_consec)) # TRUE [1] TRUE > pdim(g_consec)$balanced # FALSE [1] FALSE > nrow(g_consec) # 198 rows [1] 198 > head(g_consec) # 1st individual: years 1935, 1936 dropped, 1939 is NA firm year inv value capital 1 1 1937 410.6 5387.1 156.9 2 1 1938 257.7 2792.2 209.2 3 1 1939 NA NA NA 4 1 1940 461.2 4643.9 207.2 5 1 1941 512.0 4551.2 255.2 6 1 1942 448.0 3244.1 303.7 > > > # NA in 1st, 3rd time period (years 1935, 1937) for first individual > Grunfeld_NA <- Grunfeld > Grunfeld_NA[c(1, 3), "year"] <- NA > g_NA <- make.pconsecutive(Grunfeld_NA) > head(g_NA) # 1936 is begin for 1st individual, 1937: NA for non-index vars firm year inv value capital 1 1 1936 391.8 4661.7 52.6 2 1 1937 NA NA NA 3 1 1938 257.7 2792.2 209.2 4 1 1939 330.8 4313.2 203.4 5 1 1940 461.2 4643.9 207.2 6 1 1941 512.0 4551.2 255.2 > nrow(g_NA) # 199, year 1935 from original data is dropped [1] 199 > > > # pdata.frame interface > pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period) > make.pconsecutive(Grunfeld_missing_period) firm year inv value capital 1 1 1935 317.60 3078.50 2.80 2 1 1936 NA NA NA 3 1 1937 410.60 5387.10 156.90 4 1 1938 257.70 2792.20 209.20 5 1 1939 330.80 4313.20 203.40 6 1 1940 461.20 4643.90 207.20 7 1 1941 512.00 4551.20 255.20 8 1 1942 448.00 3244.10 303.70 9 1 1943 499.60 4053.70 264.10 10 1 1944 547.50 4379.30 201.60 11 1 1945 561.20 4840.90 265.00 12 1 1946 688.10 4900.90 402.20 13 1 1947 568.90 3526.50 761.50 14 1 1948 529.20 3254.70 922.40 15 1 1949 555.10 3700.20 1020.10 16 1 1950 642.90 3755.60 1099.00 17 1 1951 755.90 4833.00 1207.70 18 1 1952 891.20 4924.90 1430.50 19 1 1953 1304.40 6241.70 1777.30 20 1 1954 1486.70 5593.60 2226.30 21 2 1935 209.90 1362.40 53.80 22 2 1936 355.30 1807.10 50.50 23 2 1937 469.90 2676.30 118.10 24 2 1938 262.30 1801.90 260.20 25 2 1939 230.40 1957.30 312.70 26 2 1940 361.60 2202.90 254.20 27 2 1941 472.80 2380.50 261.40 28 2 1942 445.60 2168.60 298.70 29 2 1943 361.60 1985.10 301.80 30 2 1944 288.20 1813.90 279.10 31 2 1945 258.70 1850.20 213.80 32 2 1946 420.30 2067.70 132.60 33 2 1947 420.50 1796.70 264.80 34 2 1948 494.50 1625.80 306.90 35 2 1949 405.10 1667.00 351.10 36 2 1950 418.80 1677.40 357.80 37 2 1951 588.20 2289.50 342.10 38 2 1952 645.50 2159.40 444.20 39 2 1953 641.00 2031.30 623.60 40 2 1954 459.30 2115.50 669.70 41 3 1935 33.10 1170.60 97.80 42 3 1936 45.00 2015.80 104.40 43 3 1937 77.20 2803.30 118.00 44 3 1938 44.60 2039.70 156.20 45 3 1939 48.10 2256.20 172.60 46 3 1940 74.40 2132.20 186.60 47 3 1941 113.00 1834.10 220.90 48 3 1942 91.90 1588.00 287.80 49 3 1943 61.30 1749.40 319.90 50 3 1944 56.80 1687.20 321.30 51 3 1945 93.60 2007.70 319.60 52 3 1946 159.90 2208.30 346.00 53 3 1947 147.20 1656.70 456.40 54 3 1948 146.30 1604.40 543.40 55 3 1949 98.30 1431.80 618.30 56 3 1950 93.50 1610.50 647.40 57 3 1951 135.20 1819.40 671.30 58 3 1952 157.30 2079.70 726.10 59 3 1953 179.50 2371.60 800.30 60 3 1954 189.60 2759.90 888.90 61 4 1935 40.29 417.50 10.50 62 4 1936 72.76 837.80 10.20 63 4 1937 66.26 883.90 34.70 64 4 1938 51.60 437.90 51.80 65 4 1939 52.41 679.70 64.30 66 4 1940 69.41 727.80 67.10 67 4 1941 68.35 643.60 75.20 68 4 1942 46.80 410.90 71.40 69 4 1943 47.40 588.40 67.10 70 4 1944 59.57 698.40 60.50 71 4 1945 88.78 846.40 54.60 72 4 1946 74.12 893.80 84.80 73 4 1947 62.68 579.00 96.80 74 4 1948 89.36 694.60 110.20 75 4 1949 78.98 590.30 147.40 76 4 1950 100.66 693.50 163.20 77 4 1951 160.62 809.00 203.50 78 4 1952 145.00 727.00 290.60 79 4 1953 174.93 1001.50 346.10 80 4 1954 172.49 703.20 414.90 81 5 1935 39.68 157.70 183.20 82 5 1936 50.73 167.90 204.00 83 5 1937 74.24 192.90 236.00 84 5 1938 53.51 156.70 291.70 85 5 1939 42.65 191.40 323.10 86 5 1940 46.48 185.50 344.00 87 5 1941 61.40 199.60 367.70 88 5 1942 39.67 189.50 407.20 89 5 1943 62.24 151.20 426.60 90 5 1944 52.32 187.70 470.00 91 5 1945 63.21 214.70 499.20 92 5 1946 59.37 232.90 534.60 93 5 1947 58.02 249.00 566.60 94 5 1948 70.34 224.50 595.30 95 5 1949 67.42 237.30 631.40 96 5 1950 55.74 240.10 662.30 97 5 1951 80.30 327.30 683.90 98 5 1952 85.40 359.40 729.30 99 5 1953 91.90 398.40 774.30 100 5 1954 81.43 365.70 804.90 101 6 1935 20.36 197.00 6.50 102 6 1936 25.98 210.30 15.80 103 6 1937 25.94 223.10 27.70 104 6 1938 27.53 216.70 39.20 105 6 1939 24.60 286.40 48.60 106 6 1940 28.54 298.00 52.50 107 6 1941 43.41 276.90 61.50 108 6 1942 42.81 272.60 80.50 109 6 1943 27.84 287.40 94.40 110 6 1944 32.60 330.30 92.60 111 6 1945 39.03 324.40 92.30 112 6 1946 50.17 401.90 94.20 113 6 1947 51.85 407.40 111.40 114 6 1948 64.03 409.20 127.40 115 6 1949 68.16 482.20 149.30 116 6 1950 77.34 673.80 164.40 117 6 1951 95.30 676.90 177.20 118 6 1952 99.49 702.00 200.00 119 6 1953 127.52 793.50 211.50 120 6 1954 135.72 927.30 238.70 121 7 1935 24.43 138.00 100.20 122 7 1936 23.21 200.10 125.00 123 7 1937 32.78 210.10 142.40 124 7 1938 32.54 161.20 165.10 125 7 1939 26.65 161.70 194.80 126 7 1940 33.71 145.10 222.90 127 7 1941 43.50 110.60 252.10 128 7 1942 34.46 98.10 276.30 129 7 1943 44.28 108.80 300.30 130 7 1944 70.80 118.20 318.20 131 7 1945 44.12 126.50 336.20 132 7 1946 48.98 156.70 351.20 133 7 1947 48.51 119.40 373.60 134 7 1948 50.00 129.10 389.40 135 7 1949 50.59 134.80 406.70 136 7 1950 42.53 140.80 429.50 137 7 1951 64.77 179.00 450.60 138 7 1952 72.68 178.10 466.90 139 7 1953 73.86 186.80 486.20 140 7 1954 89.51 192.70 511.30 141 8 1935 12.93 191.50 1.80 142 8 1936 25.90 516.00 0.80 143 8 1937 35.05 729.00 7.40 144 8 1938 22.89 560.40 18.10 145 8 1939 18.84 519.90 23.50 146 8 1940 28.57 628.50 26.50 147 8 1941 48.51 537.10 36.20 148 8 1942 43.34 561.20 60.80 149 8 1943 37.02 617.20 84.40 150 8 1944 37.81 626.70 91.20 151 8 1945 39.27 737.20 92.40 152 8 1946 53.46 760.50 86.00 153 8 1947 55.56 581.40 111.10 154 8 1948 49.56 662.30 130.60 155 8 1949 32.04 583.80 141.80 156 8 1950 32.24 635.20 136.70 157 8 1951 54.38 723.80 129.70 158 8 1952 71.78 864.10 145.50 159 8 1953 90.08 1193.50 174.80 160 8 1954 68.60 1188.90 213.50 161 9 1935 26.63 290.60 162.00 162 9 1936 23.39 291.10 174.00 163 9 1937 30.65 335.00 183.00 164 9 1938 20.89 246.00 198.00 165 9 1939 28.78 356.20 208.00 166 9 1940 26.93 289.80 223.00 167 9 1941 32.08 268.20 234.00 168 9 1942 32.21 213.30 248.00 169 9 1943 35.69 348.20 274.00 170 9 1944 62.47 374.20 282.00 171 9 1945 52.32 387.20 316.00 172 9 1946 56.95 347.40 302.00 173 9 1947 54.32 291.90 333.00 174 9 1948 40.53 297.20 359.00 175 9 1949 32.54 276.90 370.00 176 9 1950 43.48 274.60 376.00 177 9 1951 56.49 339.90 391.00 178 9 1952 65.98 474.80 414.00 179 9 1953 66.11 496.00 443.00 180 9 1954 49.34 474.50 468.00 181 10 1935 2.54 70.91 4.50 182 10 1936 2.00 87.94 4.71 183 10 1937 2.19 82.20 4.57 184 10 1938 1.99 58.72 4.56 185 10 1939 2.03 80.54 4.38 186 10 1940 1.81 86.47 4.21 187 10 1941 2.14 77.68 4.12 188 10 1942 1.86 62.16 3.83 189 10 1943 0.93 62.24 3.58 190 10 1944 1.18 61.82 3.41 191 10 1945 1.36 65.85 3.31 192 10 1946 2.24 69.54 3.23 193 10 1947 3.81 64.97 3.90 194 10 1948 5.66 68.00 5.38 195 10 1949 4.21 71.24 7.39 196 10 1950 3.42 69.05 8.74 197 10 1951 4.67 83.04 9.07 198 10 1952 6.00 74.42 9.93 199 10 1953 6.53 63.51 11.68 200 10 1954 5.12 58.12 14.33 > > > # pseries interface > make.pconsecutive(pGrunfeld_missing_period$inv) 1-1935 1-1936 1-1937 1-1938 1-1939 1-1940 1-1941 1-1942 1-1943 1-1944 317.60 NA 410.60 257.70 330.80 461.20 512.00 448.00 499.60 547.50 1-1945 1-1946 1-1947 1-1948 1-1949 1-1950 1-1951 1-1952 1-1953 1-1954 561.20 688.10 568.90 529.20 555.10 642.90 755.90 891.20 1304.40 1486.70 2-1935 2-1936 2-1937 2-1938 2-1939 2-1940 2-1941 2-1942 2-1943 2-1944 209.90 355.30 469.90 262.30 230.40 361.60 472.80 445.60 361.60 288.20 2-1945 2-1946 2-1947 2-1948 2-1949 2-1950 2-1951 2-1952 2-1953 2-1954 258.70 420.30 420.50 494.50 405.10 418.80 588.20 645.50 641.00 459.30 3-1935 3-1936 3-1937 3-1938 3-1939 3-1940 3-1941 3-1942 3-1943 3-1944 33.10 45.00 77.20 44.60 48.10 74.40 113.00 91.90 61.30 56.80 3-1945 3-1946 3-1947 3-1948 3-1949 3-1950 3-1951 3-1952 3-1953 3-1954 93.60 159.90 147.20 146.30 98.30 93.50 135.20 157.30 179.50 189.60 4-1935 4-1936 4-1937 4-1938 4-1939 4-1940 4-1941 4-1942 4-1943 4-1944 40.29 72.76 66.26 51.60 52.41 69.41 68.35 46.80 47.40 59.57 4-1945 4-1946 4-1947 4-1948 4-1949 4-1950 4-1951 4-1952 4-1953 4-1954 88.78 74.12 62.68 89.36 78.98 100.66 160.62 145.00 174.93 172.49 5-1935 5-1936 5-1937 5-1938 5-1939 5-1940 5-1941 5-1942 5-1943 5-1944 39.68 50.73 74.24 53.51 42.65 46.48 61.40 39.67 62.24 52.32 5-1945 5-1946 5-1947 5-1948 5-1949 5-1950 5-1951 5-1952 5-1953 5-1954 63.21 59.37 58.02 70.34 67.42 55.74 80.30 85.40 91.90 81.43 6-1935 6-1936 6-1937 6-1938 6-1939 6-1940 6-1941 6-1942 6-1943 6-1944 20.36 25.98 25.94 27.53 24.60 28.54 43.41 42.81 27.84 32.60 6-1945 6-1946 6-1947 6-1948 6-1949 6-1950 6-1951 6-1952 6-1953 6-1954 39.03 50.17 51.85 64.03 68.16 77.34 95.30 99.49 127.52 135.72 7-1935 7-1936 7-1937 7-1938 7-1939 7-1940 7-1941 7-1942 7-1943 7-1944 24.43 23.21 32.78 32.54 26.65 33.71 43.50 34.46 44.28 70.80 7-1945 7-1946 7-1947 7-1948 7-1949 7-1950 7-1951 7-1952 7-1953 7-1954 44.12 48.98 48.51 50.00 50.59 42.53 64.77 72.68 73.86 89.51 8-1935 8-1936 8-1937 8-1938 8-1939 8-1940 8-1941 8-1942 8-1943 8-1944 12.93 25.90 35.05 22.89 18.84 28.57 48.51 43.34 37.02 37.81 8-1945 8-1946 8-1947 8-1948 8-1949 8-1950 8-1951 8-1952 8-1953 8-1954 39.27 53.46 55.56 49.56 32.04 32.24 54.38 71.78 90.08 68.60 9-1935 9-1936 9-1937 9-1938 9-1939 9-1940 9-1941 9-1942 9-1943 9-1944 26.63 23.39 30.65 20.89 28.78 26.93 32.08 32.21 35.69 62.47 9-1945 9-1946 9-1947 9-1948 9-1949 9-1950 9-1951 9-1952 9-1953 9-1954 52.32 56.95 54.32 40.53 32.54 43.48 56.49 65.98 66.11 49.34 10-1935 10-1936 10-1937 10-1938 10-1939 10-1940 10-1941 10-1942 10-1943 10-1944 2.54 2.00 2.19 1.99 2.03 1.81 2.14 1.86 0.93 1.18 10-1945 10-1946 10-1947 10-1948 10-1949 10-1950 10-1951 10-1952 10-1953 10-1954 1.36 2.24 3.81 5.66 4.21 3.42 4.67 6.00 6.53 5.12 > > > # comparison to make.pbalanced (makes the data only balanced, not consecutive) > g_bal <- make.pbalanced(Grunfeld_unbalanced) > all(is.pconsecutive(g_bal)) # FALSE [1] FALSE > pdim(g_bal)$balanced # TRUE [1] TRUE > nrow(g_bal) # 190 rows [1] 190 > > > > > cleanEx() > nameEx("model.frame.pdata.frame") > ### * model.frame.pdata.frame > > flush(stderr()); flush(stdout()) > > ### Name: model.frame.pdata.frame > ### Title: model.frame and model.matrix for panel data > ### Aliases: model.frame.pdata.frame formula.pdata.frame model.matrix.plm > ### model.matrix.pdata.frame > ### Keywords: classes > > ### ** Examples > > > # First, make a pdata.frame > data("Grunfeld", package = "plm") > pGrunfeld <- pdata.frame(Grunfeld) > > # then make a model frame from a pFormula and a pdata.frame > #pform <- pFormula(inv ~ value + capital) > #mf <- model.frame(pform, data = pGrunfeld) > form <- inv ~ value > mf <- model.frame(pGrunfeld, form) > > # then construct the (transformed) model matrix (design matrix) > # from formula and model frame > #modmat <- model.matrix(pform, data = mf, model = "within") > modmat <- model.matrix(mf, model = "within") > > ## retrieve model frame and model matrix from an estimated plm object > #fe_model <- plm(pform, data = pGrunfeld, model = "within") > fe_model <- plm(form, data = pGrunfeld, model = "within") > model.frame(fe_model) inv value 1-1935 317.60 3078.50 1-1936 391.80 4661.70 1-1937 410.60 5387.10 1-1938 257.70 2792.20 1-1939 330.80 4313.20 1-1940 461.20 4643.90 1-1941 512.00 4551.20 1-1942 448.00 3244.10 1-1943 499.60 4053.70 1-1944 547.50 4379.30 1-1945 561.20 4840.90 1-1946 688.10 4900.90 1-1947 568.90 3526.50 1-1948 529.20 3254.70 1-1949 555.10 3700.20 1-1950 642.90 3755.60 1-1951 755.90 4833.00 1-1952 891.20 4924.90 1-1953 1304.40 6241.70 1-1954 1486.70 5593.60 2-1935 209.90 1362.40 2-1936 355.30 1807.10 2-1937 469.90 2676.30 2-1938 262.30 1801.90 2-1939 230.40 1957.30 2-1940 361.60 2202.90 2-1941 472.80 2380.50 2-1942 445.60 2168.60 2-1943 361.60 1985.10 2-1944 288.20 1813.90 2-1945 258.70 1850.20 2-1946 420.30 2067.70 2-1947 420.50 1796.70 2-1948 494.50 1625.80 2-1949 405.10 1667.00 2-1950 418.80 1677.40 2-1951 588.20 2289.50 2-1952 645.50 2159.40 2-1953 641.00 2031.30 2-1954 459.30 2115.50 3-1935 33.10 1170.60 3-1936 45.00 2015.80 3-1937 77.20 2803.30 3-1938 44.60 2039.70 3-1939 48.10 2256.20 3-1940 74.40 2132.20 3-1941 113.00 1834.10 3-1942 91.90 1588.00 3-1943 61.30 1749.40 3-1944 56.80 1687.20 3-1945 93.60 2007.70 3-1946 159.90 2208.30 3-1947 147.20 1656.70 3-1948 146.30 1604.40 3-1949 98.30 1431.80 3-1950 93.50 1610.50 3-1951 135.20 1819.40 3-1952 157.30 2079.70 3-1953 179.50 2371.60 3-1954 189.60 2759.90 4-1935 40.29 417.50 4-1936 72.76 837.80 4-1937 66.26 883.90 4-1938 51.60 437.90 4-1939 52.41 679.70 4-1940 69.41 727.80 4-1941 68.35 643.60 4-1942 46.80 410.90 4-1943 47.40 588.40 4-1944 59.57 698.40 4-1945 88.78 846.40 4-1946 74.12 893.80 4-1947 62.68 579.00 4-1948 89.36 694.60 4-1949 78.98 590.30 4-1950 100.66 693.50 4-1951 160.62 809.00 4-1952 145.00 727.00 4-1953 174.93 1001.50 4-1954 172.49 703.20 5-1935 39.68 157.70 5-1936 50.73 167.90 5-1937 74.24 192.90 5-1938 53.51 156.70 5-1939 42.65 191.40 5-1940 46.48 185.50 5-1941 61.40 199.60 5-1942 39.67 189.50 5-1943 62.24 151.20 5-1944 52.32 187.70 5-1945 63.21 214.70 5-1946 59.37 232.90 5-1947 58.02 249.00 5-1948 70.34 224.50 5-1949 67.42 237.30 5-1950 55.74 240.10 5-1951 80.30 327.30 5-1952 85.40 359.40 5-1953 91.90 398.40 5-1954 81.43 365.70 6-1935 20.36 197.00 6-1936 25.98 210.30 6-1937 25.94 223.10 6-1938 27.53 216.70 6-1939 24.60 286.40 6-1940 28.54 298.00 6-1941 43.41 276.90 6-1942 42.81 272.60 6-1943 27.84 287.40 6-1944 32.60 330.30 6-1945 39.03 324.40 6-1946 50.17 401.90 6-1947 51.85 407.40 6-1948 64.03 409.20 6-1949 68.16 482.20 6-1950 77.34 673.80 6-1951 95.30 676.90 6-1952 99.49 702.00 6-1953 127.52 793.50 6-1954 135.72 927.30 7-1935 24.43 138.00 7-1936 23.21 200.10 7-1937 32.78 210.10 7-1938 32.54 161.20 7-1939 26.65 161.70 7-1940 33.71 145.10 7-1941 43.50 110.60 7-1942 34.46 98.10 7-1943 44.28 108.80 7-1944 70.80 118.20 7-1945 44.12 126.50 7-1946 48.98 156.70 7-1947 48.51 119.40 7-1948 50.00 129.10 7-1949 50.59 134.80 7-1950 42.53 140.80 7-1951 64.77 179.00 7-1952 72.68 178.10 7-1953 73.86 186.80 7-1954 89.51 192.70 8-1935 12.93 191.50 8-1936 25.90 516.00 8-1937 35.05 729.00 8-1938 22.89 560.40 8-1939 18.84 519.90 8-1940 28.57 628.50 8-1941 48.51 537.10 8-1942 43.34 561.20 8-1943 37.02 617.20 8-1944 37.81 626.70 8-1945 39.27 737.20 8-1946 53.46 760.50 8-1947 55.56 581.40 8-1948 49.56 662.30 8-1949 32.04 583.80 8-1950 32.24 635.20 8-1951 54.38 723.80 8-1952 71.78 864.10 8-1953 90.08 1193.50 8-1954 68.60 1188.90 9-1935 26.63 290.60 9-1936 23.39 291.10 9-1937 30.65 335.00 9-1938 20.89 246.00 9-1939 28.78 356.20 9-1940 26.93 289.80 9-1941 32.08 268.20 9-1942 32.21 213.30 9-1943 35.69 348.20 9-1944 62.47 374.20 9-1945 52.32 387.20 9-1946 56.95 347.40 9-1947 54.32 291.90 9-1948 40.53 297.20 9-1949 32.54 276.90 9-1950 43.48 274.60 9-1951 56.49 339.90 9-1952 65.98 474.80 9-1953 66.11 496.00 9-1954 49.34 474.50 10-1935 2.54 70.91 10-1936 2.00 87.94 10-1937 2.19 82.20 10-1938 1.99 58.72 10-1939 2.03 80.54 10-1940 1.81 86.47 10-1941 2.14 77.68 10-1942 1.86 62.16 10-1943 0.93 62.24 10-1944 1.18 61.82 10-1945 1.36 65.85 10-1946 2.24 69.54 10-1947 3.81 64.97 10-1948 5.66 68.00 10-1949 4.21 71.24 10-1950 3.42 69.05 10-1951 4.67 83.04 10-1952 6.00 74.42 10-1953 6.53 63.51 10-1954 5.12 58.12 > model.matrix(fe_model) value 1-1935 -1255.345 1-1936 327.855 1-1937 1053.255 1-1938 -1541.645 1-1939 -20.645 1-1940 310.055 1-1941 217.355 1-1942 -1089.745 1-1943 -280.145 1-1944 45.455 1-1945 507.055 1-1946 567.055 1-1947 -807.345 1-1948 -1079.145 1-1949 -633.645 1-1950 -578.245 1-1951 499.155 1-1952 591.055 1-1953 1907.855 1-1954 1259.755 2-1935 -609.425 2-1936 -164.725 2-1937 704.475 2-1938 -169.925 2-1939 -14.525 2-1940 231.075 2-1941 408.675 2-1942 196.775 2-1943 13.275 2-1944 -157.925 2-1945 -121.625 2-1946 95.875 2-1947 -175.125 2-1948 -346.025 2-1949 -304.825 2-1950 -294.425 2-1951 317.675 2-1952 187.575 2-1953 59.475 2-1954 143.675 3-1935 -770.725 3-1936 74.475 3-1937 861.975 3-1938 98.375 3-1939 314.875 3-1940 190.875 3-1941 -107.225 3-1942 -353.325 3-1943 -191.925 3-1944 -254.125 3-1945 66.375 3-1946 266.975 3-1947 -284.625 3-1948 -336.925 3-1949 -509.525 3-1950 -330.825 3-1951 -121.925 3-1952 138.375 3-1953 430.275 3-1954 818.575 4-1935 -275.710 4-1936 144.590 4-1937 190.690 4-1938 -255.310 4-1939 -13.510 4-1940 34.590 4-1941 -49.610 4-1942 -282.310 4-1943 -104.810 4-1944 5.190 4-1945 153.190 4-1946 200.590 4-1947 -114.210 4-1948 1.390 4-1949 -102.910 4-1950 0.290 4-1951 115.790 4-1952 33.790 4-1953 308.290 4-1954 9.990 5-1935 -73.770 5-1936 -63.570 5-1937 -38.570 5-1938 -74.770 5-1939 -40.070 5-1940 -45.970 5-1941 -31.870 5-1942 -41.970 5-1943 -80.270 5-1944 -43.770 5-1945 -16.770 5-1946 1.430 5-1947 17.530 5-1948 -6.970 5-1949 5.830 5-1950 8.630 5-1951 95.830 5-1952 127.930 5-1953 166.930 5-1954 134.230 6-1935 -222.865 6-1936 -209.565 6-1937 -196.765 6-1938 -203.165 6-1939 -133.465 6-1940 -121.865 6-1941 -142.965 6-1942 -147.265 6-1943 -132.465 6-1944 -89.565 6-1945 -95.465 6-1946 -17.965 6-1947 -12.465 6-1948 -10.665 6-1949 62.335 6-1950 253.935 6-1951 257.035 6-1952 282.135 6-1953 373.635 6-1954 507.435 7-1935 -11.790 7-1936 50.310 7-1937 60.310 7-1938 11.410 7-1939 11.910 7-1940 -4.690 7-1941 -39.190 7-1942 -51.690 7-1943 -40.990 7-1944 -31.590 7-1945 -23.290 7-1946 6.910 7-1947 -30.390 7-1948 -20.690 7-1949 -14.990 7-1950 -8.990 7-1951 29.210 7-1952 28.310 7-1953 37.010 7-1954 42.910 8-1935 -479.410 8-1936 -154.910 8-1937 58.090 8-1938 -110.510 8-1939 -151.010 8-1940 -42.410 8-1941 -133.810 8-1942 -109.710 8-1943 -53.710 8-1944 -44.210 8-1945 66.290 8-1946 89.590 8-1947 -89.510 8-1948 -8.610 8-1949 -87.110 8-1950 -35.710 8-1951 52.890 8-1952 193.190 8-1953 522.590 8-1954 517.990 9-1935 -43.050 9-1936 -42.550 9-1937 1.350 9-1938 -87.650 9-1939 22.550 9-1940 -43.850 9-1941 -65.450 9-1942 -120.350 9-1943 14.550 9-1944 40.550 9-1945 53.550 9-1946 13.750 9-1947 -41.750 9-1948 -36.450 9-1949 -56.750 9-1950 -59.050 9-1951 6.250 9-1952 141.150 9-1953 162.350 9-1954 140.850 10-1935 -0.011 10-1936 17.019 10-1937 11.279 10-1938 -12.201 10-1939 9.619 10-1940 15.549 10-1941 6.759 10-1942 -8.761 10-1943 -8.681 10-1944 -9.101 10-1945 -5.071 10-1946 -1.381 10-1947 -5.951 10-1948 -2.921 10-1949 0.319 10-1950 -1.871 10-1951 12.119 10-1952 3.499 10-1953 -7.411 10-1954 -12.801 attr(,"assign") [1] 0 1 attr(,"index") firm year 1 1 1935 2 1 1936 3 1 1937 4 1 1938 5 1 1939 6 1 1940 7 1 1941 8 1 1942 9 1 1943 10 1 1944 11 1 1945 12 1 1946 13 1 1947 14 1 1948 15 1 1949 16 1 1950 17 1 1951 18 1 1952 19 1 1953 20 1 1954 21 2 1935 22 2 1936 23 2 1937 24 2 1938 25 2 1939 26 2 1940 27 2 1941 28 2 1942 29 2 1943 30 2 1944 31 2 1945 32 2 1946 33 2 1947 34 2 1948 35 2 1949 36 2 1950 37 2 1951 38 2 1952 39 2 1953 40 2 1954 41 3 1935 42 3 1936 43 3 1937 44 3 1938 45 3 1939 46 3 1940 47 3 1941 48 3 1942 49 3 1943 50 3 1944 51 3 1945 52 3 1946 53 3 1947 54 3 1948 55 3 1949 56 3 1950 57 3 1951 58 3 1952 59 3 1953 60 3 1954 61 4 1935 62 4 1936 63 4 1937 64 4 1938 65 4 1939 66 4 1940 67 4 1941 68 4 1942 69 4 1943 70 4 1944 71 4 1945 72 4 1946 73 4 1947 74 4 1948 75 4 1949 76 4 1950 77 4 1951 78 4 1952 79 4 1953 80 4 1954 81 5 1935 82 5 1936 83 5 1937 84 5 1938 85 5 1939 86 5 1940 87 5 1941 88 5 1942 89 5 1943 90 5 1944 91 5 1945 92 5 1946 93 5 1947 94 5 1948 95 5 1949 96 5 1950 97 5 1951 98 5 1952 99 5 1953 100 5 1954 101 6 1935 102 6 1936 103 6 1937 104 6 1938 105 6 1939 106 6 1940 107 6 1941 108 6 1942 109 6 1943 110 6 1944 111 6 1945 112 6 1946 113 6 1947 114 6 1948 115 6 1949 116 6 1950 117 6 1951 118 6 1952 119 6 1953 120 6 1954 121 7 1935 122 7 1936 123 7 1937 124 7 1938 125 7 1939 126 7 1940 127 7 1941 128 7 1942 129 7 1943 130 7 1944 131 7 1945 132 7 1946 133 7 1947 134 7 1948 135 7 1949 136 7 1950 137 7 1951 138 7 1952 139 7 1953 140 7 1954 141 8 1935 142 8 1936 143 8 1937 144 8 1938 145 8 1939 146 8 1940 147 8 1941 148 8 1942 149 8 1943 150 8 1944 151 8 1945 152 8 1946 153 8 1947 154 8 1948 155 8 1949 156 8 1950 157 8 1951 158 8 1952 159 8 1953 160 8 1954 161 9 1935 162 9 1936 163 9 1937 164 9 1938 165 9 1939 166 9 1940 167 9 1941 168 9 1942 169 9 1943 170 9 1944 171 9 1945 172 9 1946 173 9 1947 174 9 1948 175 9 1949 176 9 1950 177 9 1951 178 9 1952 179 9 1953 180 9 1954 181 10 1935 182 10 1936 183 10 1937 184 10 1938 185 10 1939 186 10 1940 187 10 1941 188 10 1942 189 10 1943 190 10 1944 191 10 1945 192 10 1946 193 10 1947 194 10 1948 195 10 1949 196 10 1950 197 10 1951 198 10 1952 199 10 1953 200 10 1954 > > # same as constructed before > all.equal(mf, model.frame(fe_model), check.attributes = FALSE) # TRUE [1] TRUE > all.equal(modmat, model.matrix(fe_model), check.attributes = FALSE) # TRUE [1] TRUE > > > > > cleanEx() > nameEx("mtest") > ### * mtest > > flush(stderr()); flush(stdout()) > > ### Name: mtest > ### Title: Arellano-Bond test of Serial Correlation > ### Aliases: mtest > ### Keywords: htest > > ### ** Examples > > > data("EmplUK", package = "plm") > ar <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + + lag(log(capital), 0:2) + lag(log(output), 0:2) | lag(log(emp), 2:99), + data = EmplUK, effect = "twoways", model = "twosteps") > mtest(ar, order = 1) Autocorrelation test of degree 1 data: log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + lag(log(capital), ... normal = -2.9998, p-value = 0.002702 > mtest(ar, order = 2, vcov = vcovHC) Autocorrelation test of degree 2 data: log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + lag(log(capital), ... normal = -0.36744, p-value = 0.7133 > > > > > cleanEx() > nameEx("nobs.plm") > ### * nobs.plm > > flush(stderr()); flush(stdout()) > > ### Name: nobs.plm > ### Title: Extract Total Number of Observations Used in Estimated > ### Panelmodel > ### Aliases: nobs.plm nobs.panelmodel nobs.pgmm > ### Keywords: attribute > > ### ** Examples > > > # estimate a panelmodel > data("Produc", package = "plm") > z <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=Produc, + model="random", subset = gsp > 5000) > > nobs(z) # total observations used in estimation [1] 808 > pdim(z)$nT$N # same information [1] 808 > pdim(z) # more information about the dimensions (no. of individuals and time periods) Unbalanced Panel: n = 48, T = 9-17, N = 808 > > # illustrate difference between nobs and pdim for first-difference model > data("Grunfeld", package = "plm") > fdmod <- plm(inv ~ value + capital, data = Grunfeld, model = "fd") > nobs(fdmod) # 190 [1] 190 > pdim(fdmod)$nT$N # 200 [1] 200 > > > > > cleanEx() > nameEx("pFtest") > ### * pFtest > > flush(stderr()); flush(stdout()) > > ### Name: pFtest > ### Title: F Test for Individual and/or Time Effects > ### Aliases: pFtest pFtest.formula pFtest.plm > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package="plm") > gp <- plm(inv ~ value + capital, data = Grunfeld, model = "pooling") > gi <- plm(inv ~ value + capital, data = Grunfeld, + effect = "individual", model = "within") > gt <- plm(inv ~ value + capital, data = Grunfeld, + effect = "time", model = "within") > gd <- plm(inv ~ value + capital, data = Grunfeld, + effect = "twoways", model = "within") > pFtest(gi, gp) F test for individual effects data: inv ~ value + capital F = 49.177, df1 = 9, df2 = 188, p-value < 2.2e-16 alternative hypothesis: significant effects > pFtest(gt, gp) F test for time effects data: inv ~ value + capital F = 0.23451, df1 = 19, df2 = 178, p-value = 0.9997 alternative hypothesis: significant effects > pFtest(gd, gp) F test for twoways effects data: inv ~ value + capital F = 17.403, df1 = 28, df2 = 169, p-value < 2.2e-16 alternative hypothesis: significant effects > pFtest(inv ~ value + capital, data = Grunfeld, effect = "twoways") F test for twoways effects data: inv ~ value + capital F = 17.403, df1 = 28, df2 = 169, p-value < 2.2e-16 alternative hypothesis: significant effects > > > > > cleanEx() > nameEx("pbgtest") > ### * pbgtest > > flush(stderr()); flush(stdout()) > > ### Name: pbgtest > ### Title: Breusch-Godfrey Test for Panel Models > ### Aliases: pbgtest pbgtest.panelmodel pbgtest.formula > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > g <- plm(inv ~ value + capital, data = Grunfeld, model = "random") > > # panelmodel interface > pbgtest(g) Breusch-Godfrey/Wooldridge test for serial correlation in panel models data: inv ~ value + capital chisq = 69.95, df = 20, p-value = 1.856e-07 alternative hypothesis: serial correlation in idiosyncratic errors > pbgtest(g, order = 4) Breusch-Godfrey/Wooldridge test for serial correlation in panel models data: inv ~ value + capital chisq = 50.991, df = 4, p-value = 2.242e-10 alternative hypothesis: serial correlation in idiosyncratic errors > > # formula interface > pbgtest(inv ~ value + capital, data = Grunfeld, model = "random") Breusch-Godfrey/Wooldridge test for serial correlation in panel models data: inv ~ value + capital chisq = 69.95, df = 20, p-value = 1.856e-07 alternative hypothesis: serial correlation in idiosyncratic errors > > # F test statistic (instead of default type="Chisq") > pbgtest(g, type="F") Breusch-Godfrey/Wooldridge test for serial correlation in panel models data: inv ~ value + capital F = 4.7601, df1 = 20, df2 = 177, p-value = 4.12e-09 alternative hypothesis: serial correlation in idiosyncratic errors > pbgtest(inv ~ value + capital, data = Grunfeld, model = "random", type = "F") Breusch-Godfrey/Wooldridge test for serial correlation in panel models data: inv ~ value + capital F = 4.7601, df1 = 20, df2 = 177, p-value = 4.12e-09 alternative hypothesis: serial correlation in idiosyncratic errors > > > > > cleanEx() > nameEx("pbltest") > ### * pbltest > > flush(stderr()); flush(stdout()) > > ### Name: pbltest > ### Title: Baltagi and Li Serial Dependence Test For Random Effects Models > ### Aliases: pbltest pbltest.formula pbltest.plm > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > > # formula interface > pbltest(inv ~ value + capital, data = Grunfeld) Baltagi and Li two-sided LM test data: inv ~ value + capital chisq = 69.532, df = 1, p-value < 2.2e-16 alternative hypothesis: AR(1)/MA(1) errors in RE panel model > > # plm interface > re_mod <- plm(inv ~ value + capital, data = Grunfeld, model = "random") > pbltest(re_mod) Baltagi and Li two-sided LM test data: formula(x$formula) chisq = 69.532, df = 1, p-value < 2.2e-16 alternative hypothesis: AR(1)/MA(1) errors in RE panel model > pbltest(re_mod, alternative = "onesided") Baltagi and Li one-sided LM test data: formula(x$formula) z = 8.3386, p-value < 2.2e-16 alternative hypothesis: AR(1)/MA(1) errors in RE panel model > > > > > cleanEx() > nameEx("pbnftest") > ### * pbnftest > > flush(stderr()); flush(stdout()) > > ### Name: pbnftest > ### Title: Modified BNF-Durbin-Watson Test and Baltagi-Wu's LBI Test for > ### Panel Models > ### Aliases: pbnftest pbnftest.panelmodel pbnftest.formula > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > > # formula interface, replicate Baltagi/Wu (1999), table 1, test case A: > data_A <- Grunfeld[!Grunfeld[["year"]] %in% c("1943", "1944"), ] > pbnftest(inv ~ value + capital, data = data_A, model = "within") modified Bhargava/Franzini/Narendranathan Panel Durbin-Watson Test data: inv ~ value + capital DW = 0.70579 alternative hypothesis: serial correlation in idiosyncratic errors > pbnftest(inv ~ value + capital, data = data_A, test = "lbi", model = "within") Baltagi/Wu LBI Test for Serial Correlation in Panel Models data: inv ~ value + capital LBI = 1.0219 alternative hypothesis: serial correlation in idiosyncratic errors > > # replicate Baltagi (2013), p. 101, table 5.1: > re <- plm(inv ~ value + capital, data = Grunfeld, model = "random") > pbnftest(re) Bhargava/Franzini/Narendranathan Panel Durbin-Watson Test data: inv ~ value + capital DW = 0.68448 alternative hypothesis: serial correlation in idiosyncratic errors > pbnftest(re, test = "lbi") Baltagi/Wu LBI Test for Serial Correlation in Panel Models data: inv ~ value + capital LBI = 0.95636 alternative hypothesis: serial correlation in idiosyncratic errors > > > > > cleanEx() > nameEx("pbsytest") > ### * pbsytest > > flush(stderr()); flush(stdout()) > > ### Name: pbsytest > ### Title: Bera, Sosa-Escudero and Yoon Locally-Robust Lagrange Multiplier > ### Tests for Panel Models and Joint Test by Baltagi and Li > ### Aliases: pbsytest pbsytest.formula pbsytest.panelmodel > ### Keywords: htest > > ### ** Examples > > > ## Bera et. al (2001), p. 13, table 1 use > ## a subset of the original Grunfeld > ## data which contains three errors -> construct this subset: > data("Grunfeld", package = "plm") > Grunsubset <- rbind(Grunfeld[1:80, ], Grunfeld[141:160, ]) > Grunsubset[Grunsubset$firm == 2 & Grunsubset$year %in% c(1940, 1952), ][["inv"]] <- c(261.6, 645.2) > Grunsubset[Grunsubset$firm == 2 & Grunsubset$year == 1946, ][["capital"]] <- 232.6 > > ## default is AR testing (formula interface) > pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year")) Bera, Sosa-Escudero and Yoon locally robust test - balanced panel data: formula chisq = 3.7125, df = 1, p-value = 0.05401 alternative hypothesis: AR(1) errors sub random effects > pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year"), test = "re") Bera, Sosa-Escudero and Yoon locally robust test (one-sided) - balanced panel data: formula z = 19.601, p-value < 2.2e-16 alternative hypothesis: random effects sub AR(1) errors > pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year"), + test = "re", re.normal = FALSE) Bera, Sosa-Escudero and Yoon locally robust test (two-sided) - balanced panel data: formula chisq = 384.18, df = 1, p-value < 2.2e-16 alternative hypothesis: random effects sub AR(1) errors > pbsytest(inv ~ value + capital, data = Grunsubset, index = c("firm", "year"), test = "j") Baltagi and Li AR-RE joint test - balanced panel data: formula chisq = 457.53, df = 2, p-value < 2.2e-16 alternative hypothesis: AR(1) errors or random effects > > ## plm interface > mod <- plm(inv ~ value + capital, data = Grunsubset, model = "pooling") > pbsytest(mod) Bera, Sosa-Escudero and Yoon locally robust test - balanced panel data: formula chisq = 3.7125, df = 1, p-value = 0.05401 alternative hypothesis: AR(1) errors sub random effects > > > > > cleanEx() > nameEx("pcce") > ### * pcce > > flush(stderr()); flush(stdout()) > > ### Name: pcce > ### Title: Common Correlated Effects estimators > ### Aliases: pcce summary.pcce print.summary.pcce residuals.pcce > ### model.matrix.pcce pmodel.response.pcce > ### Keywords: regression > > ### ** Examples > > > data("Produc", package = "plm") > ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p") > summary(ccepmod) Common Correlated Effects Pooled model Call: pcce(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "p") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0918843 -0.0060964 0.0005035 0.0000000 0.0059796 0.0682325 Coefficients: Estimate Std. Error z-value Pr(>|z|) log(pcap) 0.0432376 0.1041125 0.4153 0.6779 log(pc) 0.0363922 0.0368432 0.9878 0.3233 log(emp) 0.8209632 0.1390202 5.9054 3.519e-09 *** unemp -0.0020925 0.0014973 -1.3976 0.1622 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.11927 HPY R-squared: 0.99077 > summary(ccepmod, vcov = vcovHC) # use argument vcov for robust std. errors Common Correlated Effects Pooled model Note: Coefficient variance-covariance matrix supplied: vcovHC Call: pcce(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "p") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0918843 -0.0060964 0.0005035 0.0000000 0.0059796 0.0682325 Coefficients: Estimate Std. Error z-value Pr(>|z|) log(pcap) 0.0432376 0.0972332 0.4447 0.6566 log(pc) 0.0363922 0.0322477 1.1285 0.2591 log(emp) 0.8209632 0.1104438 7.4333 1.059e-13 *** unemp -0.0020925 0.0013959 -1.4990 0.1339 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.11927 HPY R-squared: 0.99077 > > ccemgmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="mg") > summary(ccemgmod) Common Correlated Effects Mean Groups model Call: pcce(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "mg") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0806338 -0.0037117 0.0003147 0.0000000 0.0040207 0.0438957 Coefficients: Estimate Std. Error z-value Pr(>|z|) log(pcap) 0.0899850 0.1176039 0.7652 0.44418 log(pc) 0.0335784 0.0423362 0.7931 0.42770 log(emp) 0.6258659 0.1071719 5.8398 5.225e-09 *** unemp -0.0031178 0.0014389 -2.1668 0.03025 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.056978 HPY R-squared: 0.99312 > > > > > cleanEx() > nameEx("pcdtest") > ### * pcdtest > > flush(stderr()); flush(stdout()) > > ### Name: pcdtest > ### Title: Tests of cross-section dependence for panel models > ### Aliases: pcdtest pcdtest.formula pcdtest.panelmodel pcdtest.pseries > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > ## test on heterogeneous model (separate time series regressions) > pcdtest(inv ~ value + capital, data = Grunfeld, + index = c("firm", "year")) Pesaran CD test for cross-sectional dependence in panels data: inv ~ value + capital z = 5.3401, p-value = 9.292e-08 alternative hypothesis: cross-sectional dependence > > ## test on two-way fixed effects homogeneous model > pcdtest(inv ~ value + capital, data = Grunfeld, model = "within", + effect = "twoways", index = c("firm", "year")) Pesaran CD test for cross-sectional dependence in panels data: inv ~ value + capital z = 0.1162, p-value = 0.9075 alternative hypothesis: cross-sectional dependence > > ## test on panelmodel object > g <- plm(inv ~ value + capital, data = Grunfeld, index = c("firm", "year")) > pcdtest(g) Pesaran CD test for cross-sectional dependence in panels data: inv ~ value + capital z = 4.6612, p-value = 3.144e-06 alternative hypothesis: cross-sectional dependence > > ## scaled LM test > pcdtest(g, test = "sclm") Scaled LM test for cross-sectional dependence in panels data: inv ~ value + capital z = 21.222, p-value < 2.2e-16 alternative hypothesis: cross-sectional dependence > > ## test on pseries > pGrunfeld <- pdata.frame(Grunfeld) > pcdtest(pGrunfeld$value) Pesaran CD test for cross-sectional dependence in panels data: pGrunfeld$value z = 13.843, p-value < 2.2e-16 alternative hypothesis: cross-sectional dependence > > ## local test > ## define neighbours for individual 2: 1, 3, 4, 5 in lower triangular matrix > w <- matrix(0, ncol= 10, nrow=10) > w[2,1] <- w[3,2] <- w[4,2] <- w[5,2] <- 1 > pcdtest(g, w = w) Pesaran CD test for local cross-sectional dependence in panels data: inv ~ value + capital z = -0.87759, p-value = 0.3802 alternative hypothesis: cross-sectional dependence > > > > > cleanEx() > nameEx("pdata.frame") > ### * pdata.frame > > flush(stderr()); flush(stdout()) > > ### Name: pdata.frame > ### Title: data.frame for panel data > ### Aliases: pdata.frame $<-.pdata.frame [.pdata.frame [[.pdata.frame > ### $.pdata.frame print.pdata.frame as.list.pdata.frame > ### as.data.frame.pdata.frame > ### Keywords: classes > > ### ** Examples > > > # Gasoline contains two variables which are individual and time > # indexes > data("Gasoline", package = "plm") > Gas <- pdata.frame(Gasoline, index = c("country", "year"), drop.index = TRUE) > > # Hedonic is an unbalanced panel, townid is the individual index > data("Hedonic", package = "plm") > Hed <- pdata.frame(Hedonic, index = "townid", row.names = FALSE) > > # In case of balanced panel, it is sufficient to give number of > # individuals data set 'Wages' is organized as a stacked time > # series > data("Wages", package = "plm") > Wag <- pdata.frame(Wages, 595) > > # lapply on a pdata.frame by making it a list of pseries first > lapply(as.list(Wag[ , c("ed", "lwage")], keep.attributes = TRUE), lag) $ed 1-1 1-2 1-3 1-4 1-5 1-6 1-7 2-1 2-2 2-3 2-4 2-5 2-6 NA 9 9 9 9 9 9 NA 11 11 11 11 11 2-7 3-1 3-2 3-3 3-4 3-5 3-6 3-7 4-1 4-2 4-3 4-4 4-5 11 NA 12 12 12 12 12 12 NA 10 10 10 10 4-6 4-7 5-1 5-2 5-3 5-4 5-5 5-6 5-7 6-1 6-2 6-3 6-4 10 10 NA 16 16 16 16 16 16 NA 12 12 12 6-5 6-6 6-7 7-1 7-2 7-3 7-4 7-5 7-6 7-7 8-1 8-2 8-3 12 12 12 NA 12 12 12 12 12 12 NA 10 10 8-4 8-5 8-6 8-7 9-1 9-2 9-3 9-4 9-5 9-6 9-7 10-1 10-2 10 10 10 10 NA 16 16 16 16 16 16 NA 16 10-3 10-4 10-5 10-6 10-7 11-1 11-2 11-3 11-4 11-5 11-6 11-7 12-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 12-2 12-3 12-4 12-5 12-6 12-7 13-1 13-2 13-3 13-4 13-5 13-6 13-7 12 12 12 12 12 12 NA 12 12 12 12 12 12 14-1 14-2 14-3 14-4 14-5 14-6 14-7 15-1 15-2 15-3 15-4 15-5 15-6 NA 17 17 17 17 17 17 NA 14 14 14 14 14 15-7 16-1 16-2 16-3 16-4 16-5 16-6 16-7 17-1 17-2 17-3 17-4 17-5 14 NA 12 12 12 12 12 12 NA 14 14 14 14 17-6 17-7 18-1 18-2 18-3 18-4 18-5 18-6 18-7 19-1 19-2 19-3 19-4 14 14 NA 16 16 16 16 16 16 NA 12 12 12 19-5 19-6 19-7 20-1 20-2 20-3 20-4 20-5 20-6 20-7 21-1 21-2 21-3 12 12 12 NA 16 16 16 16 16 16 NA 4 4 21-4 21-5 21-6 21-7 22-1 22-2 22-3 22-4 22-5 22-6 22-7 23-1 23-2 4 4 4 4 NA 14 14 14 14 14 14 NA 16 23-3 23-4 23-5 23-6 23-7 24-1 24-2 24-3 24-4 24-5 24-6 24-7 25-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 25-2 25-3 25-4 25-5 25-6 25-7 26-1 26-2 26-3 26-4 26-5 26-6 26-7 17 17 17 17 17 17 NA 15 15 15 15 15 15 27-1 27-2 27-3 27-4 27-5 27-6 27-7 28-1 28-2 28-3 28-4 28-5 28-6 NA 16 16 16 16 16 16 NA 12 12 12 12 12 28-7 29-1 29-2 29-3 29-4 29-5 29-6 29-7 30-1 30-2 30-3 30-4 30-5 12 NA 14 14 14 14 14 14 NA 12 12 12 12 30-6 30-7 31-1 31-2 31-3 31-4 31-5 31-6 31-7 32-1 32-2 32-3 32-4 12 12 NA 17 17 17 17 17 17 NA 16 16 16 32-5 32-6 32-7 33-1 33-2 33-3 33-4 33-5 33-6 33-7 34-1 34-2 34-3 16 16 16 NA 16 16 16 16 16 16 NA 8 8 34-4 34-5 34-6 34-7 35-1 35-2 35-3 35-4 35-5 35-6 35-7 36-1 36-2 8 8 8 8 NA 13 13 13 13 13 13 NA 10 36-3 36-4 36-5 36-6 36-7 37-1 37-2 37-3 37-4 37-5 37-6 37-7 38-1 10 10 10 10 10 NA 14 14 14 14 14 14 NA 38-2 38-3 38-4 38-5 38-6 38-7 39-1 39-2 39-3 39-4 39-5 39-6 39-7 12 12 12 12 12 12 NA 13 13 13 13 13 13 40-1 40-2 40-3 40-4 40-5 40-6 40-7 41-1 41-2 41-3 41-4 41-5 41-6 NA 8 8 8 8 8 8 NA 16 16 16 16 16 41-7 42-1 42-2 42-3 42-4 42-5 42-6 42-7 43-1 43-2 43-3 43-4 43-5 16 NA 12 12 12 12 12 12 NA 16 16 16 16 43-6 43-7 44-1 44-2 44-3 44-4 44-5 44-6 44-7 45-1 45-2 45-3 45-4 16 16 NA 17 17 17 17 17 17 NA 13 13 13 45-5 45-6 45-7 46-1 46-2 46-3 46-4 46-5 46-6 46-7 47-1 47-2 47-3 13 13 13 NA 12 12 12 12 12 12 NA 14 14 47-4 47-5 47-6 47-7 48-1 48-2 48-3 48-4 48-5 48-6 48-7 49-1 49-2 14 14 14 14 NA 16 16 16 16 16 16 NA 16 49-3 49-4 49-5 49-6 49-7 50-1 50-2 50-3 50-4 50-5 50-6 50-7 51-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 51-2 51-3 51-4 51-5 51-6 51-7 52-1 52-2 52-3 52-4 52-5 52-6 52-7 17 17 17 17 17 17 NA 16 16 16 16 16 16 53-1 53-2 53-3 53-4 53-5 53-6 53-7 54-1 54-2 54-3 54-4 54-5 54-6 NA 13 13 13 13 13 13 NA 11 11 11 11 11 54-7 55-1 55-2 55-3 55-4 55-5 55-6 55-7 56-1 56-2 56-3 56-4 56-5 11 NA 8 8 8 8 8 8 NA 12 12 12 12 56-6 56-7 57-1 57-2 57-3 57-4 57-5 57-6 57-7 58-1 58-2 58-3 58-4 12 12 NA 11 11 11 11 11 11 NA 14 14 14 58-5 58-6 58-7 59-1 59-2 59-3 59-4 59-5 59-6 59-7 60-1 60-2 60-3 14 14 14 NA 17 17 17 17 17 17 NA 11 11 60-4 60-5 60-6 60-7 61-1 61-2 61-3 61-4 61-5 61-6 61-7 62-1 62-2 11 11 11 11 NA 17 17 17 17 17 17 NA 16 62-3 62-4 62-5 62-6 62-7 63-1 63-2 63-3 63-4 63-5 63-6 63-7 64-1 16 16 16 16 16 NA 17 17 17 17 17 17 NA 64-2 64-3 64-4 64-5 64-6 64-7 65-1 65-2 65-3 65-4 65-5 65-6 65-7 12 12 12 12 12 12 NA 8 8 8 8 8 8 66-1 66-2 66-3 66-4 66-5 66-6 66-7 67-1 67-2 67-3 67-4 67-5 67-6 NA 8 8 8 8 8 8 NA 12 12 12 12 12 67-7 68-1 68-2 68-3 68-4 68-5 68-6 68-7 69-1 69-2 69-3 69-4 69-5 12 NA 12 12 12 12 12 12 NA 13 13 13 13 69-6 69-7 70-1 70-2 70-3 70-4 70-5 70-6 70-7 71-1 71-2 71-3 71-4 13 13 NA 16 16 16 16 16 16 NA 16 16 16 71-5 71-6 71-7 72-1 72-2 72-3 72-4 72-5 72-6 72-7 73-1 73-2 73-3 16 16 16 NA 17 17 17 17 17 17 NA 16 16 73-4 73-5 73-6 73-7 74-1 74-2 74-3 74-4 74-5 74-6 74-7 75-1 75-2 16 16 16 16 NA 13 13 13 13 13 13 NA 8 75-3 75-4 75-5 75-6 75-7 76-1 76-2 76-3 76-4 76-5 76-6 76-7 77-1 8 8 8 8 8 NA 13 13 13 13 13 13 NA 77-2 77-3 77-4 77-5 77-6 77-7 78-1 78-2 78-3 78-4 78-5 78-6 78-7 10 10 10 10 10 10 NA 12 12 12 12 12 12 79-1 79-2 79-3 79-4 79-5 79-6 79-7 80-1 80-2 80-3 80-4 80-5 80-6 NA 8 8 8 8 8 8 NA 17 17 17 17 17 80-7 81-1 81-2 81-3 81-4 81-5 81-6 81-7 82-1 82-2 82-3 82-4 82-5 17 NA 16 16 16 16 16 16 NA 12 12 12 12 82-6 82-7 83-1 83-2 83-3 83-4 83-5 83-6 83-7 84-1 84-2 84-3 84-4 12 12 NA 12 12 12 12 12 12 NA 17 17 17 84-5 84-6 84-7 85-1 85-2 85-3 85-4 85-5 85-6 85-7 86-1 86-2 86-3 17 17 17 NA 7 7 7 7 7 7 NA 12 12 86-4 86-5 86-6 86-7 87-1 87-2 87-3 87-4 87-5 87-6 87-7 88-1 88-2 12 12 12 12 NA 14 14 14 14 14 14 NA 15 88-3 88-4 88-5 88-6 88-7 89-1 89-2 89-3 89-4 89-5 89-6 89-7 90-1 15 15 15 15 15 NA 12 12 12 12 12 12 NA 90-2 90-3 90-4 90-5 90-6 90-7 91-1 91-2 91-3 91-4 91-5 91-6 91-7 12 12 12 12 12 12 NA 7 7 7 7 7 7 92-1 92-2 92-3 92-4 92-5 92-6 92-7 93-1 93-2 93-3 93-4 93-5 93-6 NA 14 14 14 14 14 14 NA 7 7 7 7 7 93-7 94-1 94-2 94-3 94-4 94-5 94-6 94-7 95-1 95-2 95-3 95-4 95-5 7 NA 17 17 17 17 17 17 NA 8 8 8 8 95-6 95-7 96-1 96-2 96-3 96-4 96-5 96-6 96-7 97-1 97-2 97-3 97-4 8 8 NA 16 16 16 16 16 16 NA 11 11 11 97-5 97-6 97-7 98-1 98-2 98-3 98-4 98-5 98-6 98-7 99-1 99-2 99-3 11 11 11 NA 17 17 17 17 17 17 NA 12 12 99-4 99-5 99-6 99-7 100-1 100-2 100-3 100-4 100-5 100-6 100-7 101-1 101-2 12 12 12 12 NA 11 11 11 11 11 11 NA 14 101-3 101-4 101-5 101-6 101-7 102-1 102-2 102-3 102-4 102-5 102-6 102-7 103-1 14 14 14 14 14 NA 17 17 17 17 17 17 NA 103-2 103-3 103-4 103-5 103-6 103-7 104-1 104-2 104-3 104-4 104-5 104-6 104-7 16 16 16 16 16 16 NA 7 7 7 7 7 7 105-1 105-2 105-3 105-4 105-5 105-6 105-7 106-1 106-2 106-3 106-4 106-5 106-6 NA 14 14 14 14 14 14 NA 12 12 12 12 12 106-7 107-1 107-2 107-3 107-4 107-5 107-6 107-7 108-1 108-2 108-3 108-4 108-5 12 NA 12 12 12 12 12 12 NA 12 12 12 12 108-6 108-7 109-1 109-2 109-3 109-4 109-5 109-6 109-7 110-1 110-2 110-3 110-4 12 12 NA 12 12 12 12 12 12 NA 12 12 12 110-5 110-6 110-7 111-1 111-2 111-3 111-4 111-5 111-6 111-7 112-1 112-2 112-3 12 12 12 NA 13 13 13 13 13 13 NA 13 13 112-4 112-5 112-6 112-7 113-1 113-2 113-3 113-4 113-5 113-6 113-7 114-1 114-2 13 13 13 13 NA 17 17 17 17 17 17 NA 17 114-3 114-4 114-5 114-6 114-7 115-1 115-2 115-3 115-4 115-5 115-6 115-7 116-1 17 17 17 17 17 NA 12 12 12 12 12 12 NA 116-2 116-3 116-4 116-5 116-6 116-7 117-1 117-2 117-3 117-4 117-5 117-6 117-7 14 14 14 14 14 14 NA 17 17 17 17 17 17 118-1 118-2 118-3 118-4 118-5 118-6 118-7 119-1 119-2 119-3 119-4 119-5 119-6 NA 9 9 9 9 9 9 NA 17 17 17 17 17 119-7 120-1 120-2 120-3 120-4 120-5 120-6 120-7 121-1 121-2 121-3 121-4 121-5 17 NA 12 12 12 12 12 12 NA 16 16 16 16 121-6 121-7 122-1 122-2 122-3 122-4 122-5 122-6 122-7 123-1 123-2 123-3 123-4 16 16 NA 12 12 12 12 12 12 NA 8 8 8 123-5 123-6 123-7 124-1 124-2 124-3 124-4 124-5 124-6 124-7 125-1 125-2 125-3 8 8 8 NA 16 16 16 16 16 16 NA 12 12 125-4 125-5 125-6 125-7 126-1 126-2 126-3 126-4 126-5 126-6 126-7 127-1 127-2 12 12 12 12 NA 12 12 12 12 12 12 NA 14 127-3 127-4 127-5 127-6 127-7 128-1 128-2 128-3 128-4 128-5 128-6 128-7 129-1 14 14 14 14 14 NA 12 12 12 12 12 12 NA 129-2 129-3 129-4 129-5 129-6 129-7 130-1 130-2 130-3 130-4 130-5 130-6 130-7 8 8 8 8 8 8 NA 8 8 8 8 8 8 131-1 131-2 131-3 131-4 131-5 131-6 131-7 132-1 132-2 132-3 132-4 132-5 132-6 NA 8 8 8 8 8 8 NA 17 17 17 17 17 132-7 133-1 133-2 133-3 133-4 133-5 133-6 133-7 134-1 134-2 134-3 134-4 134-5 17 NA 12 12 12 12 12 12 NA 12 12 12 12 134-6 134-7 135-1 135-2 135-3 135-4 135-5 135-6 135-7 136-1 136-2 136-3 136-4 12 12 NA 12 12 12 12 12 12 NA 13 13 13 136-5 136-6 136-7 137-1 137-2 137-3 137-4 137-5 137-6 137-7 138-1 138-2 138-3 13 13 13 NA 11 11 11 11 11 11 NA 16 16 138-4 138-5 138-6 138-7 139-1 139-2 139-3 139-4 139-5 139-6 139-7 140-1 140-2 16 16 16 16 NA 16 16 16 16 16 16 NA 9 140-3 140-4 140-5 140-6 140-7 141-1 141-2 141-3 141-4 141-5 141-6 141-7 142-1 9 9 9 9 9 NA 8 8 8 8 8 8 NA 142-2 142-3 142-4 142-5 142-6 142-7 143-1 143-2 143-3 143-4 143-5 143-6 143-7 16 16 16 16 16 16 NA 12 12 12 12 12 12 144-1 144-2 144-3 144-4 144-5 144-6 144-7 145-1 145-2 145-3 145-4 145-5 145-6 NA 14 14 14 14 14 14 NA 16 16 16 16 16 145-7 146-1 146-2 146-3 146-4 146-5 146-6 146-7 147-1 147-2 147-3 147-4 147-5 16 NA 15 15 15 15 15 15 NA 12 12 12 12 147-6 147-7 148-1 148-2 148-3 148-4 148-5 148-6 148-7 149-1 149-2 149-3 149-4 12 12 NA 14 14 14 14 14 14 NA 16 16 16 149-5 149-6 149-7 150-1 150-2 150-3 150-4 150-5 150-6 150-7 151-1 151-2 151-3 16 16 16 NA 12 12 12 12 12 12 NA 16 16 151-4 151-5 151-6 151-7 152-1 152-2 152-3 152-4 152-5 152-6 152-7 153-1 153-2 16 16 16 16 NA 8 8 8 8 8 8 NA 12 153-3 153-4 153-5 153-6 153-7 154-1 154-2 154-3 154-4 154-5 154-6 154-7 155-1 12 12 12 12 12 NA 17 17 17 17 17 17 NA 155-2 155-3 155-4 155-5 155-6 155-7 156-1 156-2 156-3 156-4 156-5 156-6 156-7 16 16 16 16 16 16 NA 13 13 13 13 13 13 157-1 157-2 157-3 157-4 157-5 157-6 157-7 158-1 158-2 158-3 158-4 158-5 158-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 158-7 159-1 159-2 159-3 159-4 159-5 159-6 159-7 160-1 160-2 160-3 160-4 160-5 12 NA 12 12 12 12 12 12 NA 17 17 17 17 160-6 160-7 161-1 161-2 161-3 161-4 161-5 161-6 161-7 162-1 162-2 162-3 162-4 17 17 NA 12 12 12 12 12 12 NA 12 12 12 162-5 162-6 162-7 163-1 163-2 163-3 163-4 163-5 163-6 163-7 164-1 164-2 164-3 12 12 12 NA 12 12 12 12 12 12 NA 11 11 164-4 164-5 164-6 164-7 165-1 165-2 165-3 165-4 165-5 165-6 165-7 166-1 166-2 11 11 11 11 NA 12 12 12 12 12 12 NA 15 166-3 166-4 166-5 166-6 166-7 167-1 167-2 167-3 167-4 167-5 167-6 167-7 168-1 15 15 15 15 15 NA 10 10 10 10 10 10 NA 168-2 168-3 168-4 168-5 168-6 168-7 169-1 169-2 169-3 169-4 169-5 169-6 169-7 17 17 17 17 17 17 NA 17 17 17 17 17 17 170-1 170-2 170-3 170-4 170-5 170-6 170-7 171-1 171-2 171-3 171-4 171-5 171-6 NA 9 9 9 9 9 9 NA 12 12 12 12 12 171-7 172-1 172-2 172-3 172-4 172-5 172-6 172-7 173-1 173-2 173-3 173-4 173-5 12 NA 16 16 16 16 16 16 NA 16 16 16 16 173-6 173-7 174-1 174-2 174-3 174-4 174-5 174-6 174-7 175-1 175-2 175-3 175-4 16 16 NA 12 12 12 12 12 12 NA 12 12 12 175-5 175-6 175-7 176-1 176-2 176-3 176-4 176-5 176-6 176-7 177-1 177-2 177-3 12 12 12 NA 14 14 14 14 14 14 NA 15 15 177-4 177-5 177-6 177-7 178-1 178-2 178-3 178-4 178-5 178-6 178-7 179-1 179-2 15 15 15 15 NA 9 9 9 9 9 9 NA 16 179-3 179-4 179-5 179-6 179-7 180-1 180-2 180-3 180-4 180-5 180-6 180-7 181-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 181-2 181-3 181-4 181-5 181-6 181-7 182-1 182-2 182-3 182-4 182-5 182-6 182-7 12 12 12 12 12 12 NA 14 14 14 14 14 14 183-1 183-2 183-3 183-4 183-5 183-6 183-7 184-1 184-2 184-3 184-4 184-5 184-6 NA 8 8 8 8 8 8 NA 12 12 12 12 12 184-7 185-1 185-2 185-3 185-4 185-5 185-6 185-7 186-1 186-2 186-3 186-4 186-5 12 NA 12 12 12 12 12 12 NA 12 12 12 12 186-6 186-7 187-1 187-2 187-3 187-4 187-5 187-6 187-7 188-1 188-2 188-3 188-4 12 12 NA 12 12 12 12 12 12 NA 12 12 12 188-5 188-6 188-7 189-1 189-2 189-3 189-4 189-5 189-6 189-7 190-1 190-2 190-3 12 12 12 NA 17 17 17 17 17 17 NA 9 9 190-4 190-5 190-6 190-7 191-1 191-2 191-3 191-4 191-5 191-6 191-7 192-1 192-2 9 9 9 9 NA 6 6 6 6 6 6 NA 12 192-3 192-4 192-5 192-6 192-7 193-1 193-2 193-3 193-4 193-5 193-6 193-7 194-1 12 12 12 12 12 NA 16 16 16 16 16 16 NA 194-2 194-3 194-4 194-5 194-6 194-7 195-1 195-2 195-3 195-4 195-5 195-6 195-7 12 12 12 12 12 12 NA 16 16 16 16 16 16 196-1 196-2 196-3 196-4 196-5 196-6 196-7 197-1 197-2 197-3 197-4 197-5 197-6 NA 9 9 9 9 9 9 NA 7 7 7 7 7 197-7 198-1 198-2 198-3 198-4 198-5 198-6 198-7 199-1 199-2 199-3 199-4 199-5 7 NA 12 12 12 12 12 12 NA 13 13 13 13 199-6 199-7 200-1 200-2 200-3 200-4 200-5 200-6 200-7 201-1 201-2 201-3 201-4 13 13 NA 17 17 17 17 17 17 NA 11 11 11 201-5 201-6 201-7 202-1 202-2 202-3 202-4 202-5 202-6 202-7 203-1 203-2 203-3 11 11 11 NA 12 12 12 12 12 12 NA 9 9 203-4 203-5 203-6 203-7 204-1 204-2 204-3 204-4 204-5 204-6 204-7 205-1 205-2 9 9 9 9 NA 9 9 9 9 9 9 NA 12 205-3 205-4 205-5 205-6 205-7 206-1 206-2 206-3 206-4 206-5 206-6 206-7 207-1 12 12 12 12 12 NA 13 13 13 13 13 13 NA 207-2 207-3 207-4 207-5 207-6 207-7 208-1 208-2 208-3 208-4 208-5 208-6 208-7 17 17 17 17 17 17 NA 11 11 11 11 11 11 209-1 209-2 209-3 209-4 209-5 209-6 209-7 210-1 210-2 210-3 210-4 210-5 210-6 NA 17 17 17 17 17 17 NA 8 8 8 8 8 210-7 211-1 211-2 211-3 211-4 211-5 211-6 211-7 212-1 212-2 212-3 212-4 212-5 8 NA 10 10 10 10 10 10 NA 17 17 17 17 212-6 212-7 213-1 213-2 213-3 213-4 213-5 213-6 213-7 214-1 214-2 214-3 214-4 17 17 NA 11 11 11 11 11 11 NA 12 12 12 214-5 214-6 214-7 215-1 215-2 215-3 215-4 215-5 215-6 215-7 216-1 216-2 216-3 12 12 12 NA 16 16 16 16 16 16 NA 17 17 216-4 216-5 216-6 216-7 217-1 217-2 217-3 217-4 217-5 217-6 217-7 218-1 218-2 17 17 17 17 NA 13 13 13 13 13 13 NA 12 218-3 218-4 218-5 218-6 218-7 219-1 219-2 219-3 219-4 219-5 219-6 219-7 220-1 12 12 12 12 12 NA 12 12 12 12 12 12 NA 220-2 220-3 220-4 220-5 220-6 220-7 221-1 221-2 221-3 221-4 221-5 221-6 221-7 12 12 12 12 12 12 NA 17 17 17 17 17 17 222-1 222-2 222-3 222-4 222-5 222-6 222-7 223-1 223-2 223-3 223-4 223-5 223-6 NA 11 11 11 11 11 11 NA 14 14 14 14 14 223-7 224-1 224-2 224-3 224-4 224-5 224-6 224-7 225-1 225-2 225-3 225-4 225-5 14 NA 5 5 5 5 5 5 NA 12 12 12 12 225-6 225-7 226-1 226-2 226-3 226-4 226-5 226-6 226-7 227-1 227-2 227-3 227-4 12 12 NA 12 12 12 12 12 12 NA 16 16 16 227-5 227-6 227-7 228-1 228-2 228-3 228-4 228-5 228-6 228-7 229-1 229-2 229-3 16 16 16 NA 16 16 16 16 16 16 NA 17 17 229-4 229-5 229-6 229-7 230-1 230-2 230-3 230-4 230-5 230-6 230-7 231-1 231-2 17 17 17 17 NA 12 12 12 12 12 12 NA 16 231-3 231-4 231-5 231-6 231-7 232-1 232-2 232-3 232-4 232-5 232-6 232-7 233-1 16 16 16 16 16 NA 17 17 17 17 17 17 NA 233-2 233-3 233-4 233-5 233-6 233-7 234-1 234-2 234-3 234-4 234-5 234-6 234-7 10 10 10 10 10 10 NA 12 12 12 12 12 12 235-1 235-2 235-3 235-4 235-5 235-6 235-7 236-1 236-2 236-3 236-4 236-5 236-6 NA 7 7 7 7 7 7 NA 17 17 17 17 17 236-7 237-1 237-2 237-3 237-4 237-5 237-6 237-7 238-1 238-2 238-3 238-4 238-5 17 NA 16 16 16 16 16 16 NA 12 12 12 12 238-6 238-7 239-1 239-2 239-3 239-4 239-5 239-6 239-7 240-1 240-2 240-3 240-4 12 12 NA 16 16 16 16 16 16 NA 12 12 12 240-5 240-6 240-7 241-1 241-2 241-3 241-4 241-5 241-6 241-7 242-1 242-2 242-3 12 12 12 NA 10 10 10 10 10 10 NA 10 10 242-4 242-5 242-6 242-7 243-1 243-2 243-3 243-4 243-5 243-6 243-7 244-1 244-2 10 10 10 10 NA 12 12 12 12 12 12 NA 10 244-3 244-4 244-5 244-6 244-7 245-1 245-2 245-3 245-4 245-5 245-6 245-7 246-1 10 10 10 10 10 NA 7 7 7 7 7 7 NA 246-2 246-3 246-4 246-5 246-6 246-7 247-1 247-2 247-3 247-4 247-5 247-6 247-7 10 10 10 10 10 10 NA 7 7 7 7 7 7 248-1 248-2 248-3 248-4 248-5 248-6 248-7 249-1 249-2 249-3 249-4 249-5 249-6 NA 11 11 11 11 11 11 NA 16 16 16 16 16 249-7 250-1 250-2 250-3 250-4 250-5 250-6 250-7 251-1 251-2 251-3 251-4 251-5 16 NA 10 10 10 10 10 10 NA 16 16 16 16 251-6 251-7 252-1 252-2 252-3 252-4 252-5 252-6 252-7 253-1 253-2 253-3 253-4 16 16 NA 17 17 17 17 17 17 NA 12 12 12 253-5 253-6 253-7 254-1 254-2 254-3 254-4 254-5 254-6 254-7 255-1 255-2 255-3 12 12 12 NA 12 12 12 12 12 12 NA 12 12 255-4 255-5 255-6 255-7 256-1 256-2 256-3 256-4 256-5 256-6 256-7 257-1 257-2 12 12 12 12 NA 16 16 16 16 16 16 NA 12 257-3 257-4 257-5 257-6 257-7 258-1 258-2 258-3 258-4 258-5 258-6 258-7 259-1 12 12 12 12 12 NA 17 17 17 17 17 17 NA 259-2 259-3 259-4 259-5 259-6 259-7 260-1 260-2 260-3 260-4 260-5 260-6 260-7 10 10 10 10 10 10 NA 12 12 12 12 12 12 261-1 261-2 261-3 261-4 261-5 261-6 261-7 262-1 262-2 262-3 262-4 262-5 262-6 NA 10 10 10 10 10 10 NA 16 16 16 16 16 262-7 263-1 263-2 263-3 263-4 263-5 263-6 263-7 264-1 264-2 264-3 264-4 264-5 16 NA 12 12 12 12 12 12 NA 16 16 16 16 264-6 264-7 265-1 265-2 265-3 265-4 265-5 265-6 265-7 266-1 266-2 266-3 266-4 16 16 NA 12 12 12 12 12 12 NA 17 17 17 266-5 266-6 266-7 267-1 267-2 267-3 267-4 267-5 267-6 267-7 268-1 268-2 268-3 17 17 17 NA 12 12 12 12 12 12 NA 14 14 268-4 268-5 268-6 268-7 269-1 269-2 269-3 269-4 269-5 269-6 269-7 270-1 270-2 14 14 14 14 NA 9 9 9 9 9 9 NA 16 270-3 270-4 270-5 270-6 270-7 271-1 271-2 271-3 271-4 271-5 271-6 271-7 272-1 16 16 16 16 16 NA 16 16 16 16 16 16 NA 272-2 272-3 272-4 272-5 272-6 272-7 273-1 273-2 273-3 273-4 273-5 273-6 273-7 16 16 16 16 16 16 NA 12 12 12 12 12 12 274-1 274-2 274-3 274-4 274-5 274-6 274-7 275-1 275-2 275-3 275-4 275-5 275-6 NA 17 17 17 17 17 17 NA 17 17 17 17 17 275-7 276-1 276-2 276-3 276-4 276-5 276-6 276-7 277-1 277-2 277-3 277-4 277-5 17 NA 12 12 12 12 12 12 NA 8 8 8 8 277-6 277-7 278-1 278-2 278-3 278-4 278-5 278-6 278-7 279-1 279-2 279-3 279-4 8 8 NA 15 15 15 15 15 15 NA 12 12 12 279-5 279-6 279-7 280-1 280-2 280-3 280-4 280-5 280-6 280-7 281-1 281-2 281-3 12 12 12 NA 13 13 13 13 13 13 NA 12 12 281-4 281-5 281-6 281-7 282-1 282-2 282-3 282-4 282-5 282-6 282-7 283-1 283-2 12 12 12 12 NA 17 17 17 17 17 17 NA 9 283-3 283-4 283-5 283-6 283-7 284-1 284-2 284-3 284-4 284-5 284-6 284-7 285-1 9 9 9 9 9 NA 12 12 12 12 12 12 NA 285-2 285-3 285-4 285-5 285-6 285-7 286-1 286-2 286-3 286-4 286-5 286-6 286-7 12 12 12 12 12 12 NA 10 10 10 10 10 10 287-1 287-2 287-3 287-4 287-5 287-6 287-7 288-1 288-2 288-3 288-4 288-5 288-6 NA 10 10 10 10 10 10 NA 12 12 12 12 12 288-7 289-1 289-2 289-3 289-4 289-5 289-6 289-7 290-1 290-2 290-3 290-4 290-5 12 NA 16 16 16 16 16 16 NA 13 13 13 13 290-6 290-7 291-1 291-2 291-3 291-4 291-5 291-6 291-7 292-1 292-2 292-3 292-4 13 13 NA 12 12 12 12 12 12 NA 12 12 12 292-5 292-6 292-7 293-1 293-2 293-3 293-4 293-5 293-6 293-7 294-1 294-2 294-3 12 12 12 NA 12 12 12 12 12 12 NA 12 12 294-4 294-5 294-6 294-7 295-1 295-2 295-3 295-4 295-5 295-6 295-7 296-1 296-2 12 12 12 12 NA 12 12 12 12 12 12 NA 8 296-3 296-4 296-5 296-6 296-7 297-1 297-2 297-3 297-4 297-5 297-6 297-7 298-1 8 8 8 8 8 NA 12 12 12 12 12 12 NA 298-2 298-3 298-4 298-5 298-6 298-7 299-1 299-2 299-3 299-4 299-5 299-6 299-7 12 12 12 12 12 12 NA 16 16 16 16 16 16 300-1 300-2 300-3 300-4 300-5 300-6 300-7 301-1 301-2 301-3 301-4 301-5 301-6 NA 12 12 12 12 12 12 NA 14 14 14 14 14 301-7 302-1 302-2 302-3 302-4 302-5 302-6 302-7 303-1 303-2 303-3 303-4 303-5 14 NA 17 17 17 17 17 17 NA 13 13 13 13 303-6 303-7 304-1 304-2 304-3 304-4 304-5 304-6 304-7 305-1 305-2 305-3 305-4 13 13 NA 9 9 9 9 9 9 NA 16 16 16 305-5 305-6 305-7 306-1 306-2 306-3 306-4 306-5 306-6 306-7 307-1 307-2 307-3 16 16 16 NA 12 12 12 12 12 12 NA 8 8 307-4 307-5 307-6 307-7 308-1 308-2 308-3 308-4 308-5 308-6 308-7 309-1 309-2 8 8 8 8 NA 11 11 11 11 11 11 NA 9 309-3 309-4 309-5 309-6 309-7 310-1 310-2 310-3 310-4 310-5 310-6 310-7 311-1 9 9 9 9 9 NA 12 12 12 12 12 12 NA 311-2 311-3 311-4 311-5 311-6 311-7 312-1 312-2 312-3 312-4 312-5 312-6 312-7 12 12 12 12 12 12 NA 14 14 14 14 14 14 313-1 313-2 313-3 313-4 313-5 313-6 313-7 314-1 314-2 314-3 314-4 314-5 314-6 NA 16 16 16 16 16 16 NA 12 12 12 12 12 314-7 315-1 315-2 315-3 315-4 315-5 315-6 315-7 316-1 316-2 316-3 316-4 316-5 12 NA 17 17 17 17 17 17 NA 17 17 17 17 316-6 316-7 317-1 317-2 317-3 317-4 317-5 317-6 317-7 318-1 318-2 318-3 318-4 17 17 NA 12 12 12 12 12 12 NA 15 15 15 318-5 318-6 318-7 319-1 319-2 319-3 319-4 319-5 319-6 319-7 320-1 320-2 320-3 15 15 15 NA 12 12 12 12 12 12 NA 6 6 320-4 320-5 320-6 320-7 321-1 321-2 321-3 321-4 321-5 321-6 321-7 322-1 322-2 6 6 6 6 NA 12 12 12 12 12 12 NA 12 322-3 322-4 322-5 322-6 322-7 323-1 323-2 323-3 323-4 323-5 323-6 323-7 324-1 12 12 12 12 12 NA 16 16 16 16 16 16 NA 324-2 324-3 324-4 324-5 324-6 324-7 325-1 325-2 325-3 325-4 325-5 325-6 325-7 14 14 14 14 14 14 NA 15 15 15 15 15 15 326-1 326-2 326-3 326-4 326-5 326-6 326-7 327-1 327-2 327-3 327-4 327-5 327-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 327-7 328-1 328-2 328-3 328-4 328-5 328-6 328-7 329-1 329-2 329-3 329-4 329-5 12 NA 10 10 10 10 10 10 NA 15 15 15 15 329-6 329-7 330-1 330-2 330-3 330-4 330-5 330-6 330-7 331-1 331-2 331-3 331-4 15 15 NA 17 17 17 17 17 17 NA 11 11 11 331-5 331-6 331-7 332-1 332-2 332-3 332-4 332-5 332-6 332-7 333-1 333-2 333-3 11 11 11 NA 12 12 12 12 12 12 NA 12 12 333-4 333-5 333-6 333-7 334-1 334-2 334-3 334-4 334-5 334-6 334-7 335-1 335-2 12 12 12 12 NA 5 5 5 5 5 5 NA 16 335-3 335-4 335-5 335-6 335-7 336-1 336-2 336-3 336-4 336-5 336-6 336-7 337-1 16 16 16 16 16 NA 14 14 14 14 14 14 NA 337-2 337-3 337-4 337-5 337-6 337-7 338-1 338-2 338-3 338-4 338-5 338-6 338-7 12 12 12 12 12 12 NA 12 12 12 12 12 12 339-1 339-2 339-3 339-4 339-5 339-6 339-7 340-1 340-2 340-3 340-4 340-5 340-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 340-7 341-1 341-2 341-3 341-4 341-5 341-6 341-7 342-1 342-2 342-3 342-4 342-5 12 NA 17 17 17 17 17 17 NA 9 9 9 9 342-6 342-7 343-1 343-2 343-3 343-4 343-5 343-6 343-7 344-1 344-2 344-3 344-4 9 9 NA 12 12 12 12 12 12 NA 17 17 17 344-5 344-6 344-7 345-1 345-2 345-3 345-4 345-5 345-6 345-7 346-1 346-2 346-3 17 17 17 NA 11 11 11 11 11 11 NA 17 17 346-4 346-5 346-6 346-7 347-1 347-2 347-3 347-4 347-5 347-6 347-7 348-1 348-2 17 17 17 17 NA 17 17 17 17 17 17 NA 16 348-3 348-4 348-5 348-6 348-7 349-1 349-2 349-3 349-4 349-5 349-6 349-7 350-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 350-2 350-3 350-4 350-5 350-6 350-7 351-1 351-2 351-3 351-4 351-5 351-6 351-7 11 11 11 11 11 11 NA 8 8 8 8 8 8 352-1 352-2 352-3 352-4 352-5 352-6 352-7 353-1 353-2 353-3 353-4 353-5 353-6 NA 14 14 14 14 14 14 NA 17 17 17 17 17 353-7 354-1 354-2 354-3 354-4 354-5 354-6 354-7 355-1 355-2 355-3 355-4 355-5 17 NA 12 12 12 12 12 12 NA 12 12 12 12 355-6 355-7 356-1 356-2 356-3 356-4 356-5 356-6 356-7 357-1 357-2 357-3 357-4 12 12 NA 12 12 12 12 12 12 NA 12 12 12 357-5 357-6 357-7 358-1 358-2 358-3 358-4 358-5 358-6 358-7 359-1 359-2 359-3 12 12 12 NA 16 16 16 16 16 16 NA 10 10 359-4 359-5 359-6 359-7 360-1 360-2 360-3 360-4 360-5 360-6 360-7 361-1 361-2 10 10 10 10 NA 12 12 12 12 12 12 NA 17 361-3 361-4 361-5 361-6 361-7 362-1 362-2 362-3 362-4 362-5 362-6 362-7 363-1 17 17 17 17 17 NA 10 10 10 10 10 10 NA 363-2 363-3 363-4 363-5 363-6 363-7 364-1 364-2 364-3 364-4 364-5 364-6 364-7 12 12 12 12 12 12 NA 13 13 13 13 13 13 365-1 365-2 365-3 365-4 365-5 365-6 365-7 366-1 366-2 366-3 366-4 366-5 366-6 NA 17 17 17 17 17 17 NA 12 12 12 12 12 366-7 367-1 367-2 367-3 367-4 367-5 367-6 367-7 368-1 368-2 368-3 368-4 368-5 12 NA 9 9 9 9 9 9 NA 16 16 16 16 368-6 368-7 369-1 369-2 369-3 369-4 369-5 369-6 369-7 370-1 370-2 370-3 370-4 16 16 NA 14 14 14 14 14 14 NA 12 12 12 370-5 370-6 370-7 371-1 371-2 371-3 371-4 371-5 371-6 371-7 372-1 372-2 372-3 12 12 12 NA 10 10 10 10 10 10 NA 16 16 372-4 372-5 372-6 372-7 373-1 373-2 373-3 373-4 373-5 373-6 373-7 374-1 374-2 16 16 16 16 NA 11 11 11 11 11 11 NA 9 374-3 374-4 374-5 374-6 374-7 375-1 375-2 375-3 375-4 375-5 375-6 375-7 376-1 9 9 9 9 9 NA 11 11 11 11 11 11 NA 376-2 376-3 376-4 376-5 376-6 376-7 377-1 377-2 377-3 377-4 377-5 377-6 377-7 12 12 12 12 12 12 NA 12 12 12 12 12 12 378-1 378-2 378-3 378-4 378-5 378-6 378-7 379-1 379-2 379-3 379-4 379-5 379-6 NA 15 15 15 15 15 15 NA 7 7 7 7 7 379-7 380-1 380-2 380-3 380-4 380-5 380-6 380-7 381-1 381-2 381-3 381-4 381-5 7 NA 10 10 10 10 10 10 NA 11 11 11 11 381-6 381-7 382-1 382-2 382-3 382-4 382-5 382-6 382-7 383-1 383-2 383-3 383-4 11 11 NA 12 12 12 12 12 12 NA 12 12 12 383-5 383-6 383-7 384-1 384-2 384-3 384-4 384-5 384-6 384-7 385-1 385-2 385-3 12 12 12 NA 12 12 12 12 12 12 NA 12 12 385-4 385-5 385-6 385-7 386-1 386-2 386-3 386-4 386-5 386-6 386-7 387-1 387-2 12 12 12 12 NA 14 14 14 14 14 14 NA 17 387-3 387-4 387-5 387-6 387-7 388-1 388-2 388-3 388-4 388-5 388-6 388-7 389-1 17 17 17 17 17 NA 14 14 14 14 14 14 NA 389-2 389-3 389-4 389-5 389-6 389-7 390-1 390-2 390-3 390-4 390-5 390-6 390-7 14 14 14 14 14 14 NA 11 11 11 11 11 11 391-1 391-2 391-3 391-4 391-5 391-6 391-7 392-1 392-2 392-3 392-4 392-5 392-6 NA 9 9 9 9 9 9 NA 16 16 16 16 16 392-7 393-1 393-2 393-3 393-4 393-5 393-6 393-7 394-1 394-2 394-3 394-4 394-5 16 NA 12 12 12 12 12 12 NA 12 12 12 12 394-6 394-7 395-1 395-2 395-3 395-4 395-5 395-6 395-7 396-1 396-2 396-3 396-4 12 12 NA 7 7 7 7 7 7 NA 12 12 12 396-5 396-6 396-7 397-1 397-2 397-3 397-4 397-5 397-6 397-7 398-1 398-2 398-3 12 12 12 NA 17 17 17 17 17 17 NA 16 16 398-4 398-5 398-6 398-7 399-1 399-2 399-3 399-4 399-5 399-6 399-7 400-1 400-2 16 16 16 16 NA 6 6 6 6 6 6 NA 10 400-3 400-4 400-5 400-6 400-7 401-1 401-2 401-3 401-4 401-5 401-6 401-7 402-1 10 10 10 10 10 NA 12 12 12 12 12 12 NA 402-2 402-3 402-4 402-5 402-6 402-7 403-1 403-2 403-3 403-4 403-5 403-6 403-7 16 16 16 16 16 16 NA 12 12 12 12 12 12 404-1 404-2 404-3 404-4 404-5 404-6 404-7 405-1 405-2 405-3 405-4 405-5 405-6 NA 12 12 12 12 12 12 NA 16 16 16 16 16 405-7 406-1 406-2 406-3 406-4 406-5 406-6 406-7 407-1 407-2 407-3 407-4 407-5 16 NA 13 13 13 13 13 13 NA 17 17 17 17 407-6 407-7 408-1 408-2 408-3 408-4 408-5 408-6 408-7 409-1 409-2 409-3 409-4 17 17 NA 16 16 16 16 16 16 NA 10 10 10 409-5 409-6 409-7 410-1 410-2 410-3 410-4 410-5 410-6 410-7 411-1 411-2 411-3 10 10 10 NA 12 12 12 12 12 12 NA 16 16 411-4 411-5 411-6 411-7 412-1 412-2 412-3 412-4 412-5 412-6 412-7 413-1 413-2 16 16 16 16 NA 16 16 16 16 16 16 NA 16 413-3 413-4 413-5 413-6 413-7 414-1 414-2 414-3 414-4 414-5 414-6 414-7 415-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 415-2 415-3 415-4 415-5 415-6 415-7 416-1 416-2 416-3 416-4 416-5 416-6 416-7 9 9 9 9 9 9 NA 11 11 11 11 11 11 417-1 417-2 417-3 417-4 417-5 417-6 417-7 418-1 418-2 418-3 418-4 418-5 418-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 418-7 419-1 419-2 419-3 419-4 419-5 419-6 419-7 420-1 420-2 420-3 420-4 420-5 12 NA 17 17 17 17 17 17 NA 14 14 14 14 420-6 420-7 421-1 421-2 421-3 421-4 421-5 421-6 421-7 422-1 422-2 422-3 422-4 14 14 NA 16 16 16 16 16 16 NA 12 12 12 422-5 422-6 422-7 423-1 423-2 423-3 423-4 423-5 423-6 423-7 424-1 424-2 424-3 12 12 12 NA 10 10 10 10 10 10 NA 17 17 424-4 424-5 424-6 424-7 425-1 425-2 425-3 425-4 425-5 425-6 425-7 426-1 426-2 17 17 17 17 NA 13 13 13 13 13 13 NA 12 426-3 426-4 426-5 426-6 426-7 427-1 427-2 427-3 427-4 427-5 427-6 427-7 428-1 12 12 12 12 12 NA 17 17 17 17 17 17 NA 428-2 428-3 428-4 428-5 428-6 428-7 429-1 429-2 429-3 429-4 429-5 429-6 429-7 17 17 17 17 17 17 NA 12 12 12 12 12 12 430-1 430-2 430-3 430-4 430-5 430-6 430-7 431-1 431-2 431-3 431-4 431-5 431-6 NA 4 4 4 4 4 4 NA 12 12 12 12 12 431-7 432-1 432-2 432-3 432-4 432-5 432-6 432-7 433-1 433-2 433-3 433-4 433-5 12 NA 12 12 12 12 12 12 NA 14 14 14 14 433-6 433-7 434-1 434-2 434-3 434-4 434-5 434-6 434-7 435-1 435-2 435-3 435-4 14 14 NA 12 12 12 12 12 12 NA 9 9 9 435-5 435-6 435-7 436-1 436-2 436-3 436-4 436-5 436-6 436-7 437-1 437-2 437-3 9 9 9 NA 12 12 12 12 12 12 NA 12 12 437-4 437-5 437-6 437-7 438-1 438-2 438-3 438-4 438-5 438-6 438-7 439-1 439-2 12 12 12 12 NA 10 10 10 10 10 10 NA 12 439-3 439-4 439-5 439-6 439-7 440-1 440-2 440-3 440-4 440-5 440-6 440-7 441-1 12 12 12 12 12 NA 14 14 14 14 14 14 NA 441-2 441-3 441-4 441-5 441-6 441-7 442-1 442-2 442-3 442-4 442-5 442-6 442-7 12 12 12 12 12 12 NA 12 12 12 12 12 12 443-1 443-2 443-3 443-4 443-5 443-6 443-7 444-1 444-2 444-3 444-4 444-5 444-6 NA 12 12 12 12 12 12 NA 9 9 9 9 9 444-7 445-1 445-2 445-3 445-4 445-5 445-6 445-7 446-1 446-2 446-3 446-4 446-5 9 NA 17 17 17 17 17 17 NA 16 16 16 16 446-6 446-7 447-1 447-2 447-3 447-4 447-5 447-6 447-7 448-1 448-2 448-3 448-4 16 16 NA 8 8 8 8 8 8 NA 10 10 10 448-5 448-6 448-7 449-1 449-2 449-3 449-4 449-5 449-6 449-7 450-1 450-2 450-3 10 10 10 NA 12 12 12 12 12 12 NA 11 11 450-4 450-5 450-6 450-7 451-1 451-2 451-3 451-4 451-5 451-6 451-7 452-1 452-2 11 11 11 11 NA 11 11 11 11 11 11 NA 12 452-3 452-4 452-5 452-6 452-7 453-1 453-2 453-3 453-4 453-5 453-6 453-7 454-1 12 12 12 12 12 NA 12 12 12 12 12 12 NA 454-2 454-3 454-4 454-5 454-6 454-7 455-1 455-2 455-3 455-4 455-5 455-6 455-7 12 12 12 12 12 12 NA 8 8 8 8 8 8 456-1 456-2 456-3 456-4 456-5 456-6 456-7 457-1 457-2 457-3 457-4 457-5 457-6 NA 11 11 11 11 11 11 NA 10 10 10 10 10 457-7 458-1 458-2 458-3 458-4 458-5 458-6 458-7 459-1 459-2 459-3 459-4 459-5 10 NA 17 17 17 17 17 17 NA 14 14 14 14 459-6 459-7 460-1 460-2 460-3 460-4 460-5 460-6 460-7 461-1 461-2 461-3 461-4 14 14 NA 12 12 12 12 12 12 NA 12 12 12 461-5 461-6 461-7 462-1 462-2 462-3 462-4 462-5 462-6 462-7 463-1 463-2 463-3 12 12 12 NA 6 6 6 6 6 6 NA 17 17 463-4 463-5 463-6 463-7 464-1 464-2 464-3 464-4 464-5 464-6 464-7 465-1 465-2 17 17 17 17 NA 12 12 12 12 12 12 NA 16 465-3 465-4 465-5 465-6 465-7 466-1 466-2 466-3 466-4 466-5 466-6 466-7 467-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 467-2 467-3 467-4 467-5 467-6 467-7 468-1 468-2 468-3 468-4 468-5 468-6 468-7 12 12 12 12 12 12 NA 17 17 17 17 17 17 469-1 469-2 469-3 469-4 469-5 469-6 469-7 470-1 470-2 470-3 470-4 470-5 470-6 NA 9 9 9 9 9 9 NA 12 12 12 12 12 470-7 471-1 471-2 471-3 471-4 471-5 471-6 471-7 472-1 472-2 472-3 472-4 472-5 12 NA 16 16 16 16 16 16 NA 12 12 12 12 472-6 472-7 473-1 473-2 473-3 473-4 473-5 473-6 473-7 474-1 474-2 474-3 474-4 12 12 NA 14 14 14 14 14 14 NA 17 17 17 474-5 474-6 474-7 475-1 475-2 475-3 475-4 475-5 475-6 475-7 476-1 476-2 476-3 17 17 17 NA 12 12 12 12 12 12 NA 10 10 476-4 476-5 476-6 476-7 477-1 477-2 477-3 477-4 477-5 477-6 477-7 478-1 478-2 10 10 10 10 NA 12 12 12 12 12 12 NA 16 478-3 478-4 478-5 478-6 478-7 479-1 479-2 479-3 479-4 479-5 479-6 479-7 480-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 480-2 480-3 480-4 480-5 480-6 480-7 481-1 481-2 481-3 481-4 481-5 481-6 481-7 12 12 12 12 12 12 NA 7 7 7 7 7 7 482-1 482-2 482-3 482-4 482-5 482-6 482-7 483-1 483-2 483-3 483-4 483-5 483-6 NA 11 11 11 11 11 11 NA 14 14 14 14 14 483-7 484-1 484-2 484-3 484-4 484-5 484-6 484-7 485-1 485-2 485-3 485-4 485-5 14 NA 10 10 10 10 10 10 NA 12 12 12 12 485-6 485-7 486-1 486-2 486-3 486-4 486-5 486-6 486-7 487-1 487-2 487-3 487-4 12 12 NA 9 9 9 9 9 9 NA 15 15 15 487-5 487-6 487-7 488-1 488-2 488-3 488-4 488-5 488-6 488-7 489-1 489-2 489-3 15 15 15 NA 12 12 12 12 12 12 NA 12 12 489-4 489-5 489-6 489-7 490-1 490-2 490-3 490-4 490-5 490-6 490-7 491-1 491-2 12 12 12 12 NA 16 16 16 16 16 16 NA 12 491-3 491-4 491-5 491-6 491-7 492-1 492-2 492-3 492-4 492-5 492-6 492-7 493-1 12 12 12 12 12 NA 12 12 12 12 12 12 NA 493-2 493-3 493-4 493-5 493-6 493-7 494-1 494-2 494-3 494-4 494-5 494-6 494-7 14 14 14 14 14 14 NA 14 14 14 14 14 14 495-1 495-2 495-3 495-4 495-5 495-6 495-7 496-1 496-2 496-3 496-4 496-5 496-6 NA 14 14 14 14 14 14 NA 8 8 8 8 8 496-7 497-1 497-2 497-3 497-4 497-5 497-6 497-7 498-1 498-2 498-3 498-4 498-5 8 NA 14 14 14 14 14 14 NA 12 12 12 12 498-6 498-7 499-1 499-2 499-3 499-4 499-5 499-6 499-7 500-1 500-2 500-3 500-4 12 12 NA 11 11 11 11 11 11 NA 17 17 17 500-5 500-6 500-7 501-1 501-2 501-3 501-4 501-5 501-6 501-7 502-1 502-2 502-3 17 17 17 NA 12 12 12 12 12 12 NA 17 17 502-4 502-5 502-6 502-7 503-1 503-2 503-3 503-4 503-5 503-6 503-7 504-1 504-2 17 17 17 17 NA 12 12 12 12 12 12 NA 12 504-3 504-4 504-5 504-6 504-7 505-1 505-2 505-3 505-4 505-5 505-6 505-7 506-1 12 12 12 12 12 NA 16 16 16 16 16 16 NA 506-2 506-3 506-4 506-5 506-6 506-7 507-1 507-2 507-3 507-4 507-5 507-6 507-7 12 12 12 12 12 12 NA 12 12 12 12 12 12 508-1 508-2 508-3 508-4 508-5 508-6 508-7 509-1 509-2 509-3 509-4 509-5 509-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 509-7 510-1 510-2 510-3 510-4 510-5 510-6 510-7 511-1 511-2 511-3 511-4 511-5 12 NA 16 16 16 16 16 16 NA 13 13 13 13 511-6 511-7 512-1 512-2 512-3 512-4 512-5 512-6 512-7 513-1 513-2 513-3 513-4 13 13 NA 10 10 10 10 10 10 NA 5 5 5 513-5 513-6 513-7 514-1 514-2 514-3 514-4 514-5 514-6 514-7 515-1 515-2 515-3 5 5 5 NA 14 14 14 14 14 14 NA 12 12 515-4 515-5 515-6 515-7 516-1 516-2 516-3 516-4 516-5 516-6 516-7 517-1 517-2 12 12 12 12 NA 14 14 14 14 14 14 NA 13 517-3 517-4 517-5 517-6 517-7 518-1 518-2 518-3 518-4 518-5 518-6 518-7 519-1 13 13 13 13 13 NA 11 11 11 11 11 11 NA 519-2 519-3 519-4 519-5 519-6 519-7 520-1 520-2 520-3 520-4 520-5 520-6 520-7 12 12 12 12 12 12 NA 12 12 12 12 12 12 521-1 521-2 521-3 521-4 521-5 521-6 521-7 522-1 522-2 522-3 522-4 522-5 522-6 NA 16 16 16 16 16 16 NA 9 9 9 9 9 522-7 523-1 523-2 523-3 523-4 523-5 523-6 523-7 524-1 524-2 524-3 524-4 524-5 9 NA 10 10 10 10 10 10 NA 16 16 16 16 524-6 524-7 525-1 525-2 525-3 525-4 525-5 525-6 525-7 526-1 526-2 526-3 526-4 16 16 NA 12 12 12 12 12 12 NA 14 14 14 526-5 526-6 526-7 527-1 527-2 527-3 527-4 527-5 527-6 527-7 528-1 528-2 528-3 14 14 14 NA 16 16 16 16 16 16 NA 17 17 528-4 528-5 528-6 528-7 529-1 529-2 529-3 529-4 529-5 529-6 529-7 530-1 530-2 17 17 17 17 NA 12 12 12 12 12 12 NA 17 530-3 530-4 530-5 530-6 530-7 531-1 531-2 531-3 531-4 531-5 531-6 531-7 532-1 17 17 17 17 17 NA 16 16 16 16 16 16 NA 532-2 532-3 532-4 532-5 532-6 532-7 533-1 533-2 533-3 533-4 533-5 533-6 533-7 12 12 12 12 12 12 NA 13 13 13 13 13 13 534-1 534-2 534-3 534-4 534-5 534-6 534-7 535-1 535-2 535-3 535-4 535-5 535-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 535-7 536-1 536-2 536-3 536-4 536-5 536-6 536-7 537-1 537-2 537-3 537-4 537-5 12 NA 17 17 17 17 17 17 NA 16 16 16 16 537-6 537-7 538-1 538-2 538-3 538-4 538-5 538-6 538-7 539-1 539-2 539-3 539-4 16 16 NA 16 16 16 16 16 16 NA 17 17 17 539-5 539-6 539-7 540-1 540-2 540-3 540-4 540-5 540-6 540-7 541-1 541-2 541-3 17 17 17 NA 16 16 16 16 16 16 NA 14 14 541-4 541-5 541-6 541-7 542-1 542-2 542-3 542-4 542-5 542-6 542-7 543-1 543-2 14 14 14 14 NA 13 13 13 13 13 13 NA 17 543-3 543-4 543-5 543-6 543-7 544-1 544-2 544-3 544-4 544-5 544-6 544-7 545-1 17 17 17 17 17 NA 14 14 14 14 14 14 NA 545-2 545-3 545-4 545-5 545-6 545-7 546-1 546-2 546-3 546-4 546-5 546-6 546-7 12 12 12 12 12 12 NA 12 12 12 12 12 12 547-1 547-2 547-3 547-4 547-5 547-6 547-7 548-1 548-2 548-3 548-4 548-5 548-6 NA 12 12 12 12 12 12 NA 16 16 16 16 16 548-7 549-1 549-2 549-3 549-4 549-5 549-6 549-7 550-1 550-2 550-3 550-4 550-5 16 NA 12 12 12 12 12 12 NA 14 14 14 14 550-6 550-7 551-1 551-2 551-3 551-4 551-5 551-6 551-7 552-1 552-2 552-3 552-4 14 14 NA 16 16 16 16 16 16 NA 17 17 17 552-5 552-6 552-7 553-1 553-2 553-3 553-4 553-5 553-6 553-7 554-1 554-2 554-3 17 17 17 NA 16 16 16 16 16 16 NA 14 14 554-4 554-5 554-6 554-7 555-1 555-2 555-3 555-4 555-5 555-6 555-7 556-1 556-2 14 14 14 14 NA 15 15 15 15 15 15 NA 16 556-3 556-4 556-5 556-6 556-7 557-1 557-2 557-3 557-4 557-5 557-6 557-7 558-1 16 16 16 16 16 NA 12 12 12 12 12 12 NA 558-2 558-3 558-4 558-5 558-6 558-7 559-1 559-2 559-3 559-4 559-5 559-6 559-7 12 12 12 12 12 12 NA 16 16 16 16 16 16 560-1 560-2 560-3 560-4 560-5 560-6 560-7 561-1 561-2 561-3 561-4 561-5 561-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 561-7 562-1 562-2 562-3 562-4 562-5 562-6 562-7 563-1 563-2 563-3 563-4 563-5 12 NA 17 17 17 17 17 17 NA 14 14 14 14 563-6 563-7 564-1 564-2 564-3 564-4 564-5 564-6 564-7 565-1 565-2 565-3 565-4 14 14 NA 14 14 14 14 14 14 NA 11 11 11 565-5 565-6 565-7 566-1 566-2 566-3 566-4 566-5 566-6 566-7 567-1 567-2 567-3 11 11 11 NA 16 16 16 16 16 16 NA 10 10 567-4 567-5 567-6 567-7 568-1 568-2 568-3 568-4 568-5 568-6 568-7 569-1 569-2 10 10 10 10 NA 14 14 14 14 14 14 NA 12 569-3 569-4 569-5 569-6 569-7 570-1 570-2 570-3 570-4 570-5 570-6 570-7 571-1 12 12 12 12 12 NA 12 12 12 12 12 12 NA 571-2 571-3 571-4 571-5 571-6 571-7 572-1 572-2 572-3 572-4 572-5 572-6 572-7 12 12 12 12 12 12 NA 11 11 11 11 11 11 573-1 573-2 573-3 573-4 573-5 573-6 573-7 574-1 574-2 574-3 574-4 574-5 574-6 NA 12 12 12 12 12 12 NA 12 12 12 12 12 574-7 575-1 575-2 575-3 575-4 575-5 575-6 575-7 576-1 576-2 576-3 576-4 576-5 12 NA 14 14 14 14 14 14 NA 12 12 12 12 576-6 576-7 577-1 577-2 577-3 577-4 577-5 577-6 577-7 578-1 578-2 578-3 578-4 12 12 NA 12 12 12 12 12 12 NA 13 13 13 578-5 578-6 578-7 579-1 579-2 579-3 579-4 579-5 579-6 579-7 580-1 580-2 580-3 13 13 13 NA 16 16 16 16 16 16 NA 12 12 580-4 580-5 580-6 580-7 581-1 581-2 581-3 581-4 581-5 581-6 581-7 582-1 582-2 12 12 12 12 NA 17 17 17 17 17 17 NA 12 582-3 582-4 582-5 582-6 582-7 583-1 583-2 583-3 583-4 583-5 583-6 583-7 584-1 12 12 12 12 12 NA 12 12 12 12 12 12 NA 584-2 584-3 584-4 584-5 584-6 584-7 585-1 585-2 585-3 585-4 585-5 585-6 585-7 12 12 12 12 12 12 NA 16 16 16 16 16 16 586-1 586-2 586-3 586-4 586-5 586-6 586-7 587-1 587-2 587-3 587-4 587-5 587-6 NA 10 10 10 10 10 10 NA 12 12 12 12 12 587-7 588-1 588-2 588-3 588-4 588-5 588-6 588-7 589-1 589-2 589-3 589-4 589-5 12 NA 16 16 16 16 16 16 NA 12 12 12 12 589-6 589-7 590-1 590-2 590-3 590-4 590-5 590-6 590-7 591-1 591-2 591-3 591-4 12 12 NA 12 12 12 12 12 12 NA 8 8 8 591-5 591-6 591-7 592-1 592-2 592-3 592-4 592-5 592-6 592-7 593-1 593-2 593-3 8 8 8 NA 13 13 13 13 13 13 NA 8 8 593-4 593-5 593-6 593-7 594-1 594-2 594-3 594-4 594-5 594-6 594-7 595-1 595-2 8 8 8 8 NA 12 12 12 12 12 12 NA 12 595-3 595-4 595-5 595-6 595-7 12 12 12 12 12 $lwage 1-1 1-2 1-3 1-4 1-5 1-6 1-7 2-1 2-2 2-3 NA 5.56068 5.72031 5.99645 5.99645 6.06146 6.17379 NA 6.16331 6.21461 2-4 2-5 2-6 2-7 3-1 3-2 3-3 3-4 3-5 3-6 6.26340 6.54391 6.69703 6.79122 NA 5.65249 6.43615 6.54822 6.60259 6.69580 3-7 4-1 4-2 4-3 4-4 4-5 4-6 4-7 5-1 5-2 6.77878 NA 6.15698 6.23832 6.30079 6.35957 6.46925 6.56244 NA 6.43775 5-3 5-4 5-5 5-6 5-7 6-1 6-2 6-3 6-4 6-5 6.62007 6.63332 6.98286 7.04752 7.31322 NA 6.90575 6.90575 6.90776 7.00307 6-6 6-7 7-1 7-2 7-3 7-4 7-5 7-6 7-7 8-1 7.06902 7.52023 NA 6.13340 6.17379 6.21261 6.31355 6.37502 6.44572 NA 8-2 8-3 8-4 8-5 8-6 8-7 9-1 9-2 9-3 9-4 6.33150 6.40357 6.54391 6.56244 6.59167 6.81783 NA 6.55108 6.55108 6.80239 9-5 9-6 9-7 10-1 10-2 10-3 10-4 10-5 10-6 10-7 6.90776 7.09008 7.17012 NA 6.39693 6.43775 6.43775 6.43775 6.52209 6.61338 11-1 11-2 11-3 11-4 11-5 11-6 11-7 12-1 12-2 12-3 NA 6.65801 6.72623 6.80239 6.90776 7.03966 7.12769 NA 6.55108 6.62936 12-4 12-5 12-6 12-7 13-1 13-2 13-3 13-4 13-5 13-6 6.72263 6.73340 6.72263 6.95177 NA 6.90575 6.90073 7.17012 7.26543 7.25912 13-7 14-1 14-2 14-3 14-4 14-5 14-6 14-7 15-1 15-2 7.25488 NA 6.80239 6.80239 6.92560 7.00307 7.13090 7.12448 NA 5.94017 15-3 15-4 15-5 15-6 15-7 16-1 16-2 16-3 16-4 16-5 5.86079 5.99894 6.08450 6.39526 6.47697 NA 5.97889 5.78383 5.85793 5.95324 16-6 16-7 17-1 17-2 17-3 17-4 17-5 17-6 17-7 18-1 6.14633 6.17587 NA 6.62007 6.49224 6.77992 6.74524 7.09008 7.13090 NA 18-2 18-3 18-4 18-5 18-6 18-7 19-1 19-2 19-3 19-4 6.90575 6.90575 7.60090 7.64969 7.71869 7.78322 NA 6.38688 6.47389 6.54247 19-5 19-6 19-7 20-1 20-2 20-3 20-4 20-5 20-6 20-7 6.67582 6.74170 6.83195 NA 6.78333 6.85751 6.98472 7.05790 7.20564 7.10988 21-1 21-2 21-3 21-4 21-5 21-6 21-7 22-1 22-2 22-3 NA 6.05912 6.05209 5.99146 6.21461 6.32436 6.44254 NA 6.59987 6.59987 22-4 22-5 22-6 22-7 23-1 23-2 23-3 23-4 23-5 23-6 6.68461 6.72743 6.88244 6.95655 NA 6.90575 6.90575 7.49554 7.66153 7.86327 23-7 24-1 24-2 24-3 24-4 24-5 24-6 24-7 25-1 25-2 8.03916 NA 5.43808 5.52146 5.57973 5.66988 5.73657 5.84354 NA 6.52942 25-3 25-4 25-5 25-6 25-7 26-1 26-2 26-3 26-4 26-5 6.57925 6.71538 6.68461 6.79122 6.97541 NA 6.47697 6.55108 6.86693 6.85646 26-6 26-7 27-1 27-2 27-3 27-4 27-5 27-6 27-7 28-1 7.27932 7.54961 NA 6.01616 6.90575 7.04752 7.26193 7.09008 6.96319 NA 28-2 28-3 28-4 28-5 28-6 28-7 29-1 29-2 29-3 29-4 6.73340 6.69703 6.68461 6.74524 6.73102 6.80239 NA 6.90575 6.90575 7.06219 29-5 29-6 29-7 30-1 30-2 30-3 30-4 30-5 30-6 30-7 7.57558 7.67322 7.47307 NA 6.19644 6.29895 6.33150 6.36303 6.48158 6.57786 31-1 31-2 31-3 31-4 31-5 31-6 31-7 32-1 32-2 32-3 NA 6.90575 6.90575 7.31322 7.24423 7.48437 7.54961 NA 6.90575 6.90575 32-4 32-5 32-6 32-7 33-1 33-2 33-3 33-4 33-5 33-6 7.46737 7.40853 7.60589 8.04879 NA 6.90575 6.90575 7.17012 7.27932 7.24065 33-7 34-1 34-2 34-3 34-4 34-5 34-6 34-7 35-1 35-2 7.37776 NA 5.75890 5.86079 5.82305 5.94803 6.24804 6.20658 NA 6.51471 35-3 35-4 35-5 35-6 35-7 36-1 36-2 36-3 36-4 36-5 6.61070 6.39693 6.75227 6.88653 7.01212 NA 6.62007 6.77422 7.13090 7.08841 36-6 36-7 37-1 37-2 37-3 37-4 37-5 37-6 37-7 38-1 7.13090 6.90776 NA 6.90575 6.74170 7.00307 7.13090 7.14283 7.28276 NA 38-2 38-3 38-4 38-5 38-6 38-7 39-1 39-2 39-3 39-4 6.05209 6.05209 6.05209 6.36819 6.43615 6.54965 NA 6.47697 6.58617 6.47697 39-5 39-6 39-7 40-1 40-2 40-3 40-4 40-5 40-6 40-7 6.52942 6.80239 6.77992 NA 6.35784 6.49979 6.58203 6.69456 6.78784 6.90776 41-1 41-2 41-3 41-4 41-5 41-6 41-7 42-1 42-2 42-3 NA 6.90575 6.90575 7.37776 7.49109 7.57558 7.52833 NA 6.21860 6.49375 42-4 42-5 42-6 42-7 43-1 43-2 43-3 43-4 43-5 43-6 6.58064 6.68960 6.79347 6.92461 NA 6.68461 6.84375 6.90776 7.00307 7.13090 43-7 44-1 44-2 44-3 44-4 44-5 44-6 44-7 45-1 45-2 7.40853 NA 6.90575 6.90575 7.20786 7.31322 7.43838 7.49332 NA 6.22456 45-3 45-4 45-5 45-6 45-7 46-1 46-2 46-3 46-4 46-5 6.33328 6.30992 6.47697 6.63332 6.68211 NA 6.12687 6.23245 6.32615 6.32436 46-6 46-7 47-1 47-2 47-3 47-4 47-5 47-6 47-7 48-1 6.46770 6.60665 NA 6.90575 6.90575 7.04752 7.09008 7.20786 7.11802 NA 48-2 48-3 48-4 48-5 48-6 48-7 49-1 49-2 49-3 49-4 6.90575 6.90575 7.13090 7.22621 7.13090 7.31322 NA 6.90575 6.90575 7.60090 49-5 49-6 49-7 50-1 50-2 50-3 50-4 50-5 50-6 50-7 7.67601 7.70210 7.69621 NA 6.27099 6.39859 6.48311 6.55678 6.65544 6.74876 51-1 51-2 51-3 51-4 51-5 51-6 51-7 52-1 52-2 52-3 NA 6.90575 6.90575 7.60090 7.64969 7.82405 7.86327 NA 6.90575 6.90575 52-4 52-5 52-6 52-7 53-1 53-2 53-3 53-4 53-5 53-6 7.24423 7.13090 7.46737 7.60090 NA 6.43615 6.90575 6.95655 6.95655 7.06048 53-7 54-1 54-2 54-3 54-4 54-5 54-6 54-7 55-1 55-2 7.20786 NA 6.51175 6.59030 6.68461 6.74524 6.89770 7.07750 NA 6.65415 55-3 55-4 55-5 55-6 55-7 56-1 56-2 56-3 56-4 56-5 6.85013 6.85646 6.92363 7.04141 7.13330 NA 6.10925 6.16331 6.33328 6.33328 56-6 56-7 57-1 57-2 57-3 57-4 57-5 57-6 57-7 58-1 6.43775 6.58617 NA 6.04025 6.28972 6.01616 6.57786 6.55108 6.53233 NA 58-2 58-3 58-4 58-5 58-6 58-7 59-1 59-2 59-3 59-4 6.55108 6.68461 6.69703 6.90776 7.00307 7.16240 NA 6.90575 6.90575 7.34601 59-5 59-6 59-7 60-1 60-2 60-3 60-4 60-5 60-6 60-7 7.43838 7.48156 7.60090 NA 5.97635 6.04025 6.04025 6.04025 6.12030 6.21860 61-1 61-2 61-3 61-4 61-5 61-6 61-7 62-1 62-2 62-3 NA 6.78559 6.88857 6.88755 6.96885 6.85646 6.73815 NA 6.58064 6.83518 62-4 62-5 62-6 62-7 63-1 63-2 63-3 63-4 63-5 63-6 6.91274 6.95845 6.92560 7.23778 NA 6.90575 6.90575 7.49554 7.54961 7.52564 63-7 64-1 64-2 64-3 64-4 64-5 64-6 64-7 65-1 65-2 7.72886 NA 6.35611 6.42162 6.47697 6.53669 6.55108 6.68461 NA 6.55536 65-3 65-4 65-5 65-6 65-7 66-1 66-2 66-3 66-4 66-5 6.62274 6.72263 6.77992 6.93828 7.03439 NA 6.30810 6.39693 6.51767 6.61070 66-6 66-7 67-1 67-2 67-3 67-4 67-5 67-6 67-7 68-1 6.68711 6.77079 NA 5.96615 6.02828 6.15273 6.21461 6.30992 6.44572 NA 68-2 68-3 68-4 68-5 68-6 68-7 69-1 69-2 69-3 69-4 6.45834 6.50728 6.56808 6.66823 6.75577 6.80239 NA 6.40523 6.55678 6.62007 69-5 69-6 69-7 70-1 70-2 70-3 70-4 70-5 70-6 70-7 6.62007 6.70319 6.83518 NA 6.47697 6.47697 6.62007 6.80239 6.90776 6.90776 71-1 71-2 71-3 71-4 71-5 71-6 71-7 72-1 72-2 72-3 NA 6.69703 6.80239 6.89770 6.95655 7.13090 7.24423 NA 6.47697 6.62007 72-4 72-5 72-6 72-7 73-1 73-2 73-3 73-4 73-5 73-6 6.62007 6.68461 6.85646 6.90776 NA 6.46147 6.65544 6.73459 6.81344 6.91473 73-7 74-1 74-2 74-3 74-4 74-5 74-6 74-7 75-1 75-2 6.85330 NA 5.41610 5.52146 5.59842 5.70378 5.76832 5.88610 NA 5.66988 75-3 75-4 75-5 75-6 75-7 76-1 76-2 76-3 76-4 76-5 5.99146 6.08677 6.17379 6.25575 6.43775 NA 5.16479 5.62762 5.84354 5.96101 76-6 76-7 77-1 77-2 77-3 77-4 77-5 77-6 77-7 78-1 6.02828 6.15273 NA 5.52146 5.70378 6.20456 6.05912 6.19848 6.16121 NA 78-2 78-3 78-4 78-5 78-6 78-7 79-1 79-2 79-3 79-4 5.70378 5.78383 5.78383 6.16331 6.21461 6.21461 NA 5.42935 5.47227 5.64545 79-5 79-6 79-7 80-1 80-2 80-3 80-4 80-5 80-6 80-7 5.68698 5.85793 5.95324 NA 6.74524 6.81674 6.65929 7.09008 7.07918 6.98472 81-1 81-2 81-3 81-4 81-5 81-6 81-7 82-1 82-2 82-3 NA 6.35437 6.33683 6.43775 6.39693 7.23346 7.35883 NA 6.62007 6.66568 82-4 82-5 82-6 82-7 83-1 83-2 83-3 83-4 83-5 83-6 6.81454 6.88857 6.95750 6.93342 NA 6.12249 6.08904 6.29157 6.39693 6.49072 83-7 84-1 84-2 84-3 84-4 84-5 84-6 84-7 85-1 85-2 6.59715 NA 6.58617 6.62007 6.62007 6.40192 6.77422 6.92560 NA 6.16331 85-3 85-4 85-5 85-6 85-7 86-1 86-2 86-3 86-4 86-5 6.22654 6.32077 6.45520 6.51471 6.62007 NA 6.21461 6.71538 6.43775 6.65673 86-6 86-7 87-1 87-2 87-3 87-4 87-5 87-6 87-7 88-1 6.65929 6.86485 NA 6.54247 6.60259 6.74641 6.86485 6.95845 7.01392 NA 88-2 88-3 88-4 88-5 88-6 88-7 89-1 89-2 89-3 89-4 6.36990 6.39693 6.55962 6.73102 6.86485 7.15462 NA 6.05209 6.02345 6.30992 89-5 89-6 89-7 90-1 90-2 90-3 90-4 90-5 90-6 90-7 6.38012 6.65929 6.68461 NA 6.21461 6.21461 6.30992 6.39693 6.21461 6.39693 91-1 91-2 91-3 91-4 91-5 91-6 91-7 92-1 92-2 92-3 NA 6.34388 6.41346 6.72743 6.76504 6.85646 7.00307 NA 6.47697 6.58617 92-4 92-5 92-6 92-7 93-1 93-2 93-3 93-4 93-5 93-6 6.73340 6.81344 6.96130 6.50279 NA 5.99146 6.17794 6.17794 6.39693 6.50129 93-7 94-1 94-2 94-3 94-4 94-5 94-6 94-7 95-1 95-2 6.60665 NA 6.79122 6.84588 6.87213 6.95655 7.09672 7.16085 NA 6.28413 95-3 95-4 95-5 95-6 95-7 96-1 96-2 96-3 96-4 96-5 6.32794 6.47697 6.50429 6.58755 6.76504 NA 6.90575 6.90575 7.34601 7.20786 96-6 96-7 97-1 97-2 97-3 97-4 97-5 97-6 97-7 98-1 7.52294 7.67601 NA 6.10925 6.22654 6.31355 6.41999 6.51471 6.62804 NA 98-2 98-3 98-4 98-5 98-6 98-7 99-1 99-2 99-3 99-4 6.72743 6.89770 6.98379 7.07918 7.17012 7.14756 NA 5.78074 5.82600 5.84644 99-5 99-6 99-7 100-1 100-2 100-3 100-4 100-5 100-6 100-7 5.95584 6.06611 6.19032 NA 5.99146 6.10925 6.39693 6.39693 6.47697 6.47697 101-1 101-2 101-3 101-4 101-5 101-6 101-7 102-1 102-2 102-3 NA 6.69703 6.79682 6.89467 6.96885 6.92560 7.18007 NA 6.68461 6.80239 102-4 102-5 102-6 102-7 103-1 103-2 103-3 103-4 103-5 103-6 6.90776 7.04752 7.04752 7.31322 NA 6.30992 6.58617 6.62007 6.90776 6.96602 103-7 104-1 104-2 104-3 104-4 104-5 104-6 104-7 105-1 105-2 7.06902 NA 5.73657 5.81413 5.89990 5.94017 6.13123 6.13123 NA 6.67203 105-3 105-4 105-5 105-6 105-7 106-1 106-2 106-3 106-4 106-5 6.84268 6.84055 6.85646 6.99485 7.05618 NA 6.36303 6.47697 6.57228 6.65929 106-6 106-7 107-1 107-2 107-3 107-4 107-5 107-6 107-7 108-1 6.80239 6.58893 NA 6.90575 6.90575 7.13090 7.13090 7.31322 7.32975 NA 108-2 108-3 108-4 108-5 108-6 108-7 109-1 109-2 109-3 109-4 6.24611 6.21461 6.47697 6.49677 6.61473 6.74524 NA 6.74524 6.81344 6.87213 109-5 109-6 109-7 110-1 110-2 110-3 110-4 110-5 110-6 110-7 7.10661 7.24423 7.29980 NA 6.50728 6.55108 6.87730 6.55108 7.04752 6.83411 111-1 111-2 111-3 111-4 111-5 111-6 111-7 112-1 112-2 112-3 NA 6.10925 6.31355 6.45362 6.68461 6.79122 6.83518 NA 6.90575 6.90575 112-4 112-5 112-6 112-7 113-1 113-2 113-3 113-4 113-5 113-6 7.11070 7.25134 7.34601 7.34601 NA 6.90575 6.90575 7.17012 7.45530 7.56008 113-7 114-1 114-2 114-3 114-4 114-5 114-6 114-7 115-1 115-2 7.37212 NA 6.28972 6.36130 6.43615 6.43455 6.41182 6.36990 NA 6.69456 115-3 115-4 115-5 115-6 115-7 116-1 116-2 116-3 116-4 116-5 6.76849 6.90776 7.03966 7.02554 7.22766 NA 6.74052 6.82979 6.80239 6.94698 116-6 116-7 117-1 117-2 117-3 117-4 117-5 117-6 117-7 118-1 7.01031 7.13569 NA 6.57925 6.39693 6.62007 6.70930 6.76849 6.93537 NA 118-2 118-3 118-4 118-5 118-6 118-7 119-1 119-2 119-3 119-4 6.58617 6.68461 6.71538 6.77422 6.85646 6.91672 NA 6.90575 6.90575 8.08641 119-5 119-6 119-7 120-1 120-2 120-3 120-4 120-5 120-6 120-7 8.22951 7.90986 8.12089 NA 6.24417 6.39693 6.90776 6.90776 6.77194 6.86693 121-1 121-2 121-3 121-4 121-5 121-6 121-7 122-1 122-2 122-3 NA 6.90575 6.90575 7.46737 7.52294 7.60090 7.50769 NA 6.62007 6.71417 122-4 122-5 122-6 122-7 123-1 123-2 123-3 123-4 123-5 123-6 6.81892 6.97635 6.92658 7.00307 NA 6.30992 6.55108 6.56526 6.74524 6.93925 123-7 124-1 124-2 124-3 124-4 124-5 124-6 124-7 125-1 125-2 6.95655 NA 6.89770 6.90575 7.17012 7.31322 7.37776 7.49554 NA 6.22059 125-3 125-4 125-5 125-6 125-7 126-1 126-2 126-3 126-4 126-5 6.30079 6.37673 6.50129 6.62007 6.78672 NA 6.41673 6.48920 6.61740 6.70564 126-6 126-7 127-1 127-2 127-3 127-4 127-5 127-6 127-7 128-1 6.85646 6.43775 NA 6.83518 6.90575 7.08171 7.09838 7.37776 7.43838 NA 128-2 128-3 128-4 128-5 128-6 128-7 129-1 129-2 129-3 129-4 5.52146 5.89715 6.35611 6.47697 6.54679 6.65673 NA 6.07074 6.12687 6.21461 129-5 129-6 129-7 130-1 130-2 130-3 130-4 130-5 130-6 130-7 6.27288 6.32794 6.44413 NA 6.48311 6.52503 6.60800 6.71538 6.84055 6.96979 131-1 131-2 131-3 131-4 131-5 131-6 131-7 132-1 132-2 132-3 NA 6.48311 6.55393 6.59987 6.70930 6.85646 6.96791 NA 6.90575 6.90575 132-4 132-5 132-6 132-7 133-1 133-2 133-3 133-4 133-5 133-6 7.43838 7.49554 7.57558 7.64969 NA 6.32794 6.47697 6.58617 6.68461 6.68461 133-7 134-1 134-2 134-3 134-4 134-5 134-6 134-7 135-1 135-2 6.94119 NA 5.72031 5.95842 6.02102 6.21461 6.29157 6.37161 NA 6.65286 135-3 135-4 135-5 135-6 135-7 136-1 136-2 136-3 136-4 136-5 6.77422 7.09008 6.90776 7.10824 7.02820 NA 6.90575 6.57925 7.18311 7.31322 136-6 136-7 137-1 137-2 137-3 137-4 137-5 137-6 137-7 138-1 7.40853 7.38647 NA 6.63726 6.78559 6.91274 6.98286 6.95655 7.01930 NA 138-2 138-3 138-4 138-5 138-6 138-7 139-1 139-2 139-3 139-4 6.90575 6.90575 7.46737 7.50384 7.58579 7.66388 NA 6.43775 6.62007 6.62007 139-5 139-6 139-7 140-1 140-2 140-3 140-4 140-5 140-6 140-7 6.77422 7.13090 7.15070 NA 6.23048 6.33328 6.36475 6.45047 6.57228 6.99393 141-1 141-2 141-3 141-4 141-5 141-6 141-7 142-1 142-2 142-3 NA 5.88610 5.78383 5.99146 6.10925 6.32077 6.43775 NA 6.72143 6.68085 142-4 142-5 142-6 142-7 143-1 143-2 143-3 143-4 143-5 143-6 6.72623 6.87730 6.87730 6.96130 NA 6.37161 6.47697 6.58617 6.68461 6.81344 143-7 144-1 144-2 144-3 144-4 144-5 144-6 144-7 145-1 145-2 6.87730 NA 6.23048 6.30992 6.39693 6.62007 6.77422 6.74993 NA 6.77422 145-3 145-4 145-5 145-6 145-7 146-1 146-2 146-3 146-4 146-5 6.90575 7.31322 7.49554 6.91374 7.10988 NA 6.90575 6.90575 7.13090 7.17012 146-6 146-7 147-1 147-2 147-3 147-4 147-5 147-6 147-7 148-1 7.24423 7.31322 NA 6.47697 6.61338 6.72143 7.00307 7.00307 7.19818 NA 148-2 148-3 148-4 148-5 148-6 148-7 149-1 149-2 149-3 149-4 5.75257 5.75257 6.23441 6.47697 6.62007 6.95655 NA 6.53669 6.84162 6.68461 149-5 149-6 149-7 150-1 150-2 150-3 150-4 150-5 150-6 150-7 6.84055 6.90776 6.85646 NA 6.27099 6.53959 6.56667 6.59578 6.83841 6.94022 151-1 151-2 151-3 151-4 151-5 151-6 151-7 152-1 152-2 152-3 NA 6.30992 6.43294 6.48158 6.60665 6.63332 6.74524 NA 5.41610 5.45959 152-4 152-5 152-6 152-7 153-1 153-2 153-3 153-4 153-5 153-6 5.45959 5.57973 5.70378 5.70378 NA 6.22456 6.30992 6.30992 6.38519 6.43455 153-7 154-1 154-2 154-3 154-4 154-5 154-6 154-7 155-1 155-2 6.55108 NA 6.74524 6.72743 6.88244 6.92756 6.89972 7.02731 NA 6.52209 155-3 155-4 155-5 155-6 155-7 156-1 156-2 156-3 156-4 156-5 6.68461 6.80239 7.00307 7.00307 7.27932 NA 6.65286 6.77422 6.80239 6.85646 156-6 156-7 157-1 157-2 157-3 157-4 157-5 157-6 157-7 158-1 7.00307 7.09008 NA 6.54535 6.45362 6.57786 6.67456 6.77308 6.86901 NA 158-2 158-3 158-4 158-5 158-6 158-7 159-1 159-2 159-3 159-4 6.63857 6.68461 6.73340 6.88244 6.92560 7.04141 NA 6.55108 6.58617 6.65286 159-5 159-6 159-7 160-1 160-2 160-3 160-4 160-5 160-6 160-7 6.43775 6.62007 6.80239 NA 6.62007 6.80239 6.85646 6.90776 6.95655 7.09008 161-1 161-2 161-3 161-4 161-5 161-6 161-7 162-1 162-2 162-3 NA 6.10925 6.21461 6.21461 6.55820 6.66568 6.86171 NA 5.74300 6.05209 162-4 162-5 162-6 162-7 163-1 163-2 163-3 163-4 163-5 163-6 6.07993 6.30992 6.39693 6.47697 NA 6.04025 6.18415 6.21461 6.30992 6.45520 163-7 164-1 164-2 164-3 164-4 164-5 164-6 164-7 165-1 165-2 6.52209 NA 6.07304 6.19441 6.31897 6.39526 6.47697 6.59715 NA 6.17794 165-3 165-4 165-5 165-6 165-7 166-1 166-2 166-3 166-4 166-5 6.31716 6.41182 6.39693 6.51471 6.61338 NA 6.71538 6.78559 6.88449 7.00307 166-6 166-7 167-1 167-2 167-3 167-4 167-5 167-6 167-7 168-1 7.09008 7.17012 NA 5.01064 5.01064 5.52146 5.66988 5.73657 5.81413 NA 168-2 168-3 168-4 168-5 168-6 168-7 169-1 169-2 169-3 169-4 6.23245 6.57925 6.65286 6.74524 7.49554 8.16052 NA 6.90575 6.90575 7.40853 169-5 169-6 169-7 170-1 170-2 170-3 170-4 170-5 170-6 170-7 7.46737 7.52294 7.46737 NA 5.67332 5.74620 5.82895 6.07993 5.99146 5.99146 171-1 171-2 171-3 171-4 171-5 171-6 171-7 172-1 172-2 172-3 NA 5.99146 5.92693 6.07993 6.07993 6.26340 6.43775 NA 6.90575 6.90575 172-4 172-5 172-6 172-7 173-1 173-2 173-3 173-4 173-5 173-6 7.22766 7.29302 7.33368 7.57353 NA 6.90575 6.90575 7.34601 7.31322 7.55486 173-7 174-1 174-2 174-3 174-4 174-5 174-6 174-7 175-1 175-2 7.71869 NA 6.08222 6.51323 6.62141 6.69950 6.79571 6.89669 NA 5.96615 175-3 175-4 175-5 175-6 175-7 176-1 176-2 176-3 176-4 176-5 6.11147 6.17379 6.22059 6.28972 6.36475 NA 6.43615 6.65929 6.74524 6.85646 176-6 176-7 177-1 177-2 177-3 177-4 177-5 177-6 177-7 178-1 6.90776 7.13330 NA 6.77422 6.82546 6.07993 6.90776 6.21461 6.70073 NA 178-2 178-3 178-4 178-5 178-6 178-7 179-1 179-2 179-3 179-4 6.10032 6.15698 6.24222 6.38351 6.42487 6.57228 NA 5.74620 5.92693 6.01616 179-5 179-6 179-7 180-1 180-2 180-3 180-4 180-5 180-6 180-7 5.94803 6.05209 6.32436 NA 6.28972 6.39693 6.54535 6.73102 7.49332 6.73102 181-1 181-2 181-3 181-4 181-5 181-6 181-7 182-1 182-2 182-3 NA 6.55108 6.86693 6.92560 7.06476 7.13090 7.43838 NA 5.85793 5.88610 182-4 182-5 182-6 182-7 183-1 183-2 183-3 183-4 183-5 183-6 5.99146 6.02828 6.10925 6.16331 NA 5.42053 5.51745 5.19296 5.34711 5.64191 183-7 184-1 184-2 184-3 184-4 184-5 184-6 184-7 185-1 185-2 5.65948 NA 6.55393 6.71538 6.62407 6.83087 6.95655 6.79010 NA 6.55108 185-3 185-4 185-5 185-6 185-7 186-1 186-2 186-3 186-4 186-5 6.62007 6.70564 6.78559 6.88857 7.00760 NA 6.80239 6.85646 6.98008 7.09589 186-6 186-7 187-1 187-2 187-3 187-4 187-5 187-6 187-7 188-1 7.48829 7.45124 NA 6.90575 6.90575 7.11639 7.07412 7.05790 7.02643 NA 188-2 188-3 188-4 188-5 188-6 188-7 189-1 189-2 189-3 189-4 6.62007 6.62007 6.83303 6.92560 7.02376 7.06305 NA 6.90575 6.90575 7.04752 189-5 189-6 189-7 190-1 190-2 190-3 190-4 190-5 190-6 190-7 7.09008 7.13090 6.99942 NA 6.09582 6.29157 6.40523 6.62007 6.71901 6.82546 191-1 191-2 191-3 191-4 191-5 191-6 191-7 192-1 192-2 192-3 NA 6.10032 6.29157 6.29157 6.37161 6.47389 6.54679 NA 6.28600 6.45990 192-4 192-5 192-6 192-7 193-1 193-2 193-3 193-4 193-5 193-6 6.54965 6.64639 6.76849 6.84162 NA 6.90575 6.90575 7.36518 7.42476 7.46737 193-7 194-1 194-2 194-3 194-4 194-5 194-6 194-7 195-1 195-2 7.28139 NA 6.54965 6.83518 6.79794 6.80128 6.91075 6.69084 NA 6.44572 195-3 195-4 195-5 195-6 195-7 196-1 196-2 196-3 196-4 196-5 6.62007 6.69703 6.82979 6.90776 6.72263 NA 6.05209 6.21461 6.43775 6.58617 196-6 196-7 197-1 197-2 197-3 197-4 197-5 197-6 197-7 198-1 6.68461 6.74524 NA 5.53339 5.82008 5.89990 5.97635 6.05209 6.12905 NA 198-2 198-3 198-4 198-5 198-6 198-7 199-1 199-2 199-3 199-4 6.68461 6.68461 6.77422 6.84588 7.02554 7.08841 NA 6.39693 6.51915 6.60394 199-5 199-6 199-7 200-1 200-2 200-3 200-4 200-5 200-6 200-7 6.73815 6.84268 6.90776 NA 6.17379 6.33683 6.35437 6.39526 6.55108 6.68461 201-1 201-2 201-3 201-4 201-5 201-6 201-7 202-1 202-2 202-3 NA 6.16331 6.24222 6.36647 6.44254 6.51026 6.62407 NA 5.90808 5.92693 202-4 202-5 202-6 202-7 203-1 203-2 203-3 203-4 203-5 203-6 6.10702 6.26149 6.26149 6.31716 NA 6.07993 6.07993 6.13773 6.18826 6.18826 203-7 204-1 204-2 204-3 204-4 204-5 204-6 204-7 205-1 205-2 5.96615 NA 6.47697 6.53669 6.58617 6.67203 6.80239 6.93731 NA 6.54679 205-3 205-4 205-5 205-6 205-7 206-1 206-2 206-3 206-4 206-5 6.68461 6.90776 6.69456 6.90776 6.93828 NA 6.90575 6.90575 7.34278 7.47591 206-6 206-7 207-1 207-2 207-3 207-4 207-5 207-6 207-7 208-1 7.50108 7.21744 NA 6.90575 6.90575 7.31322 7.70661 6.98286 7.46737 NA 208-2 208-3 208-4 208-5 208-6 208-7 209-1 209-2 209-3 209-4 5.39363 5.52146 5.75257 5.75257 5.81413 5.95324 NA 6.55108 6.67203 6.66568 209-5 209-6 209-7 210-1 210-2 210-3 210-4 210-5 210-6 210-7 6.67203 6.71538 6.80239 NA 6.62007 6.62007 6.71538 6.80239 6.85646 6.90776 211-1 211-2 211-3 211-4 211-5 211-6 211-7 212-1 212-2 212-3 NA 6.77422 6.90575 6.90776 7.36960 7.45298 7.64969 NA 6.90575 6.90575 212-4 212-5 212-6 212-7 213-1 213-2 213-3 213-4 213-5 213-6 7.09008 7.20786 7.31322 7.20117 NA 6.90575 6.90575 7.00307 7.09008 7.13090 213-7 214-1 214-2 214-3 214-4 214-5 214-6 214-7 215-1 215-2 7.08171 NA 6.28600 6.35437 6.43775 6.43775 6.47697 6.55108 NA 6.26340 215-3 215-4 215-5 215-6 215-7 216-1 216-2 216-3 216-4 216-5 6.17379 6.10925 6.10925 6.30992 6.21461 NA 6.85646 6.90575 7.04752 7.13090 216-6 216-7 217-1 217-2 217-3 217-4 217-5 217-6 217-7 218-1 7.24423 6.99485 NA 6.05209 6.05209 6.21461 6.33328 6.43775 6.19236 NA 218-2 218-3 218-4 218-5 218-6 218-7 219-1 219-2 219-3 219-4 6.31536 6.33328 6.40192 6.54103 6.54679 6.65028 NA 6.81344 6.80239 6.95655 219-5 219-6 219-7 220-1 220-2 220-3 220-4 220-5 220-6 220-7 7.08841 7.36455 7.49554 NA 6.47851 6.55393 6.62804 6.74524 6.82979 7.09008 221-1 221-2 221-3 221-4 221-5 221-6 221-7 222-1 222-2 222-3 NA 6.90575 6.90575 7.20786 7.29302 7.37776 7.15462 NA 5.70378 6.26340 222-4 222-5 222-6 222-7 223-1 223-2 223-3 223-4 223-5 223-6 5.92693 5.52146 5.99146 6.16331 NA 6.10925 6.24417 6.28227 6.38519 6.48920 223-7 224-1 224-2 224-3 224-4 224-5 224-6 224-7 225-1 225-2 6.55678 NA 6.90575 6.90575 7.03878 7.12287 7.12287 7.17012 NA 6.02345 225-3 225-4 225-5 225-6 225-7 226-1 226-2 226-3 226-4 226-5 6.25383 6.33328 6.47697 6.49072 6.84268 NA 5.57973 5.66988 5.82600 5.87493 226-6 226-7 227-1 227-2 227-3 227-4 227-5 227-6 227-7 228-1 5.89990 6.10925 NA 6.90575 6.90575 7.11070 7.13090 6.67077 6.90776 NA 228-2 228-3 228-4 228-5 228-6 228-7 229-1 229-2 229-3 229-4 6.14633 6.55108 6.80239 6.90776 7.09008 7.24423 NA 6.68461 6.74524 6.74524 229-5 229-6 229-7 230-1 230-2 230-3 230-4 230-5 230-6 230-7 6.85646 6.90776 6.90776 NA 5.91350 6.24028 6.36303 6.55108 6.71538 6.80239 231-1 231-2 231-3 231-4 231-5 231-6 231-7 232-1 232-2 232-3 NA 6.90575 6.90575 7.64969 7.88231 7.82405 7.97247 NA 6.04025 6.90575 232-4 232-5 232-6 232-7 233-1 233-2 233-3 233-4 233-5 233-6 6.95655 7.01571 7.09008 7.13090 NA 6.63332 6.71538 6.82979 6.90776 7.00307 233-7 234-1 234-2 234-3 234-4 234-5 234-6 234-7 235-1 235-2 7.08255 NA 6.23245 6.30262 6.34564 6.47235 6.48768 6.71538 NA 5.92693 235-3 235-4 235-5 235-6 235-7 236-1 236-2 236-3 236-4 236-5 5.95324 6.10925 6.14204 6.17379 6.17379 NA 6.90575 6.90575 7.18917 7.19293 236-6 236-7 237-1 237-2 237-3 237-4 237-5 237-6 237-7 238-1 7.37776 7.37776 NA 6.16331 6.15273 6.36990 6.38012 6.31716 6.57925 NA 238-2 238-3 238-4 238-5 238-6 238-7 239-1 239-2 239-3 239-4 6.74524 6.85118 6.90776 6.96602 7.03439 6.92067 NA 6.90575 6.90575 7.82405 239-5 239-6 239-7 240-1 240-2 240-3 240-4 240-5 240-6 240-7 7.90101 7.67322 8.00637 NA 6.90575 6.90575 7.12367 7.23921 7.42357 7.46737 241-1 241-2 241-3 241-4 241-5 241-6 241-7 242-1 242-2 242-3 NA 6.53814 6.62936 6.72263 6.86485 6.87626 6.75809 NA 5.75257 5.75257 242-4 242-5 242-6 242-7 243-1 243-2 243-3 243-4 243-5 243-6 5.88053 5.94542 6.03548 6.02587 NA 6.90575 6.90575 7.14520 7.22548 7.33302 243-7 244-1 244-2 244-3 244-4 244-5 244-6 244-7 245-1 245-2 7.40853 NA 6.37161 6.46925 6.56948 6.51175 6.56526 6.64639 NA 6.39693 245-3 245-4 245-5 245-6 245-7 246-1 246-2 246-3 246-4 246-5 6.47697 6.55108 6.62007 6.66568 6.68461 NA 6.47697 6.35437 6.35437 6.50728 246-6 246-7 247-1 247-2 247-3 247-4 247-5 247-6 247-7 248-1 6.59715 6.66313 NA 5.43808 5.56068 5.57973 5.73657 5.76832 5.81413 NA 248-2 248-3 248-4 248-5 248-6 248-7 249-1 249-2 249-3 249-4 5.74620 5.73657 5.41610 5.46383 6.21461 5.85793 NA 6.62007 6.55108 6.74524 249-5 249-6 249-7 250-1 250-2 250-3 250-4 250-5 250-6 250-7 6.80239 6.95655 7.02554 NA 6.05678 6.10925 6.18415 6.28413 6.39693 6.51471 251-1 251-2 251-3 251-4 251-5 251-6 251-7 252-1 252-2 252-3 NA 5.88610 6.21461 6.44572 6.39693 6.65673 6.62007 NA 6.90575 6.90575 252-4 252-5 252-6 252-7 253-1 253-2 253-3 253-4 253-5 253-6 7.54961 7.60090 7.67322 7.60090 NA 6.23832 6.49224 6.52209 6.58617 6.80239 253-7 254-1 254-2 254-3 254-4 254-5 254-6 254-7 255-1 255-2 6.85646 NA 6.22654 6.30992 6.39693 6.52942 6.61204 6.70441 NA 6.05209 255-3 255-4 255-5 255-6 255-7 256-1 256-2 256-3 256-4 256-5 6.01616 6.27852 6.36819 6.45834 6.56244 NA 6.23048 6.35437 6.49979 6.68461 256-6 256-7 257-1 257-2 257-3 257-4 257-5 257-6 257-7 258-1 6.98008 7.00307 NA 6.64379 6.74406 6.79906 6.89366 7.02731 7.06476 NA 258-2 258-3 258-4 258-5 258-6 258-7 259-1 259-2 259-3 259-4 6.51175 6.49527 6.63726 6.63857 6.69456 6.69827 NA 5.85793 5.85793 6.21461 259-5 259-6 259-7 260-1 260-2 260-3 260-4 260-5 260-6 260-7 6.18415 6.23441 6.18415 NA 6.55108 6.63332 6.74524 6.95655 7.02554 7.15070 261-1 261-2 261-3 261-4 261-5 261-6 261-7 262-1 262-2 262-3 NA 6.31716 6.36130 6.42972 6.73102 6.81014 6.95177 NA 6.83841 6.85224 262-4 262-5 262-6 262-7 263-1 263-2 263-3 263-4 263-5 263-6 6.91175 6.91175 7.31122 7.18917 NA 6.10925 6.20456 6.37161 6.39693 6.55108 263-7 264-1 264-2 264-3 264-4 264-5 264-6 264-7 265-1 265-2 6.60259 NA 6.30992 6.29157 6.30992 6.65286 6.62007 6.57228 NA 6.67834 265-3 265-4 265-5 265-6 265-7 266-1 266-2 266-3 266-4 266-5 6.75227 6.84162 6.95655 7.00307 7.15462 NA 6.90575 6.90575 7.02997 7.09008 266-6 266-7 267-1 267-2 267-3 267-4 267-5 267-6 267-7 268-1 7.20786 7.26193 NA 6.55962 6.60123 6.73221 6.79122 6.90776 7.00307 NA 268-2 268-3 268-4 268-5 268-6 268-7 269-1 269-2 269-3 269-4 5.89715 6.07993 5.98141 6.33328 6.27476 6.20456 NA 6.44572 6.74524 6.81892 269-5 269-6 269-7 270-1 270-2 270-3 270-4 270-5 270-6 270-7 6.85646 7.00307 7.09008 NA 6.90575 6.90575 7.91936 8.13153 8.02290 7.92516 271-1 271-2 271-3 271-4 271-5 271-6 271-7 272-1 272-2 272-3 NA 6.48920 6.55820 6.57786 6.68461 6.39693 7.09008 NA 6.35437 6.44572 272-4 272-5 272-6 272-7 273-1 273-2 273-3 273-4 273-5 273-6 6.80239 6.80239 6.90776 6.96602 NA 6.31173 6.54535 6.56386 6.63200 6.80239 273-7 274-1 274-2 274-3 274-4 274-5 274-6 274-7 275-1 275-2 6.84162 NA 6.64249 6.75344 6.81783 6.91473 6.95655 7.01571 NA 6.21461 275-3 275-4 275-5 275-6 275-7 276-1 276-2 276-3 276-4 276-5 6.17379 6.35437 6.55108 6.45362 6.21461 NA 6.55251 6.62007 6.68461 6.72383 276-6 276-7 277-1 277-2 277-3 277-4 277-5 277-6 277-7 278-1 6.82437 6.95655 NA 6.42162 6.48920 6.72022 6.76964 6.80239 6.87730 NA 278-2 278-3 278-4 278-5 278-6 278-7 279-1 279-2 279-3 279-4 6.90575 6.90575 7.17012 7.28276 7.36201 7.33498 NA 6.57368 6.67960 6.85857 279-5 279-6 279-7 280-1 280-2 280-3 280-4 280-5 280-6 280-7 6.84268 6.96791 7.13090 NA 6.05209 6.05209 6.10925 6.21461 6.26340 6.26340 281-1 281-2 281-3 281-4 281-5 281-6 281-7 282-1 282-2 282-3 NA 6.53088 6.55962 6.58064 6.72383 6.87626 6.97541 NA 6.74524 6.74524 282-4 282-5 282-6 282-7 283-1 283-2 283-3 283-4 283-5 283-6 6.81344 6.85118 7.02554 7.09008 NA 6.90575 6.21461 7.13090 6.90776 7.31322 283-7 284-1 284-2 284-3 284-4 284-5 284-6 284-7 285-1 285-2 7.46737 NA 6.44413 6.45362 6.52062 6.72743 6.59167 6.73815 NA 6.48311 285-3 285-4 285-5 285-6 285-7 286-1 286-2 286-3 286-4 286-5 6.53233 6.75227 6.80017 6.75926 6.96885 NA 5.99146 6.38012 6.56526 6.73340 286-6 286-7 287-1 287-2 287-3 287-4 287-5 287-6 287-7 288-1 6.74524 6.84588 NA 6.50429 6.47389 6.56386 6.75110 6.88755 6.99485 NA 288-2 288-3 288-4 288-5 288-6 288-7 289-1 289-2 289-3 289-4 6.86485 6.90575 7.02554 7.15070 7.24423 6.95940 NA 6.90575 6.90575 7.46737 289-5 289-6 289-7 290-1 290-2 290-3 290-4 290-5 290-6 290-7 7.69621 7.60090 7.91936 NA 6.28786 6.20456 6.24417 6.32794 6.53669 6.55108 291-1 291-2 291-3 291-4 291-5 291-6 291-7 292-1 292-2 292-3 NA 6.80239 6.82979 6.98472 7.00307 6.70808 6.96130 NA 6.61607 6.57647 292-4 292-5 292-6 292-7 293-1 293-2 293-3 293-4 293-5 293-6 6.68461 6.74288 7.03615 7.02465 NA 6.06611 6.18826 6.31173 6.55678 6.55393 293-7 294-1 294-2 294-3 294-4 294-5 294-6 294-7 295-1 295-2 6.75227 NA 6.19644 6.26530 6.36475 6.43935 6.54391 6.61473 NA 6.33328 295-3 295-4 295-5 295-6 295-7 296-1 296-2 296-3 296-4 296-5 6.61473 6.73459 6.77422 6.86485 6.97073 NA 6.44572 6.33328 6.39693 6.48158 296-6 296-7 297-1 297-2 297-3 297-4 297-5 297-6 297-7 298-1 6.59167 6.59304 NA 6.42972 6.43133 6.59441 6.71174 6.74524 6.87730 NA 298-2 298-3 298-4 298-5 298-6 298-7 299-1 299-2 299-3 299-4 6.49224 6.21461 6.72743 6.66568 6.30992 6.21461 NA 6.30992 6.30992 6.30992 299-5 299-6 299-7 300-1 300-2 300-3 300-4 300-5 300-6 300-7 6.30992 6.55108 6.62007 NA 6.14633 6.25383 6.30992 6.35089 6.46147 6.50279 301-1 301-2 301-3 301-4 301-5 301-6 301-7 302-1 302-2 302-3 NA 6.53669 6.55108 6.65929 6.82979 6.85646 6.95655 NA 6.22456 6.30992 302-4 302-5 302-6 302-7 303-1 303-2 303-3 303-4 303-5 303-6 6.47697 6.67834 6.77992 6.80461 NA 5.75257 5.85793 6.07535 8.51719 6.51471 303-7 304-1 304-2 304-3 304-4 304-5 304-6 304-7 305-1 305-2 6.58617 NA 6.16331 6.26340 6.35437 6.43775 6.47697 6.55108 NA 6.90575 305-3 305-4 305-5 305-6 305-7 306-1 306-2 306-3 306-4 306-5 6.90575 7.25134 7.25134 6.90776 7.23634 NA 6.49527 6.60394 6.70073 6.58617 306-6 306-7 307-1 307-2 307-3 307-4 307-5 307-6 307-7 308-1 6.41836 6.21461 NA 6.45047 6.67203 6.56386 6.60665 6.83518 6.86171 NA 308-2 308-3 308-4 308-5 308-6 308-7 309-1 309-2 309-3 309-4 6.48158 6.55393 6.61874 6.75693 6.87626 6.99026 NA 6.00389 6.10925 6.23441 309-5 309-6 309-7 310-1 310-2 310-3 310-4 310-5 310-6 310-7 6.03548 6.11589 6.19441 NA 6.33150 6.39359 6.48464 6.56948 6.70319 6.79010 311-1 311-2 311-3 311-4 311-5 311-6 311-7 312-1 312-2 312-3 NA 6.41182 6.56526 6.59987 6.69084 6.84055 6.93731 NA 6.57508 6.65157 312-4 312-5 312-6 312-7 313-1 313-2 313-3 313-4 313-5 313-6 6.75577 6.84055 6.90675 6.68336 NA 6.22258 6.34564 6.42972 6.49224 6.63595 313-7 314-1 314-2 314-3 314-4 314-5 314-6 314-7 315-1 315-2 6.65929 NA 6.30262 6.35611 6.41510 6.49224 6.62407 6.79571 NA 5.70711 315-3 315-4 315-5 315-6 315-7 316-1 316-2 316-3 316-4 316-5 6.39693 6.39693 6.63463 6.62007 6.68461 NA 6.90575 6.90575 7.67322 7.75148 316-6 316-7 317-1 317-2 317-3 317-4 317-5 317-6 317-7 318-1 7.81883 7.60090 NA 5.99146 5.90263 6.55108 6.49979 6.21461 6.68461 NA 318-2 318-3 318-4 318-5 318-6 318-7 319-1 319-2 319-3 319-4 6.35437 6.47697 6.74524 6.55108 6.80239 6.57925 NA 6.50129 6.57368 6.65544 319-5 319-6 319-7 320-1 320-2 320-3 320-4 320-5 320-6 320-7 6.75344 6.84801 6.94312 NA 6.09131 6.23637 6.27099 6.47697 6.59578 6.44572 321-1 321-2 321-3 321-4 321-5 321-6 321-7 322-1 322-2 322-3 NA 6.04025 6.20658 6.27288 6.39693 6.47080 6.54535 NA 6.68711 6.74524 322-4 322-5 322-6 322-7 323-1 323-2 323-3 323-4 323-5 323-6 6.85646 6.95655 7.04752 7.09340 NA 6.55108 6.55108 6.80239 6.85646 6.86693 323-7 324-1 324-2 324-3 324-4 324-5 324-6 324-7 325-1 325-2 7.00307 NA 6.18826 6.60665 6.68461 6.76849 6.86066 6.96602 NA 6.26340 325-3 325-4 325-5 325-6 325-7 326-1 326-2 326-3 326-4 326-5 6.29157 6.39693 6.39693 6.57786 6.62007 NA 6.55108 6.68461 6.55108 6.82871 326-6 326-7 327-1 327-2 327-3 327-4 327-5 327-6 327-7 328-1 6.80239 7.13648 NA 5.93225 6.19644 6.23637 6.30992 6.44254 6.10925 NA 328-2 328-3 328-4 328-5 328-6 328-7 329-1 329-2 329-3 329-4 6.62007 6.68461 6.73340 6.81892 6.87213 6.96602 NA 6.42162 6.47697 6.55108 329-5 329-6 329-7 330-1 330-2 330-3 330-4 330-5 330-6 330-7 6.68461 6.68461 6.80239 NA 6.75926 6.81344 6.86693 6.85118 6.94890 7.06476 331-1 331-2 331-3 331-4 331-5 331-6 331-7 332-1 332-2 332-3 NA 6.88959 6.90575 6.90776 7.00307 7.13090 7.10824 NA 5.61313 5.82008 332-4 332-5 332-6 332-7 333-1 333-2 333-3 333-4 333-5 333-6 5.76205 5.89990 5.95842 6.17379 NA 6.44572 6.62007 6.59987 6.63332 6.83518 333-7 334-1 334-2 334-3 334-4 334-5 334-6 334-7 335-1 335-2 6.99942 NA 6.33328 5.85793 6.39526 6.10925 6.10925 6.10925 NA 6.39526 335-3 335-4 335-5 335-6 335-7 336-1 336-2 336-3 336-4 336-5 6.43615 6.49224 6.73102 6.74524 7.04229 NA 6.30992 6.53669 6.46147 6.57925 336-6 336-7 337-1 337-2 337-3 337-4 337-5 337-6 337-7 338-1 6.89770 6.42487 NA 6.65286 6.77422 6.90776 6.96885 6.96224 7.10824 NA 338-2 338-3 338-4 338-5 338-6 338-7 339-1 339-2 339-3 339-4 5.98645 5.95584 6.18415 6.19236 6.62539 6.62007 NA 5.88332 5.88610 5.93489 339-5 339-6 339-7 340-1 340-2 340-3 340-4 340-5 340-6 340-7 6.07993 6.07993 6.08222 NA 6.57647 6.66441 6.73697 6.80793 6.80793 6.91771 341-1 341-2 341-3 341-4 341-5 341-6 341-7 342-1 342-2 342-3 NA 6.90575 6.90575 7.24566 7.37776 7.47307 7.57558 NA 6.45520 6.55820 342-4 342-5 342-6 342-7 343-1 343-2 343-3 343-4 343-5 343-6 6.66313 6.84588 6.92560 7.13648 NA 6.90575 6.90575 7.18539 7.24423 7.31122 343-7 344-1 344-2 344-3 344-4 344-5 344-6 344-7 345-1 345-2 7.37588 NA 6.90575 6.90575 7.60090 7.71869 7.74066 7.60090 NA 6.16331 345-3 345-4 345-5 345-6 345-7 346-1 346-2 346-3 346-4 346-5 6.30992 6.30445 6.62007 6.47697 6.55108 NA 6.90575 6.90575 6.57925 7.04752 346-6 346-7 347-1 347-2 347-3 347-4 347-5 347-6 347-7 348-1 7.09008 7.32251 NA 6.77422 6.88755 6.90776 6.96508 6.95655 7.19669 NA 348-2 348-3 348-4 348-5 348-6 348-7 349-1 349-2 349-3 349-4 6.90575 6.90575 7.84385 7.93737 8.10168 7.91936 NA 6.13988 6.56667 6.65157 349-5 349-6 349-7 350-1 350-2 350-3 350-4 350-5 350-6 350-7 6.83303 6.97167 7.07834 NA 6.55108 6.68711 6.75227 6.81344 6.96035 7.02019 351-1 351-2 351-3 351-4 351-5 351-6 351-7 352-1 352-2 352-3 NA 5.92693 5.99146 6.57368 6.66568 6.69703 6.82002 NA 6.43775 6.57368 352-4 352-5 352-6 352-7 353-1 353-2 353-3 353-4 353-5 353-6 6.65286 6.82655 6.90776 7.03615 NA 6.90575 6.90575 8.16052 8.00637 8.05516 353-7 354-1 354-2 354-3 354-4 354-5 354-6 354-7 355-1 355-2 7.94591 NA 6.50429 6.55108 6.64509 6.64639 6.90776 7.01392 NA 5.76832 355-3 355-4 355-5 355-6 355-7 356-1 356-2 356-3 356-4 356-5 5.89990 6.60800 6.84588 6.08677 6.30992 NA 6.39024 6.49072 6.61874 6.80572 356-6 356-7 357-1 357-2 357-3 357-4 357-5 357-6 357-7 358-1 6.90375 6.98101 NA 6.34564 6.51471 6.57925 6.63332 6.80239 6.88755 NA 358-2 358-3 358-4 358-5 358-6 358-7 359-1 359-2 359-3 359-4 6.49072 6.51471 6.55108 6.70808 6.82002 6.45990 NA 5.98141 5.98141 6.10925 359-5 359-6 359-7 360-1 360-2 360-3 360-4 360-5 360-6 360-7 6.21461 6.25767 6.32436 NA 6.90575 6.90575 7.24423 6.95655 7.00307 7.17319 361-1 361-2 361-3 361-4 361-5 361-6 361-7 362-1 362-2 362-3 NA 5.41610 5.48064 6.21461 6.25383 6.34388 7.17012 NA 6.58617 6.66185 362-4 362-5 362-6 362-7 363-1 363-2 363-3 363-4 363-5 363-6 6.81344 6.86171 7.06902 7.07750 NA 6.12249 6.16542 6.21661 6.56526 6.62007 363-7 364-1 364-2 364-3 364-4 364-5 364-6 364-7 365-1 365-2 6.69332 NA 6.43775 6.39693 6.47080 6.74524 6.77422 6.79010 NA 6.90575 365-3 365-4 365-5 365-6 365-7 366-1 366-2 366-3 366-4 366-5 6.90575 5.01728 7.37776 7.46737 7.37776 NA 6.62007 6.55108 6.55108 5.87212 366-6 366-7 367-1 367-2 367-3 367-4 367-5 367-6 367-7 368-1 6.92560 6.76504 NA 6.51471 6.76273 6.93731 6.82979 6.88244 6.98008 NA 368-2 368-3 368-4 368-5 368-6 368-7 369-1 369-2 369-3 369-4 6.46770 6.62007 6.76849 6.99577 7.13648 7.37776 NA 6.29711 6.49224 6.74524 369-5 369-6 369-7 370-1 370-2 370-3 370-4 370-5 370-6 370-7 6.88244 6.95655 7.04752 NA 6.60259 6.62407 6.71538 6.84268 7.02554 7.22330 371-1 371-2 371-3 371-4 371-5 371-6 371-7 372-1 372-2 372-3 NA 6.56526 6.65929 6.78784 6.85435 6.96602 7.15383 NA 6.52209 6.58617 372-4 372-5 372-6 372-7 373-1 373-2 373-3 373-4 373-5 373-6 6.65286 6.65286 6.77765 6.80239 NA 6.05678 6.23441 6.39693 6.39859 6.39693 373-7 374-1 374-2 374-3 374-4 374-5 374-6 374-7 375-1 375-2 6.39693 NA 5.76832 5.85793 6.03787 6.10702 6.17379 6.23245 NA 5.68358 375-3 375-4 375-5 375-6 375-7 376-1 376-2 376-3 376-4 376-5 5.73334 5.88610 5.94542 6.01372 6.02345 NA 6.49224 6.55108 6.60665 6.68461 376-6 376-7 377-1 377-2 377-3 377-4 377-5 377-6 377-7 378-1 6.74524 6.80239 NA 6.82979 6.90575 6.90776 7.04752 7.20786 6.94986 NA 378-2 378-3 378-4 378-5 378-6 378-7 379-1 379-2 379-3 379-4 6.49224 6.65929 6.65673 6.80239 7.08171 7.15929 NA 6.49224 6.54391 6.50728 379-5 379-6 379-7 380-1 380-2 380-3 380-4 380-5 380-6 380-7 6.71538 6.73934 6.89264 NA 6.36303 6.40523 6.41346 6.42162 6.43775 6.62007 381-1 381-2 381-3 381-4 381-5 381-6 381-7 382-1 382-2 382-3 NA 6.37161 6.41673 6.39693 6.55108 6.90776 6.80239 NA 6.43775 6.43775 382-4 382-5 382-6 382-7 383-1 383-2 383-3 383-4 383-5 383-6 6.33683 6.37673 6.39359 6.71538 NA 6.80239 6.90575 6.96602 6.95655 7.09008 383-7 384-1 384-2 384-3 384-4 384-5 384-6 384-7 385-1 385-2 7.13090 NA 5.78383 6.12249 6.10925 6.10925 6.16331 6.19236 NA 6.60665 385-3 385-4 385-5 385-6 385-7 386-1 386-2 386-3 386-4 386-5 6.73340 6.79122 6.84588 6.95559 7.12287 NA 6.90575 6.90575 7.26193 7.27932 386-6 386-7 387-1 387-2 387-3 387-4 387-5 387-6 387-7 388-1 7.39326 7.35947 NA 6.56526 6.55108 6.63332 6.61607 6.80461 6.90776 NA 388-2 388-3 388-4 388-5 388-6 388-7 389-1 389-2 389-3 389-4 6.80239 6.90575 7.09008 7.13090 7.24423 7.31322 NA 6.90575 6.90575 7.11151 389-5 389-6 389-7 390-1 390-2 390-3 390-4 390-5 390-6 390-7 7.27170 7.33889 7.34601 NA 6.74759 6.84268 6.93731 7.03878 7.13090 7.22621 391-1 391-2 391-3 391-4 391-5 391-6 391-7 392-1 392-2 392-3 NA 6.29527 6.45047 6.39192 6.57786 6.56103 6.59715 NA 6.35957 6.39693 392-4 392-5 392-6 392-7 393-1 393-2 393-3 393-4 393-5 393-6 6.47697 6.08677 6.65929 6.79122 NA 6.44413 6.56808 6.63463 6.73340 6.83411 393-7 394-1 394-2 394-3 394-4 394-5 394-6 394-7 395-1 395-2 6.93537 NA 6.21461 6.21461 6.41346 6.50728 6.69703 6.69703 NA 6.05912 395-3 395-4 395-5 395-6 395-7 396-1 396-2 396-3 396-4 396-5 6.11147 6.27288 6.31173 6.41836 6.57925 NA 6.05209 6.10925 6.21461 6.30992 396-6 396-7 397-1 397-2 397-3 397-4 397-5 397-6 397-7 398-1 6.55108 6.55108 NA 6.77992 6.86693 6.89770 6.96885 7.05876 7.13090 NA 398-2 398-3 398-4 398-5 398-6 398-7 399-1 399-2 399-3 399-4 6.53669 6.65929 6.79122 6.88755 6.98472 7.09008 NA 6.38012 6.46147 6.54822 399-5 399-6 399-7 400-1 400-2 400-3 400-4 400-5 400-6 400-7 6.67330 6.77194 6.89467 NA 6.50877 6.60259 6.69332 6.75344 6.80128 6.95559 401-1 401-2 401-3 401-4 401-5 401-6 401-7 402-1 402-2 402-3 NA 6.58617 6.68586 6.78559 6.73340 7.60090 7.18311 NA 6.90575 6.90575 402-4 402-5 402-6 402-7 403-1 403-2 403-3 403-4 403-5 403-6 6.90776 7.13090 7.20786 7.44659 NA 6.90575 6.90575 7.16627 7.13090 7.37776 403-7 404-1 404-2 404-3 404-4 404-5 404-6 404-7 405-1 405-2 7.40853 NA 6.55108 6.68461 6.80239 6.82437 6.93245 7.00307 NA 6.90575 405-3 405-4 405-5 405-6 405-7 406-1 406-2 406-3 406-4 406-5 6.90575 7.13090 7.24423 7.46737 7.45008 NA 6.39693 6.48004 6.54965 6.65415 406-6 406-7 407-1 407-2 407-3 407-4 407-5 407-6 407-7 408-1 6.68461 6.82979 NA 6.80239 6.90575 7.09506 7.18007 7.27932 7.34601 NA 408-2 408-3 408-4 408-5 408-6 408-7 409-1 409-2 409-3 409-4 6.12249 6.14847 6.20658 6.30992 6.58341 6.58617 NA 6.38856 6.43775 6.47697 409-5 409-6 409-7 410-1 410-2 410-3 410-4 410-5 410-6 410-7 6.56386 6.44572 6.88244 NA 5.59842 5.70378 5.82895 6.05209 6.10925 6.16331 411-1 411-2 411-3 411-4 411-5 411-6 411-7 412-1 412-2 412-3 NA 6.02102 6.17379 6.30992 6.57925 6.65673 6.65028 NA 6.37161 6.72143 412-4 412-5 412-6 412-7 413-1 413-2 413-3 413-4 413-5 413-6 6.75344 7.24423 7.24423 7.11070 NA 6.55108 6.78672 6.88038 6.96885 7.03703 413-7 414-1 414-2 414-3 414-4 414-5 414-6 414-7 415-1 415-2 7.00125 NA 6.47697 6.56526 6.55108 6.55108 6.74524 6.86171 NA 6.05912 415-3 415-4 415-5 415-6 415-7 416-1 416-2 416-3 416-4 416-5 6.03548 6.07993 6.16121 6.31897 6.27099 NA 6.46459 6.53233 6.60800 6.60394 416-6 416-7 417-1 417-2 417-3 417-4 417-5 417-6 417-7 418-1 6.80239 6.90675 NA 5.61677 6.00141 6.12687 6.12030 6.23048 6.30079 NA 418-2 418-3 418-4 418-5 418-6 418-7 419-1 419-2 419-3 419-4 6.49224 6.62936 6.66185 6.65929 6.75693 6.82437 NA 6.55108 6.63332 6.70319 419-5 419-6 419-7 420-1 420-2 420-3 420-4 420-5 420-6 420-7 6.79122 6.85646 7.06902 NA 6.39693 6.39693 6.47697 6.62007 6.74524 6.79010 421-1 421-2 421-3 421-4 421-5 421-6 421-7 422-1 422-2 422-3 NA 5.96615 6.10925 6.30992 6.39693 6.46925 6.51471 NA 6.21060 6.29157 422-4 422-5 422-6 422-7 423-1 423-2 423-3 423-4 423-5 423-6 6.41017 6.47697 6.57088 6.67708 NA 6.64639 6.69084 6.69084 6.86693 6.30992 423-7 424-1 424-2 424-3 424-4 424-5 424-6 424-7 425-1 425-2 6.81892 NA 6.82979 6.85646 6.90776 6.95655 7.00307 7.09008 NA 6.05209 425-3 425-4 425-5 425-6 425-7 426-1 426-2 426-3 426-4 426-5 6.62007 6.76849 6.77422 6.88244 6.97073 NA 6.46770 6.55108 6.55108 6.65929 426-6 426-7 427-1 427-2 427-3 427-4 427-5 427-6 427-7 428-1 6.68461 6.74524 NA 6.90575 6.90575 7.31255 7.31322 7.40853 7.39879 NA 428-2 428-3 428-4 428-5 428-6 428-7 429-1 429-2 429-3 429-4 6.10925 6.19236 6.25383 6.25383 6.42972 6.47697 NA 6.28227 6.54391 6.48920 429-5 429-6 429-7 430-1 430-2 430-3 430-4 430-5 430-6 430-7 6.58203 6.80239 6.88755 NA 5.41610 5.52146 5.61677 5.94017 6.06843 6.17794 431-1 431-2 431-3 431-4 431-5 431-6 431-7 432-1 432-2 432-3 NA 5.68017 5.81413 5.72359 6.21261 6.37843 6.45990 NA 6.90575 6.90575 432-4 432-5 432-6 432-7 433-1 433-2 433-3 433-4 433-5 433-6 7.03878 7.10661 7.21891 7.27932 NA 6.42162 6.42162 6.46614 6.84588 6.62007 433-7 434-1 434-2 434-3 434-4 434-5 434-6 434-7 435-1 435-2 6.62007 NA 6.54103 6.57925 6.64118 6.74052 6.85224 6.95655 NA 6.61338 435-3 435-4 435-5 435-6 435-7 436-1 436-2 436-3 436-4 436-5 6.67960 6.71538 6.86693 6.93245 7.00307 NA 6.47697 6.47697 6.55108 6.53233 436-6 436-7 437-1 437-2 437-3 437-4 437-5 437-6 437-7 438-1 6.62007 6.66823 NA 6.10032 6.22654 6.30445 6.34914 6.39359 6.39192 NA 438-2 438-3 438-4 438-5 438-6 438-7 439-1 439-2 439-3 439-4 6.24417 6.34564 6.42162 6.51471 6.58617 6.68461 NA 6.35437 6.45362 6.82111 439-5 439-6 439-7 440-1 440-2 440-3 440-4 440-5 440-6 440-7 6.94698 7.07242 7.07242 NA 6.10925 6.13773 6.05209 6.16331 6.30992 6.30992 441-1 441-2 441-3 441-4 441-5 441-6 441-7 442-1 442-2 442-3 NA 6.21461 6.21461 6.21461 6.30992 6.47697 6.62007 NA 5.95324 6.14633 442-4 442-5 442-6 442-7 443-1 443-2 443-3 443-4 443-5 443-6 6.24998 6.30992 6.47697 6.43775 NA 5.93225 6.06843 6.23245 6.29527 6.28040 443-7 444-1 444-2 444-3 444-4 444-5 444-6 444-7 445-1 445-2 6.35611 NA 6.68586 6.68461 6.74524 6.95655 6.90776 6.95655 NA 6.79122 445-3 445-4 445-5 445-6 445-7 446-1 446-2 446-3 446-4 446-5 6.90575 7.00307 7.09008 7.17012 7.20042 NA 5.89990 5.99146 6.10256 5.89440 446-6 446-7 447-1 447-2 447-3 447-4 447-5 447-6 447-7 448-1 6.44731 6.33683 NA 5.70378 5.78383 5.99146 5.99146 6.08677 6.16331 NA 448-2 448-3 448-4 448-5 448-6 448-7 449-1 449-2 449-3 449-4 6.52209 6.57925 6.70930 6.73340 6.88755 6.95655 NA 5.96615 6.08677 6.16331 449-5 449-6 449-7 450-1 450-2 450-3 450-4 450-5 450-6 450-7 6.85646 6.57647 7.10988 NA 6.48158 6.58617 6.66058 6.76964 6.88755 6.97354 451-1 451-2 451-3 451-4 451-5 451-6 451-7 452-1 452-2 452-3 NA 6.34212 6.55820 6.66568 6.75460 6.83303 6.93537 NA 5.94017 5.88610 452-4 452-5 452-6 452-7 453-1 453-2 453-3 453-4 453-5 453-6 6.06379 5.90808 6.24804 6.50279 NA 6.80239 6.83411 6.89366 6.89366 7.01392 453-7 454-1 454-2 454-3 454-4 454-5 454-6 454-7 455-1 455-2 7.17012 NA 6.52356 6.55393 6.55393 6.78333 7.04752 7.02554 NA 6.10925 455-3 455-4 455-5 455-6 455-7 456-1 456-2 456-3 456-4 456-5 6.20456 6.19441 6.28227 6.35437 6.49979 NA 6.51471 6.59578 6.66568 6.59987 456-6 456-7 457-1 457-2 457-3 457-4 457-5 457-6 457-7 458-1 6.88755 6.95273 NA 5.93754 6.08450 6.06146 6.21461 6.21461 6.30262 NA 458-2 458-3 458-4 458-5 458-6 458-7 459-1 459-2 459-3 459-4 6.51471 6.61338 6.71538 6.81892 6.95655 7.04752 NA 6.31716 6.69580 6.81564 459-5 459-6 459-7 460-1 460-2 460-3 460-4 460-5 460-6 460-7 6.87213 7.10661 7.09672 NA 6.49979 6.72743 6.71538 6.77878 6.91274 6.93245 461-1 461-2 461-3 461-4 461-5 461-6 461-7 462-1 462-2 462-3 NA 6.32794 6.44572 6.45362 6.50728 6.69703 6.81344 NA 6.16331 6.39693 462-4 462-5 462-6 462-7 463-1 463-2 463-3 463-4 463-5 463-6 5.95584 6.07535 6.26340 6.33683 NA 6.67834 6.68461 6.85646 6.85646 6.90776 463-7 464-1 464-2 464-3 464-4 464-5 464-6 464-7 465-1 465-2 7.04752 NA 5.92693 5.99146 5.85793 6.07993 6.21461 6.43775 NA 6.21461 465-3 465-4 465-5 465-6 465-7 466-1 466-2 466-3 466-4 466-5 6.38012 6.35437 6.47697 6.43775 6.52942 NA 6.24417 6.34564 6.42162 6.51471 466-6 466-7 467-1 467-2 467-3 467-4 467-5 467-6 467-7 468-1 6.58617 6.68461 NA 6.23441 6.31897 6.42162 6.49375 6.55108 7.00307 NA 468-2 468-3 468-4 468-5 468-6 468-7 469-1 469-2 469-3 469-4 6.90575 6.90575 7.31322 7.31322 7.31322 7.37776 NA 5.39363 5.43808 4.60517 469-5 469-6 469-7 470-1 470-2 470-3 470-4 470-5 470-6 470-7 5.08140 5.27300 5.81413 NA 6.52942 6.62007 6.45990 7.00307 7.20786 7.46737 471-1 471-2 471-3 471-4 471-5 471-6 471-7 472-1 472-2 472-3 NA 6.65286 6.73102 6.82979 6.93245 7.02554 7.13090 NA 5.57973 5.73334 472-4 472-5 472-6 472-7 473-1 473-2 473-3 473-4 473-5 473-6 5.76832 5.86647 6.06611 6.23441 NA 6.42162 6.37502 6.58617 6.76273 6.74876 473-7 474-1 474-2 474-3 474-4 474-5 474-6 474-7 475-1 475-2 6.80239 NA 6.71538 6.74524 6.88244 6.98008 7.24423 7.43838 NA 6.81344 475-3 475-4 475-5 475-6 475-7 476-1 476-2 476-3 476-4 476-5 6.88244 7.00307 7.17012 7.17778 7.24423 NA 5.86930 5.85793 5.87774 5.85793 476-6 476-7 477-1 477-2 477-3 477-4 477-5 477-6 477-7 478-1 6.58617 6.35437 NA 6.28040 6.44572 6.48004 6.77422 6.83195 6.90776 NA 478-2 478-3 478-4 478-5 478-6 478-7 479-1 479-2 479-3 479-4 6.25383 6.46147 6.53669 6.62007 6.63595 6.75110 NA 6.90575 6.90575 7.23418 479-5 479-6 479-7 480-1 480-2 480-3 480-4 480-5 480-6 480-7 7.29980 7.31322 7.37588 NA 5.59842 6.21461 6.10925 6.26340 6.55108 6.80239 481-1 481-2 481-3 481-4 481-5 481-6 481-7 482-1 482-2 482-3 NA 5.70378 5.72031 5.99146 6.05209 6.21461 6.26340 NA 6.35437 6.35437 482-4 482-5 482-6 482-7 483-1 483-2 483-3 483-4 483-5 483-6 6.53669 6.60665 6.60665 6.75693 NA 6.07535 6.35437 6.58617 6.58893 6.59304 483-7 484-1 484-2 484-3 484-4 484-5 484-6 484-7 485-1 485-2 6.84268 NA 6.56103 6.90575 6.73340 6.87420 7.03439 7.03527 NA 6.35611 485-3 485-4 485-5 485-6 485-7 486-1 486-2 486-3 486-4 486-5 6.49224 6.65929 6.56948 6.80017 6.73102 NA 6.90575 6.90575 6.95655 7.02554 486-6 486-7 487-1 487-2 487-3 487-4 487-5 487-6 487-7 488-1 7.09008 7.09008 NA 6.72743 6.80239 6.88653 6.95655 7.09008 7.26193 NA 488-2 488-3 488-4 488-5 488-6 488-7 489-1 489-2 489-3 489-4 6.55251 6.62407 6.78672 6.83087 7.04491 7.02643 NA 6.59030 6.62007 6.65286 489-5 489-6 489-7 490-1 490-2 490-3 490-4 490-5 490-6 490-7 6.71538 6.77422 6.85646 NA 6.80239 6.90575 6.90776 7.01481 7.03878 6.94890 491-1 491-2 491-3 491-4 491-5 491-6 491-7 492-1 492-2 492-3 NA 6.27288 6.46303 6.53088 6.66950 6.66696 7.02554 NA 6.15698 6.10925 492-4 492-5 492-6 492-7 493-1 493-2 493-3 493-4 493-5 493-6 6.33683 6.35957 6.58755 6.71296 NA 6.55108 6.62007 6.70073 6.77194 6.85646 493-7 494-1 494-2 494-3 494-4 494-5 494-6 494-7 495-1 495-2 7.09008 NA 5.94803 6.05209 6.11589 6.18002 6.25958 6.31536 NA 6.21461 495-3 495-4 495-5 495-6 495-7 496-1 496-2 496-3 496-4 496-5 6.30992 6.68461 6.74288 6.81344 6.80239 NA 6.90575 6.90575 7.18463 7.28893 496-6 496-7 497-1 497-2 497-3 497-4 497-5 497-6 497-7 498-1 7.39080 7.48661 NA 6.90575 6.90575 7.09008 7.24423 7.35436 7.29097 NA 498-2 498-3 498-4 498-5 498-6 498-7 499-1 499-2 499-3 499-4 6.26340 6.30079 6.39693 6.39693 6.55820 6.77422 NA 6.35957 6.45677 6.61338 499-5 499-6 499-7 500-1 500-2 500-3 500-4 500-5 500-6 500-7 6.63988 6.77422 6.79122 NA 6.62007 6.62007 6.68461 6.88755 6.85646 5.98896 501-1 501-2 501-3 501-4 501-5 501-6 501-7 502-1 502-2 502-3 NA 6.35957 6.45205 6.52942 6.63857 6.79122 6.95655 NA 6.64639 6.71538 502-4 502-5 502-6 502-7 503-1 503-2 503-3 503-4 503-5 503-6 6.77422 6.95655 6.95655 7.20786 NA 6.80239 6.90575 7.10250 7.12769 7.18311 503-7 504-1 504-2 504-3 504-4 504-5 504-6 504-7 505-1 505-2 7.35372 NA 6.49072 6.64509 6.80239 6.73102 7.03703 6.49072 NA 6.90575 505-3 505-4 505-5 505-6 505-7 506-1 506-2 506-3 506-4 506-5 6.90575 7.43838 7.60090 6.77422 7.62071 NA 6.35437 6.43775 6.55108 6.68461 506-6 506-7 507-1 507-2 507-3 507-4 507-5 507-6 507-7 508-1 6.74524 6.68461 NA 6.21461 6.30992 6.37161 6.39693 6.55108 6.55108 NA 508-2 508-3 508-4 508-5 508-6 508-7 509-1 509-2 509-3 509-4 6.28227 6.51471 6.58617 6.71538 6.75110 6.45520 NA 6.39526 6.49072 6.59578 509-5 509-6 509-7 510-1 510-2 510-3 510-4 510-5 510-6 510-7 6.64118 6.80239 6.91870 NA 6.90575 6.90575 7.17012 7.27932 7.31322 7.37776 511-1 511-2 511-3 511-4 511-5 511-6 511-7 512-1 512-2 512-3 NA 6.19441 6.26340 6.29157 6.13123 6.74524 6.95655 NA 6.16331 6.39693 512-4 512-5 512-6 512-7 513-1 513-2 513-3 513-4 513-5 513-6 6.71538 6.58617 6.81344 6.90776 NA 6.03787 6.17170 6.13123 6.18826 5.76519 513-7 514-1 514-2 514-3 514-4 514-5 514-6 514-7 515-1 515-2 5.98896 NA 6.90575 6.90575 6.95655 7.09008 7.04752 7.13090 NA 5.50126 515-3 515-4 515-5 515-6 515-7 516-1 516-2 516-3 516-4 516-5 5.50126 5.65249 5.82305 5.89990 6.05209 NA 6.77992 6.90575 6.98472 7.08841 516-6 516-7 517-1 517-2 517-3 517-4 517-5 517-6 517-7 518-1 6.86171 6.97635 NA 5.59842 5.59842 5.74620 5.96615 6.12030 6.21461 NA 518-2 518-3 518-4 518-5 518-6 518-7 519-1 519-2 519-3 519-4 5.93225 5.97126 6.11368 6.25767 6.36303 6.40523 NA 5.78383 5.99146 5.89715 519-5 519-6 519-7 520-1 520-2 520-3 520-4 520-5 520-6 520-7 5.83188 6.05209 6.26340 NA 6.37502 6.56948 6.53669 6.64249 6.22654 6.37502 521-1 521-2 521-3 521-4 521-5 521-6 521-7 522-1 522-2 522-3 NA 6.62007 6.80239 6.85646 6.98008 7.04316 6.86693 NA 5.76832 5.82895 522-4 522-5 522-6 522-7 523-1 523-2 523-3 523-4 523-5 523-6 5.76832 6.59851 6.67960 7.09008 NA 6.18208 6.26340 6.39359 6.44095 6.51471 523-7 524-1 524-2 524-3 524-4 524-5 524-6 524-7 525-1 525-2 6.66441 NA 6.21461 6.39693 6.39693 6.56526 6.70073 6.52209 NA 6.24611 525-3 525-4 525-5 525-6 525-7 526-1 526-2 526-3 526-4 526-5 6.33150 6.40853 6.48158 6.55962 6.67834 NA 5.70378 5.92693 6.10925 6.04025 526-6 526-7 527-1 527-2 527-3 527-4 527-5 527-6 527-7 528-1 6.35957 6.50429 NA 6.23441 6.39693 6.50728 6.65286 6.74524 7.13090 NA 528-2 528-3 528-4 528-5 528-6 528-7 529-1 529-2 529-3 529-4 6.09807 6.26910 6.34036 6.48768 6.57786 6.62007 NA 6.90575 6.90575 7.06048 529-5 529-6 529-7 530-1 530-2 530-3 530-4 530-5 530-6 530-7 7.09589 6.93342 7.46737 NA 6.90575 6.90575 7.12930 7.17012 7.27031 7.45991 531-1 531-2 531-3 531-4 531-5 531-6 531-7 532-1 532-2 532-3 NA 6.57647 6.77422 6.88244 7.09008 7.20786 7.49554 NA 5.73657 5.96615 532-4 532-5 532-6 532-7 533-1 533-2 533-3 533-4 533-5 533-6 6.07535 6.15698 6.26340 6.34739 NA 6.29157 6.39526 6.39693 6.39693 6.49072 533-7 534-1 534-2 534-3 534-4 534-5 534-6 534-7 535-1 535-2 6.59030 NA 5.88610 5.98394 5.98394 6.19848 5.78996 6.01127 NA 5.57973 535-3 535-4 535-5 535-6 535-7 536-1 536-2 536-3 536-4 536-5 5.66988 5.70378 5.82895 5.99146 6.07535 NA 6.69456 6.72743 6.85330 6.89669 536-6 536-7 537-1 537-2 537-3 537-4 537-5 537-6 537-7 538-1 7.13090 6.96508 NA 6.03787 6.55108 6.65673 6.85646 7.09008 6.74406 NA 538-2 538-3 538-4 538-5 538-6 538-7 539-1 539-2 539-3 539-4 6.62007 6.77422 6.77422 6.90776 7.01481 7.06219 NA 6.34564 6.44413 6.62007 539-5 539-6 539-7 540-1 540-2 540-3 540-4 540-5 540-6 540-7 6.95655 7.31122 7.38833 NA 6.90575 6.90575 7.55799 7.60090 7.60090 7.60090 541-1 541-2 541-3 541-4 541-5 541-6 541-7 542-1 542-2 542-3 NA 6.55536 6.55108 6.63332 6.59715 6.82437 6.90776 NA 6.53959 6.53669 542-4 542-5 542-6 542-7 543-1 543-2 543-3 543-4 543-5 543-6 6.69084 6.84375 6.88244 7.04752 NA 6.65929 6.69084 6.77422 6.68461 6.90776 543-7 544-1 544-2 544-3 544-4 544-5 544-6 544-7 545-1 545-2 6.94986 NA 6.55393 6.68461 7.11070 7.17396 7.01481 6.92264 NA 6.89770 545-3 545-4 545-5 545-6 545-7 546-1 546-2 546-3 546-4 546-5 6.90575 7.13090 7.20786 7.31322 7.09008 NA 6.63988 6.72143 6.78897 6.92560 546-6 546-7 547-1 547-2 547-3 547-4 547-5 547-6 547-7 548-1 6.95655 7.00307 NA 6.56667 6.77422 6.73340 6.78333 6.77878 6.98472 NA 548-2 548-3 548-4 548-5 548-6 548-7 549-1 549-2 549-3 549-4 6.85646 6.74524 6.85646 6.90776 7.13090 6.68461 NA 5.70378 5.99146 5.99146 549-5 549-6 549-7 550-1 550-2 550-3 550-4 550-5 550-6 550-7 5.99146 5.99146 6.39693 NA 6.81124 6.74993 6.80239 6.88551 6.98379 7.08171 551-1 551-2 551-3 551-4 551-5 551-6 551-7 552-1 552-2 552-3 NA 6.04025 5.86930 6.21461 6.43775 6.17170 6.55108 NA 6.35437 6.52209 552-4 552-5 552-6 552-7 553-1 553-2 553-3 553-4 553-5 553-6 6.52649 6.67203 6.62007 6.80239 NA 5.91350 5.88610 5.99146 6.26340 6.35437 553-7 554-1 554-2 554-3 554-4 554-5 554-6 554-7 555-1 555-2 6.55108 NA 5.82305 5.89715 5.96358 6.02828 6.10702 6.32972 NA 6.90575 555-3 555-4 555-5 555-6 555-7 556-1 556-2 556-3 556-4 556-5 6.90575 7.31322 7.34601 7.40853 7.52294 NA 6.59715 6.89264 6.98749 6.98286 556-6 556-7 557-1 557-2 557-3 557-4 557-5 557-6 557-7 558-1 7.30317 7.39388 NA 6.35957 6.42811 6.45677 6.58341 6.54391 6.89568 NA 558-2 558-3 558-4 558-5 558-6 558-7 559-1 559-2 559-3 559-4 5.89990 6.19236 6.03309 6.34388 6.41017 6.39526 NA 6.31897 6.38182 6.44889 559-5 559-6 559-7 560-1 560-2 560-3 560-4 560-5 560-6 560-7 6.51323 6.56948 6.65801 NA 6.30992 6.30992 6.39693 6.39693 6.39693 6.47697 561-1 561-2 561-3 561-4 561-5 561-6 561-7 562-1 562-2 562-3 NA 6.10925 6.07993 8.26873 6.51471 6.46925 6.79794 NA 6.43775 6.59304 562-4 562-5 562-6 562-7 563-1 563-2 563-3 563-4 563-5 563-6 6.68461 6.85646 7.09008 7.10250 NA 6.65673 6.74052 6.90776 6.90776 6.90776 563-7 564-1 564-2 564-3 564-4 564-5 564-6 564-7 565-1 565-2 6.93245 NA 6.57925 6.77422 6.84268 6.95655 7.02554 7.08423 NA 6.84268 565-3 565-4 565-5 565-6 565-7 566-1 566-2 566-3 566-4 566-5 6.90575 7.13090 7.27031 7.05359 6.96130 NA 6.35437 6.51471 6.26340 6.68461 566-6 566-7 567-1 567-2 567-3 567-4 567-5 567-6 567-7 568-1 7.14362 7.19519 NA 5.82895 5.82895 5.82895 5.84354 5.89990 6.04025 NA 568-2 568-3 568-4 568-5 568-6 568-7 569-1 569-2 569-3 569-4 6.42487 6.48004 6.58064 6.68461 6.80793 6.92363 NA 6.46614 6.62007 6.62007 569-5 569-6 569-7 570-1 570-2 570-3 570-4 570-5 570-6 570-7 6.90776 7.04752 7.25912 NA 5.81711 5.88610 5.99894 6.06611 6.14419 6.29157 571-1 571-2 571-3 571-4 571-5 571-6 571-7 572-1 572-2 572-3 NA 6.34564 6.47389 6.82437 6.89770 6.97073 7.04752 NA 5.70378 5.99146 572-4 572-5 572-6 572-7 573-1 573-2 573-3 573-4 573-5 573-6 6.21461 6.44572 6.39693 6.62007 NA 6.10925 6.21461 6.25958 6.30628 6.40688 573-7 574-1 574-2 574-3 574-4 574-5 574-6 574-7 575-1 575-2 6.48311 NA 6.28227 6.38856 6.45520 6.66696 6.83518 6.90776 NA 5.79606 575-3 575-4 575-5 575-6 575-7 576-1 576-2 576-3 576-4 576-5 5.98141 6.04025 6.17170 6.28972 6.39526 NA 6.21461 6.26340 6.30992 6.47697 576-6 576-7 577-1 577-2 577-3 577-4 577-5 577-6 577-7 578-1 6.39693 6.68461 NA 5.92693 5.99146 6.10925 6.21461 6.26340 6.39693 NA 578-2 578-3 578-4 578-5 578-6 578-7 579-1 579-2 579-3 579-4 6.39359 6.46147 6.44889 6.63726 6.75926 6.83518 NA 5.79606 5.92693 5.96615 579-5 579-6 579-7 580-1 580-2 580-3 580-4 580-5 580-6 580-7 6.17379 6.47697 6.70073 NA 6.29157 6.38856 6.46147 6.57925 6.60665 6.71538 581-1 581-2 581-3 581-4 581-5 581-6 581-7 582-1 582-2 582-3 NA 6.77422 6.77422 6.89264 6.95655 6.95655 7.18917 NA 5.70378 6.11810 582-4 582-5 582-6 582-7 583-1 583-2 583-3 583-4 583-5 583-6 6.39693 6.72743 6.78784 6.91771 NA 6.29342 6.51323 6.64898 6.88346 7.04752 583-7 584-1 584-2 584-3 584-4 584-5 584-6 584-7 585-1 585-2 7.12930 NA 6.21461 6.30992 6.65286 6.53233 6.60259 6.70073 NA 6.55108 585-3 585-4 585-5 585-6 585-7 586-1 586-2 586-3 586-4 586-5 6.58617 6.68461 6.77992 6.90776 7.05704 NA 5.68698 5.70378 5.84354 5.99146 586-6 586-7 587-1 587-2 587-3 587-4 587-5 587-6 587-7 588-1 6.06379 6.10925 NA 6.61874 6.70319 6.78897 6.88449 7.07834 7.11883 NA 588-2 588-3 588-4 588-5 588-6 588-7 589-1 589-2 589-3 589-4 5.43808 5.92693 6.05209 6.10702 6.57786 6.35611 NA 6.66696 6.73697 6.80461 589-5 589-6 589-7 590-1 590-2 590-3 590-4 590-5 590-6 590-7 6.87523 6.95081 7.07834 NA 6.33683 6.53379 6.62007 6.59304 6.81124 6.81344 591-1 591-2 591-3 591-4 591-5 591-6 591-7 592-1 592-2 592-3 NA 6.46459 6.47697 6.60935 6.55108 6.77079 6.85646 NA 6.49979 6.51619 592-4 592-5 592-6 592-7 593-1 593-2 593-3 593-4 593-5 593-6 6.65157 6.76849 6.83841 6.86797 NA 5.29832 5.41610 5.41610 5.41610 5.70378 593-7 594-1 594-2 594-3 594-4 594-5 594-6 594-7 595-1 595-2 5.70378 NA 6.42487 6.48004 6.57368 6.69084 6.78672 6.91968 NA 5.68698 595-3 595-4 595-5 595-6 595-7 5.85793 5.95324 6.06379 6.21461 6.29157 > > > > > > cleanEx() > nameEx("pdim") > ### * pdim > > flush(stderr()); flush(stdout()) > > ### Name: pdim > ### Title: Check for the Dimensions of the Panel > ### Aliases: pdim pdim.default pdim.data.frame pdim.pdata.frame > ### pdim.pseries pdim.panelmodel pdim.pgmm print.pdim > ### Keywords: attribute > > ### ** Examples > > > # There are 595 individuals > data("Wages", package = "plm") > pdim(Wages, 595) Balanced Panel: n = 595, T = 7, N = 4165 > > # Gasoline contains two variables which are individual and time > # indexes and are the first two variables > data("Gasoline", package="plm") > pdim(Gasoline) Balanced Panel: n = 18, T = 19, N = 342 > > # Hedonic is an unbalanced panel, townid is the individual index > data("Hedonic", package = "plm") > pdim(Hedonic, "townid") Unbalanced Panel: n = 92, T = 1-30, N = 506 > > # An example of the panelmodel method > data("Produc", package = "plm") > z <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=Produc, + model="random", subset = gsp > 5000) > pdim(z) Unbalanced Panel: n = 48, T = 9-17, N = 808 > > > > > cleanEx() > nameEx("pdwtest") > ### * pdwtest > > flush(stderr()); flush(stdout()) > > ### Name: pdwtest > ### Title: Durbin-Watson Test for Panel Models > ### Aliases: pdwtest pdwtest.panelmodel pdwtest.formula > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > g <- plm(inv ~ value + capital, data = Grunfeld, model="random") > pdwtest(g) Durbin-Watson test for serial correlation in panel models data: inv ~ value + capital DW = 0.99636, p-value = 2.819e-13 alternative hypothesis: serial correlation in idiosyncratic errors > pdwtest(g, alternative="two.sided") Durbin-Watson test for serial correlation in panel models data: inv ~ value + capital DW = 0.99636, p-value = 5.638e-13 alternative hypothesis: serial correlation in idiosyncratic errors > ## formula interface > pdwtest(inv ~ value + capital, data=Grunfeld, model="random") Durbin-Watson test for serial correlation in panel models data: inv ~ value + capital DW = 0.99636, p-value = 2.819e-13 alternative hypothesis: serial correlation in idiosyncratic errors > > > > > cleanEx() > nameEx("pggls") > ### * pggls > > flush(stderr()); flush(stdout()) > > ### Name: pggls > ### Title: General FGLS Estimators > ### Aliases: pggls summary.pggls print.summary.pggls residuals.pggls > ### Keywords: regression > > ### ** Examples > > > data("Produc", package = "plm") > zz_wi <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, model = "within") > summary(zz_wi) Oneway (individual) effect Within FGLS model Call: pggls(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "within") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.117505 -0.023706 -0.004717 0.000000 0.017288 0.177768 Coefficients: Estimate Std. Error z-value Pr(>|z|) log(pcap) -0.00104277 0.02900641 -0.0359 0.9713 log(pc) 0.17151298 0.01807934 9.4867 < 2.2e-16 *** log(emp) 0.84449144 0.02042362 41.3488 < 2.2e-16 *** unemp -0.00357102 0.00047319 -7.5468 4.462e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 1.1623 Multiple R-squared: 0.99863 > > zz_pool <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, model = "pooling") > summary(zz_pool) Oneway (individual) effect General FGLS model Call: pggls(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "pooling") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.255736 -0.070199 -0.014124 -0.008909 0.039118 0.455461 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.26388494 0.10077679 22.4643 < 2.2e-16 *** log(pcap) 0.10566584 0.02004106 5.2725 1.346e-07 *** log(pc) 0.21643137 0.01539471 14.0588 < 2.2e-16 *** log(emp) 0.71293894 0.01863632 38.2553 < 2.2e-16 *** unemp -0.00447265 0.00045214 -9.8921 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 7.5587 Multiple R-squared: 0.99111 > > zz_fd <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, model = "fd") > summary(zz_fd) Oneway (individual) effect First-Difference FGLS model Call: pggls(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "fd") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0847594 -0.0103758 0.0024378 0.0007254 0.0133336 0.1018213 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 0.00942926 0.00106337 8.8673 < 2e-16 *** log(pcap) -0.04400764 0.02911083 -1.5117 0.13060 log(pc) -0.03100727 0.01248722 -2.4831 0.01302 * log(emp) 0.87411813 0.02077388 42.0777 < 2e-16 *** unemp -0.00483240 0.00040668 -11.8825 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.33459 Multiple R-squared: 0.99961 > > > > > > cleanEx() > nameEx("pgmm") > ### * pgmm > > flush(stderr()); flush(stdout()) > > ### Name: pgmm > ### Title: Generalized Method of Moments (GMM) Estimation for Panel Data > ### Aliases: pgmm coef.pgmm summary.pgmm print.summary.pgmm > ### Keywords: regression > > ### ** Examples > > > data("EmplUK", package = "plm") > > ## Arellano and Bond (1991), table 4 col. b > z1 <- 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, effect = "twoways", model = "twosteps") > summary(z1, 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") Unbalanced Panel: n = 140, T = 7-9, N = 1031 Number of Observations Used: 611 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.6190677 -0.0255683 0.0000000 -0.0001339 0.0332013 0.6410272 Coefficients: Estimate Std. Error z-value Pr(>|z|) lag(log(emp), 1:2)1 0.474151 0.085303 5.5584 2.722e-08 *** lag(log(emp), 1:2)2 -0.052967 0.027284 -1.9413 0.0522200 . lag(log(wage), 0:1)0 -0.513205 0.049345 -10.4003 < 2.2e-16 *** lag(log(wage), 0:1)1 0.224640 0.080063 2.8058 0.0050192 ** log(capital) 0.292723 0.039463 7.4177 1.191e-13 *** lag(log(output), 0:1)0 0.609775 0.108524 5.6188 1.923e-08 *** lag(log(output), 0:1)1 -0.446373 0.124815 -3.5763 0.0003485 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Sargan test: chisq(25) = 30.11247 (p-value = 0.22011) Autocorrelation test (1): normal = -2.427829 (p-value = 0.01519) Autocorrelation test (2): normal = -0.3325401 (p-value = 0.73948) Wald test for coefficients: chisq(7) = 371.9877 (p-value = < 2.22e-16) Wald test for time dummies: chisq(6) = 26.9045 (p-value = 0.0001509) > > ## Blundell and Bond (1998) table 4 (cf. DPD for OX p. 12 col. 4) > z2 <- pgmm(log(emp) ~ lag(log(emp), 1)+ lag(log(wage), 0:1) + + lag(log(capital), 0:1) | lag(log(emp), 2:99) + + lag(log(wage), 2:99) + lag(log(capital), 2:99), + data = EmplUK, effect = "twoways", model = "onestep", + transformation = "ld") > summary(z2, robust = TRUE) Twoways effects One step model Call: pgmm(formula = log(emp) ~ lag(log(emp), 1) + lag(log(wage), 0:1) + lag(log(capital), 0:1) | lag(log(emp), 2:99) + lag(log(wage), 2:99) + lag(log(capital), 2:99), data = EmplUK, effect = "twoways", model = "onestep", transformation = "ld") Unbalanced Panel: n = 140, T = 7-9, N = 1031 Number of Observations Used: 1642 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.7530341 -0.0369030 0.0000000 0.0002882 0.0466069 0.6001503 Coefficients: Estimate Std. Error z-value Pr(>|z|) lag(log(emp), 1) 0.935605 0.026295 35.5810 < 2.2e-16 *** lag(log(wage), 0:1)0 -0.630976 0.118054 -5.3448 9.050e-08 *** lag(log(wage), 0:1)1 0.482620 0.136887 3.5257 0.0004224 *** lag(log(capital), 0:1)0 0.483930 0.053867 8.9838 < 2.2e-16 *** lag(log(capital), 0:1)1 -0.424393 0.058479 -7.2572 3.952e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Sargan test: chisq(100) = 118.763 (p-value = 0.097096) Autocorrelation test (1): normal = -4.808434 (p-value = 1.5212e-06) Autocorrelation test (2): normal = -0.2800133 (p-value = 0.77947) Wald test for coefficients: chisq(5) = 11174.82 (p-value = < 2.22e-16) Wald test for time dummies: chisq(7) = 14.71138 (p-value = 0.039882) > > ## Not run: > ##D ## Same with the old formula or dynformula interface > ##D ## Arellano and Bond (1991), table 4, col. b > ##D z1 <- pgmm(log(emp) ~ log(wage) + log(capital) + log(output), > ##D lag.form = list(2,1,0,1), data = EmplUK, > ##D effect = "twoways", model = "twosteps", > ##D gmm.inst = ~log(emp), lag.gmm = list(c(2,99))) > ##D summary(z1, robust = FALSE) > ##D > ##D ## Blundell and Bond (1998) table 4 (cf DPD for OX p. 12 col. 4) > ##D z2 <- pgmm(dynformula(log(emp) ~ log(wage) + log(capital), list(1,1,1)), > ##D data = EmplUK, effect = "twoways", model = "onestep", > ##D gmm.inst = ~log(emp) + log(wage) + log(capital), > ##D lag.gmm = c(2,99), transformation = "ld") > ##D summary(z2, robust = TRUE) > ## End(Not run) > > > > > cleanEx() > nameEx("pgrangertest") > ### * pgrangertest > > flush(stderr()); flush(stdout()) > > ### Name: pgrangertest > ### Title: Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) > ### Aliases: pgrangertest > ### Keywords: htest > > ### ** Examples > > > ## not meaningful, just to demonstrate usage > ## H0: 'value' does not Granger cause 'inv' for all invididuals > > data("Grunfeld", package = "plm") > pgrangertest(inv ~ value, data = Grunfeld) Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) data: inv ~ value Ztilde = 3.2896, p-value = 0.001003 alternative hypothesis: Granger causality for at least one individual > pgrangertest(inv ~ value, data = Grunfeld, order = 2L) Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) data: inv ~ value Ztilde = 1.6832, p-value = 0.09234 alternative hypothesis: Granger causality for at least one individual > pgrangertest(inv ~ value, data = Grunfeld, order = 2L, test = "Zbar") Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) data: inv ~ value Zbar = 2.9657, p-value = 0.00302 alternative hypothesis: Granger causality for at least one individual > > # varying lag order (last individual lag order 3, others lag order 2) > pgrangertest(inv ~ value, data = Grunfeld, order = c(rep(2L, 9), 3L)) Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012)) data: inv ~ value Ztilde = 1.8181, p-value = 0.06905 alternative hypothesis: Granger causality for at least one individual > > > > > > cleanEx() > nameEx("pht") > ### * pht > > flush(stderr()); flush(stdout()) > > ### Name: pht > ### Title: Hausman-Taylor Estimator for Panel Data > ### Aliases: pht summary.pht print.summary.pht > ### Keywords: regression > > ### ** Examples > > > ## replicates Baltagi (2005, 2013), table 7.4 > ## preferred way with plm() > data("Wages", package = "plm") > ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + + bluecol + ind + union + sex + black + ed | + bluecol + south + smsa + ind + sex + black | + wks + married + union + exp + I(exp ^ 2), + data = Wages, index = 595, + random.method = "ht", model = "random", inst.method = "baltagi") > summary(ht) Oneway (individual) effect Random Effect Model (Hausman-Taylor's transformation) Instrumental variable estimation (Baltagi's transformation) Call: plm(formula = lwage ~ wks + south + smsa + married + exp + I(exp^2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp^2), data = Wages, model = "random", random.method = "ht", inst.method = "baltagi", index = 595) Balanced Panel: n = 595, T = 7, N = 4165 Effects: var std.dev share idiosyncratic 0.02304 0.15180 0.025 individual 0.88699 0.94180 0.975 theta: 0.9392 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -12.643736 -0.466002 0.043285 0.524739 13.340263 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.9127e+00 2.8365e-01 10.2687 < 2.2e-16 *** wks 8.3740e-04 5.9973e-04 1.3963 0.16263 southyes 7.4398e-03 3.1955e-02 0.2328 0.81590 smsayes -4.1833e-02 1.8958e-02 -2.2066 0.02734 * marriedyes -2.9851e-02 1.8980e-02 -1.5728 0.11578 exp 1.1313e-01 2.4710e-03 45.7851 < 2.2e-16 *** I(exp^2) -4.1886e-04 5.4598e-05 -7.6718 1.696e-14 *** bluecolyes -2.0705e-02 1.3781e-02 -1.5024 0.13299 ind 1.3604e-02 1.5237e-02 0.8928 0.37196 unionyes 3.2771e-02 1.4908e-02 2.1982 0.02794 * sexfemale -1.3092e-01 1.2666e-01 -1.0337 0.30129 blackyes -2.8575e-01 1.5570e-01 -1.8352 0.06647 . ed 1.3794e-01 2.1248e-02 6.4919 8.474e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 243.04 Residual Sum of Squares: 4163.6 R-Squared: 0.60945 Adj. R-Squared: 0.60833 Chisq: 6891.87 on 12 DF, p-value: < 2.22e-16 > > am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + + bluecol + ind + union + sex + black + ed | + bluecol + south + smsa + ind + sex + black | + wks + married + union + exp + I(exp ^ 2), + data = Wages, index = 595, + random.method = "ht", model = "random", inst.method = "am") > summary(am) Oneway (individual) effect Random Effect Model (Hausman-Taylor's transformation) Instrumental variable estimation (Amemiya-MaCurdy's transformation) Call: plm(formula = lwage ~ wks + south + smsa + married + exp + I(exp^2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp^2), data = Wages, model = "random", random.method = "ht", inst.method = "am", index = 595) Balanced Panel: n = 595, T = 7, N = 4165 Effects: var std.dev share idiosyncratic 0.02304 0.15180 0.025 individual 0.88699 0.94180 0.975 theta: 0.9392 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -12.643192 -0.464811 0.043216 0.523598 13.338789 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.9273e+00 2.7513e-01 10.6399 < 2.2e-16 *** wks 8.3806e-04 5.9945e-04 1.3980 0.16210 southyes 7.2818e-03 3.1936e-02 0.2280 0.81964 smsayes -4.1951e-02 1.8947e-02 -2.2141 0.02682 * marriedyes -3.0089e-02 1.8967e-02 -1.5864 0.11266 exp 1.1297e-01 2.4688e-03 45.7584 < 2.2e-16 *** I(exp^2) -4.2140e-04 5.4554e-05 -7.7244 1.124e-14 *** bluecolyes -2.0850e-02 1.3765e-02 -1.5147 0.12986 ind 1.3629e-02 1.5229e-02 0.8949 0.37082 unionyes 3.2475e-02 1.4894e-02 2.1804 0.02922 * sexfemale -1.3201e-01 1.2660e-01 -1.0427 0.29709 blackyes -2.8590e-01 1.5549e-01 -1.8388 0.06595 . ed 1.3720e-01 2.0570e-02 6.6703 2.553e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 243.04 Residual Sum of Squares: 4160.3 R-Squared: 0.60948 Adj. R-Squared: 0.60835 Chisq: 6879.2 on 12 DF, p-value: < 2.22e-16 > > ## deprecated way with pht() for HT > #ht <- pht(lwage ~ wks + south + smsa + married + exp + I(exp^2) + > # bluecol + ind + union + sex + black + ed | > # sex + black + bluecol + south + smsa + ind, > # data = Wages, model = "ht", index = 595) > #summary(ht) > # deprecated way with pht() for AM > #am <- pht(lwage ~ wks + south + smsa + married + exp + I(exp^2) + > # bluecol + ind + union + sex + black + ed | > # sex + black + bluecol + south + smsa + ind, > # data = Wages, model = "am", index = 595) > #summary(am) > > > > > > cleanEx() > nameEx("phtest") > ### * phtest > > flush(stderr()); flush(stdout()) > > ### Name: phtest > ### Title: Hausman Test for Panel Models > ### Aliases: phtest phtest.formula phtest.panelmodel > ### Keywords: htest > > ### ** Examples > > > data("Gasoline", package = "plm") > form <- lgaspcar ~ lincomep + lrpmg + lcarpcap > wi <- plm(form, data = Gasoline, model = "within") > re <- plm(form, data = Gasoline, model = "random") > phtest(wi, re) Hausman Test data: form chisq = 302.8, df = 3, p-value < 2.2e-16 alternative hypothesis: one model is inconsistent > phtest(form, data = Gasoline) Hausman Test data: form chisq = 302.8, df = 3, p-value < 2.2e-16 alternative hypothesis: one model is inconsistent > phtest(form, data = Gasoline, method = "aux") Regression-based Hausman test data: form chisq = 26.495, df = 3, p-value = 7.512e-06 alternative hypothesis: one model is inconsistent > > # robust Hausman test (regression-based) > phtest(form, data = Gasoline, method = "aux", vcov = vcovHC) Regression-based Hausman test, vcov: vcovHC data: form chisq = 12.495, df = 3, p-value = 0.005867 alternative hypothesis: one model is inconsistent > > # robust Hausman test with vcov supplied as a > # function and additional parameters > phtest(form, data = Gasoline, method = "aux", + vcov = function(x) vcovHC(x, method="white2", type="HC3")) Regression-based Hausman test, vcov: function(x) vcovHC(x, method = "white2", type = "HC3") data: form chisq = 38.389, df = 3, p-value = 2.338e-08 alternative hypothesis: one model is inconsistent > > > > > cleanEx() > nameEx("piest") > ### * piest > > flush(stderr()); flush(stdout()) > > ### Name: piest > ### Title: Chamberlain estimator and test for fixed effects > ### Aliases: piest print.piest summary.piest print.summary.piest > ### Keywords: htest > > ### ** Examples > > > data("RiceFarms", package = "plm") > pirice <- piest(log(goutput) ~ log(seed) + log(totlabor) + log(size), RiceFarms, index = "id") > summary(pirice) Estimate Std. Error z-value Pr(>|z|) log(seed) 0.109644903 0.01570873 6.9798717 2.954498e-12 log(totlabor) 0.226122409 0.01685392 13.4166057 4.833494e-41 log(size) 0.657583316 0.02260419 29.0912160 4.636680e-186 log(seed).1 0.116874708 0.02822261 4.1411733 3.455338e-05 log(totlabor).1 0.044050487 0.03522845 1.2504237 2.111448e-01 log(size).1 -0.231526271 0.04516303 -5.1264562 2.952468e-07 log(seed).2 -0.019015159 0.01193206 -1.5936186 1.110215e-01 log(totlabor).2 -0.026159677 0.02900882 -0.9017837 3.671718e-01 log(size).2 0.031425564 0.02334159 1.3463337 1.781949e-01 log(seed).3 -0.068786787 0.02472592 -2.7819704 5.402997e-03 log(totlabor).3 0.122166690 0.02947842 4.1442759 3.408891e-05 log(size).3 0.048725309 0.02388707 2.0398196 4.136830e-02 log(seed).4 0.013214907 0.02856657 0.4626003 6.436509e-01 log(totlabor).4 0.052630352 0.02723711 1.9323033 5.332208e-02 log(size).4 -0.004638401 0.04432250 -0.1046512 9.166526e-01 log(seed).5 -0.110545590 0.02916033 -3.7909579 1.500674e-04 log(totlabor).5 -0.227715057 0.03908012 -5.8268768 5.647427e-09 log(size).5 0.237687158 0.04376374 5.4311436 5.599405e-08 log(seed).6 0.255630583 0.03642902 7.0172244 2.263190e-12 log(totlabor).6 0.153556015 0.03542873 4.3342232 1.462757e-05 log(size).6 -0.355043589 0.04222026 -8.4093186 4.123995e-17 Chamberlain's pi test data: log(goutput) ~ log(seed) + log(totlabor) + log(size) chisq = 113.72, df = 87, p-value = 0.02882 > > > > > cleanEx() > nameEx("plm-package") > ### * plm-package > > flush(stderr()); flush(stdout()) > > ### Name: plm-package > ### Title: plm package: linear models for panel data > ### Aliases: plm-package > ### Keywords: package > > ### ** Examples > > > data("Produc", package = "plm") > zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, index = c("state","year")) > summary(zz) Oneway (individual) effect Within Model Call: plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state", "year")) Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -0.120456 -0.023741 -0.002041 0.018144 0.174718 Coefficients: Estimate Std. Error t-value Pr(>|t|) log(pcap) -0.02614965 0.02900158 -0.9017 0.3675 log(pc) 0.29200693 0.02511967 11.6246 < 2.2e-16 *** log(emp) 0.76815947 0.03009174 25.5273 < 2.2e-16 *** unemp -0.00529774 0.00098873 -5.3582 1.114e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 18.941 Residual Sum of Squares: 1.1112 R-Squared: 0.94134 Adj. R-Squared: 0.93742 F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16 > > # replicates some results from Baltagi (2013), table 3.1 > data("Grunfeld", package = "plm") > p <- plm(inv ~ value + capital, + data = Grunfeld, model="pooling") > > wi <- plm(inv ~ value + capital, + data = Grunfeld, model="within", effect = "twoways") > > swar <- plm(inv ~ value + capital, + data = Grunfeld, model="random", effect = "twoways") > > amemiya <- plm(inv ~ value + capital, + data = Grunfeld, model = "random", random.method = "amemiya", + effect = "twoways") > > walhus <- plm(inv ~ value + capital, + data = Grunfeld, model = "random", random.method = "walhus", + effect = "twoways") > > > > > cleanEx() > nameEx("plm") > ### * plm > > flush(stderr()); flush(stdout()) > > ### Name: plm > ### Title: Panel Data Estimators > ### Aliases: plm print.plm.list terms.panelmodel vcov.panelmodel > ### fitted.panelmodel residuals.panelmodel df.residual.panelmodel > ### coef.panelmodel print.panelmodel update.panelmodel > ### deviance.panelmodel predict.plm formula.plm plot.plm residuals.plm > ### fitted.plm > ### Keywords: regression > > ### ** Examples > > > data("Produc", package = "plm") > zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, index = c("state","year")) > summary(zz) Oneway (individual) effect Within Model Call: plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state", "year")) Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -0.120456 -0.023741 -0.002041 0.018144 0.174718 Coefficients: Estimate Std. Error t-value Pr(>|t|) log(pcap) -0.02614965 0.02900158 -0.9017 0.3675 log(pc) 0.29200693 0.02511967 11.6246 < 2.2e-16 *** log(emp) 0.76815947 0.03009174 25.5273 < 2.2e-16 *** unemp -0.00529774 0.00098873 -5.3582 1.114e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 18.941 Residual Sum of Squares: 1.1112 R-Squared: 0.94134 Adj. R-Squared: 0.93742 F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16 > > # replicates some results from Baltagi (2013), table 3.1 > data("Grunfeld", package = "plm") > p <- plm(inv ~ value + capital, + data = Grunfeld, model = "pooling") > > wi <- plm(inv ~ value + capital, + data = Grunfeld, model = "within", effect = "twoways") > > swar <- plm(inv ~ value + capital, + data = Grunfeld, model = "random", effect = "twoways") > > amemiya <- plm(inv ~ value + capital, + data = Grunfeld, model = "random", random.method = "amemiya", + effect = "twoways") > > walhus <- plm(inv ~ value + capital, + data = Grunfeld, model = "random", random.method = "walhus", + effect = "twoways") > > # summary and summary with a funished vcov (passed as matrix, > # as function, and as function with additional argument) > summary(wi) Twoways effects Within Model Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "twoways", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -162.6094 -19.4710 -1.2669 19.1277 211.8420 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.117716 0.013751 8.5604 6.653e-15 *** capital 0.357916 0.022719 15.7540 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 1615600 Residual Sum of Squares: 452150 R-Squared: 0.72015 Adj. R-Squared: 0.67047 F-statistic: 217.442 on 2 and 169 DF, p-value: < 2.22e-16 > summary(wi, vcov = vcovHC(wi)) Twoways effects Within Model Note: Coefficient variance-covariance matrix supplied: vcovHC(wi) Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "twoways", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -162.6094 -19.4710 -1.2669 19.1277 211.8420 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.117716 0.009712 12.121 < 2.2e-16 *** capital 0.357916 0.042931 8.337 2.552e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 1615600 Residual Sum of Squares: 452150 R-Squared: 0.72015 Adj. R-Squared: 0.67047 F-statistic: 74.6338 on 2 and 9 DF, p-value: 2.4936e-06 > summary(wi, vcov = vcovHC) Twoways effects Within Model Note: Coefficient variance-covariance matrix supplied: vcovHC Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "twoways", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -162.6094 -19.4710 -1.2669 19.1277 211.8420 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.117716 0.009712 12.121 < 2.2e-16 *** capital 0.357916 0.042931 8.337 2.552e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 1615600 Residual Sum of Squares: 452150 R-Squared: 0.72015 Adj. R-Squared: 0.67047 F-statistic: 74.6338 on 2 and 9 DF, p-value: 2.4936e-06 > summary(wi, vcov = function(x) vcovHC(x, method = "white2")) Twoways effects Within Model Note: Coefficient variance-covariance matrix supplied: function(x) vcovHC(x, method = "white2") Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "twoways", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -162.6094 -19.4710 -1.2669 19.1277 211.8420 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.11772 0.01881 6.2582 3.095e-09 *** capital 0.35792 0.03178 11.2622 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 1615600 Residual Sum of Squares: 452150 R-Squared: 0.72015 Adj. R-Squared: 0.67047 F-statistic: 102.013 on 2 and 9 DF, p-value: 6.5484e-07 > > > # nested random effect model > # replicate Baltagi/Song/Jung (2001), p. 378 (table 6), columns SA, WH > # == Baltagi (2013), pp. 204-205 > data("Produc", package = "plm") > pProduc <- pdata.frame(Produc, index = c("state", "year", "region")) > form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp > summary(plm(form, data = pProduc, model = "random", effect = "nested")) Nested effects Random Effect Model (Swamy-Arora's transformation) Call: plm(formula = form, data = pProduc, effect = "nested", model = "random") Balanced Panel: n = 48, T = 17, N = 816 Effects: var std.dev share idiosyncratic 0.001352 0.036765 0.191 individual 0.004278 0.065410 0.604 group 0.001455 0.038148 0.205 theta: Min. 1st Qu. Median Mean 3rd Qu. Max. id 0.86492676 0.8649268 0.86492676 0.86492676 0.86492676 0.86492676 group 0.03960556 0.0466931 0.05713605 0.05577645 0.06458029 0.06458029 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.106171 -0.024805 -0.001816 -0.000054 0.019795 0.182810 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.08921088 0.14570204 14.3389 < 2.2e-16 *** log(pc) 0.27412419 0.02054440 13.3430 < 2.2e-16 *** log(emp) 0.73983766 0.02575046 28.7311 < 2.2e-16 *** log(hwy) 0.07273624 0.02202509 3.3024 0.0009585 *** log(water) 0.07645327 0.01385767 5.5170 3.448e-08 *** log(util) -0.09437398 0.01677289 -5.6266 1.838e-08 *** unemp -0.00616304 0.00090331 -6.8227 8.933e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 43.035 Residual Sum of Squares: 1.1245 R-Squared: 0.97387 Adj. R-Squared: 0.97368 Chisq: 30152 on 6 DF, p-value: < 2.22e-16 > summary(plm(form, data = pProduc, model = "random", effect = "nested", + random.method = "walhus")) Nested effects Random Effect Model (Wallace-Hussain's transformation) Call: plm(formula = form, data = pProduc, effect = "nested", model = "random", random.method = "walhus") Balanced Panel: n = 48, T = 17, N = 816 Effects: var std.dev share idiosyncratic 0.001415 0.037617 0.163 individual 0.004507 0.067131 0.520 group 0.002744 0.052387 0.317 theta: Min. 1st Qu. Median Mean 3rd Qu. Max. id 0.86533240 0.86533240 0.86533240 0.86533240 0.86533240 0.86533240 group 0.05409908 0.06154491 0.07179372 0.07023704 0.07867007 0.07867007 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.105014 -0.024736 -0.001879 -0.000056 0.019944 0.182082 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.08165186 0.15034855 13.8455 < 2.2e-16 *** log(pc) 0.27256322 0.02093384 13.0202 < 2.2e-16 *** log(emp) 0.74164483 0.02607167 28.4464 < 2.2e-16 *** log(hwy) 0.07493204 0.02234932 3.3528 0.0008001 *** log(water) 0.07639159 0.01386702 5.5089 3.611e-08 *** log(util) -0.09523031 0.01677247 -5.6778 1.365e-08 *** unemp -0.00614840 0.00090786 -6.7724 1.267e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 40.423 Residual Sum of Squares: 1.1195 R-Squared: 0.97231 Adj. R-Squared: 0.9721 Chisq: 28403.2 on 6 DF, p-value: < 2.22e-16 > > ## Hausman-Taylor estimator and Amemiya-MaCurdy estimator > ## replicate Baltagi (2005, 2013), table 7.4 > data("Wages", package = "plm") > ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + + bluecol + ind + union + sex + black + ed | + bluecol + south + smsa + ind + sex + black | + wks + married + union + exp + I(exp ^ 2), + data = Wages, index = 595, + random.method = "ht", model = "random", inst.method = "baltagi") > summary(ht) Oneway (individual) effect Random Effect Model (Hausman-Taylor's transformation) Instrumental variable estimation (Baltagi's transformation) Call: plm(formula = lwage ~ wks + south + smsa + married + exp + I(exp^2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp^2), data = Wages, model = "random", random.method = "ht", inst.method = "baltagi", index = 595) Balanced Panel: n = 595, T = 7, N = 4165 Effects: var std.dev share idiosyncratic 0.02304 0.15180 0.025 individual 0.88699 0.94180 0.975 theta: 0.9392 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -12.643736 -0.466002 0.043285 0.524739 13.340263 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.9127e+00 2.8365e-01 10.2687 < 2.2e-16 *** wks 8.3740e-04 5.9973e-04 1.3963 0.16263 southyes 7.4398e-03 3.1955e-02 0.2328 0.81590 smsayes -4.1833e-02 1.8958e-02 -2.2066 0.02734 * marriedyes -2.9851e-02 1.8980e-02 -1.5728 0.11578 exp 1.1313e-01 2.4710e-03 45.7851 < 2.2e-16 *** I(exp^2) -4.1886e-04 5.4598e-05 -7.6718 1.696e-14 *** bluecolyes -2.0705e-02 1.3781e-02 -1.5024 0.13299 ind 1.3604e-02 1.5237e-02 0.8928 0.37196 unionyes 3.2771e-02 1.4908e-02 2.1982 0.02794 * sexfemale -1.3092e-01 1.2666e-01 -1.0337 0.30129 blackyes -2.8575e-01 1.5570e-01 -1.8352 0.06647 . ed 1.3794e-01 2.1248e-02 6.4919 8.474e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 243.04 Residual Sum of Squares: 4163.6 R-Squared: 0.60945 Adj. R-Squared: 0.60833 Chisq: 6891.87 on 12 DF, p-value: < 2.22e-16 > > am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + + bluecol + ind + union + sex + black + ed | + bluecol + south + smsa + ind + sex + black | + wks + married + union + exp + I(exp ^ 2), + data = Wages, index = 595, + random.method = "ht", model = "random", inst.method = "am") > summary(am) Oneway (individual) effect Random Effect Model (Hausman-Taylor's transformation) Instrumental variable estimation (Amemiya-MaCurdy's transformation) Call: plm(formula = lwage ~ wks + south + smsa + married + exp + I(exp^2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp^2), data = Wages, model = "random", random.method = "ht", inst.method = "am", index = 595) Balanced Panel: n = 595, T = 7, N = 4165 Effects: var std.dev share idiosyncratic 0.02304 0.15180 0.025 individual 0.88699 0.94180 0.975 theta: 0.9392 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -12.643192 -0.464811 0.043216 0.523598 13.338789 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.9273e+00 2.7513e-01 10.6399 < 2.2e-16 *** wks 8.3806e-04 5.9945e-04 1.3980 0.16210 southyes 7.2818e-03 3.1936e-02 0.2280 0.81964 smsayes -4.1951e-02 1.8947e-02 -2.2141 0.02682 * marriedyes -3.0089e-02 1.8967e-02 -1.5864 0.11266 exp 1.1297e-01 2.4688e-03 45.7584 < 2.2e-16 *** I(exp^2) -4.2140e-04 5.4554e-05 -7.7244 1.124e-14 *** bluecolyes -2.0850e-02 1.3765e-02 -1.5147 0.12986 ind 1.3629e-02 1.5229e-02 0.8949 0.37082 unionyes 3.2475e-02 1.4894e-02 2.1804 0.02922 * sexfemale -1.3201e-01 1.2660e-01 -1.0427 0.29709 blackyes -2.8590e-01 1.5549e-01 -1.8388 0.06595 . ed 1.3720e-01 2.0570e-02 6.6703 2.553e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 243.04 Residual Sum of Squares: 4160.3 R-Squared: 0.60948 Adj. R-Squared: 0.60835 Chisq: 6879.2 on 12 DF, p-value: < 2.22e-16 > > > > > cleanEx() > nameEx("plmtest") > ### * plmtest > > flush(stderr()); flush(stdout()) > > ### Name: plmtest > ### Title: Lagrange FF Multiplier Tests for Panel Models > ### Aliases: plmtest plmtest.plm plmtest.formula > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > g <- plm(inv ~ value + capital, data = Grunfeld, model = "pooling") > plmtest(g) Lagrange Multiplier Test - (Honda) for balanced panels data: inv ~ value + capital normal = 28.252, p-value < 2.2e-16 alternative hypothesis: significant effects > plmtest(g, effect="time") Lagrange Multiplier Test - time effects (Honda) for balanced panels data: inv ~ value + capital normal = -2.5404, p-value = 0.9945 alternative hypothesis: significant effects > plmtest(inv ~ value + capital, data = Grunfeld, type = "honda") Lagrange Multiplier Test - (Honda) for balanced panels data: inv ~ value + capital normal = 28.252, p-value < 2.2e-16 alternative hypothesis: significant effects > plmtest(inv ~ value + capital, data = Grunfeld, type = "bp") Lagrange Multiplier Test - (Breusch-Pagan) for balanced panels data: inv ~ value + capital chisq = 798.16, df = 1, p-value < 2.2e-16 alternative hypothesis: significant effects > plmtest(inv ~ value + capital, data = Grunfeld, type = "bp", effect = "twoways") Lagrange Multiplier Test - two-ways effects (Breusch-Pagan) for balanced panels data: inv ~ value + capital chisq = 804.62, df = 2, p-value < 2.2e-16 alternative hypothesis: significant effects > plmtest(inv ~ value + capital, data = Grunfeld, type = "ghm", effect = "twoways") Lagrange Multiplier Test - two-ways effects (Gourieroux, Holly and Monfort) for balanced panels data: inv ~ value + capital chibarsq = 798.16, df0 = 0.00, df1 = 1.00, df2 = 2.00, w0 = 0.25, w1 = 0.50, w2 = 0.25, p-value < 2.2e-16 alternative hypothesis: significant effects > plmtest(inv ~ value + capital, data = Grunfeld, type = "kw", effect = "twoways") Lagrange Multiplier Test - two-ways effects (King and Wu) for balanced panels data: inv ~ value + capital normal = 21.832, p-value < 2.2e-16 alternative hypothesis: significant effects > > Grunfeld_unbal <- Grunfeld[1:(nrow(Grunfeld)-1), ] # create an unbalanced panel data set > g_unbal <- plm(inv ~ value + capital, data = Grunfeld_unbal, model = "pooling") > plmtest(g_unbal) # unbalanced version of test is indicated in output Lagrange Multiplier Test - (Honda) for unbalanced panels data: inv ~ value + capital normal = 28.225, p-value < 2.2e-16 alternative hypothesis: significant effects > > > > > cleanEx() > nameEx("pmg") > ### * pmg > > flush(stderr()); flush(stdout()) > > ### Name: pmg > ### Title: Mean Groups (MG), Demeaned MG and CCE MG estimators > ### Aliases: pmg summary.pmg print.summary.pmg residuals.pmg > ### Keywords: regression > > ### ** Examples > > > data("Produc", package = "plm") > ## Mean Groups estimator > mgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc) > summary(mgmod) Mean Groups model Call: pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc) Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0828079 -0.0118150 0.0004247 0.0000000 0.0126479 0.1189647 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 2.6722392 0.4126515 6.4758 9.433e-11 *** log(pcap) -0.1048507 0.0799132 -1.3121 0.18950 log(pc) 0.2182539 0.0500862 4.3576 1.315e-05 *** log(emp) 0.9334776 0.0750072 12.4452 < 2.2e-16 *** unemp -0.0037216 0.0016427 -2.2655 0.02348 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.33009 Multiple R-squared: 0.99961 > > ## demeaned Mean Groups > dmgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, model = "dmg") > summary(dmgmod) Demeaned Mean Groups model Call: pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "dmg") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0834415 -0.0076164 -0.0001226 0.0000000 0.0078109 0.1177009 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 0.0580979 0.1042881 0.5571 0.577466 log(pcap) -0.0629002 0.1021706 -0.6156 0.538133 log(pc) 0.1607882 0.0591334 2.7191 0.006546 ** log(emp) 0.8425585 0.0704896 11.9529 < 2.2e-16 *** unemp -0.0050181 0.0020770 -2.4160 0.015693 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.23666 Multiple R-squared: 0.99972 > > ## Common Correlated Effects Mean Groups > ccemgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, model = "cmg") > summary(ccemgmod) Common Correlated Effects Mean Groups model Call: pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "cmg") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0806338 -0.0037117 0.0003147 0.0000000 0.0040207 0.0438957 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) -0.6741754 1.0445518 -0.6454 0.518655 log(pcap) 0.0899850 0.1176040 0.7652 0.444180 log(pc) 0.0335784 0.0423362 0.7931 0.427698 log(emp) 0.6258659 0.1071719 5.8398 5.225e-09 *** unemp -0.0031178 0.0014389 -2.1668 0.030249 * y.bar 1.0038005 0.1078874 9.3041 < 2.2e-16 *** log(pcap).bar -0.0491919 0.2396185 -0.2053 0.837344 log(pc).bar -0.0033198 0.1576547 -0.0211 0.983200 log(emp).bar -0.6978359 0.2432887 -2.8683 0.004126 ** unemp.bar 0.0025544 0.0031848 0.8021 0.422505 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.056978 Multiple R-squared: 0.99993 > > > > cleanEx() > nameEx("pmodel.response") > ### * pmodel.response > > flush(stderr()); flush(stdout()) > > ### Name: pmodel.response > ### Title: A function to extract the model.response > ### Aliases: pmodel.response pmodel.response.plm pmodel.response.data.frame > ### pmodel.response.formula > ### Keywords: manip > > ### ** Examples > > > # First, make a pdata.frame > data("Grunfeld", package = "plm") > pGrunfeld <- pdata.frame(Grunfeld) > > # then make a model frame from a pFormula and a pdata.frame > > > form <- inv ~ value + capital > mf <- model.frame(pGrunfeld, form) > # construct (transformed) response of the within model > resp <- pmodel.response(form, data = mf, model = "within", effect = "individual") > # retrieve (transformed) response directly from model frame > resp_mf <- pmodel.response(mf, model = "within", effect = "individual") > > # retrieve (transformed) response from a plm object, i.e. an estimated model > fe_model <- plm(form, data = pGrunfeld, model = "within") > pmodel.response(fe_model) 1-1935 1-1936 1-1937 1-1938 1-1939 1-1940 1-1941 1-1942 -290.4200 -216.2200 -197.4200 -350.3200 -277.2200 -146.8200 -96.0200 -160.0200 1-1943 1-1944 1-1945 1-1946 1-1947 1-1948 1-1949 1-1950 -108.4200 -60.5200 -46.8200 80.0800 -39.1200 -78.8200 -52.9200 34.8800 1-1951 1-1952 1-1953 1-1954 2-1935 2-1936 2-1937 2-1938 147.8800 283.1800 696.3800 878.6800 -200.5750 -55.1750 59.4250 -148.1750 2-1939 2-1940 2-1941 2-1942 2-1943 2-1944 2-1945 2-1946 -180.0750 -48.8750 62.3250 35.1250 -48.8750 -122.2750 -151.7750 9.8250 2-1947 2-1948 2-1949 2-1950 2-1951 2-1952 2-1953 2-1954 10.0250 84.0250 -5.3750 8.3250 177.7250 235.0250 230.5250 48.8250 3-1935 3-1936 3-1937 3-1938 3-1939 3-1940 3-1941 3-1942 -69.1900 -57.2900 -25.0900 -57.6900 -54.1900 -27.8900 10.7100 -10.3900 3-1943 3-1944 3-1945 3-1946 3-1947 3-1948 3-1949 3-1950 -40.9900 -45.4900 -8.6900 57.6100 44.9100 44.0100 -3.9900 -8.7900 3-1951 3-1952 3-1953 3-1954 4-1935 4-1936 4-1937 4-1938 32.9100 55.0100 77.2100 87.3100 -45.8335 -13.3635 -19.8635 -34.5235 4-1939 4-1940 4-1941 4-1942 4-1943 4-1944 4-1945 4-1946 -33.7135 -16.7135 -17.7735 -39.3235 -38.7235 -26.5535 2.6565 -12.0035 4-1947 4-1948 4-1949 4-1950 4-1951 4-1952 4-1953 4-1954 -23.4435 3.2365 -7.1435 14.5365 74.4965 58.8765 88.8065 86.3665 5-1935 5-1936 5-1937 5-1938 5-1939 5-1940 5-1941 5-1942 -22.1225 -11.0725 12.4375 -8.2925 -19.1525 -15.3225 -0.4025 -22.1325 5-1943 5-1944 5-1945 5-1946 5-1947 5-1948 5-1949 5-1950 0.4375 -9.4825 1.4075 -2.4325 -3.7825 8.5375 5.6175 -6.0625 5-1951 5-1952 5-1953 5-1954 6-1935 6-1936 6-1937 6-1938 18.4975 23.5975 30.0975 19.6275 -35.0510 -29.4310 -29.4710 -27.8810 6-1939 6-1940 6-1941 6-1942 6-1943 6-1944 6-1945 6-1946 -30.8110 -26.8710 -12.0010 -12.6010 -27.5710 -22.8110 -16.3810 -5.2410 6-1947 6-1948 6-1949 6-1950 6-1951 6-1952 6-1953 6-1954 -3.5610 8.6190 12.7490 21.9290 39.8890 44.0790 72.1090 80.3090 7-1935 7-1936 7-1937 7-1938 7-1939 7-1940 7-1941 7-1942 -23.1655 -24.3855 -14.8155 -15.0555 -20.9455 -13.8855 -4.0955 -13.1355 7-1943 7-1944 7-1945 7-1946 7-1947 7-1948 7-1949 7-1950 -3.3155 23.2045 -3.4755 1.3845 0.9145 2.4045 2.9945 -5.0655 7-1951 7-1952 7-1953 7-1954 8-1935 8-1936 8-1937 8-1938 17.1745 25.0845 26.2645 41.9145 -29.9615 -16.9915 -7.8415 -20.0015 8-1939 8-1940 8-1941 8-1942 8-1943 8-1944 8-1945 8-1946 -24.0515 -14.3215 5.6185 0.4485 -5.8715 -5.0815 -3.6215 10.5685 8-1947 8-1948 8-1949 8-1950 8-1951 8-1952 8-1953 8-1954 12.6685 6.6685 -10.8515 -10.6515 11.4885 28.8885 47.1885 25.7085 9-1935 9-1936 9-1937 9-1938 9-1939 9-1940 9-1941 9-1942 -15.2590 -18.4990 -11.2390 -20.9990 -13.1090 -14.9590 -9.8090 -9.6790 9-1943 9-1944 9-1945 9-1946 9-1947 9-1948 9-1949 9-1950 -6.1990 20.5810 10.4310 15.0610 12.4310 -1.3590 -9.3490 1.5910 9-1951 9-1952 9-1953 9-1954 10-1935 10-1936 10-1937 10-1938 14.6010 24.0910 24.2210 7.4510 -0.5445 -1.0845 -0.8945 -1.0945 10-1939 10-1940 10-1941 10-1942 10-1943 10-1944 10-1945 10-1946 -1.0545 -1.2745 -0.9445 -1.2245 -2.1545 -1.9045 -1.7245 -0.8445 10-1947 10-1948 10-1949 10-1950 10-1951 10-1952 10-1953 10-1954 0.7255 2.5755 1.1255 0.3355 1.5855 2.9155 3.4455 2.0355 > > # same as constructed before > all.equal(resp, pmodel.response(fe_model), check.attributes = FALSE) # TRUE [1] TRUE > > > > > cleanEx() > nameEx("pooltest") > ### * pooltest > > flush(stderr()); flush(stdout()) > > ### Name: pooltest > ### Title: Test of Poolability > ### Aliases: pooltest pooltest.plm pooltest.formula > ### Keywords: htest > > ### ** Examples > > > data("Gasoline", package = "plm") > form <- lgaspcar ~ lincomep + lrpmg + lcarpcap > gasw <- plm(form, data = Gasoline, model = "within") > gasp <- plm(form, data = Gasoline, model = "pooling") > gasnp <- pvcm(form, data = Gasoline, model = "within") > pooltest(gasw, gasnp) F statistic data: form F = 27.335, df1 = 51, df2 = 270, p-value < 2.2e-16 alternative hypothesis: unstability > pooltest(gasp, gasnp) F statistic data: form F = 129.32, df1 = 68, df2 = 270, p-value < 2.2e-16 alternative hypothesis: unstability > > pooltest(form, data = Gasoline, effect = "individual", model = "within") F statistic data: form F = 27.335, df1 = 51, df2 = 270, p-value < 2.2e-16 alternative hypothesis: unstability > pooltest(form, data = Gasoline, effect = "individual", model = "pooling") F statistic data: form F = 129.32, df1 = 68, df2 = 270, p-value < 2.2e-16 alternative hypothesis: unstability > > > > > cleanEx() > nameEx("pseries") > ### * pseries > > flush(stderr()); flush(stdout()) > > ### Name: pseries > ### Title: panel series > ### Aliases: pseries print.pseries as.matrix.pseries plot.pseries > ### summary.pseries plot.summary.pseries print.summary.pseries Between > ### Between.default Between.pseries Between.matrix between > ### between.default between.pseries between.matrix Within Within.default > ### Within.pseries Within.matrix > ### Keywords: classes > > ### ** Examples > > > # First, create a pdata.frame > data("EmplUK", package = "plm") > Em <- pdata.frame(EmplUK) > > # Then extract a series, which becomes additionally a pseries > z <- Em$output > class(z) [1] "pseries" "numeric" > > # obtain the matrix representation > as.matrix(z) 1976 1977 1978 1979 1980 1981 1982 1983 1 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 2 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 3 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 4 NA 118.2223 120.1551 118.8319 111.9164 97.5540 92.1982 92.4041 5 94.8991 96.5038 98.8163 100.4835 100.1223 98.5270 99.5083 NA 6 102.7724 107.0270 108.6788 111.1190 101.9265 99.4971 99.3103 NA 7 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 8 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 9 102.7724 107.0270 108.6788 111.1190 101.9265 99.4971 99.3103 NA 10 95.3657 96.7315 99.2333 100.7335 100.0000 98.2324 99.7635 NA 11 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 12 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 13 125.8064 127.9649 125.4294 125.0259 106.3044 98.0729 96.8674 NA 14 NA NA 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 15 NA 105.3355 108.1833 110.6984 105.9673 96.5931 92.0013 92.2488 16 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 17 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 18 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 19 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 20 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 21 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 22 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 23 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 24 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 25 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 26 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 27 NA NA 112.8000 108.9000 100.0000 89.2000 90.5000 86.9000 28 NA 93.9500 98.2000 102.5667 102.0167 99.4917 94.2583 99.0583 29 NA 95.7000 100.2000 102.4000 101.1000 96.9500 96.0500 103.3500 30 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 31 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 32 NA 96.0500 100.6000 102.3667 100.9167 96.4417 96.4083 104.2083 33 93.6000 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 NA 34 NA 95.7000 100.2000 102.4000 101.1000 96.9500 96.0500 103.3500 35 NA 94.6500 99.0000 102.5000 101.6500 98.4750 94.9750 100.7750 36 93.6000 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 NA 37 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 38 106.1747 108.9675 107.8624 104.5668 100.0000 92.7430 90.0386 NA 39 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 40 106.5556 108.2693 108.1387 105.3907 101.1417 94.5573 90.7147 NA 41 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 42 106.1747 108.9675 107.8624 104.5668 100.0000 92.7430 90.0386 NA 43 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 44 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 45 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 46 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 47 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 48 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 49 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 NA 50 106.1747 108.9675 107.8624 104.5668 100.0000 92.7430 90.0386 NA 51 125.8064 127.9649 125.4294 125.0259 106.3044 98.0729 96.8674 NA 52 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 53 NA 116.2250 114.9750 111.8250 106.6750 97.3000 89.5250 89.6000 54 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 55 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 56 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 57 NA 117.9279 120.2607 118.9515 113.4060 98.1655 92.3142 92.1207 58 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 59 NA 117.9279 120.2607 118.9515 113.4060 98.1655 92.3142 92.1207 60 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 61 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 62 NA 117.9279 120.2607 118.9515 113.4060 98.1655 92.3142 92.1207 63 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 64 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 65 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 66 NA 109.8750 109.8750 111.6750 108.2500 97.2500 89.4750 91.6500 67 117.0448 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 NA 68 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 69 NA 109.8750 109.8750 111.6750 108.2500 97.2500 89.4750 91.6500 70 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 71 102.5303 106.1199 108.8228 111.3165 102.9836 94.8896 91.4088 NA 72 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 73 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 74 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 75 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 76 102.5303 106.1199 108.8228 111.3165 102.9836 94.8896 91.4088 NA 77 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 78 102.5303 106.1199 108.8228 111.3165 102.9836 94.8896 91.4088 NA 79 93.6000 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 NA 80 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 NA 81 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 82 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 83 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 84 94.6657 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 NA 85 93.8333 97.1000 101.8000 102.2667 100.3667 94.9167 97.4833 NA 86 94.6657 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 NA 87 116.4000 115.7000 112.8000 108.9000 100.0000 89.2000 90.5000 NA 88 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 89 110.1000 109.2000 111.9000 111.0000 100.0000 89.0000 90.9000 NA 90 110.0500 109.4250 111.2250 111.2250 102.7500 91.7500 90.4250 NA 91 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 NA 92 94.5333 95.0000 99.4000 102.4667 101.4667 97.9667 95.3333 NA 93 NA 94.6500 99.0000 102.5000 101.6500 98.4750 94.9750 100.7750 94 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 NA 95 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 96 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 NA 97 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 NA 98 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 99 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 100 NA 127.6979 126.0818 124.8981 110.5073 98.5011 96.9925 97.8807 101 NA 117.9279 120.2607 118.9515 113.4060 98.1655 92.3142 92.1207 102 NA 105.4446 107.8390 109.5789 108.6693 99.8491 99.3706 101.2430 103 93.6000 97.8000 102.6000 102.2000 100.0000 93.9000 98.2000 NA 104 NA 95.8210 97.5654 99.7333 100.4890 99.4108 98.7427 100.1189 105 NA 96.2762 98.3993 100.2334 100.2445 98.8216 99.2531 100.4744 106 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 107 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 108 NA 95.7072 97.3569 99.6083 100.5501 99.5581 98.6151 100.0301 109 NA 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 110 NA 127.1640 127.3866 124.6424 118.9132 99.3576 97.2428 97.1944 111 NA 116.0500 114.2500 110.8500 104.4500 94.6000 89.8500 88.7000 112 NA 106.8729 108.6912 107.0385 103.4251 98.1858 92.0669 89.8429 113 93.9500 96.7500 101.4000 102.3000 100.5500 95.4250 97.1250 105.9250 114 NA 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 115 NA 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 116 NA 117.6336 120.3662 119.0712 114.8955 98.7770 92.4302 91.8372 117 NA 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 107.0929 118 116.8157 118.2223 120.1551 118.8319 111.9164 97.5540 92.1982 92.4041 119 NA 109.8750 109.8750 111.6750 108.2500 97.2500 89.4750 91.6500 120 NA 105.5970 108.3964 110.9044 104.9727 96.0253 91.8038 92.4875 121 NA 104.5512 107.5437 110.0804 108.9509 98.2966 92.5937 91.5326 122 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 123 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 124 NA 110.0250 109.4250 111.8250 110.0833 99.0833 89.1583 91.1500 125 NA 117.3392 120.4718 119.1908 116.3851 99.3885 92.5462 91.5538 126 NA 118.2223 120.1551 118.8319 111.9164 97.5540 92.1982 92.4041 127 94.6657 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 128 94.6657 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 129 94.6657 96.3900 98.6078 100.3584 100.1834 98.6743 99.3807 100.5633 130 101.7754 106.8009 108.5589 110.8990 102.8898 99.5474 99.3189 105.1429 131 103.7669 106.9042 109.4623 111.9345 100.0000 93.1862 90.8164 93.6813 132 126.7636 128.3653 124.4507 125.2176 100.0000 97.4305 96.6797 98.7386 133 117.6000 115.8750 113.5250 109.8750 102.2250 91.9000 90.1750 87.8000 134 117.0448 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 135 116.9589 119.6943 119.6272 118.2336 104.4687 94.4966 91.6183 93.8212 136 117.0448 120.5774 119.3105 117.8746 100.0000 92.6621 91.2704 94.6714 137 110.0500 109.4250 111.2250 111.2250 102.7500 91.7500 90.4250 93.1500 138 101.7059 105.5970 108.3964 110.9044 104.9727 96.0253 91.8038 92.4875 139 102.5303 106.1199 108.8228 111.3165 102.9836 94.8896 91.4088 92.9650 140 104.7664 107.4791 108.9188 111.5591 100.0000 99.3965 99.2931 107.0929 1984 1 NA 2 NA 3 NA 4 NA 5 NA 6 NA 7 NA 8 NA 9 NA 10 NA 11 NA 12 NA 13 NA 14 99.9862 15 NA 16 NA 17 NA 18 NA 19 NA 20 NA 21 NA 22 NA 23 NA 24 NA 25 NA 26 NA 27 87.4000 28 NA 29 NA 30 NA 31 NA 32 NA 33 NA 34 NA 35 NA 36 NA 37 NA 38 NA 39 NA 40 NA 41 NA 42 NA 43 NA 44 NA 45 NA 46 NA 47 NA 48 NA 49 NA 50 NA 51 NA 52 NA 53 NA 54 NA 55 NA 56 NA 57 NA 58 NA 59 NA 60 NA 61 NA 62 NA 63 NA 64 NA 65 NA 66 NA 67 NA 68 NA 69 NA 70 NA 71 NA 72 NA 73 NA 74 NA 75 NA 76 NA 77 NA 78 NA 79 NA 80 NA 81 NA 82 NA 83 NA 84 NA 85 NA 86 NA 87 NA 88 NA 89 NA 90 NA 91 NA 92 NA 93 NA 94 NA 95 NA 96 NA 97 NA 98 NA 99 NA 100 NA 101 NA 102 NA 103 NA 104 101.2078 105 101.5857 106 101.6802 107 101.1133 108 101.1133 109 101.6802 110 99.9862 111 87.1500 112 88.4593 113 NA 114 97.9030 115 97.9030 116 95.2100 117 113.4571 118 NA 119 94.1750 120 96.0539 121 94.6981 122 NA 123 NA 124 93.9917 125 94.9407 126 95.7486 127 101.6802 128 101.6802 129 101.6802 130 111.8661 131 97.7487 132 103.7289 133 87.2750 134 97.9030 135 97.0951 136 97.9030 137 94.7250 138 96.0539 139 96.7318 140 113.4571 > > # compute the between and within transformations > between(z) 1 2 3 4 5 6 7 8 98.77511 98.77511 98.77511 107.32600 98.40861 104.33301 104.48757 104.48757 9 10 11 12 13 14 15 16 104.33301 98.57998 104.48757 104.48757 115.06733 109.24617 101.57539 104.78571 17 18 19 20 21 22 23 24 113.12871 103.73214 104.78571 103.73214 103.73214 104.78571 104.78571 104.78571 25 26 27 28 29 30 31 32 104.78571 104.78571 96.52857 98.50596 99.39286 104.78571 104.48757 99.57024 33 34 35 36 37 38 39 40 98.32857 99.39286 98.86071 98.32857 103.73214 101.47900 103.73214 102.10971 41 42 43 44 45 46 47 48 103.73214 101.47900 113.12871 103.73214 113.12871 114.12963 104.78571 114.12963 49 50 51 52 53 54 55 56 105.88214 101.47900 115.06733 104.78571 103.73214 113.12871 104.78571 114.12963 57 58 59 60 61 62 63 64 107.59236 104.78571 107.59236 102.29579 101.93559 107.59236 103.15714 103.15714 65 66 67 68 69 70 71 72 103.15714 102.57857 108.39140 103.15714 102.57857 101.93559 102.58164 102.29579 73 74 75 76 77 78 79 80 101.93559 101.93559 102.29579 102.58164 101.93559 102.58164 98.32857 102.29579 81 82 83 84 85 86 87 88 104.78571 104.78571 113.12871 98.32290 98.25239 98.32290 104.78571 103.15714 89 90 91 92 93 94 95 96 103.15714 103.83571 104.48757 98.02381 98.86071 105.88214 114.12963 114.12963 97 98 99 100 101 102 103 104 105.88214 113.12871 101.93559 111.79420 107.59236 104.57064 98.32857 99.13611 105 106 107 108 109 110 111 112 99.41102 99.47976 99.06739 99.06739 99.47976 111.48590 100.73750 99.32283 113 114 115 116 117 118 119 120 99.17812 104.28368 104.28368 106.27761 105.89957 108.51221 101.52813 100.78013 121 122 123 124 125 126 127 128 101.03090 101.21897 101.21897 101.84270 106.47701 105.87882 98.94487 98.94487 129 130 131 132 133 134 135 136 98.94487 105.19992 100.83339 111.26388 101.80556 105.70158 106.22377 105.70158 137 138 139 140 101.63611 100.88299 100.86314 105.77367 > Within(z) 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 -3.06791471 -1.41821071 0.83318529 1.77498629 0.78298429 -0.16001671 1-1983 2-1977 2-1978 2-1979 2-1980 2-1981 1.25498629 -3.06791471 -1.41821071 0.83318529 1.77498629 0.78298429 2-1982 2-1983 3-1977 3-1978 3-1979 3-1980 -0.16001671 1.25498629 -3.06791471 -1.41821071 0.83318529 1.77498629 3-1981 3-1982 3-1983 4-1977 4-1978 4-1979 0.78298429 -0.16001671 1.25498629 10.89630057 12.82910057 11.50590057 4-1980 4-1981 4-1982 4-1983 5-1976 5-1977 4.59040057 -9.77199843 -15.12780343 -14.92190043 -3.50951329 -1.90481529 5-1978 5-1979 5-1980 5-1981 5-1982 6-1976 0.40768471 2.07488571 1.71368571 0.11838571 1.09968671 -1.56061486 6-1977 6-1978 6-1979 6-1980 6-1981 6-1982 2.69398514 4.34578514 6.78598514 -2.40651486 -4.83591386 -5.02271186 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 7-1982 8-1976 8-1977 8-1978 8-1979 8-1980 -5.19447314 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 8-1981 8-1982 9-1976 9-1977 9-1978 9-1979 -5.09107114 -5.19447314 -1.56061486 2.69398514 4.34578514 6.78598514 9-1980 9-1981 9-1982 10-1976 10-1977 10-1978 -2.40651486 -4.83591386 -5.02271186 -3.21428471 -1.84848571 0.65331429 10-1979 10-1980 10-1981 10-1982 11-1976 11-1977 2.15351529 1.42001529 -0.34758571 1.18351129 0.27882886 2.99152886 11-1978 11-1979 11-1980 11-1981 11-1982 12-1976 4.43122886 7.07152886 -4.48757114 -5.09107114 -5.19447314 0.27882886 12-1977 12-1978 12-1979 12-1980 12-1981 12-1982 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 -5.19447314 13-1976 13-1977 13-1978 13-1979 13-1980 13-1981 10.73907143 12.89757143 10.36207143 9.95857143 -8.76292857 -16.99442957 13-1982 14-1978 14-1979 14-1980 14-1981 14-1982 -18.19992757 18.14043000 15.39623000 9.66703000 -9.88857300 -12.00337200 14-1983 14-1984 15-1977 15-1978 15-1979 15-1980 -12.05177300 -9.25997200 3.76011414 6.60791414 9.12301414 4.39191414 15-1981 15-1982 15-1983 16-1976 16-1977 16-1978 -4.98228386 -9.57408886 -9.32658386 11.61428614 10.91428614 8.01428614 16-1979 16-1980 16-1981 16-1982 17-1977 17-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 14.03528686 14.25788686 17-1979 17-1980 17-1981 17-1982 17-1983 18-1977 11.51368686 5.78448686 -13.77111614 -15.88591514 -15.93431614 12.49285671 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 11.24285671 8.09285671 2.94285671 -6.43214029 -14.20714129 -14.13214529 19-1976 19-1977 19-1978 19-1979 19-1980 19-1981 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 19-1982 20-1977 20-1978 20-1979 20-1980 20-1981 -14.28571386 12.49285671 11.24285671 8.09285671 2.94285671 -6.43214029 20-1982 20-1983 21-1977 21-1978 21-1979 21-1980 -14.20714129 -14.13214529 12.49285671 11.24285671 8.09285671 2.94285671 21-1981 21-1982 21-1983 22-1976 22-1977 22-1978 -6.43214029 -14.20714129 -14.13214529 11.61428614 10.91428614 8.01428614 22-1979 22-1980 22-1981 22-1982 23-1976 23-1977 4.11428614 -4.78571386 -15.58571686 -14.28571386 11.61428614 10.91428614 23-1978 23-1979 23-1980 23-1981 23-1982 24-1976 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 11.61428614 24-1977 24-1978 24-1979 24-1980 24-1981 24-1982 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 25-1976 25-1977 25-1978 25-1979 25-1980 25-1981 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 25-1982 26-1976 26-1977 26-1978 26-1979 26-1980 -14.28571386 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 26-1981 26-1982 27-1978 27-1979 27-1980 27-1981 -15.58571686 -14.28571386 16.27142843 12.37142843 3.47142843 -7.32857457 27-1982 27-1983 27-1984 28-1977 28-1978 28-1979 -6.02857157 -9.62856957 -9.12856957 -4.55595871 -0.30595871 4.06074429 28-1980 28-1981 28-1982 28-1983 29-1977 29-1978 3.51074429 0.98574329 -4.24765471 0.55234029 -3.69285971 0.80714329 29-1979 29-1980 29-1981 29-1982 29-1983 30-1976 3.00714329 1.70714329 -2.44285971 -3.34285371 3.95714329 11.61428614 30-1977 30-1978 30-1979 30-1980 30-1981 30-1982 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 31-1976 31-1977 31-1978 31-1979 31-1980 31-1981 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 31-1982 32-1977 32-1978 32-1979 32-1980 32-1981 -5.19447314 -3.52024114 1.02975586 2.79645586 1.34645586 -3.12854014 32-1982 32-1983 33-1976 33-1977 33-1978 33-1979 -3.16194214 4.63805586 -4.72857343 -0.52856843 4.27142857 3.87142857 33-1980 33-1981 33-1982 34-1977 34-1978 34-1979 1.67142857 -4.42856943 -0.12857443 -3.69285971 0.80714329 3.00714329 34-1980 34-1981 34-1982 34-1983 35-1977 35-1978 1.70714329 -2.44285971 -3.34285371 3.95714329 -4.21071200 0.13928600 35-1979 35-1980 35-1981 35-1982 35-1983 36-1976 3.63928600 2.78928600 -0.38571600 -3.88571600 1.91428600 -4.72857343 36-1977 36-1978 36-1979 36-1980 36-1981 36-1982 -0.52856843 4.27142857 3.87142857 1.67142857 -4.42856943 -0.12857443 37-1977 37-1978 37-1979 37-1980 37-1981 37-1982 12.49285671 11.24285671 8.09285671 2.94285671 -6.43214029 -14.20714129 37-1983 38-1976 38-1977 38-1978 38-1979 38-1980 -14.13214529 4.69570100 7.48850100 6.38340100 3.08780100 -1.47899900 38-1981 38-1982 39-1977 39-1978 39-1979 39-1980 -8.73600300 -11.44040200 12.49285671 11.24285671 8.09285671 2.94285671 39-1981 39-1982 39-1983 40-1976 40-1977 40-1978 -6.43214029 -14.20714129 -14.13214529 4.44588629 6.15958629 6.02898629 40-1979 40-1980 40-1981 40-1982 41-1977 41-1978 3.28098629 -0.96801371 -7.55241671 -11.39501471 12.49285671 11.24285671 41-1979 41-1980 41-1981 41-1982 41-1983 42-1976 8.09285671 2.94285671 -6.43214029 -14.20714129 -14.13214529 4.69570100 42-1977 42-1978 42-1979 42-1980 42-1981 42-1982 7.48850100 6.38340100 3.08780100 -1.47899900 -8.73600300 -11.44040200 43-1977 43-1978 43-1979 43-1980 43-1981 43-1982 14.03528686 14.25788686 11.51368686 5.78448686 -13.77111614 -15.88591514 43-1983 44-1977 44-1978 44-1979 44-1980 44-1981 -15.93431614 12.49285671 11.24285671 8.09285671 2.94285671 -6.43214029 44-1982 44-1983 45-1977 45-1978 45-1979 45-1980 -14.20714129 -14.13214529 14.03528686 14.25788686 11.51368686 5.78448686 45-1981 45-1982 45-1983 46-1976 46-1977 46-1978 -13.77111614 -15.88591514 -15.93431614 12.63397157 14.23567157 10.32107157 46-1979 46-1980 46-1981 46-1982 47-1976 47-1977 11.08797157 -14.12962843 -16.69913243 -17.44992543 11.61428614 10.91428614 47-1978 47-1979 47-1980 47-1981 47-1982 48-1976 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 12.63397157 48-1977 48-1978 48-1979 48-1980 48-1981 48-1982 14.23567157 10.32107157 11.08797157 -14.12962843 -16.69913243 -17.44992543 49-1976 49-1977 49-1978 49-1979 49-1980 49-1981 11.71785643 9.99285643 7.64285643 3.99285643 -3.65714357 -13.98214157 49-1982 50-1976 50-1977 50-1978 50-1979 50-1980 -15.70714057 4.69570100 7.48850100 6.38340100 3.08780100 -1.47899900 50-1981 50-1982 51-1976 51-1977 51-1978 51-1979 -8.73600300 -11.44040200 10.73907143 12.89757143 10.36207143 9.95857143 51-1980 51-1981 51-1982 52-1976 52-1977 52-1978 -8.76292857 -16.99442957 -18.19992757 11.61428614 10.91428614 8.01428614 52-1979 52-1980 52-1981 52-1982 53-1977 53-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 12.49285671 11.24285671 53-1979 53-1980 53-1981 53-1982 53-1983 54-1977 8.09285671 2.94285671 -6.43214029 -14.20714129 -14.13214529 14.03528686 54-1978 54-1979 54-1980 54-1981 54-1982 54-1983 14.25788686 11.51368686 5.78448686 -13.77111614 -15.88591514 -15.93431614 55-1976 55-1977 55-1978 55-1979 55-1980 55-1981 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 55-1982 56-1976 56-1977 56-1978 56-1979 56-1980 -14.28571386 12.63397157 14.23567157 10.32107157 11.08797157 -14.12962843 56-1981 56-1982 57-1977 57-1978 57-1979 57-1980 -16.69913243 -17.44992543 10.33554357 12.66834357 11.35914357 5.81364357 57-1981 57-1982 57-1983 58-1976 58-1977 58-1978 -9.42685943 -15.27815543 -15.47165943 11.61428614 10.91428614 8.01428614 58-1979 58-1980 58-1981 58-1982 59-1977 59-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 10.33554357 12.66834357 59-1979 59-1980 59-1981 59-1982 59-1983 60-1976 11.35914357 5.81364357 -9.42685943 -15.27815543 -15.47165943 1.47111400 60-1977 60-1978 60-1979 60-1980 60-1981 60-1982 4.60841400 7.16651400 9.63871400 -2.29578600 -9.10958300 -11.47938700 61-1977 61-1978 61-1979 61-1980 61-1981 61-1982 2.61561471 5.60811471 8.14481471 7.01531471 -3.63898529 -9.34188829 61-1983 62-1977 62-1978 62-1979 62-1980 62-1981 -10.40298529 10.33554357 12.66834357 11.35914357 5.81364357 -9.42685943 62-1982 62-1983 63-1976 63-1977 63-1978 63-1979 -15.27815543 -15.47165943 6.94285686 6.04285686 8.74285686 7.84285686 63-1980 63-1981 63-1982 64-1976 64-1977 64-1978 -3.15714314 -14.15714314 -12.25714114 6.94285686 6.04285686 8.74285686 64-1979 64-1980 64-1981 64-1982 65-1976 65-1977 7.84285686 -3.15714314 -14.15714314 -12.25714114 6.94285686 6.04285686 65-1978 65-1979 65-1980 65-1981 65-1982 66-1977 8.74285686 7.84285686 -3.15714314 -14.15714314 -12.25714114 7.29642857 66-1978 66-1979 66-1980 66-1981 66-1982 66-1983 7.29642857 9.09642857 5.67142857 -5.32857143 -13.10357343 -10.92856943 67-1976 67-1977 67-1978 67-1979 67-1980 67-1981 8.65339957 12.18599957 10.91909957 9.48319957 -8.39140043 -15.72929843 67-1982 68-1976 68-1977 68-1978 68-1979 68-1980 -17.12099943 6.94285686 6.04285686 8.74285686 7.84285686 -3.15714314 68-1981 68-1982 69-1977 69-1978 69-1979 69-1980 -14.15714314 -12.25714114 7.29642857 7.29642857 9.09642857 5.67142857 69-1981 69-1982 69-1983 70-1977 70-1978 70-1979 -5.32857143 -13.10357343 -10.92856943 2.61561471 5.60811471 8.14481471 70-1980 70-1981 70-1982 70-1983 71-1976 71-1977 7.01531471 -3.63898529 -9.34188829 -10.40298529 -0.05134300 3.53825700 71-1978 71-1979 71-1980 71-1981 71-1982 72-1976 6.24115700 8.73485700 0.40195700 -7.69204000 -11.17284500 1.47111400 72-1977 72-1978 72-1979 72-1980 72-1981 72-1982 4.60841400 7.16651400 9.63871400 -2.29578600 -9.10958300 -11.47938700 73-1977 73-1978 73-1979 73-1980 73-1981 73-1982 2.61561471 5.60811471 8.14481471 7.01531471 -3.63898529 -9.34188829 73-1983 74-1977 74-1978 74-1979 74-1980 74-1981 -10.40298529 2.61561471 5.60811471 8.14481471 7.01531471 -3.63898529 74-1982 74-1983 75-1976 75-1977 75-1978 75-1979 -9.34188829 -10.40298529 1.47111400 4.60841400 7.16651400 9.63871400 75-1980 75-1981 75-1982 76-1976 76-1977 76-1978 -2.29578600 -9.10958300 -11.47938700 -0.05134300 3.53825700 6.24115700 76-1979 76-1980 76-1981 76-1982 77-1977 77-1978 8.73485700 0.40195700 -7.69204000 -11.17284500 2.61561471 5.60811471 77-1979 77-1980 77-1981 77-1982 77-1983 78-1976 8.14481471 7.01531471 -3.63898529 -9.34188829 -10.40298529 -0.05134300 78-1977 78-1978 78-1979 78-1980 78-1981 78-1982 3.53825700 6.24115700 8.73485700 0.40195700 -7.69204000 -11.17284500 79-1976 79-1977 79-1978 79-1979 79-1980 79-1981 -4.72857343 -0.52856843 4.27142857 3.87142857 1.67142857 -4.42856943 79-1982 80-1976 80-1977 80-1978 80-1979 80-1980 -0.12857443 1.47111400 4.60841400 7.16651400 9.63871400 -2.29578600 80-1981 80-1982 81-1976 81-1977 81-1978 81-1979 -9.10958300 -11.47938700 11.61428614 10.91428614 8.01428614 4.11428614 81-1980 81-1981 81-1982 82-1976 82-1977 82-1978 -4.78571386 -15.58571686 -14.28571386 11.61428614 10.91428614 8.01428614 82-1979 82-1980 82-1981 82-1982 83-1977 83-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 14.03528686 14.25788686 83-1979 83-1980 83-1981 83-1982 83-1983 84-1976 11.51368686 5.78448686 -13.77111614 -15.88591514 -15.93431614 -3.65719771 84-1977 84-1978 84-1979 84-1980 84-1981 84-1982 -1.93290171 0.28490229 2.03549929 1.86049929 0.35140029 1.05779829 85-1976 85-1977 85-1978 85-1979 85-1980 85-1981 -4.41908729 -1.15238729 3.54761471 4.01431471 2.11431471 -3.33568329 85-1982 86-1976 86-1977 86-1978 86-1979 86-1980 -0.76908629 -3.65719771 -1.93290171 0.28490229 2.03549929 1.86049929 86-1981 86-1982 87-1976 87-1977 87-1978 87-1979 0.35140029 1.05779829 11.61428614 10.91428614 8.01428614 4.11428614 87-1980 87-1981 87-1982 88-1976 88-1977 88-1978 -4.78571386 -15.58571686 -14.28571386 6.94285686 6.04285686 8.74285686 88-1979 88-1980 88-1981 88-1982 89-1976 89-1977 7.84285686 -3.15714314 -14.15714314 -12.25714114 6.94285686 6.04285686 89-1978 89-1979 89-1980 89-1981 89-1982 90-1976 8.74285686 7.84285686 -3.15714314 -14.15714314 -12.25714114 6.21428529 90-1977 90-1978 90-1979 90-1980 90-1981 90-1982 5.58928529 7.38928529 7.38928529 -1.08571471 -12.08571471 -13.41071171 91-1976 91-1977 91-1978 91-1979 91-1980 91-1981 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 91-1982 92-1976 92-1977 92-1978 92-1979 92-1980 -5.19447314 -3.49051229 -3.02381429 1.37618771 4.44288571 3.44288571 92-1981 92-1982 93-1977 93-1978 93-1979 93-1980 -0.05711629 -2.69051629 -4.21071200 0.13928600 3.63928600 2.78928600 93-1981 93-1982 93-1983 94-1976 94-1977 94-1978 -0.38571600 -3.88571600 1.91428600 11.71785643 9.99285643 7.64285643 94-1979 94-1980 94-1981 94-1982 95-1976 95-1977 3.99285643 -3.65714357 -13.98214157 -15.70714057 12.63397157 14.23567157 95-1978 95-1979 95-1980 95-1981 95-1982 96-1976 10.32107157 11.08797157 -14.12962843 -16.69913243 -17.44992543 12.63397157 96-1977 96-1978 96-1979 96-1980 96-1981 96-1982 14.23567157 10.32107157 11.08797157 -14.12962843 -16.69913243 -17.44992543 97-1976 97-1977 97-1978 97-1979 97-1980 97-1981 11.71785643 9.99285643 7.64285643 3.99285643 -3.65714357 -13.98214157 97-1982 98-1977 98-1978 98-1979 98-1980 98-1981 -15.70714057 14.03528686 14.25788686 11.51368686 5.78448686 -13.77111614 98-1982 98-1983 99-1977 99-1978 99-1979 99-1980 -15.88591514 -15.93431614 2.61561471 5.60811471 8.14481471 7.01531471 99-1981 99-1982 99-1983 100-1977 100-1978 100-1979 -3.63898529 -9.34188829 -10.40298529 15.90370029 14.28760029 13.10390029 100-1980 100-1981 100-1982 100-1983 101-1977 101-1978 -1.28689971 -13.29310071 -14.80169971 -13.91350071 10.33554357 12.66834357 101-1979 101-1980 101-1981 101-1982 101-1983 102-1977 11.35914357 5.81364357 -9.42685943 -15.27815543 -15.47165943 0.87395771 102-1978 102-1979 102-1980 102-1981 102-1982 102-1983 3.26835771 5.00825771 4.09865771 -4.72154429 -5.20004429 -3.32764229 103-1976 103-1977 103-1978 103-1979 103-1980 103-1981 -4.72857343 -0.52856843 4.27142857 3.87142857 1.67142857 -4.42856943 103-1982 104-1977 104-1978 104-1979 104-1980 104-1981 -0.12857443 -3.31511262 -1.57071262 0.59718738 1.35288838 0.27468538 104-1982 104-1983 104-1984 105-1977 105-1978 105-1979 -0.39341262 0.98278837 2.07168838 -3.13482587 -1.01172487 0.82237513 105-1980 105-1981 105-1982 105-1983 105-1984 106-1977 0.83347513 -0.58942287 -0.15792688 1.06337513 2.17467513 -3.08976375 106-1978 106-1979 106-1980 106-1981 106-1982 106-1983 -0.87195975 0.87863725 0.70363725 -0.80546175 -0.09906375 1.08353725 106-1984 107-1977 107-1978 107-1979 107-1980 107-1981 2.20043725 -3.36018800 -1.71048400 0.54091200 1.48271300 0.49071100 107-1982 107-1983 107-1984 108-1977 108-1978 108-1979 -0.45229000 0.96271300 2.04591300 -3.36018800 -1.71048400 0.54091200 108-1980 108-1981 108-1982 108-1983 108-1984 109-1977 1.48271300 0.49071100 -0.45229000 0.96271300 2.04591300 -3.08976375 109-1978 109-1979 109-1980 109-1981 109-1982 109-1983 -0.87195975 0.87863725 0.70363725 -0.80546175 -0.09906375 1.08353725 109-1984 110-1977 110-1978 110-1979 110-1980 110-1981 2.20043725 15.67810125 15.90070125 13.15650125 7.42730125 -12.12830175 110-1982 110-1983 110-1984 111-1977 111-1978 111-1979 -14.24310075 -14.29150175 -11.49970075 15.31250062 13.51250063 10.11250062 111-1980 111-1981 111-1982 111-1983 111-1984 112-1977 3.71250063 -6.13750137 -10.88750137 -12.03750238 -13.58749737 7.55007487 112-1978 112-1979 112-1980 112-1981 112-1982 112-1983 9.36837487 7.71567487 4.10227487 -1.13702613 -7.25592313 -9.47992213 112-1984 113-1976 113-1977 113-1978 113-1979 113-1980 -10.86352813 -5.22812800 -2.42812500 2.22187500 3.12187500 1.37187500 113-1981 113-1982 113-1983 114-1977 114-1978 114-1979 -3.75312200 -2.05312500 6.74687500 16.29372437 15.02682438 13.59092438 114-1980 114-1981 114-1982 114-1983 114-1984 115-1977 -4.28367563 -11.62157362 -13.01327462 -9.61227363 -6.38067562 16.29372437 115-1978 115-1979 115-1980 115-1981 115-1982 115-1983 15.02682438 13.59092438 -4.28367563 -11.62157362 -13.01327462 -9.61227363 115-1984 116-1977 116-1978 116-1979 116-1980 116-1981 -6.38067562 11.35598825 14.08858825 12.79358825 8.61788825 -7.50061175 116-1982 116-1983 116-1984 117-1977 117-1978 117-1979 -13.84741275 -14.44041575 -11.06761275 1.57952525 3.01922525 5.65952525 117-1980 117-1981 117-1982 117-1983 117-1984 118-1976 -5.89957475 -6.50307475 -6.60647675 1.19332525 7.55752525 8.30348800 118-1977 118-1978 118-1979 118-1980 118-1981 118-1982 9.71008800 11.64288800 10.31968800 3.40418800 -10.95821100 -16.31401600 118-1983 119-1977 119-1978 119-1979 119-1980 119-1981 -16.10811300 8.34687462 8.34687462 10.14687462 6.72187462 -4.27812538 119-1982 119-1983 119-1984 120-1977 120-1978 120-1979 -12.05312738 -9.87812338 -7.35312237 4.81687425 7.61627425 10.12427425 120-1980 120-1981 120-1982 120-1983 120-1984 121-1977 4.19257425 -4.75482675 -8.97632375 -8.29262275 -4.72622375 3.52030075 121-1978 121-1979 121-1980 121-1981 121-1982 121-1983 6.51280075 9.04950075 7.92000075 -2.73429925 -8.43720225 -9.49829925 121-1984 122-1976 122-1977 122-1978 122-1979 122-1980 -6.33280225 2.54792513 5.68522513 8.24332512 10.71552512 -1.21897488 122-1981 122-1982 122-1983 123-1976 123-1977 123-1978 -8.03277187 -10.40257587 -7.53767787 2.54792513 5.68522513 8.24332512 123-1979 123-1980 123-1981 123-1982 123-1983 124-1977 10.71552512 -1.21897488 -8.03277187 -10.40257587 -7.53767787 8.18229988 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 7.58229988 9.98229988 8.24059988 -2.75940212 -12.68439812 -10.69269812 124-1984 125-1977 125-1978 125-1979 125-1980 125-1981 -7.85100112 10.86218763 13.99478763 12.71378762 9.90808762 -7.08851637 125-1982 125-1983 125-1984 126-1977 126-1978 126-1979 -13.93080837 -14.92321037 -11.53631538 12.34347600 14.27627600 12.95307600 126-1980 126-1981 126-1982 126-1983 126-1984 127-1976 6.03757600 -8.32482300 -13.68062800 -13.47472500 -10.13022800 -4.27916422 127-1977 127-1978 127-1979 127-1980 127-1981 127-1982 -2.55486822 -0.33706422 1.41353278 1.23853278 -0.27056622 0.43583178 127-1983 127-1984 128-1976 128-1977 128-1978 128-1979 1.61843278 2.73533278 -4.27916422 -2.55486822 -0.33706422 1.41353278 128-1980 128-1981 128-1982 128-1983 128-1984 129-1976 1.23853278 -0.27056622 0.43583178 1.61843278 2.73533278 -4.27916422 129-1977 129-1978 129-1979 129-1980 129-1981 129-1982 -2.55486822 -0.33706422 1.41353278 1.23853278 -0.27056622 0.43583178 129-1983 129-1984 130-1976 130-1977 130-1978 130-1979 1.61843278 2.73533278 -3.42452244 1.60097756 3.35897756 5.69907756 130-1980 130-1981 130-1982 130-1983 130-1984 131-1976 -2.31012244 -5.65252144 -5.88102144 -0.05702244 6.66617756 2.93351089 131-1977 131-1978 131-1979 131-1980 131-1981 131-1982 6.07081089 8.62891089 11.10111089 -0.83338911 -7.64718611 -10.01699011 131-1983 131-1984 132-1976 132-1977 132-1978 132-1979 -7.15209211 -3.08468611 15.49972211 17.10142211 13.18682211 13.95372211 132-1980 132-1981 132-1982 132-1983 132-1984 133-1976 -11.26387789 -13.83338189 -14.58417489 -12.52527589 -7.53497789 15.79444333 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 14.06944333 11.71944333 8.06944333 0.41944333 -9.90555467 -11.63055367 133-1983 133-1984 134-1976 134-1977 134-1978 134-1979 -14.00555367 -14.53055467 11.34322167 14.87582167 13.60892167 12.17302167 134-1980 134-1981 134-1982 134-1983 134-1984 135-1976 -5.70157833 -13.03947633 -14.43117733 -11.03017633 -7.79857833 10.73513378 135-1977 135-1978 135-1979 135-1980 135-1981 135-1982 13.47053378 13.40343378 12.00983378 -1.75506622 -11.72716922 -14.60546522 135-1983 135-1984 136-1976 136-1977 136-1978 136-1979 -12.40256822 -9.12866622 11.34322167 14.87582167 13.60892167 12.17302167 136-1980 136-1981 136-1982 136-1983 136-1984 137-1976 -5.70157833 -13.03947633 -14.43117733 -11.03017633 -7.79857833 8.41388856 137-1977 137-1978 137-1979 137-1980 137-1981 137-1982 7.78888856 9.58888856 9.58888856 1.11388856 -9.88611144 -11.21110844 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 -8.48610944 -6.91111344 0.82291044 4.71401044 7.51341044 10.02141044 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 4.08971044 -4.85769056 -9.07918756 -8.39548656 -4.82908756 1.66715633 139-1977 139-1978 139-1979 139-1980 139-1981 139-1982 5.25675633 7.95965633 10.45335633 2.12045633 -5.97354067 -9.45434567 139-1983 139-1984 140-1976 140-1977 140-1978 140-1979 -7.89814767 -4.13134767 -1.00726644 1.70543356 3.14513356 5.78543356 140-1980 140-1981 140-1982 140-1983 140-1984 -5.77366644 -6.37716644 -6.48056844 1.31923356 7.68343356 > > # Between replicates the values for each time observation > Between(z) 1 1 1 1 1 1 1 2 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 2 2 2 2 2 2 3 3 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 3 3 3 3 3 4 4 4 98.77511 98.77511 98.77511 98.77511 98.77511 107.32600 107.32600 107.32600 4 4 4 4 5 5 5 5 107.32600 107.32600 107.32600 107.32600 98.40861 98.40861 98.40861 98.40861 5 5 5 6 6 6 6 6 98.40861 98.40861 98.40861 104.33301 104.33301 104.33301 104.33301 104.33301 6 6 7 7 7 7 7 7 104.33301 104.33301 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 7 8 8 8 8 8 8 8 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 9 9 9 9 9 9 9 10 104.33301 104.33301 104.33301 104.33301 104.33301 104.33301 104.33301 98.57998 10 10 10 10 10 10 11 11 98.57998 98.57998 98.57998 98.57998 98.57998 98.57998 104.48757 104.48757 11 11 11 11 11 12 12 12 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 12 12 12 12 13 13 13 13 104.48757 104.48757 104.48757 104.48757 115.06733 115.06733 115.06733 115.06733 13 13 13 14 14 14 14 14 115.06733 115.06733 115.06733 109.24617 109.24617 109.24617 109.24617 109.24617 14 14 15 15 15 15 15 15 109.24617 109.24617 101.57539 101.57539 101.57539 101.57539 101.57539 101.57539 15 16 16 16 16 16 16 16 101.57539 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 17 17 17 17 17 17 17 18 113.12871 113.12871 113.12871 113.12871 113.12871 113.12871 113.12871 103.73214 18 18 18 18 18 18 19 19 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 104.78571 104.78571 19 19 19 19 19 20 20 20 104.78571 104.78571 104.78571 104.78571 104.78571 103.73214 103.73214 103.73214 20 20 20 20 21 21 21 21 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 21 21 21 22 22 22 22 22 103.73214 103.73214 103.73214 104.78571 104.78571 104.78571 104.78571 104.78571 22 22 23 23 23 23 23 23 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 23 24 24 24 24 24 24 24 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 25 25 25 25 25 25 25 26 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 26 26 26 26 26 26 27 27 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 96.52857 96.52857 27 27 27 27 27 28 28 28 96.52857 96.52857 96.52857 96.52857 96.52857 98.50596 98.50596 98.50596 28 28 28 28 29 29 29 29 98.50596 98.50596 98.50596 98.50596 99.39286 99.39286 99.39286 99.39286 29 29 29 30 30 30 30 30 99.39286 99.39286 99.39286 104.78571 104.78571 104.78571 104.78571 104.78571 30 30 31 31 31 31 31 31 104.78571 104.78571 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 31 32 32 32 32 32 32 32 104.48757 99.57024 99.57024 99.57024 99.57024 99.57024 99.57024 99.57024 33 33 33 33 33 33 33 34 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 99.39286 34 34 34 34 34 34 35 35 99.39286 99.39286 99.39286 99.39286 99.39286 99.39286 98.86071 98.86071 35 35 35 35 35 36 36 36 98.86071 98.86071 98.86071 98.86071 98.86071 98.32857 98.32857 98.32857 36 36 36 36 37 37 37 37 98.32857 98.32857 98.32857 98.32857 103.73214 103.73214 103.73214 103.73214 37 37 37 38 38 38 38 38 103.73214 103.73214 103.73214 101.47900 101.47900 101.47900 101.47900 101.47900 38 38 39 39 39 39 39 39 101.47900 101.47900 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 39 40 40 40 40 40 40 40 103.73214 102.10971 102.10971 102.10971 102.10971 102.10971 102.10971 102.10971 41 41 41 41 41 41 41 42 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 101.47900 42 42 42 42 42 42 43 43 101.47900 101.47900 101.47900 101.47900 101.47900 101.47900 113.12871 113.12871 43 43 43 43 43 44 44 44 113.12871 113.12871 113.12871 113.12871 113.12871 103.73214 103.73214 103.73214 44 44 44 44 45 45 45 45 103.73214 103.73214 103.73214 103.73214 113.12871 113.12871 113.12871 113.12871 45 45 45 46 46 46 46 46 113.12871 113.12871 113.12871 114.12963 114.12963 114.12963 114.12963 114.12963 46 46 47 47 47 47 47 47 114.12963 114.12963 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 47 48 48 48 48 48 48 48 104.78571 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 49 49 49 49 49 49 49 50 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 101.47900 50 50 50 50 50 50 51 51 101.47900 101.47900 101.47900 101.47900 101.47900 101.47900 115.06733 115.06733 51 51 51 51 51 52 52 52 115.06733 115.06733 115.06733 115.06733 115.06733 104.78571 104.78571 104.78571 52 52 52 52 53 53 53 53 104.78571 104.78571 104.78571 104.78571 103.73214 103.73214 103.73214 103.73214 53 53 53 54 54 54 54 54 103.73214 103.73214 103.73214 113.12871 113.12871 113.12871 113.12871 113.12871 54 54 55 55 55 55 55 55 113.12871 113.12871 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 55 56 56 56 56 56 56 56 104.78571 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 57 57 57 57 57 57 57 58 107.59236 107.59236 107.59236 107.59236 107.59236 107.59236 107.59236 104.78571 58 58 58 58 58 58 59 59 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 107.59236 107.59236 59 59 59 59 59 60 60 60 107.59236 107.59236 107.59236 107.59236 107.59236 102.29579 102.29579 102.29579 60 60 60 60 61 61 61 61 102.29579 102.29579 102.29579 102.29579 101.93559 101.93559 101.93559 101.93559 61 61 61 62 62 62 62 62 101.93559 101.93559 101.93559 107.59236 107.59236 107.59236 107.59236 107.59236 62 62 63 63 63 63 63 63 107.59236 107.59236 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 63 64 64 64 64 64 64 64 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 65 65 65 65 65 65 65 66 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 102.57857 66 66 66 66 66 66 67 67 102.57857 102.57857 102.57857 102.57857 102.57857 102.57857 108.39140 108.39140 67 67 67 67 67 68 68 68 108.39140 108.39140 108.39140 108.39140 108.39140 103.15714 103.15714 103.15714 68 68 68 68 69 69 69 69 103.15714 103.15714 103.15714 103.15714 102.57857 102.57857 102.57857 102.57857 69 69 69 70 70 70 70 70 102.57857 102.57857 102.57857 101.93559 101.93559 101.93559 101.93559 101.93559 70 70 71 71 71 71 71 71 101.93559 101.93559 102.58164 102.58164 102.58164 102.58164 102.58164 102.58164 71 72 72 72 72 72 72 72 102.58164 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 73 73 73 73 73 73 73 74 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 74 74 74 74 74 74 75 75 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 102.29579 102.29579 75 75 75 75 75 76 76 76 102.29579 102.29579 102.29579 102.29579 102.29579 102.58164 102.58164 102.58164 76 76 76 76 77 77 77 77 102.58164 102.58164 102.58164 102.58164 101.93559 101.93559 101.93559 101.93559 77 77 77 78 78 78 78 78 101.93559 101.93559 101.93559 102.58164 102.58164 102.58164 102.58164 102.58164 78 78 79 79 79 79 79 79 102.58164 102.58164 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 79 80 80 80 80 80 80 80 98.32857 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 81 81 81 81 81 81 81 82 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 82 82 82 82 82 82 83 83 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 113.12871 113.12871 83 83 83 83 83 84 84 84 113.12871 113.12871 113.12871 113.12871 113.12871 98.32290 98.32290 98.32290 84 84 84 84 85 85 85 85 98.32290 98.32290 98.32290 98.32290 98.25239 98.25239 98.25239 98.25239 85 85 85 86 86 86 86 86 98.25239 98.25239 98.25239 98.32290 98.32290 98.32290 98.32290 98.32290 86 86 87 87 87 87 87 87 98.32290 98.32290 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 87 88 88 88 88 88 88 88 104.78571 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 89 89 89 89 89 89 89 90 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.83571 90 90 90 90 90 90 91 91 103.83571 103.83571 103.83571 103.83571 103.83571 103.83571 104.48757 104.48757 91 91 91 91 91 92 92 92 104.48757 104.48757 104.48757 104.48757 104.48757 98.02381 98.02381 98.02381 92 92 92 92 93 93 93 93 98.02381 98.02381 98.02381 98.02381 98.86071 98.86071 98.86071 98.86071 93 93 93 94 94 94 94 94 98.86071 98.86071 98.86071 105.88214 105.88214 105.88214 105.88214 105.88214 94 94 95 95 95 95 95 95 105.88214 105.88214 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 95 96 96 96 96 96 96 96 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 97 97 97 97 97 97 97 98 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 113.12871 98 98 98 98 98 98 99 99 113.12871 113.12871 113.12871 113.12871 113.12871 113.12871 101.93559 101.93559 99 99 99 99 99 100 100 100 101.93559 101.93559 101.93559 101.93559 101.93559 111.79420 111.79420 111.79420 100 100 100 100 101 101 101 101 111.79420 111.79420 111.79420 111.79420 107.59236 107.59236 107.59236 107.59236 101 101 101 102 102 102 102 102 107.59236 107.59236 107.59236 104.57064 104.57064 104.57064 104.57064 104.57064 102 102 103 103 103 103 103 103 104.57064 104.57064 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 103 104 104 104 104 104 104 104 98.32857 99.13611 99.13611 99.13611 99.13611 99.13611 99.13611 99.13611 104 105 105 105 105 105 105 105 99.13611 99.41102 99.41102 99.41102 99.41102 99.41102 99.41102 99.41102 105 106 106 106 106 106 106 106 99.41102 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 106 107 107 107 107 107 107 107 99.47976 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 107 108 108 108 108 108 108 108 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 108 109 109 109 109 109 109 109 99.06739 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 109 110 110 110 110 110 110 110 99.47976 111.48590 111.48590 111.48590 111.48590 111.48590 111.48590 111.48590 110 111 111 111 111 111 111 111 111.48590 100.73750 100.73750 100.73750 100.73750 100.73750 100.73750 100.73750 111 112 112 112 112 112 112 112 100.73750 99.32283 99.32283 99.32283 99.32283 99.32283 99.32283 99.32283 112 113 113 113 113 113 113 113 99.32283 99.17812 99.17812 99.17812 99.17812 99.17812 99.17812 99.17812 113 114 114 114 114 114 114 114 99.17812 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 114 115 115 115 115 115 115 115 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 115 116 116 116 116 116 116 116 104.28368 106.27761 106.27761 106.27761 106.27761 106.27761 106.27761 106.27761 116 117 117 117 117 117 117 117 106.27761 105.89957 105.89957 105.89957 105.89957 105.89957 105.89957 105.89957 117 118 118 118 118 118 118 118 105.89957 108.51221 108.51221 108.51221 108.51221 108.51221 108.51221 108.51221 118 119 119 119 119 119 119 119 108.51221 101.52813 101.52813 101.52813 101.52813 101.52813 101.52813 101.52813 119 120 120 120 120 120 120 120 101.52813 100.78013 100.78013 100.78013 100.78013 100.78013 100.78013 100.78013 120 121 121 121 121 121 121 121 100.78013 101.03090 101.03090 101.03090 101.03090 101.03090 101.03090 101.03090 121 122 122 122 122 122 122 122 101.03090 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 122 123 123 123 123 123 123 123 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 123 124 124 124 124 124 124 124 101.21897 101.84270 101.84270 101.84270 101.84270 101.84270 101.84270 101.84270 124 125 125 125 125 125 125 125 101.84270 106.47701 106.47701 106.47701 106.47701 106.47701 106.47701 106.47701 125 126 126 126 126 126 126 126 106.47701 105.87882 105.87882 105.87882 105.87882 105.87882 105.87882 105.87882 126 127 127 127 127 127 127 127 105.87882 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 127 127 128 128 128 128 128 128 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 128 128 128 129 129 129 129 129 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 129 129 129 129 130 130 130 130 98.94487 98.94487 98.94487 98.94487 105.19992 105.19992 105.19992 105.19992 130 130 130 130 130 131 131 131 105.19992 105.19992 105.19992 105.19992 105.19992 100.83339 100.83339 100.83339 131 131 131 131 131 131 132 132 100.83339 100.83339 100.83339 100.83339 100.83339 100.83339 111.26388 111.26388 132 132 132 132 132 132 132 133 111.26388 111.26388 111.26388 111.26388 111.26388 111.26388 111.26388 101.80556 133 133 133 133 133 133 133 133 101.80556 101.80556 101.80556 101.80556 101.80556 101.80556 101.80556 101.80556 134 134 134 134 134 134 134 134 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 134 135 135 135 135 135 135 135 105.70158 106.22377 106.22377 106.22377 106.22377 106.22377 106.22377 106.22377 135 135 136 136 136 136 136 136 106.22377 106.22377 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 136 136 136 137 137 137 137 137 105.70158 105.70158 105.70158 101.63611 101.63611 101.63611 101.63611 101.63611 137 137 137 137 138 138 138 138 101.63611 101.63611 101.63611 101.63611 100.88299 100.88299 100.88299 100.88299 138 138 138 138 138 139 139 139 100.88299 100.88299 100.88299 100.88299 100.88299 100.86314 100.86314 100.86314 139 139 139 139 139 139 140 140 100.86314 100.86314 100.86314 100.86314 100.86314 100.86314 105.77367 105.77367 140 140 140 140 140 140 140 105.77367 105.77367 105.77367 105.77367 105.77367 105.77367 105.77367 > > # between, Between, and Within transformations on other dimension > between(z, effect = "time") 1976 1977 1978 1979 1980 1981 1982 1983 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 98.42776 > Between(z, effect = "time") 1977 1978 1979 1980 1981 1982 1983 1977 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 1978 1979 1980 1981 1982 1983 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 1979 1980 1981 1982 1983 1977 1978 1979 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 1980 1981 1982 1983 1976 1977 1978 1979 103.80583 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 1980 1981 1982 1976 1977 1978 1979 1980 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1976 1977 1978 1979 1980 1981 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1976 1977 1978 1979 1980 1981 1982 1976 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 1977 1978 1979 1980 1981 1982 1976 1977 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 1978 1979 1980 1981 1982 1976 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 1979 1980 1981 1982 1976 1977 1978 1979 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 1980 1981 1982 1978 1979 1980 1981 1982 103.80583 95.70594 93.83759 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1984 1977 1978 1979 1980 1981 1982 95.36660 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1976 1977 1978 1979 1980 1981 1982 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1977 1978 1979 1980 1981 1982 1983 1977 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 1978 1979 1980 1981 1982 1983 1976 1977 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 110.32734 1978 1979 1980 1981 1982 1977 1978 1979 111.23714 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 111.06937 1980 1981 1982 1983 1977 1978 1979 1980 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1976 1977 1978 1979 1980 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1976 1977 1978 1979 1980 1981 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1976 1977 1978 1979 1980 1981 1982 1976 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 1977 1978 1979 1980 1981 1982 1978 1979 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 111.23714 111.06937 1980 1981 1982 1983 1984 1977 1978 1979 103.80583 95.70594 93.83759 95.36660 98.42776 110.32734 111.23714 111.06937 1980 1981 1982 1983 1977 1978 1979 1980 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1976 1977 1978 1979 1980 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1976 1977 1978 1979 1980 1981 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1977 1978 1979 1980 1981 1982 1983 93.83759 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1976 1977 1978 1979 1980 1981 1982 1977 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 110.32734 1978 1979 1980 1981 1982 1983 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 1979 1980 1981 1982 1983 1976 1977 1978 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 1979 1980 1981 1982 1977 1978 1979 1980 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1976 1977 1978 1979 1980 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1977 1978 1979 1980 1981 1982 95.70594 93.83759 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1976 1977 1978 1979 1980 1981 1982 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1977 1978 1979 1980 1981 1982 1983 1976 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 1977 1978 1979 1980 1981 1982 1977 1978 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 1979 1980 1981 1982 1983 1977 1978 1979 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 1980 1981 1982 1983 1977 1978 1979 1980 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1976 1977 1978 1979 1980 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1976 1977 1978 1979 1980 1981 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1976 1977 1978 1979 1980 1981 1982 1976 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 1977 1978 1979 1980 1981 1982 1976 1977 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 1978 1979 1980 1981 1982 1976 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 1979 1980 1981 1982 1977 1978 1979 1980 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1977 1978 1979 1980 1981 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1983 1976 1977 1978 1979 1980 1981 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1977 1978 1979 1980 1981 1982 1983 1976 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 1977 1978 1979 1980 1981 1982 1977 1978 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 1979 1980 1981 1982 1983 1976 1977 1978 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 1979 1980 1981 1982 1977 1978 1979 1980 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1977 1978 1979 1980 1981 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1983 1976 1977 1978 1979 1980 1981 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1976 1977 1978 1979 1980 1981 1982 1977 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 110.32734 1978 1979 1980 1981 1982 1983 1976 1977 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 110.32734 1978 1979 1980 1981 1982 1976 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 1979 1980 1981 1982 1977 1978 1979 1980 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1977 1978 1979 1980 1981 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1983 1976 1977 1978 1979 1980 1981 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1977 1978 1979 1980 1981 1982 1983 1977 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 1978 1979 1980 1981 1982 1983 1976 1977 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 110.32734 1978 1979 1980 1981 1982 1976 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 1979 1980 1981 1982 1977 1978 1979 1980 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1976 1977 1978 1979 1980 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1976 1977 1978 1979 1980 1981 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1976 1977 1978 1979 1980 1981 1982 1976 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 1977 1978 1979 1980 1981 1982 1977 1978 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 1979 1980 1981 1982 1983 1976 1977 1978 111.06937 103.80583 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 1979 1980 1981 1982 1976 1977 1978 1979 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 1980 1981 1982 1976 1977 1978 1979 1980 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1976 1977 1978 1979 1980 1981 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1976 1977 1978 1979 1980 1981 1982 1976 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 1977 1978 1979 1980 1981 1982 1976 1977 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 1978 1979 1980 1981 1982 1976 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 108.98092 110.32734 111.23714 1979 1980 1981 1982 1977 1978 1979 1980 111.06937 103.80583 95.70594 93.83759 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1976 1977 1978 1979 1980 95.70594 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1976 1977 1978 1979 1980 1981 95.70594 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1976 1977 1978 1979 1980 1981 1982 93.83759 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1976 1977 1978 1979 1980 1981 1982 1977 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 110.32734 1978 1979 1980 1981 1982 1983 1977 1978 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 1979 1980 1981 1982 1983 1977 1978 1979 111.06937 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 1980 1981 1982 1983 1977 1978 1979 1980 103.80583 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1977 1978 1979 1980 1981 95.70594 93.83759 95.36660 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1983 1976 1977 1978 1979 1980 1981 93.83759 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1977 1978 1979 1980 1981 1982 1983 93.83759 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1976 1977 1978 1979 1980 1981 1982 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1977 1978 1979 1980 1981 1982 1983 95.36660 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1976 1977 1978 1979 1980 1981 1982 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1977 1978 1979 1980 1981 1982 1983 95.36660 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1976 1977 1978 1979 1980 1981 1982 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1976 1977 1978 1979 1980 1981 1982 95.36660 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1977 1978 1979 1980 1981 1982 1983 95.36660 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1977 1978 1979 1980 1981 1982 1983 98.42776 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1976 1977 1978 1979 1980 1981 1982 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1984 1976 1977 1978 1979 1980 1981 95.36660 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1983 1984 1976 1977 1978 1979 1980 93.83759 95.36660 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1984 1976 1977 1978 1979 95.70594 93.83759 95.36660 98.42776 108.98092 110.32734 111.23714 111.06937 1980 1981 1982 1983 1984 1976 1977 1978 103.80583 95.70594 93.83759 95.36660 98.42776 108.98092 110.32734 111.23714 1979 1980 1981 1982 1983 1984 1976 1977 111.06937 103.80583 95.70594 93.83759 95.36660 98.42776 108.98092 110.32734 1978 1979 1980 1981 1982 1983 1984 1976 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 98.42776 108.98092 1977 1978 1979 1980 1981 1982 1983 1984 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 98.42776 1976 1977 1978 1979 1980 1981 1982 1983 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 1984 1976 1977 1978 1979 1980 1981 1982 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 93.83759 1983 1984 1976 1977 1978 1979 1980 1981 95.36660 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 95.70594 1982 1983 1984 1976 1977 1978 1979 1980 93.83759 95.36660 98.42776 108.98092 110.32734 111.23714 111.06937 103.80583 1981 1982 1983 1984 1976 1977 1978 1979 95.70594 93.83759 95.36660 98.42776 108.98092 110.32734 111.23714 111.06937 1980 1981 1982 1983 1984 1976 1977 1978 103.80583 95.70594 93.83759 95.36660 98.42776 108.98092 110.32734 111.23714 1979 1980 1981 1982 1983 1984 1976 1977 111.06937 103.80583 95.70594 93.83759 95.36660 98.42776 108.98092 110.32734 1978 1979 1980 1981 1982 1983 1984 111.23714 111.06937 103.80583 95.70594 93.83759 95.36660 98.42776 > Within(z, effect = "time") 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 -14.62013790 -13.88023366 -11.46106953 -3.25573357 3.85216057 4.77750734 1-1983 2-1977 2-1978 2-1979 2-1980 2-1981 4.66350056 -14.62013790 -13.88023366 -11.46106953 -3.25573357 3.85216057 2-1982 2-1983 3-1977 3-1978 3-1979 3-1980 4.77750734 4.66350056 -14.62013790 -13.88023366 -11.46106953 -3.25573357 3-1981 3-1982 3-1983 4-1977 4-1978 4-1979 3.85216057 4.77750734 4.66350056 7.89496310 8.91796334 7.76253147 4-1980 4-1981 4-1982 4-1983 5-1976 5-1977 8.11056643 1.84806357 -1.63939366 -2.96250044 -14.08181656 -13.82353790 5-1978 5-1979 5-1980 5-1981 5-1982 6-1976 -12.42083766 -10.58586853 -3.68353357 2.82106257 5.67071134 -6.20851756 6-1977 6-1978 6-1979 6-1980 6-1981 6-1982 -3.30033690 -2.55833666 0.04963147 -1.87933357 3.79116357 5.47271334 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 -4.21451756 -2.84823690 -2.31833666 0.48973147 -3.80583357 3.69056257 7-1982 8-1976 8-1977 8-1978 8-1979 8-1980 5.45550834 -4.21451756 -2.84823690 -2.31833666 0.48973147 -3.80583357 8-1981 8-1982 9-1976 9-1977 9-1978 9-1979 3.69056257 5.45550834 -6.20851756 -3.30033690 -2.55833666 0.04963147 9-1980 9-1981 9-1982 10-1976 10-1977 10-1978 -1.87933357 3.79116357 5.47271334 -13.61521756 -13.59583790 -12.00383766 10-1979 10-1980 10-1981 10-1982 11-1976 11-1977 -10.33586853 -3.80583357 2.52646157 5.92590634 -4.21451756 -2.84823690 11-1978 11-1979 11-1980 11-1981 11-1982 12-1976 -2.31833666 0.48973147 -3.80583357 3.69056257 5.45550834 -4.21451756 12-1977 12-1978 12-1979 12-1980 12-1981 12-1982 -2.84823690 -2.31833666 0.48973147 -3.80583357 3.69056257 5.45550834 13-1976 13-1977 13-1978 13-1979 13-1980 13-1981 16.82548244 17.63756310 14.19226334 13.95653147 2.49856643 2.36696157 13-1982 14-1978 14-1979 14-1980 14-1981 14-1982 3.02981134 16.14946334 13.57303147 15.10736643 3.65165957 3.40520834 14-1983 14-1984 15-1977 15-1978 15-1979 15-1980 1.82779756 1.55844111 -4.99183690 -3.05383666 -0.37096853 2.16146643 15-1981 15-1982 15-1983 16-1976 16-1977 16-1978 0.88716457 -1.83629266 -3.11779744 7.41908244 5.37266310 1.56286334 16-1979 16-1980 16-1981 16-1982 17-1977 17-1978 -2.16936853 -3.80583357 -6.50594043 -3.33758966 16.83666310 16.14946334 17-1979 17-1980 17-1981 17-1982 17-1983 18-1977 13.57303147 15.10736643 3.65165957 3.40520834 1.82779756 5.89766310 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 3.73786334 0.75563147 2.86916643 1.59406557 -4.31258766 -5.76660144 19-1976 19-1977 19-1978 19-1979 19-1980 19-1981 7.41908244 5.37266310 1.56286334 -2.16936853 -3.80583357 -6.50594043 19-1982 20-1977 20-1978 20-1979 20-1980 20-1981 -3.33758966 5.89766310 3.73786334 0.75563147 2.86916643 1.59406557 20-1982 20-1983 21-1977 21-1978 21-1979 21-1980 -4.31258766 -5.76660144 5.89766310 3.73786334 0.75563147 2.86916643 21-1981 21-1982 21-1983 22-1976 22-1977 22-1978 1.59406557 -4.31258766 -5.76660144 7.41908244 5.37266310 1.56286334 22-1979 22-1980 22-1981 22-1982 23-1976 23-1977 -2.16936853 -3.80583357 -6.50594043 -3.33758966 7.41908244 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0.31936157 -2.03378766 -2.87909644 -2.37385489 -5.77613690 121-1978 121-1979 121-1980 121-1981 121-1982 121-1983 -3.69343666 -0.98896853 5.14506643 2.59066257 -1.24389266 -3.83399944 121-1984 122-1976 122-1977 122-1978 122-1979 122-1980 -3.72965989 -5.21401756 -3.42313690 -1.77483666 0.86513147 -3.80583357 122-1981 122-1982 122-1983 123-1976 123-1977 123-1978 -2.51973443 -3.02119066 -1.68530244 -5.21401756 -3.42313690 -1.77483666 123-1979 123-1980 123-1981 123-1982 123-1983 124-1977 0.86513147 -3.80583357 -2.51973443 -3.02119066 -1.68530244 -0.30233690 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 -1.81213666 0.75563147 6.27746643 3.37736057 -4.67928766 -4.21659744 124-1984 125-1977 125-1978 125-1979 125-1980 125-1981 -4.43605789 7.01186310 9.23466334 8.12143147 12.57926643 3.68255857 125-1982 125-1983 125-1984 126-1977 126-1978 126-1979 -1.29138566 -3.81279744 -3.48705989 7.89496310 8.91796334 7.76253147 126-1980 126-1981 126-1982 126-1983 126-1984 127-1976 8.11056643 1.84806357 -1.63939366 -2.96250044 -2.67916089 -14.31521456 127-1977 127-1978 127-1979 127-1980 127-1981 127-1982 -13.93733790 -12.62933366 -10.71096853 -3.62243357 2.96836357 5.54310934 127-1983 127-1984 128-1976 128-1977 128-1978 128-1979 5.19670056 3.25244311 -14.31521456 -13.93733790 -12.62933366 -10.71096853 128-1980 128-1981 128-1982 128-1983 128-1984 129-1976 -3.62243357 2.96836357 5.54310934 5.19670056 3.25244311 -14.31521456 129-1977 129-1978 129-1979 129-1980 129-1981 129-1982 -13.93733790 -12.62933366 -10.71096853 -3.62243357 2.96836357 5.54310934 129-1983 129-1984 130-1976 130-1977 130-1978 130-1979 5.19670056 3.25244311 -7.20551756 -3.52643690 -2.67823666 -0.17036853 130-1980 130-1981 130-1982 130-1983 130-1984 131-1976 -0.91603357 3.84146357 5.48131134 9.77630056 13.43834311 -5.21401756 131-1977 131-1978 131-1979 131-1980 131-1981 131-1982 -3.42313690 -1.77483666 0.86513147 -3.80583357 -2.51973443 -3.02119066 131-1983 131-1984 132-1976 132-1977 132-1978 132-1979 -1.68530244 -0.67905389 17.78268244 18.03796310 13.21356334 14.14823147 132-1980 132-1981 132-1982 132-1983 132-1984 133-1976 -3.80583357 1.72455857 2.84211334 3.37200256 5.30114311 8.61908244 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 5.54766310 2.28786334 -1.19436853 -1.58083357 -3.80593543 -3.66258666 133-1983 133-1984 134-1976 134-1977 134-1978 134-1979 -7.56659644 -11.15275489 8.06388244 10.25006310 8.07336334 6.80523147 134-1980 134-1981 134-1982 134-1983 134-1984 135-1976 -3.80583357 -3.04383543 -2.56718866 -0.69519744 -0.52475689 7.97798244 135-1977 135-1978 135-1979 135-1980 135-1981 135-1982 9.36696310 8.39006334 7.16423147 0.66286643 -1.20934043 -2.21928866 135-1983 135-1984 136-1976 136-1977 136-1978 136-1979 -1.54540144 -1.33265689 8.06388244 10.25006310 8.07336334 6.80523147 136-1980 136-1981 136-1982 136-1983 136-1984 137-1976 -3.80583357 -3.04383543 -2.56718866 -0.69519744 -0.52475689 1.06908244 137-1977 137-1978 137-1979 137-1980 137-1981 137-1982 -0.90233690 -0.01213666 0.15563147 -1.05583357 -3.95593743 -3.41258666 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 -2.21659744 -3.70275889 -7.27501756 -4.73033690 -2.84073666 -0.16496853 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 1.16686643 0.31936157 -2.03378766 -2.87909644 -2.37385489 -6.45061756 139-1977 139-1978 139-1979 139-1980 139-1981 139-1982 -4.20743690 -2.41433666 0.24713147 -0.82223357 -0.81633443 -2.42879166 139-1983 139-1984 140-1976 140-1977 140-1978 140-1979 -2.40160344 -1.69596089 -4.21451756 -2.84823690 -2.31833666 0.48973147 140-1980 140-1981 140-1982 140-1983 140-1984 -3.80583357 3.69056257 5.45550834 11.72630056 15.02934311 > > # NA treatment for between, Between, and Within > z2 <- z > z2[length(z2)] <- NA # set last value to NA > between(z2, na.rm = TRUE) # non-NA value for last individual 1 2 3 4 5 6 7 8 98.77511 98.77511 98.77511 107.32600 98.40861 104.33301 104.48757 104.48757 9 10 11 12 13 14 15 16 104.33301 98.57998 104.48757 104.48757 115.06733 109.24617 101.57539 104.78571 17 18 19 20 21 22 23 24 113.12871 103.73214 104.78571 103.73214 103.73214 104.78571 104.78571 104.78571 25 26 27 28 29 30 31 32 104.78571 104.78571 96.52857 98.50596 99.39286 104.78571 104.48757 99.57024 33 34 35 36 37 38 39 40 98.32857 99.39286 98.86071 98.32857 103.73214 101.47900 103.73214 102.10971 41 42 43 44 45 46 47 48 103.73214 101.47900 113.12871 103.73214 113.12871 114.12963 104.78571 114.12963 49 50 51 52 53 54 55 56 105.88214 101.47900 115.06733 104.78571 103.73214 113.12871 104.78571 114.12963 57 58 59 60 61 62 63 64 107.59236 104.78571 107.59236 102.29579 101.93559 107.59236 103.15714 103.15714 65 66 67 68 69 70 71 72 103.15714 102.57857 108.39140 103.15714 102.57857 101.93559 102.58164 102.29579 73 74 75 76 77 78 79 80 101.93559 101.93559 102.29579 102.58164 101.93559 102.58164 98.32857 102.29579 81 82 83 84 85 86 87 88 104.78571 104.78571 113.12871 98.32290 98.25239 98.32290 104.78571 103.15714 89 90 91 92 93 94 95 96 103.15714 103.83571 104.48757 98.02381 98.86071 105.88214 114.12963 114.12963 97 98 99 100 101 102 103 104 105.88214 113.12871 101.93559 111.79420 107.59236 104.57064 98.32857 99.13611 105 106 107 108 109 110 111 112 99.41102 99.47976 99.06739 99.06739 99.47976 111.48590 100.73750 99.32283 113 114 115 116 117 118 119 120 99.17812 104.28368 104.28368 106.27761 105.89957 108.51221 101.52813 100.78013 121 122 123 124 125 126 127 128 101.03090 101.21897 101.21897 101.84270 106.47701 105.87882 98.94487 98.94487 129 130 131 132 133 134 135 136 98.94487 105.19992 100.83339 111.26388 101.80556 105.70158 106.22377 105.70158 137 138 139 140 101.63611 100.88299 100.86314 104.81324 > Between(z2, na.rm = TRUE) # only the NA observation is lost 1 1 1 1 1 1 1 2 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 2 2 2 2 2 2 3 3 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 98.77511 3 3 3 3 3 4 4 4 98.77511 98.77511 98.77511 98.77511 98.77511 107.32600 107.32600 107.32600 4 4 4 4 5 5 5 5 107.32600 107.32600 107.32600 107.32600 98.40861 98.40861 98.40861 98.40861 5 5 5 6 6 6 6 6 98.40861 98.40861 98.40861 104.33301 104.33301 104.33301 104.33301 104.33301 6 6 7 7 7 7 7 7 104.33301 104.33301 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 7 8 8 8 8 8 8 8 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 9 9 9 9 9 9 9 10 104.33301 104.33301 104.33301 104.33301 104.33301 104.33301 104.33301 98.57998 10 10 10 10 10 10 11 11 98.57998 98.57998 98.57998 98.57998 98.57998 98.57998 104.48757 104.48757 11 11 11 11 11 12 12 12 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 12 12 12 12 13 13 13 13 104.48757 104.48757 104.48757 104.48757 115.06733 115.06733 115.06733 115.06733 13 13 13 14 14 14 14 14 115.06733 115.06733 115.06733 109.24617 109.24617 109.24617 109.24617 109.24617 14 14 15 15 15 15 15 15 109.24617 109.24617 101.57539 101.57539 101.57539 101.57539 101.57539 101.57539 15 16 16 16 16 16 16 16 101.57539 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 17 17 17 17 17 17 17 18 113.12871 113.12871 113.12871 113.12871 113.12871 113.12871 113.12871 103.73214 18 18 18 18 18 18 19 19 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 104.78571 104.78571 19 19 19 19 19 20 20 20 104.78571 104.78571 104.78571 104.78571 104.78571 103.73214 103.73214 103.73214 20 20 20 20 21 21 21 21 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 21 21 21 22 22 22 22 22 103.73214 103.73214 103.73214 104.78571 104.78571 104.78571 104.78571 104.78571 22 22 23 23 23 23 23 23 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 23 24 24 24 24 24 24 24 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 25 25 25 25 25 25 25 26 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 26 26 26 26 26 26 27 27 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 96.52857 96.52857 27 27 27 27 27 28 28 28 96.52857 96.52857 96.52857 96.52857 96.52857 98.50596 98.50596 98.50596 28 28 28 28 29 29 29 29 98.50596 98.50596 98.50596 98.50596 99.39286 99.39286 99.39286 99.39286 29 29 29 30 30 30 30 30 99.39286 99.39286 99.39286 104.78571 104.78571 104.78571 104.78571 104.78571 30 30 31 31 31 31 31 31 104.78571 104.78571 104.48757 104.48757 104.48757 104.48757 104.48757 104.48757 31 32 32 32 32 32 32 32 104.48757 99.57024 99.57024 99.57024 99.57024 99.57024 99.57024 99.57024 33 33 33 33 33 33 33 34 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 99.39286 34 34 34 34 34 34 35 35 99.39286 99.39286 99.39286 99.39286 99.39286 99.39286 98.86071 98.86071 35 35 35 35 35 36 36 36 98.86071 98.86071 98.86071 98.86071 98.86071 98.32857 98.32857 98.32857 36 36 36 36 37 37 37 37 98.32857 98.32857 98.32857 98.32857 103.73214 103.73214 103.73214 103.73214 37 37 37 38 38 38 38 38 103.73214 103.73214 103.73214 101.47900 101.47900 101.47900 101.47900 101.47900 38 38 39 39 39 39 39 39 101.47900 101.47900 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 39 40 40 40 40 40 40 40 103.73214 102.10971 102.10971 102.10971 102.10971 102.10971 102.10971 102.10971 41 41 41 41 41 41 41 42 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 103.73214 101.47900 42 42 42 42 42 42 43 43 101.47900 101.47900 101.47900 101.47900 101.47900 101.47900 113.12871 113.12871 43 43 43 43 43 44 44 44 113.12871 113.12871 113.12871 113.12871 113.12871 103.73214 103.73214 103.73214 44 44 44 44 45 45 45 45 103.73214 103.73214 103.73214 103.73214 113.12871 113.12871 113.12871 113.12871 45 45 45 46 46 46 46 46 113.12871 113.12871 113.12871 114.12963 114.12963 114.12963 114.12963 114.12963 46 46 47 47 47 47 47 47 114.12963 114.12963 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 47 48 48 48 48 48 48 48 104.78571 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 49 49 49 49 49 49 49 50 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 101.47900 50 50 50 50 50 50 51 51 101.47900 101.47900 101.47900 101.47900 101.47900 101.47900 115.06733 115.06733 51 51 51 51 51 52 52 52 115.06733 115.06733 115.06733 115.06733 115.06733 104.78571 104.78571 104.78571 52 52 52 52 53 53 53 53 104.78571 104.78571 104.78571 104.78571 103.73214 103.73214 103.73214 103.73214 53 53 53 54 54 54 54 54 103.73214 103.73214 103.73214 113.12871 113.12871 113.12871 113.12871 113.12871 54 54 55 55 55 55 55 55 113.12871 113.12871 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 55 56 56 56 56 56 56 56 104.78571 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 57 57 57 57 57 57 57 58 107.59236 107.59236 107.59236 107.59236 107.59236 107.59236 107.59236 104.78571 58 58 58 58 58 58 59 59 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 107.59236 107.59236 59 59 59 59 59 60 60 60 107.59236 107.59236 107.59236 107.59236 107.59236 102.29579 102.29579 102.29579 60 60 60 60 61 61 61 61 102.29579 102.29579 102.29579 102.29579 101.93559 101.93559 101.93559 101.93559 61 61 61 62 62 62 62 62 101.93559 101.93559 101.93559 107.59236 107.59236 107.59236 107.59236 107.59236 62 62 63 63 63 63 63 63 107.59236 107.59236 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 63 64 64 64 64 64 64 64 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 65 65 65 65 65 65 65 66 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 102.57857 66 66 66 66 66 66 67 67 102.57857 102.57857 102.57857 102.57857 102.57857 102.57857 108.39140 108.39140 67 67 67 67 67 68 68 68 108.39140 108.39140 108.39140 108.39140 108.39140 103.15714 103.15714 103.15714 68 68 68 68 69 69 69 69 103.15714 103.15714 103.15714 103.15714 102.57857 102.57857 102.57857 102.57857 69 69 69 70 70 70 70 70 102.57857 102.57857 102.57857 101.93559 101.93559 101.93559 101.93559 101.93559 70 70 71 71 71 71 71 71 101.93559 101.93559 102.58164 102.58164 102.58164 102.58164 102.58164 102.58164 71 72 72 72 72 72 72 72 102.58164 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 73 73 73 73 73 73 73 74 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 74 74 74 74 74 74 75 75 101.93559 101.93559 101.93559 101.93559 101.93559 101.93559 102.29579 102.29579 75 75 75 75 75 76 76 76 102.29579 102.29579 102.29579 102.29579 102.29579 102.58164 102.58164 102.58164 76 76 76 76 77 77 77 77 102.58164 102.58164 102.58164 102.58164 101.93559 101.93559 101.93559 101.93559 77 77 77 78 78 78 78 78 101.93559 101.93559 101.93559 102.58164 102.58164 102.58164 102.58164 102.58164 78 78 79 79 79 79 79 79 102.58164 102.58164 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 79 80 80 80 80 80 80 80 98.32857 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 102.29579 81 81 81 81 81 81 81 82 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 82 82 82 82 82 82 83 83 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 113.12871 113.12871 83 83 83 83 83 84 84 84 113.12871 113.12871 113.12871 113.12871 113.12871 98.32290 98.32290 98.32290 84 84 84 84 85 85 85 85 98.32290 98.32290 98.32290 98.32290 98.25239 98.25239 98.25239 98.25239 85 85 85 86 86 86 86 86 98.25239 98.25239 98.25239 98.32290 98.32290 98.32290 98.32290 98.32290 86 86 87 87 87 87 87 87 98.32290 98.32290 104.78571 104.78571 104.78571 104.78571 104.78571 104.78571 87 88 88 88 88 88 88 88 104.78571 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 89 89 89 89 89 89 89 90 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.15714 103.83571 90 90 90 90 90 90 91 91 103.83571 103.83571 103.83571 103.83571 103.83571 103.83571 104.48757 104.48757 91 91 91 91 91 92 92 92 104.48757 104.48757 104.48757 104.48757 104.48757 98.02381 98.02381 98.02381 92 92 92 92 93 93 93 93 98.02381 98.02381 98.02381 98.02381 98.86071 98.86071 98.86071 98.86071 93 93 93 94 94 94 94 94 98.86071 98.86071 98.86071 105.88214 105.88214 105.88214 105.88214 105.88214 94 94 95 95 95 95 95 95 105.88214 105.88214 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 95 96 96 96 96 96 96 96 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 114.12963 97 97 97 97 97 97 97 98 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 105.88214 113.12871 98 98 98 98 98 98 99 99 113.12871 113.12871 113.12871 113.12871 113.12871 113.12871 101.93559 101.93559 99 99 99 99 99 100 100 100 101.93559 101.93559 101.93559 101.93559 101.93559 111.79420 111.79420 111.79420 100 100 100 100 101 101 101 101 111.79420 111.79420 111.79420 111.79420 107.59236 107.59236 107.59236 107.59236 101 101 101 102 102 102 102 102 107.59236 107.59236 107.59236 104.57064 104.57064 104.57064 104.57064 104.57064 102 102 103 103 103 103 103 103 104.57064 104.57064 98.32857 98.32857 98.32857 98.32857 98.32857 98.32857 103 104 104 104 104 104 104 104 98.32857 99.13611 99.13611 99.13611 99.13611 99.13611 99.13611 99.13611 104 105 105 105 105 105 105 105 99.13611 99.41102 99.41102 99.41102 99.41102 99.41102 99.41102 99.41102 105 106 106 106 106 106 106 106 99.41102 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 106 107 107 107 107 107 107 107 99.47976 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 107 108 108 108 108 108 108 108 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 99.06739 108 109 109 109 109 109 109 109 99.06739 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 99.47976 109 110 110 110 110 110 110 110 99.47976 111.48590 111.48590 111.48590 111.48590 111.48590 111.48590 111.48590 110 111 111 111 111 111 111 111 111.48590 100.73750 100.73750 100.73750 100.73750 100.73750 100.73750 100.73750 111 112 112 112 112 112 112 112 100.73750 99.32283 99.32283 99.32283 99.32283 99.32283 99.32283 99.32283 112 113 113 113 113 113 113 113 99.32283 99.17812 99.17812 99.17812 99.17812 99.17812 99.17812 99.17812 113 114 114 114 114 114 114 114 99.17812 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 114 115 115 115 115 115 115 115 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 104.28368 115 116 116 116 116 116 116 116 104.28368 106.27761 106.27761 106.27761 106.27761 106.27761 106.27761 106.27761 116 117 117 117 117 117 117 117 106.27761 105.89957 105.89957 105.89957 105.89957 105.89957 105.89957 105.89957 117 118 118 118 118 118 118 118 105.89957 108.51221 108.51221 108.51221 108.51221 108.51221 108.51221 108.51221 118 119 119 119 119 119 119 119 108.51221 101.52813 101.52813 101.52813 101.52813 101.52813 101.52813 101.52813 119 120 120 120 120 120 120 120 101.52813 100.78013 100.78013 100.78013 100.78013 100.78013 100.78013 100.78013 120 121 121 121 121 121 121 121 100.78013 101.03090 101.03090 101.03090 101.03090 101.03090 101.03090 101.03090 121 122 122 122 122 122 122 122 101.03090 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 122 123 123 123 123 123 123 123 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 101.21897 123 124 124 124 124 124 124 124 101.21897 101.84270 101.84270 101.84270 101.84270 101.84270 101.84270 101.84270 124 125 125 125 125 125 125 125 101.84270 106.47701 106.47701 106.47701 106.47701 106.47701 106.47701 106.47701 125 126 126 126 126 126 126 126 106.47701 105.87882 105.87882 105.87882 105.87882 105.87882 105.87882 105.87882 126 127 127 127 127 127 127 127 105.87882 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 127 127 128 128 128 128 128 128 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 128 128 128 129 129 129 129 129 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 98.94487 129 129 129 129 130 130 130 130 98.94487 98.94487 98.94487 98.94487 105.19992 105.19992 105.19992 105.19992 130 130 130 130 130 131 131 131 105.19992 105.19992 105.19992 105.19992 105.19992 100.83339 100.83339 100.83339 131 131 131 131 131 131 132 132 100.83339 100.83339 100.83339 100.83339 100.83339 100.83339 111.26388 111.26388 132 132 132 132 132 132 132 133 111.26388 111.26388 111.26388 111.26388 111.26388 111.26388 111.26388 101.80556 133 133 133 133 133 133 133 133 101.80556 101.80556 101.80556 101.80556 101.80556 101.80556 101.80556 101.80556 134 134 134 134 134 134 134 134 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 134 135 135 135 135 135 135 135 105.70158 106.22377 106.22377 106.22377 106.22377 106.22377 106.22377 106.22377 135 135 136 136 136 136 136 136 106.22377 106.22377 105.70158 105.70158 105.70158 105.70158 105.70158 105.70158 136 136 136 137 137 137 137 137 105.70158 105.70158 105.70158 101.63611 101.63611 101.63611 101.63611 101.63611 137 137 137 137 138 138 138 138 101.63611 101.63611 101.63611 101.63611 100.88299 100.88299 100.88299 100.88299 138 138 138 138 138 139 139 139 100.88299 100.88299 100.88299 100.88299 100.88299 100.86314 100.86314 100.86314 139 139 139 139 139 139 140 140 100.86314 100.86314 100.86314 100.86314 100.86314 100.86314 104.81324 104.81324 140 140 140 140 140 140 140 104.81324 104.81324 104.81324 104.81324 104.81324 104.81324 NA > Within(z2, na.rm = TRUE) # only the NA observation is lost 1-1977 1-1978 1-1979 1-1980 1-1981 1-1982 -3.06791471 -1.41821071 0.83318529 1.77498629 0.78298429 -0.16001671 1-1983 2-1977 2-1978 2-1979 2-1980 2-1981 1.25498629 -3.06791471 -1.41821071 0.83318529 1.77498629 0.78298429 2-1982 2-1983 3-1977 3-1978 3-1979 3-1980 -0.16001671 1.25498629 -3.06791471 -1.41821071 0.83318529 1.77498629 3-1981 3-1982 3-1983 4-1977 4-1978 4-1979 0.78298429 -0.16001671 1.25498629 10.89630057 12.82910057 11.50590057 4-1980 4-1981 4-1982 4-1983 5-1976 5-1977 4.59040057 -9.77199843 -15.12780343 -14.92190043 -3.50951329 -1.90481529 5-1978 5-1979 5-1980 5-1981 5-1982 6-1976 0.40768471 2.07488571 1.71368571 0.11838571 1.09968671 -1.56061486 6-1977 6-1978 6-1979 6-1980 6-1981 6-1982 2.69398514 4.34578514 6.78598514 -2.40651486 -4.83591386 -5.02271186 7-1976 7-1977 7-1978 7-1979 7-1980 7-1981 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 7-1982 8-1976 8-1977 8-1978 8-1979 8-1980 -5.19447314 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 8-1981 8-1982 9-1976 9-1977 9-1978 9-1979 -5.09107114 -5.19447314 -1.56061486 2.69398514 4.34578514 6.78598514 9-1980 9-1981 9-1982 10-1976 10-1977 10-1978 -2.40651486 -4.83591386 -5.02271186 -3.21428471 -1.84848571 0.65331429 10-1979 10-1980 10-1981 10-1982 11-1976 11-1977 2.15351529 1.42001529 -0.34758571 1.18351129 0.27882886 2.99152886 11-1978 11-1979 11-1980 11-1981 11-1982 12-1976 4.43122886 7.07152886 -4.48757114 -5.09107114 -5.19447314 0.27882886 12-1977 12-1978 12-1979 12-1980 12-1981 12-1982 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 -5.19447314 13-1976 13-1977 13-1978 13-1979 13-1980 13-1981 10.73907143 12.89757143 10.36207143 9.95857143 -8.76292857 -16.99442957 13-1982 14-1978 14-1979 14-1980 14-1981 14-1982 -18.19992757 18.14043000 15.39623000 9.66703000 -9.88857300 -12.00337200 14-1983 14-1984 15-1977 15-1978 15-1979 15-1980 -12.05177300 -9.25997200 3.76011414 6.60791414 9.12301414 4.39191414 15-1981 15-1982 15-1983 16-1976 16-1977 16-1978 -4.98228386 -9.57408886 -9.32658386 11.61428614 10.91428614 8.01428614 16-1979 16-1980 16-1981 16-1982 17-1977 17-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 14.03528686 14.25788686 17-1979 17-1980 17-1981 17-1982 17-1983 18-1977 11.51368686 5.78448686 -13.77111614 -15.88591514 -15.93431614 12.49285671 18-1978 18-1979 18-1980 18-1981 18-1982 18-1983 11.24285671 8.09285671 2.94285671 -6.43214029 -14.20714129 -14.13214529 19-1976 19-1977 19-1978 19-1979 19-1980 19-1981 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 19-1982 20-1977 20-1978 20-1979 20-1980 20-1981 -14.28571386 12.49285671 11.24285671 8.09285671 2.94285671 -6.43214029 20-1982 20-1983 21-1977 21-1978 21-1979 21-1980 -14.20714129 -14.13214529 12.49285671 11.24285671 8.09285671 2.94285671 21-1981 21-1982 21-1983 22-1976 22-1977 22-1978 -6.43214029 -14.20714129 -14.13214529 11.61428614 10.91428614 8.01428614 22-1979 22-1980 22-1981 22-1982 23-1976 23-1977 4.11428614 -4.78571386 -15.58571686 -14.28571386 11.61428614 10.91428614 23-1978 23-1979 23-1980 23-1981 23-1982 24-1976 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 11.61428614 24-1977 24-1978 24-1979 24-1980 24-1981 24-1982 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 25-1976 25-1977 25-1978 25-1979 25-1980 25-1981 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 25-1982 26-1976 26-1977 26-1978 26-1979 26-1980 -14.28571386 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 26-1981 26-1982 27-1978 27-1979 27-1980 27-1981 -15.58571686 -14.28571386 16.27142843 12.37142843 3.47142843 -7.32857457 27-1982 27-1983 27-1984 28-1977 28-1978 28-1979 -6.02857157 -9.62856957 -9.12856957 -4.55595871 -0.30595871 4.06074429 28-1980 28-1981 28-1982 28-1983 29-1977 29-1978 3.51074429 0.98574329 -4.24765471 0.55234029 -3.69285971 0.80714329 29-1979 29-1980 29-1981 29-1982 29-1983 30-1976 3.00714329 1.70714329 -2.44285971 -3.34285371 3.95714329 11.61428614 30-1977 30-1978 30-1979 30-1980 30-1981 30-1982 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 31-1976 31-1977 31-1978 31-1979 31-1980 31-1981 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 31-1982 32-1977 32-1978 32-1979 32-1980 32-1981 -5.19447314 -3.52024114 1.02975586 2.79645586 1.34645586 -3.12854014 32-1982 32-1983 33-1976 33-1977 33-1978 33-1979 -3.16194214 4.63805586 -4.72857343 -0.52856843 4.27142857 3.87142857 33-1980 33-1981 33-1982 34-1977 34-1978 34-1979 1.67142857 -4.42856943 -0.12857443 -3.69285971 0.80714329 3.00714329 34-1980 34-1981 34-1982 34-1983 35-1977 35-1978 1.70714329 -2.44285971 -3.34285371 3.95714329 -4.21071200 0.13928600 35-1979 35-1980 35-1981 35-1982 35-1983 36-1976 3.63928600 2.78928600 -0.38571600 -3.88571600 1.91428600 -4.72857343 36-1977 36-1978 36-1979 36-1980 36-1981 36-1982 -0.52856843 4.27142857 3.87142857 1.67142857 -4.42856943 -0.12857443 37-1977 37-1978 37-1979 37-1980 37-1981 37-1982 12.49285671 11.24285671 8.09285671 2.94285671 -6.43214029 -14.20714129 37-1983 38-1976 38-1977 38-1978 38-1979 38-1980 -14.13214529 4.69570100 7.48850100 6.38340100 3.08780100 -1.47899900 38-1981 38-1982 39-1977 39-1978 39-1979 39-1980 -8.73600300 -11.44040200 12.49285671 11.24285671 8.09285671 2.94285671 39-1981 39-1982 39-1983 40-1976 40-1977 40-1978 -6.43214029 -14.20714129 -14.13214529 4.44588629 6.15958629 6.02898629 40-1979 40-1980 40-1981 40-1982 41-1977 41-1978 3.28098629 -0.96801371 -7.55241671 -11.39501471 12.49285671 11.24285671 41-1979 41-1980 41-1981 41-1982 41-1983 42-1976 8.09285671 2.94285671 -6.43214029 -14.20714129 -14.13214529 4.69570100 42-1977 42-1978 42-1979 42-1980 42-1981 42-1982 7.48850100 6.38340100 3.08780100 -1.47899900 -8.73600300 -11.44040200 43-1977 43-1978 43-1979 43-1980 43-1981 43-1982 14.03528686 14.25788686 11.51368686 5.78448686 -13.77111614 -15.88591514 43-1983 44-1977 44-1978 44-1979 44-1980 44-1981 -15.93431614 12.49285671 11.24285671 8.09285671 2.94285671 -6.43214029 44-1982 44-1983 45-1977 45-1978 45-1979 45-1980 -14.20714129 -14.13214529 14.03528686 14.25788686 11.51368686 5.78448686 45-1981 45-1982 45-1983 46-1976 46-1977 46-1978 -13.77111614 -15.88591514 -15.93431614 12.63397157 14.23567157 10.32107157 46-1979 46-1980 46-1981 46-1982 47-1976 47-1977 11.08797157 -14.12962843 -16.69913243 -17.44992543 11.61428614 10.91428614 47-1978 47-1979 47-1980 47-1981 47-1982 48-1976 8.01428614 4.11428614 -4.78571386 -15.58571686 -14.28571386 12.63397157 48-1977 48-1978 48-1979 48-1980 48-1981 48-1982 14.23567157 10.32107157 11.08797157 -14.12962843 -16.69913243 -17.44992543 49-1976 49-1977 49-1978 49-1979 49-1980 49-1981 11.71785643 9.99285643 7.64285643 3.99285643 -3.65714357 -13.98214157 49-1982 50-1976 50-1977 50-1978 50-1979 50-1980 -15.70714057 4.69570100 7.48850100 6.38340100 3.08780100 -1.47899900 50-1981 50-1982 51-1976 51-1977 51-1978 51-1979 -8.73600300 -11.44040200 10.73907143 12.89757143 10.36207143 9.95857143 51-1980 51-1981 51-1982 52-1976 52-1977 52-1978 -8.76292857 -16.99442957 -18.19992757 11.61428614 10.91428614 8.01428614 52-1979 52-1980 52-1981 52-1982 53-1977 53-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 12.49285671 11.24285671 53-1979 53-1980 53-1981 53-1982 53-1983 54-1977 8.09285671 2.94285671 -6.43214029 -14.20714129 -14.13214529 14.03528686 54-1978 54-1979 54-1980 54-1981 54-1982 54-1983 14.25788686 11.51368686 5.78448686 -13.77111614 -15.88591514 -15.93431614 55-1976 55-1977 55-1978 55-1979 55-1980 55-1981 11.61428614 10.91428614 8.01428614 4.11428614 -4.78571386 -15.58571686 55-1982 56-1976 56-1977 56-1978 56-1979 56-1980 -14.28571386 12.63397157 14.23567157 10.32107157 11.08797157 -14.12962843 56-1981 56-1982 57-1977 57-1978 57-1979 57-1980 -16.69913243 -17.44992543 10.33554357 12.66834357 11.35914357 5.81364357 57-1981 57-1982 57-1983 58-1976 58-1977 58-1978 -9.42685943 -15.27815543 -15.47165943 11.61428614 10.91428614 8.01428614 58-1979 58-1980 58-1981 58-1982 59-1977 59-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 10.33554357 12.66834357 59-1979 59-1980 59-1981 59-1982 59-1983 60-1976 11.35914357 5.81364357 -9.42685943 -15.27815543 -15.47165943 1.47111400 60-1977 60-1978 60-1979 60-1980 60-1981 60-1982 4.60841400 7.16651400 9.63871400 -2.29578600 -9.10958300 -11.47938700 61-1977 61-1978 61-1979 61-1980 61-1981 61-1982 2.61561471 5.60811471 8.14481471 7.01531471 -3.63898529 -9.34188829 61-1983 62-1977 62-1978 62-1979 62-1980 62-1981 -10.40298529 10.33554357 12.66834357 11.35914357 5.81364357 -9.42685943 62-1982 62-1983 63-1976 63-1977 63-1978 63-1979 -15.27815543 -15.47165943 6.94285686 6.04285686 8.74285686 7.84285686 63-1980 63-1981 63-1982 64-1976 64-1977 64-1978 -3.15714314 -14.15714314 -12.25714114 6.94285686 6.04285686 8.74285686 64-1979 64-1980 64-1981 64-1982 65-1976 65-1977 7.84285686 -3.15714314 -14.15714314 -12.25714114 6.94285686 6.04285686 65-1978 65-1979 65-1980 65-1981 65-1982 66-1977 8.74285686 7.84285686 -3.15714314 -14.15714314 -12.25714114 7.29642857 66-1978 66-1979 66-1980 66-1981 66-1982 66-1983 7.29642857 9.09642857 5.67142857 -5.32857143 -13.10357343 -10.92856943 67-1976 67-1977 67-1978 67-1979 67-1980 67-1981 8.65339957 12.18599957 10.91909957 9.48319957 -8.39140043 -15.72929843 67-1982 68-1976 68-1977 68-1978 68-1979 68-1980 -17.12099943 6.94285686 6.04285686 8.74285686 7.84285686 -3.15714314 68-1981 68-1982 69-1977 69-1978 69-1979 69-1980 -14.15714314 -12.25714114 7.29642857 7.29642857 9.09642857 5.67142857 69-1981 69-1982 69-1983 70-1977 70-1978 70-1979 -5.32857143 -13.10357343 -10.92856943 2.61561471 5.60811471 8.14481471 70-1980 70-1981 70-1982 70-1983 71-1976 71-1977 7.01531471 -3.63898529 -9.34188829 -10.40298529 -0.05134300 3.53825700 71-1978 71-1979 71-1980 71-1981 71-1982 72-1976 6.24115700 8.73485700 0.40195700 -7.69204000 -11.17284500 1.47111400 72-1977 72-1978 72-1979 72-1980 72-1981 72-1982 4.60841400 7.16651400 9.63871400 -2.29578600 -9.10958300 -11.47938700 73-1977 73-1978 73-1979 73-1980 73-1981 73-1982 2.61561471 5.60811471 8.14481471 7.01531471 -3.63898529 -9.34188829 73-1983 74-1977 74-1978 74-1979 74-1980 74-1981 -10.40298529 2.61561471 5.60811471 8.14481471 7.01531471 -3.63898529 74-1982 74-1983 75-1976 75-1977 75-1978 75-1979 -9.34188829 -10.40298529 1.47111400 4.60841400 7.16651400 9.63871400 75-1980 75-1981 75-1982 76-1976 76-1977 76-1978 -2.29578600 -9.10958300 -11.47938700 -0.05134300 3.53825700 6.24115700 76-1979 76-1980 76-1981 76-1982 77-1977 77-1978 8.73485700 0.40195700 -7.69204000 -11.17284500 2.61561471 5.60811471 77-1979 77-1980 77-1981 77-1982 77-1983 78-1976 8.14481471 7.01531471 -3.63898529 -9.34188829 -10.40298529 -0.05134300 78-1977 78-1978 78-1979 78-1980 78-1981 78-1982 3.53825700 6.24115700 8.73485700 0.40195700 -7.69204000 -11.17284500 79-1976 79-1977 79-1978 79-1979 79-1980 79-1981 -4.72857343 -0.52856843 4.27142857 3.87142857 1.67142857 -4.42856943 79-1982 80-1976 80-1977 80-1978 80-1979 80-1980 -0.12857443 1.47111400 4.60841400 7.16651400 9.63871400 -2.29578600 80-1981 80-1982 81-1976 81-1977 81-1978 81-1979 -9.10958300 -11.47938700 11.61428614 10.91428614 8.01428614 4.11428614 81-1980 81-1981 81-1982 82-1976 82-1977 82-1978 -4.78571386 -15.58571686 -14.28571386 11.61428614 10.91428614 8.01428614 82-1979 82-1980 82-1981 82-1982 83-1977 83-1978 4.11428614 -4.78571386 -15.58571686 -14.28571386 14.03528686 14.25788686 83-1979 83-1980 83-1981 83-1982 83-1983 84-1976 11.51368686 5.78448686 -13.77111614 -15.88591514 -15.93431614 -3.65719771 84-1977 84-1978 84-1979 84-1980 84-1981 84-1982 -1.93290171 0.28490229 2.03549929 1.86049929 0.35140029 1.05779829 85-1976 85-1977 85-1978 85-1979 85-1980 85-1981 -4.41908729 -1.15238729 3.54761471 4.01431471 2.11431471 -3.33568329 85-1982 86-1976 86-1977 86-1978 86-1979 86-1980 -0.76908629 -3.65719771 -1.93290171 0.28490229 2.03549929 1.86049929 86-1981 86-1982 87-1976 87-1977 87-1978 87-1979 0.35140029 1.05779829 11.61428614 10.91428614 8.01428614 4.11428614 87-1980 87-1981 87-1982 88-1976 88-1977 88-1978 -4.78571386 -15.58571686 -14.28571386 6.94285686 6.04285686 8.74285686 88-1979 88-1980 88-1981 88-1982 89-1976 89-1977 7.84285686 -3.15714314 -14.15714314 -12.25714114 6.94285686 6.04285686 89-1978 89-1979 89-1980 89-1981 89-1982 90-1976 8.74285686 7.84285686 -3.15714314 -14.15714314 -12.25714114 6.21428529 90-1977 90-1978 90-1979 90-1980 90-1981 90-1982 5.58928529 7.38928529 7.38928529 -1.08571471 -12.08571471 -13.41071171 91-1976 91-1977 91-1978 91-1979 91-1980 91-1981 0.27882886 2.99152886 4.43122886 7.07152886 -4.48757114 -5.09107114 91-1982 92-1976 92-1977 92-1978 92-1979 92-1980 -5.19447314 -3.49051229 -3.02381429 1.37618771 4.44288571 3.44288571 92-1981 92-1982 93-1977 93-1978 93-1979 93-1980 -0.05711629 -2.69051629 -4.21071200 0.13928600 3.63928600 2.78928600 93-1981 93-1982 93-1983 94-1976 94-1977 94-1978 -0.38571600 -3.88571600 1.91428600 11.71785643 9.99285643 7.64285643 94-1979 94-1980 94-1981 94-1982 95-1976 95-1977 3.99285643 -3.65714357 -13.98214157 -15.70714057 12.63397157 14.23567157 95-1978 95-1979 95-1980 95-1981 95-1982 96-1976 10.32107157 11.08797157 -14.12962843 -16.69913243 -17.44992543 12.63397157 96-1977 96-1978 96-1979 96-1980 96-1981 96-1982 14.23567157 10.32107157 11.08797157 -14.12962843 -16.69913243 -17.44992543 97-1976 97-1977 97-1978 97-1979 97-1980 97-1981 11.71785643 9.99285643 7.64285643 3.99285643 -3.65714357 -13.98214157 97-1982 98-1977 98-1978 98-1979 98-1980 98-1981 -15.70714057 14.03528686 14.25788686 11.51368686 5.78448686 -13.77111614 98-1982 98-1983 99-1977 99-1978 99-1979 99-1980 -15.88591514 -15.93431614 2.61561471 5.60811471 8.14481471 7.01531471 99-1981 99-1982 99-1983 100-1977 100-1978 100-1979 -3.63898529 -9.34188829 -10.40298529 15.90370029 14.28760029 13.10390029 100-1980 100-1981 100-1982 100-1983 101-1977 101-1978 -1.28689971 -13.29310071 -14.80169971 -13.91350071 10.33554357 12.66834357 101-1979 101-1980 101-1981 101-1982 101-1983 102-1977 11.35914357 5.81364357 -9.42685943 -15.27815543 -15.47165943 0.87395771 102-1978 102-1979 102-1980 102-1981 102-1982 102-1983 3.26835771 5.00825771 4.09865771 -4.72154429 -5.20004429 -3.32764229 103-1976 103-1977 103-1978 103-1979 103-1980 103-1981 -4.72857343 -0.52856843 4.27142857 3.87142857 1.67142857 -4.42856943 103-1982 104-1977 104-1978 104-1979 104-1980 104-1981 -0.12857443 -3.31511262 -1.57071262 0.59718738 1.35288838 0.27468538 104-1982 104-1983 104-1984 105-1977 105-1978 105-1979 -0.39341262 0.98278837 2.07168838 -3.13482587 -1.01172487 0.82237513 105-1980 105-1981 105-1982 105-1983 105-1984 106-1977 0.83347513 -0.58942287 -0.15792688 1.06337513 2.17467513 -3.08976375 106-1978 106-1979 106-1980 106-1981 106-1982 106-1983 -0.87195975 0.87863725 0.70363725 -0.80546175 -0.09906375 1.08353725 106-1984 107-1977 107-1978 107-1979 107-1980 107-1981 2.20043725 -3.36018800 -1.71048400 0.54091200 1.48271300 0.49071100 107-1982 107-1983 107-1984 108-1977 108-1978 108-1979 -0.45229000 0.96271300 2.04591300 -3.36018800 -1.71048400 0.54091200 108-1980 108-1981 108-1982 108-1983 108-1984 109-1977 1.48271300 0.49071100 -0.45229000 0.96271300 2.04591300 -3.08976375 109-1978 109-1979 109-1980 109-1981 109-1982 109-1983 -0.87195975 0.87863725 0.70363725 -0.80546175 -0.09906375 1.08353725 109-1984 110-1977 110-1978 110-1979 110-1980 110-1981 2.20043725 15.67810125 15.90070125 13.15650125 7.42730125 -12.12830175 110-1982 110-1983 110-1984 111-1977 111-1978 111-1979 -14.24310075 -14.29150175 -11.49970075 15.31250062 13.51250063 10.11250062 111-1980 111-1981 111-1982 111-1983 111-1984 112-1977 3.71250063 -6.13750137 -10.88750137 -12.03750238 -13.58749737 7.55007487 112-1978 112-1979 112-1980 112-1981 112-1982 112-1983 9.36837487 7.71567487 4.10227487 -1.13702613 -7.25592313 -9.47992213 112-1984 113-1976 113-1977 113-1978 113-1979 113-1980 -10.86352813 -5.22812800 -2.42812500 2.22187500 3.12187500 1.37187500 113-1981 113-1982 113-1983 114-1977 114-1978 114-1979 -3.75312200 -2.05312500 6.74687500 16.29372437 15.02682438 13.59092438 114-1980 114-1981 114-1982 114-1983 114-1984 115-1977 -4.28367563 -11.62157362 -13.01327462 -9.61227363 -6.38067562 16.29372437 115-1978 115-1979 115-1980 115-1981 115-1982 115-1983 15.02682438 13.59092438 -4.28367563 -11.62157362 -13.01327462 -9.61227363 115-1984 116-1977 116-1978 116-1979 116-1980 116-1981 -6.38067562 11.35598825 14.08858825 12.79358825 8.61788825 -7.50061175 116-1982 116-1983 116-1984 117-1977 117-1978 117-1979 -13.84741275 -14.44041575 -11.06761275 1.57952525 3.01922525 5.65952525 117-1980 117-1981 117-1982 117-1983 117-1984 118-1976 -5.89957475 -6.50307475 -6.60647675 1.19332525 7.55752525 8.30348800 118-1977 118-1978 118-1979 118-1980 118-1981 118-1982 9.71008800 11.64288800 10.31968800 3.40418800 -10.95821100 -16.31401600 118-1983 119-1977 119-1978 119-1979 119-1980 119-1981 -16.10811300 8.34687462 8.34687462 10.14687462 6.72187462 -4.27812538 119-1982 119-1983 119-1984 120-1977 120-1978 120-1979 -12.05312738 -9.87812338 -7.35312237 4.81687425 7.61627425 10.12427425 120-1980 120-1981 120-1982 120-1983 120-1984 121-1977 4.19257425 -4.75482675 -8.97632375 -8.29262275 -4.72622375 3.52030075 121-1978 121-1979 121-1980 121-1981 121-1982 121-1983 6.51280075 9.04950075 7.92000075 -2.73429925 -8.43720225 -9.49829925 121-1984 122-1976 122-1977 122-1978 122-1979 122-1980 -6.33280225 2.54792513 5.68522513 8.24332512 10.71552512 -1.21897488 122-1981 122-1982 122-1983 123-1976 123-1977 123-1978 -8.03277187 -10.40257587 -7.53767787 2.54792513 5.68522513 8.24332512 123-1979 123-1980 123-1981 123-1982 123-1983 124-1977 10.71552512 -1.21897488 -8.03277187 -10.40257587 -7.53767787 8.18229988 124-1978 124-1979 124-1980 124-1981 124-1982 124-1983 7.58229988 9.98229988 8.24059988 -2.75940212 -12.68439812 -10.69269812 124-1984 125-1977 125-1978 125-1979 125-1980 125-1981 -7.85100112 10.86218763 13.99478763 12.71378762 9.90808762 -7.08851637 125-1982 125-1983 125-1984 126-1977 126-1978 126-1979 -13.93080837 -14.92321037 -11.53631538 12.34347600 14.27627600 12.95307600 126-1980 126-1981 126-1982 126-1983 126-1984 127-1976 6.03757600 -8.32482300 -13.68062800 -13.47472500 -10.13022800 -4.27916422 127-1977 127-1978 127-1979 127-1980 127-1981 127-1982 -2.55486822 -0.33706422 1.41353278 1.23853278 -0.27056622 0.43583178 127-1983 127-1984 128-1976 128-1977 128-1978 128-1979 1.61843278 2.73533278 -4.27916422 -2.55486822 -0.33706422 1.41353278 128-1980 128-1981 128-1982 128-1983 128-1984 129-1976 1.23853278 -0.27056622 0.43583178 1.61843278 2.73533278 -4.27916422 129-1977 129-1978 129-1979 129-1980 129-1981 129-1982 -2.55486822 -0.33706422 1.41353278 1.23853278 -0.27056622 0.43583178 129-1983 129-1984 130-1976 130-1977 130-1978 130-1979 1.61843278 2.73533278 -3.42452244 1.60097756 3.35897756 5.69907756 130-1980 130-1981 130-1982 130-1983 130-1984 131-1976 -2.31012244 -5.65252144 -5.88102144 -0.05702244 6.66617756 2.93351089 131-1977 131-1978 131-1979 131-1980 131-1981 131-1982 6.07081089 8.62891089 11.10111089 -0.83338911 -7.64718611 -10.01699011 131-1983 131-1984 132-1976 132-1977 132-1978 132-1979 -7.15209211 -3.08468611 15.49972211 17.10142211 13.18682211 13.95372211 132-1980 132-1981 132-1982 132-1983 132-1984 133-1976 -11.26387789 -13.83338189 -14.58417489 -12.52527589 -7.53497789 15.79444333 133-1977 133-1978 133-1979 133-1980 133-1981 133-1982 14.06944333 11.71944333 8.06944333 0.41944333 -9.90555467 -11.63055367 133-1983 133-1984 134-1976 134-1977 134-1978 134-1979 -14.00555367 -14.53055467 11.34322167 14.87582167 13.60892167 12.17302167 134-1980 134-1981 134-1982 134-1983 134-1984 135-1976 -5.70157833 -13.03947633 -14.43117733 -11.03017633 -7.79857833 10.73513378 135-1977 135-1978 135-1979 135-1980 135-1981 135-1982 13.47053378 13.40343378 12.00983378 -1.75506622 -11.72716922 -14.60546522 135-1983 135-1984 136-1976 136-1977 136-1978 136-1979 -12.40256822 -9.12866622 11.34322167 14.87582167 13.60892167 12.17302167 136-1980 136-1981 136-1982 136-1983 136-1984 137-1976 -5.70157833 -13.03947633 -14.43117733 -11.03017633 -7.79857833 8.41388856 137-1977 137-1978 137-1979 137-1980 137-1981 137-1982 7.78888856 9.58888856 9.58888856 1.11388856 -9.88611144 -11.21110844 137-1983 137-1984 138-1976 138-1977 138-1978 138-1979 -8.48610944 -6.91111344 0.82291044 4.71401044 7.51341044 10.02141044 138-1980 138-1981 138-1982 138-1983 138-1984 139-1976 4.08971044 -4.85769056 -9.07918756 -8.39548656 -4.82908756 1.66715633 139-1977 139-1978 139-1979 139-1980 139-1981 139-1982 5.25675633 7.95965633 10.45335633 2.12045633 -5.97354067 -9.45434567 139-1983 139-1984 140-1976 140-1977 140-1978 140-1979 -7.89814767 -4.13134767 -0.04683725 2.66586275 4.10556275 6.74586275 140-1980 140-1981 140-1982 140-1983 140-1984 -4.81323725 -5.41673725 -5.52013925 2.27966275 NA > > sum(is.na(Between(z2))) # 9 observations lost due to one NA value [1] 9 > sum(is.na(Between(z2, na.rm = TRUE))) # only the NA observation is lost [1] 1 > sum(is.na(Within(z2))) # 9 observations lost due to one NA value [1] 9 > sum(is.na(Within(z2, na.rm = TRUE))) # only the NA observation is lost [1] 1 > > > > > cleanEx() > nameEx("punbalancedness") > ### * punbalancedness > > flush(stderr()); flush(stdout()) > > ### Name: punbalancedness > ### Title: Measures for Unbalancedness of Panel Data > ### Aliases: punbalancedness punbalancedness.pdata.frame > ### punbalancedness.data.frame punbalancedness.panelmodel > ### Keywords: attribute > > ### ** Examples > > > # Grunfeld is a balanced panel, Hedonic is an unbalanced panel > data(list=c("Grunfeld", "Hedonic"), package="plm") > > # Grunfeld has individual and time index in first two columns > punbalancedness(Grunfeld) # c(1,1) indicates balanced panel gamma nu 1 1 > pdim(Grunfeld)$balanced # TRUE [1] TRUE > > # Hedonic has individual index in column "townid" (in last column) > punbalancedness(Hedonic, index="townid") # c(0.472, 0.519) gamma nu 0.4715336 0.5188292 > pdim(Hedonic, index="townid")$balanced # FALSE [1] FALSE > > # punbalancedness on estimated models > plm_mod_pool <- plm(inv ~ value + capital, data = Grunfeld) > punbalancedness(plm_mod_pool) gamma nu 1 1 > > plm_mod_fe <- plm(inv ~ value + capital, data = Grunfeld[1:99, ], model = "within") > punbalancedness(plm_mod_fe) gamma nu 0.9995791 0.9995920 > > # replicate results for panel data design no. 1 in Ahrens/Pincus (1981), p. 234 > ind_d1 <- c(1,1,1,2,2,2,3,3,3,3,3,4,4,4,4,4,4,4,5,5,5,5,5,5,5) > time_d1 <- c(1,2,3,1,2,3,1,2,3,4,5,1,2,3,4,5,6,7,1,2,3,4,5,6,7) > df_d1 <- data.frame(individual = ind_d1, time = time_d1) > punbalancedness(df_d1) # c(0.868, 0.887) gamma nu 0.8677686 0.8865248 > > # example for a nested panel structure with a third index variable > # specifying a group (states are grouped by region) and without grouping > data("Produc", package = "plm") > punbalancedness(Produc, index = c("state", "year", "region")) c1 c2 c3 0.8760816 1.0000000 0.8760816 > punbalancedness(Produc, index = c("state", "year")) gamma nu 1 1 > > > > > cleanEx() > nameEx("purtest") > ### * purtest > > flush(stderr()); flush(stdout()) > > ### Name: purtest > ### Title: Unit root tests for panel data > ### Aliases: purtest print.purtest summary.purtest print.summary.purtest > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > y <- data.frame(split(Grunfeld$inv, Grunfeld$firm)) # individuals in columns > > purtest(y, pmax = 4, exo = "intercept", test = "madwu") Maddala-Wu Unit-Root Test (ex. var.: Individual Intercepts) data: y chisq = 14.719, df = 20, p-value = 0.7923 alternative hypothesis: stationarity > > ## same via formula interface > purtest(inv ~ 1, data = Grunfeld, index = c("firm", "year"), pmax = 4, test = "madwu") Maddala-Wu Unit-Root Test (ex. var.: Individual Intercepts) data: inv ~ 1 chisq = 14.719, df = 20, p-value = 0.7923 alternative hypothesis: stationarity > > > > > cleanEx() > nameEx("pvar") > ### * pvar > > flush(stderr()); flush(stdout()) > > ### Name: pvar > ### Title: Check for Cross-Sectional and Time Variation > ### Aliases: pvar pvar.matrix pvar.data.frame pvar.pdata.frame pvar.pseries > ### print.pvar > ### Keywords: attribute > > ### ** Examples > > > > # Gasoline contains two variables which are individual and time > # indexes and are the first two variables > data("Gasoline", package = "plm") > pvar(Gasoline) no time variation: country no individual variation: year > > # Hedonic is an unbalanced panel, townid is the individual index; > # the drop.index argument is passed to pdata.frame > data("Hedonic", package = "plm") > pvar(Hedonic, "townid", drop.index = TRUE) no time variation: zn indus rad tax ptratio > > # same using pdata.frame > Hed <- pdata.frame(Hedonic, "townid", drop.index = TRUE) > pvar(Hed) no time variation: zn indus rad tax ptratio > > # Gasoline with pvar's matrix interface > Gasoline_mat <- as.matrix(Gasoline) > pvar(Gasoline_mat) no time variation: country no individual variation: year > pvar(Gasoline_mat, index=c("country", "year")) no time variation: country no individual variation: year > > > > > cleanEx() > nameEx("pvcm") > ### * pvcm > > flush(stderr()); flush(stdout()) > > ### Name: pvcm > ### Title: Variable Coefficients Models for Panel Data > ### Aliases: pvcm summary.pvcm print.summary.pvcm > ### Keywords: regression > > ### ** Examples > > > data("Produc", package = "plm") > zw <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "within") > zr <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "random") > > ## replicate Greene (2012), p. 419, table 11.14 > summary(pvcm(log(gsp) ~ log(pc) + log(hwy) + log(water) + log(util) + log(emp) + unemp, + data = Produc, model = "random")) Oneway (individual) effect Random coefficients model Call: pvcm(formula = log(gsp) ~ log(pc) + log(hwy) + log(water) + log(util) + log(emp) + unemp, data = Produc, model = "random") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.23533 -0.06035 0.08087 0.09229 0.20421 0.97490 Estimated mean of the coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 1.6530780 1.0833134 1.5259 0.12702 log(pc) 0.0940755 0.0515162 1.8261 0.06783 . log(hwy) 0.1050114 0.1736406 0.6048 0.54534 log(water) 0.0767189 0.0674273 1.1378 0.25520 log(util) -0.0149021 0.0988643 -0.1507 0.88019 log(emp) 0.9190594 0.1044486 8.7992 < 2e-16 *** unemp -0.0047055 0.0020673 -2.2761 0.02284 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Estimated variance of the coefficients: (Intercept) log(pc) log(hwy) log(water) log(util) log(emp) (Intercept) 50.101152 -0.1269537 -5.7011050 1.1490999 0.9323094 -1.5405556 log(pc) -0.126954 0.0921826 0.0050351 -0.0178555 -0.0306629 -0.0649625 log(hwy) -5.701105 0.0050351 1.2347643 -0.1657787 -0.4550976 -0.0467022 log(water) 1.149100 -0.0178555 -0.1657787 0.1883437 -0.0095582 -0.1125142 log(util) 0.932309 -0.0306629 -0.4550976 -0.0095582 0.3996351 0.0118384 log(emp) -1.540556 -0.0649625 -0.0467022 -0.1125142 0.0118384 0.4348876 unemp -0.027161 -0.0013129 0.0020316 -0.0024191 -0.0013977 0.0068745 unemp (Intercept) -0.02716134 log(pc) -0.00131287 log(hwy) 0.00203161 log(water) -0.00241907 log(util) -0.00139775 log(emp) 0.00687449 unemp 0.00016044 Total Sum of Squares: 21431 Residual Sum of Squares: 36.691 Multiple R-Squared: 0.99829 Chisq: 434.623 on 6 DF, p-value: < 2.22e-16 > > ## Not run: > ##D # replicate Swamy (1970), p. 166, table 5.2 > ##D data(Grunfeld, package = "AER") # 11 firm Grunfeld data needed from package AER > ##D gw <- pvcm(invest ~ value + capital, data = Grunfeld, index = c("firm", "year")) > ## End(Not run) > > > > > > cleanEx() > nameEx("pwaldtest") > ### * pwaldtest > > flush(stderr()); flush(stdout()) > > ### Name: pwaldtest > ### Title: Wald-style Chi-square Test and F Test > ### Aliases: pwaldtest pwaldtest.plm pwaldtest.pvcm > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > mod_fe <- plm(inv ~ value + capital, data = Grunfeld, model = "within") > mod_re <- plm(inv ~ value + capital, data = Grunfeld, model = "random") > pwaldtest(mod_fe, test = "F") F test data: inv ~ value + capital F = 309.01, df1 = 2, df2 = 188, p-value < 2.2e-16 > pwaldtest(mod_re, test = "Chisq") Wald test data: inv ~ value + capital Chisq = 657.67, df = 2, p-value < 2.2e-16 > > # with robust vcov (matrix, function) > pwaldtest(mod_fe, vcov = vcovHC(mod_fe)) Wald test (robust), vcov: vcovHC(mod_fe) data: inv ~ value + capital Chisq = 63.549, df = 2, p-value = 1.587e-14 > pwaldtest(mod_fe, vcov = function(x) vcovHC(x, type = "HC3")) Wald test (robust), vcov: function(x) vcovHC(x, type = "HC3") data: inv ~ value + capital Chisq = 45.836, df = 2, p-value = 1.114e-10 > > pwaldtest(mod_fe, vcov = vcovHC(mod_fe), df2adj = FALSE) # w/o df2 adjustment Wald test (robust), vcov: vcovHC(mod_fe) data: inv ~ value + capital Chisq = 63.549, df = 2, p-value = 1.587e-14 > > # example without attribute "cluster" in the vcov > vcov_mat <- vcovHC(mod_fe) > attr(vcov_mat, "cluster") <- NULL # remove attribute > pwaldtest(mod_fe, vcov = vcov_mat) # no df2 adjustment performed Wald test (robust), vcov: vcov_mat data: inv ~ value + capital Chisq = 63.549, df = 2, p-value = 1.587e-14 > > > > > > cleanEx() > nameEx("pwartest") > ### * pwartest > > flush(stderr()); flush(stdout()) > > ### Name: pwartest > ### Title: Wooldridge Test for AR(1) Errors in FE Panel Models > ### Aliases: pwartest pwartest.formula pwartest.panelmodel > ### Keywords: htest > > ### ** Examples > > > data("EmplUK", package = "plm") > pwartest(log(emp) ~ log(wage) + log(capital), data = EmplUK) Wooldridge's test for serial correlation in FE panels data: plm.model F = 312.3, df1 = 1, df2 = 889, p-value < 2.2e-16 alternative hypothesis: serial correlation > > # pass argument 'type' to vcovHC used in test > pwartest(log(emp) ~ log(wage) + log(capital), data = EmplUK, type = "HC3") Wooldridge's test for serial correlation in FE panels data: plm.model F = 305.17, df1 = 1, df2 = 889, p-value < 2.2e-16 alternative hypothesis: serial correlation > > > > > > cleanEx() > nameEx("pwfdtest") > ### * pwfdtest > > flush(stderr()); flush(stdout()) > > ### Name: pwfdtest > ### Title: Wooldridge first-difference-based test for AR(1) errors in > ### levels or first-differenced panel models > ### Aliases: pwfdtest pwfdtest.formula pwfdtest.panelmodel > ### Keywords: htest > > ### ** Examples > > > data("EmplUK" , package = "plm") > pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK) Wooldridge's first-difference test for serial correlation in panels data: plm.model F = 1.5251, df1 = 1, df2 = 749, p-value = 0.2172 alternative hypothesis: serial correlation in differenced errors > pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK, h0 = "fe") Wooldridge's first-difference test for serial correlation in panels data: plm.model F = 131.55, df1 = 1, df2 = 749, p-value < 2.2e-16 alternative hypothesis: serial correlation in original errors > > # pass argument 'type' to vcovHC used in test > pwfdtest(log(emp) ~ log(wage) + log(capital), data = EmplUK, type = "HC3", h0 = "fe") Wooldridge's first-difference test for serial correlation in panels data: plm.model F = 123.79, df1 = 1, df2 = 749, p-value < 2.2e-16 alternative hypothesis: serial correlation in original errors > > > # same with panelmodel interface > mod <- plm(log(emp) ~ log(wage) + log(capital), data = EmplUK, model = "fd") > pwfdtest(mod) Wooldridge's first-difference test for serial correlation in panels data: mod F = 1.5251, df1 = 1, df2 = 749, p-value = 0.2172 alternative hypothesis: serial correlation in differenced errors > pwfdtest(mod, h0 = "fe") Wooldridge's first-difference test for serial correlation in panels data: mod F = 131.55, df1 = 1, df2 = 749, p-value < 2.2e-16 alternative hypothesis: serial correlation in original errors > pwfdtest(mod, type = "HC3", h0 = "fe") Wooldridge's first-difference test for serial correlation in panels data: mod F = 123.79, df1 = 1, df2 = 749, p-value < 2.2e-16 alternative hypothesis: serial correlation in original errors > > > > > > cleanEx() > nameEx("pwtest") > ### * pwtest > > flush(stderr()); flush(stdout()) > > ### Name: pwtest > ### Title: Wooldridge's Test for Unobserved Effects in Panel Models > ### Aliases: pwtest pwtest.formula pwtest.panelmodel > ### Keywords: htest > > ### ** Examples > > > data("Produc", package = "plm") > ## formula interface > pwtest(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc) Wooldridge's test for unobserved individual effects data: formula z = 3.9383, p-value = 8.207e-05 alternative hypothesis: unobserved effect > pwtest(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, effect = "time") Wooldridge's test for unobserved time effects data: formula z = 1.3143, p-value = 0.1888 alternative hypothesis: unobserved effect > > ## panelmodel interface > # first, estimate a pooling model, than compute test statistics > form <- formula(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp) > pool_prodc <- plm(form, data = Produc, model = "pooling") > pwtest(pool_prodc) # == effect="individual" Wooldridge's test for unobserved individual effects data: formula z = 3.9383, p-value = 8.207e-05 alternative hypothesis: unobserved effect > pwtest(pool_prodc, effect="time") Wooldridge's test for unobserved time effects data: formula z = 1.3143, p-value = 0.1888 alternative hypothesis: unobserved effect > > > > > cleanEx() > nameEx("r.squared") > ### * r.squared > > flush(stderr()); flush(stdout()) > > ### Name: r.squared > ### Title: R squared and adjusted R squared for panel models > ### Aliases: r.squared > ### Keywords: htest > > ### ** Examples > > > data("Grunfeld", package = "plm") > p <- plm(inv ~ value + capital, data = Grunfeld, model = "pooling") > r.squared(p) [1] 0.812408 > r.squared(p, dfcor = TRUE) [1] 0.8105035 > > > > > cleanEx() > nameEx("ranef.plm") > ### * ranef.plm > > flush(stderr()); flush(stdout()) > > ### Name: ranef.plm > ### Title: Extract the Random Effects > ### Aliases: ranef.plm > ### Keywords: regression > > ### ** Examples > > > data("Grunfeld", package = "plm") > m1 <- plm(inv ~ value + capital, data = Grunfeld, model = "random") > ranef(m1) # individual random effects 1 2 3 4 5 6 -9.5242955 157.8910235 -172.8958044 29.9119801 -54.6790089 34.3461316 7 8 9 10 -7.8977584 0.6726376 -28.1393497 50.3144442 > > # compare to random effects by ML estimation via lmer from package > # lme4 > ## Not run: > ##D library(lme4) > ##D m2 <- lmer(inv ~ value + capital + (1 | firm), data = Grunfeld) > ##D cbind("plm" = ranef(m1), "lmer" = unname(ranef(m2)$firm)) > ## End(Not run) > > # two-ways RE model, calculate individual and time random effects > data("Cigar", package = "plm") > tw <- plm(sales ~ pop + price, data = Cigar, model = "random", effect = "twoways") > ranef(tw) # individual random effects 1 3 4 5 7 8 -16.2313983 -10.9407671 -8.4070839 -8.0035988 -3.5394647 26.4991483 9 10 11 13 14 15 39.7177104 3.8777211 -5.1844126 -19.6522390 2.6503121 16.3006653 16 17 18 19 20 21 -11.6829305 -11.4349650 52.1389070 0.1959530 10.1000599 -0.7371054 22 23 24 25 26 27 -2.5582962 6.0052270 -13.9084124 -15.9415817 7.2335306 -13.1071681 28 29 30 31 32 33 -16.2253164 50.1149877 110.3969356 -1.5881308 -31.7198448 -2.6728683 35 36 37 39 40 41 -19.5275399 1.7670879 -3.3623632 -8.4818063 11.7369530 -6.5745407 42 43 44 45 46 47 -20.8494465 -5.4741136 -11.0335402 -55.6627609 16.3784126 7.7327316 48 49 50 51 -25.6449876 -9.0717648 -14.3557950 10.7278996 > ranef(tw, effect = "time") # time random effects 63 64 65 66 67 68 69 -4.8730347 -7.3464635 -5.9340750 -5.1545167 -4.9379129 -5.9884844 -6.6989482 70 71 72 73 74 75 76 -8.2130475 -5.6496310 -2.2865468 -0.9936552 1.3543836 3.0309237 5.7378570 77 78 79 80 81 82 83 5.6665356 6.4982251 5.3839356 6.2673107 6.9524675 6.8306273 5.2637423 84 85 86 87 88 89 90 3.0305218 2.9219323 2.3839785 1.3038537 0.2722306 -1.0413612 -1.9688282 91 92 -2.3758022 0.5637823 > > > > > > > cleanEx() > nameEx("sargan") > ### * sargan > > flush(stderr()); flush(stdout()) > > ### Name: sargan > ### Title: Hansen-Sargan Test of Overidentifying Restrictions > ### Aliases: sargan > ### Keywords: htest > > ### ** Examples > > > data("EmplUK", package = "plm") > ar <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + + lag(log(capital), 0:2) + lag(log(output), 0:2) | lag(log(emp), 2:99), + data = EmplUK, effect = "twoways", model = "twosteps") > sargan(ar) Sargan test data: log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + lag(log(capital), ... chisq = 31.381, df = 25, p-value = 0.1767 > > > > > cleanEx() > nameEx("summary.plm") > ### * summary.plm > > flush(stderr()); flush(stdout()) > > ### Name: summary.plm.list > ### Title: Summary for plm objects > ### Aliases: summary.plm.list coef.summary.plm.list print.summary.plm.list > ### summary.plm print.summary.plm > ### Keywords: regression > > ### ** Examples > > > data("Produc", package = "plm") > zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, index = c("state","year")) > summary(zz) Oneway (individual) effect Within Model Call: plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state", "year")) Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -0.120456 -0.023741 -0.002041 0.018144 0.174718 Coefficients: Estimate Std. Error t-value Pr(>|t|) log(pcap) -0.02614965 0.02900158 -0.9017 0.3675 log(pc) 0.29200693 0.02511967 11.6246 < 2.2e-16 *** log(emp) 0.76815947 0.03009174 25.5273 < 2.2e-16 *** unemp -0.00529774 0.00098873 -5.3582 1.114e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 18.941 Residual Sum of Squares: 1.1112 R-Squared: 0.94134 Adj. R-Squared: 0.93742 F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16 > > # summary with a funished vcov, passed as matrix, as function, and > # as function with additional argument > data("Grunfeld", package = "plm") > wi <- plm(inv ~ value + capital, + data = Grunfeld, model="within", effect = "individual") > summary(wi, vcov = vcovHC(wi)) Oneway (individual) effect Within Model Note: Coefficient variance-covariance matrix supplied: vcovHC(wi) Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "individual", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -184.00857 -17.64316 0.56337 19.19222 250.70974 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.110124 0.014342 7.6783 8.566e-13 *** capital 0.310065 0.049793 6.2271 3.033e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 2244400 Residual Sum of Squares: 523480 R-Squared: 0.76676 Adj. R-Squared: 0.75311 F-statistic: 31.7744 on 2 and 9 DF, p-value: 8.3417e-05 > summary(wi, vcov = vcovHC) Oneway (individual) effect Within Model Note: Coefficient variance-covariance matrix supplied: vcovHC Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "individual", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -184.00857 -17.64316 0.56337 19.19222 250.70974 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.110124 0.014342 7.6783 8.566e-13 *** capital 0.310065 0.049793 6.2271 3.033e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 2244400 Residual Sum of Squares: 523480 R-Squared: 0.76676 Adj. R-Squared: 0.75311 F-statistic: 31.7744 on 2 and 9 DF, p-value: 8.3417e-05 > summary(wi, vcov = function(x) vcovHC(x, method = "white2")) Oneway (individual) effect Within Model Note: Coefficient variance-covariance matrix supplied: function(x) vcovHC(x, method = "white2") Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "individual", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -184.00857 -17.64316 0.56337 19.19222 250.70974 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.110124 0.018925 5.8191 2.51e-08 *** capital 0.310065 0.027787 11.1585 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 2244400 Residual Sum of Squares: 523480 R-Squared: 0.76676 Adj. R-Squared: 0.75311 F-statistic: 117.915 on 2 and 9 DF, p-value: 3.5011e-07 > > # extract F statistic > wi_summary <- summary(wi) > Fstat <- wi_summary[["fstatistic"]] > > # extract estimates and p-values > est <- wi_summary[["coefficients"]][ , "Estimate"] > pval <- wi_summary[["coefficients"]][ , "Pr(>|t|)"] > > # print summary only for coefficent "value" > print(wi_summary, subset = "value") Oneway (individual) effect Within Model Call: plm(formula = inv ~ value + capital, data = Grunfeld, effect = "individual", model = "within") Balanced Panel: n = 10, T = 20, N = 200 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -184.00857 -17.64316 0.56337 19.19222 250.70974 Coefficients: Estimate Std. Error t-value Pr(>|t|) value 0.110124 0.011857 9.2879 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 2244400 Residual Sum of Squares: 523480 R-Squared: 0.76676 Adj. R-Squared: 0.75311 F-statistic: 309.014 on 2 and 188 DF, p-value: < 2.22e-16 > > > > > cleanEx() > nameEx("vcovBK") > ### * vcovBK > > flush(stderr()); flush(stdout()) > > ### Name: vcovBK > ### Title: Beck and Katz Robust Covariance Matrix Estimators > ### Aliases: vcovBK vcovBK.plm > ### Keywords: regression > > ### ** Examples > > > library(lmtest) Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric > library(car) Loading required package: carData > data("Produc", package="plm") > zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="random") > ## standard coefficient significance test > coeftest(zz) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.13541100 0.13346149 16.0002 < 2.2e-16 *** log(pcap) 0.00443859 0.02341732 0.1895 0.8497 log(pc) 0.31054843 0.01980475 15.6805 < 2.2e-16 *** log(emp) 0.72967053 0.02492022 29.2803 < 2.2e-16 *** unemp -0.00617247 0.00090728 -6.8033 1.986e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## robust significance test, cluster by group > ## (robust vs. serial correlation), default arguments > coeftest(zz, vcov.=vcovBK) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2562311 8.3339 3.327e-16 *** log(pcap) 0.0044386 0.0494249 0.0898 0.9284646 log(pc) 0.3105484 0.0427824 7.2588 9.153e-13 *** log(emp) 0.7296705 0.0497995 14.6522 < 2.2e-16 *** unemp -0.0061725 0.0017190 -3.5908 0.0003495 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem with parameters, pass vcov as a function argument > coeftest(zz, vcov.=function(x) vcovBK(x, type="HC1")) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2570198 8.3084 4.059e-16 *** log(pcap) 0.0044386 0.0495770 0.0895 0.9286835 log(pc) 0.3105484 0.0429141 7.2365 1.068e-12 *** log(emp) 0.7296705 0.0499528 14.6072 < 2.2e-16 *** unemp -0.0061725 0.0017242 -3.5798 0.0003643 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem, cluster by time period > ## (robust vs. cross-sectional correlation) > coeftest(zz, vcov.=function(x) vcovBK(x, + type="HC1", cluster="time")) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2776015 7.6924 4.188e-14 *** log(pcap) 0.0044386 0.0395528 0.1122 0.9106772 log(pc) 0.3105484 0.0458243 6.7769 2.360e-11 *** log(emp) 0.7296705 0.0562371 12.9749 < 2.2e-16 *** unemp -0.0061725 0.0018531 -3.3309 0.0009049 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem with parameters, pass vcov as a matrix argument > coeftest(zz, vcov.=vcovBK(zz, type="HC1")) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2570198 8.3084 4.059e-16 *** log(pcap) 0.0044386 0.0495770 0.0895 0.9286835 log(pc) 0.3105484 0.0429141 7.2365 1.068e-12 *** log(emp) 0.7296705 0.0499528 14.6072 < 2.2e-16 *** unemp -0.0061725 0.0017242 -3.5798 0.0003643 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## joint restriction test > waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovBK) Wald test Model 1: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Model 2: log(gsp) ~ log(pcap) + log(pc) Res.Df Df Chisq Pr(>Chisq) 1 811 2 813 -2 411.05 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## test of hyp.: 2*log(pc)=log(emp) > linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovBK) Linear hypothesis test Hypothesis: 2 log(pc) - log(emp) = 0 Model 1: restricted model Model 2: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Note: Coefficient covariance matrix supplied. Res.Df Df Chisq Pr(>Chisq) 1 812 2 811 1 0.8152 0.3666 > > > > > cleanEx() detaching ‘package:car’, ‘package:carData’, ‘package:lmtest’, ‘package:zoo’ > nameEx("vcovDC") > ### * vcovDC > > flush(stderr()); flush(stdout()) > > ### Name: vcovDC > ### Title: Double-Clustering Robust Covariance Matrix Estimator > ### Aliases: vcovDC vcovDC.plm > ### Keywords: regression > > ### ** Examples > > > library(lmtest) Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric > library(car) Loading required package: carData > data("Produc", package="plm") > zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling") > ## standard coefficient significance test > coeftest(zz) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.0575873 28.5359 < 2.2e-16 *** log(pcap) 0.1550070 0.0171538 9.0363 < 2.2e-16 *** log(pc) 0.3091902 0.0102720 30.1003 < 2.2e-16 *** log(emp) 0.5939349 0.0137475 43.2032 < 2.2e-16 *** unemp -0.0067330 0.0014164 -4.7537 2.363e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## DC robust significance test, default > coeftest(zz, vcov.=vcovDC) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.643302 0.252047 6.5198 1.237e-10 *** log(pcap) 0.155007 0.061718 2.5115 0.01221 * log(pc) 0.309190 0.044957 6.8774 1.217e-11 *** log(emp) 0.593935 0.070203 8.4603 < 2.2e-16 *** unemp -0.006733 0.003330 -2.0219 0.04351 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem with parameters, pass vcov as a function argument > coeftest(zz, vcov.=function(x) vcovDC(x, type="HC1", maxlag=4)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.2528223 6.4998 1.403e-10 *** log(pcap) 0.1550070 0.0619079 2.5038 0.01248 * log(pc) 0.3091902 0.0450955 6.8563 1.400e-11 *** log(emp) 0.5939349 0.0704186 8.4343 < 2.2e-16 *** unemp -0.0067330 0.0033403 -2.0157 0.04416 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## joint restriction test > waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovDC) Wald test Model 1: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Model 2: log(gsp) ~ log(pcap) + log(pc) Res.Df Df Chisq Pr(>Chisq) 1 811 2 813 -2 109.61 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## test of hyp.: 2*log(pc)=log(emp) > linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovDC) Linear hypothesis test Hypothesis: 2 log(pc) - log(emp) = 0 Model 1: restricted model Model 2: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Note: Coefficient covariance matrix supplied. Res.Df Df Chisq Pr(>Chisq) 1 812 2 811 1 0.0299 0.8626 > > > > > cleanEx() detaching ‘package:car’, ‘package:carData’, ‘package:lmtest’, ‘package:zoo’ > nameEx("vcovG") > ### * vcovG > > flush(stderr()); flush(stdout()) > > ### Name: vcovG > ### Title: Generic Lego building block for Robust Covariance Matrix > ### Estimators > ### Aliases: vcovG vcovG.plm vcovG.pcce > ### Keywords: regression > > ### ** Examples > > > data("Produc", package="plm") > zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, + model="pooling") > ## reproduce Arellano's covariance matrix > vcovG(zz, cluster="group", inner="cluster", l=0) (Intercept) log(pcap) log(pc) log(emp) (Intercept) 0.0596248904 -9.637916e-03 -0.0068911857 0.0148866870 log(pcap) -0.0096379163 3.614354e-03 -0.0002956929 -0.0031157168 log(pc) -0.0068911857 -2.956929e-04 0.0021371841 -0.0017597732 log(emp) 0.0148866870 -3.115717e-03 -0.0017597732 0.0047067982 unemp 0.0003700792 -8.058266e-05 -0.0000586966 0.0001366349 unemp (Intercept) 3.700792e-04 log(pcap) -8.058266e-05 log(pc) -5.869660e-05 log(emp) 1.366349e-04 unemp 9.550671e-06 attr(,"cluster") [1] "group" > ## use in coefficient significance test > library(lmtest) Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric > ## define custom covariance function > ## (in this example, same as vcovHC) > myvcov <- function(x) vcovG(x, cluster="group", inner="cluster", l=0) > ## robust significance test > coeftest(zz, vcov.=myvcov) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.2441821 6.7298 3.211e-11 *** log(pcap) 0.1550070 0.0601195 2.5783 0.01010 * log(pc) 0.3091902 0.0462297 6.6881 4.209e-11 *** log(emp) 0.5939349 0.0686061 8.6572 < 2.2e-16 *** unemp -0.0067330 0.0030904 -2.1787 0.02964 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > cleanEx() detaching ‘package:lmtest’, ‘package:zoo’ > nameEx("vcovHC.plm") > ### * vcovHC.plm > > flush(stderr()); flush(stdout()) > > ### Name: vcovHC.plm > ### Title: Robust Covariance Matrix Estimators > ### Aliases: vcovHC.plm vcovHC.pcce vcovHC.pgmm > ### Keywords: regression > > ### ** Examples > > > library(lmtest) Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric > library(car) Loading required package: carData > data("Produc", package = "plm") > zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, + data = Produc, model = "random") > ## standard coefficient significance test > coeftest(zz) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.13541100 0.13346149 16.0002 < 2.2e-16 *** log(pcap) 0.00443859 0.02341732 0.1895 0.8497 log(pc) 0.31054843 0.01980475 15.6805 < 2.2e-16 *** log(emp) 0.72967053 0.02492022 29.2803 < 2.2e-16 *** unemp -0.00617247 0.00090728 -6.8033 1.986e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## robust significance test, cluster by group > ## (robust vs. serial correlation) > coeftest(zz, vcov.=vcovHC) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2386676 8.9472 < 2.2e-16 *** log(pcap) 0.0044386 0.0545970 0.0813 0.935226 log(pc) 0.3105484 0.0435922 7.1239 2.317e-12 *** log(emp) 0.7296705 0.0699680 10.4286 < 2.2e-16 *** unemp -0.0061725 0.0023326 -2.6461 0.008299 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem with parameters, pass vcov as a function argument > coeftest(zz, vcov.=function(x) vcovHC(x, method="arellano", type="HC1")) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2394021 8.9198 < 2.2e-16 *** log(pcap) 0.0044386 0.0547651 0.0810 0.935424 log(pc) 0.3105484 0.0437264 7.1021 2.689e-12 *** log(emp) 0.7296705 0.0701833 10.3966 < 2.2e-16 *** unemp -0.0061725 0.0023398 -2.6380 0.008499 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem, cluster by time period > ## (robust vs. cross-sectional correlation) > coeftest(zz, vcov.=function(x) vcovHC(x, method="arellano", + type="HC1", cluster="group")) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2394021 8.9198 < 2.2e-16 *** log(pcap) 0.0044386 0.0547651 0.0810 0.935424 log(pc) 0.3105484 0.0437264 7.1021 2.689e-12 *** log(emp) 0.7296705 0.0701833 10.3966 < 2.2e-16 *** unemp -0.0061725 0.0023398 -2.6380 0.008499 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem with parameters, pass vcov as a matrix argument > coeftest(zz, vcov.=vcovHC(zz, method="arellano", type="HC1")) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1354110 0.2394021 8.9198 < 2.2e-16 *** log(pcap) 0.0044386 0.0547651 0.0810 0.935424 log(pc) 0.3105484 0.0437264 7.1021 2.689e-12 *** log(emp) 0.7296705 0.0701833 10.3966 < 2.2e-16 *** unemp -0.0061725 0.0023398 -2.6380 0.008499 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## joint restriction test > waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovHC) Wald test Model 1: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Model 2: log(gsp) ~ log(pcap) + log(pc) Res.Df Df Chisq Pr(>Chisq) 1 811 2 813 -2 404.16 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## test of hyp.: 2*log(pc)=log(emp) > linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovHC) Linear hypothesis test Hypothesis: 2 log(pc) - log(emp) = 0 Model 1: restricted model Model 2: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Note: Coefficient covariance matrix supplied. Res.Df Df Chisq Pr(>Chisq) 1 812 2 811 1 0.5878 0.4433 > > ## Robust inference for CCE models > data("Produc", package = "plm") > ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p") > summary(ccepmod, vcov = vcovHC) Common Correlated Effects Pooled model Note: Coefficient variance-covariance matrix supplied: vcovHC Call: pcce(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "p") Balanced Panel: n = 48, T = 17, N = 816 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.0918843 -0.0060964 0.0005035 0.0000000 0.0059796 0.0682325 Coefficients: Estimate Std. Error z-value Pr(>|z|) log(pcap) 0.0432376 0.0972332 0.4447 0.6566 log(pc) 0.0363922 0.0322477 1.1285 0.2591 log(emp) 0.8209632 0.1104438 7.4333 1.059e-13 *** unemp -0.0020925 0.0013959 -1.4990 0.1339 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 849.81 Residual Sum of Squares: 0.11927 HPY R-squared: 0.99077 > > ## Robust inference for GMM models > data("EmplUK", package="plm") > ar <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + + log(capital) + lag(log(capital), 2) + log(output) + + lag(log(output),2) | lag(log(emp), 2:99), + data = EmplUK, effect = "twoways", model = "twosteps") > rv <- vcovHC(ar) > mtest(ar, order = 2, vcov = rv) Autocorrelation test of degree 2 data: log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + ... normal = -0.1165, p-value = 0.9073 > > > > cleanEx() detaching ‘package:car’, ‘package:carData’, ‘package:lmtest’, ‘package:zoo’ > nameEx("vcovNW") > ### * vcovNW > > flush(stderr()); flush(stdout()) > > ### Name: vcovNW > ### Title: Newey and West (1987) Robust Covariance Matrix Estimator > ### Aliases: vcovNW vcovNW.plm vcovNW.pcce > ### Keywords: regression > > ### ** Examples > > > library(lmtest) Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric > library(car) Loading required package: carData > data("Produc", package="plm") > zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling") > ## standard coefficient significance test > coeftest(zz) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.0575873 28.5359 < 2.2e-16 *** log(pcap) 0.1550070 0.0171538 9.0363 < 2.2e-16 *** log(pc) 0.3091902 0.0102720 30.1003 < 2.2e-16 *** log(emp) 0.5939349 0.0137475 43.2032 < 2.2e-16 *** unemp -0.0067330 0.0014164 -4.7537 2.363e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## NW robust significance test, default > coeftest(zz, vcov.=vcovNW) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.1143540 14.3703 < 2.2e-16 *** log(pcap) 0.1550070 0.0299283 5.1793 2.812e-07 *** log(pc) 0.3091902 0.0206394 14.9806 < 2.2e-16 *** log(emp) 0.5939349 0.0316213 18.7827 < 2.2e-16 *** unemp -0.0067330 0.0020247 -3.3254 0.0009225 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem with parameters, pass vcov as a function argument > coeftest(zz, vcov.=function(x) vcovNW(x, type="HC1", maxlag=4)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.1399123 11.7452 < 2.2e-16 *** log(pcap) 0.1550070 0.0367023 4.2234 2.679e-05 *** log(pc) 0.3091902 0.0256858 12.0374 < 2.2e-16 *** log(emp) 0.5939349 0.0388977 15.2691 < 2.2e-16 *** unemp -0.0067330 0.0023634 -2.8488 0.004499 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## joint restriction test > waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovNW) Wald test Model 1: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Model 2: log(gsp) ~ log(pcap) + log(pc) Res.Df Df Chisq Pr(>Chisq) 1 811 2 813 -2 480.18 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## test of hyp.: 2*log(pc)=log(emp) > linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovNW) Linear hypothesis test Hypothesis: 2 log(pc) - log(emp) = 0 Model 1: restricted model Model 2: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Note: Coefficient covariance matrix supplied. Res.Df Df Chisq Pr(>Chisq) 1 812 2 811 1 0.1531 0.6956 > > > > > cleanEx() detaching ‘package:car’, ‘package:carData’, ‘package:lmtest’, ‘package:zoo’ > nameEx("vcovSCC") > ### * vcovSCC > > flush(stderr()); flush(stdout()) > > ### Name: vcovSCC > ### Title: Driscoll and Kraay (1998) Robust Covariance Matrix Estimator > ### Aliases: vcovSCC vcovSCC.plm vcovSCC.pcce > ### Keywords: regression > > ### ** Examples > > > library(lmtest) Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric > library(car) Loading required package: carData > data("Produc", package="plm") > zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling") > ## standard coefficient significance test > coeftest(zz) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.0575873 28.5359 < 2.2e-16 *** log(pcap) 0.1550070 0.0171538 9.0363 < 2.2e-16 *** log(pc) 0.3091902 0.0102720 30.1003 < 2.2e-16 *** log(emp) 0.5939349 0.0137475 43.2032 < 2.2e-16 *** unemp -0.0067330 0.0014164 -4.7537 2.363e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## SCC robust significance test, default > coeftest(zz, vcov.=vcovSCC) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.1503485 10.9300 < 2.2e-16 *** log(pcap) 0.1550070 0.0369734 4.1924 3.064e-05 *** log(pc) 0.3091902 0.0076442 40.4479 < 2.2e-16 *** log(emp) 0.5939349 0.0387024 15.3462 < 2.2e-16 *** unemp -0.0067330 0.0025389 -2.6520 0.008159 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## idem with parameters, pass vcov as a function argument > coeftest(zz, vcov.=function(x) vcovSCC(x, type="HC1", maxlag=4)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6433023 0.1793363 9.1632 < 2.2e-16 *** log(pcap) 0.1550070 0.0441052 3.5145 0.000465 *** log(pc) 0.3091902 0.0069837 44.2731 < 2.2e-16 *** log(emp) 0.5939349 0.0454539 13.0668 < 2.2e-16 *** unemp -0.0067330 0.0029520 -2.2808 0.022817 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## joint restriction test > waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovSCC) Wald test Model 1: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Model 2: log(gsp) ~ log(pcap) + log(pc) Res.Df Df Chisq Pr(>Chisq) 1 811 2 813 -2 531 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## test of hyp.: 2*log(pc)=log(emp) > linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovSCC) Linear hypothesis test Hypothesis: 2 log(pc) - log(emp) = 0 Model 1: restricted model Model 2: log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp Note: Coefficient covariance matrix supplied. Res.Df Df Chisq Pr(>Chisq) 1 812 2 811 1 0.2413 0.6233 > > > > > cleanEx() detaching ‘package:car’, ‘package:carData’, ‘package:lmtest’, ‘package:zoo’ > nameEx("within_intercept") > ### * within_intercept > > flush(stderr()); flush(stdout()) > > ### Name: within_intercept > ### Title: Overall Intercept for Within Models Along its Standard Error > ### Aliases: within_intercept within_intercept.plm > ### Keywords: attribute > > ### ** Examples > > > data("Hedonic", package = "plm") > mod_fe <- plm(mv ~ age + crim, data = Hedonic, index = "townid") > overallint <- within_intercept(mod_fe) > attr(overallint, "se") # standard error [1] 0.04853606 > > # overall intercept is the weighted mean of fixed effects in the > # one-way case > weighted.mean(fixef(mod_fe), as.numeric(table(index(mod_fe)[[1]]))) [1] 10.25964 > > # relationship of type="dmean", "level" and within_intercept in the > # one-way case > data("Grunfeld", package = "plm") > gi <- plm(inv ~ value + capital, data = Grunfeld, model = "within") > fx_level <- fixef(gi, type = "level") > fx_dmean <- fixef(gi, type = "dmean") > overallint <- within_intercept(gi) > all.equal(overallint + fx_dmean, fx_level, check.attributes = FALSE) # TRUE [1] TRUE > > # overall intercept with robust standard error > within_intercept(gi, vcov = function(x) vcovHC(x, method="arellano", type="HC0")) (overall_intercept) -58.74394 attr(,"se") [1] 26.05445 > > > > > ### *