1% Generated by roxygen2: do not edit by hand 2% Please edit documentation in R/est_cce.R 3\name{pcce} 4\alias{pcce} 5\alias{summary.pcce} 6\alias{print.summary.pcce} 7\alias{residuals.pcce} 8\alias{model.matrix.pcce} 9\alias{pmodel.response.pcce} 10\title{Common Correlated Effects estimators} 11\usage{ 12pcce( 13 formula, 14 data, 15 subset, 16 na.action, 17 model = c("mg", "p"), 18 index = NULL, 19 trend = FALSE, 20 ... 21) 22 23\method{summary}{pcce}(object, vcov = NULL, ...) 24 25\method{print}{summary.pcce}( 26 x, 27 digits = max(3, getOption("digits") - 2), 28 width = getOption("width"), 29 ... 30) 31 32\method{residuals}{pcce}(object, type = c("defactored", "standard"), ...) 33 34\method{model.matrix}{pcce}(object, ...) 35 36\method{pmodel.response}{pcce}(object, ...) 37} 38\arguments{ 39\item{formula}{a symbolic description of the model to be estimated,} 40 41\item{data}{a \code{data.frame},} 42 43\item{subset}{see \code{lm},} 44 45\item{na.action}{see \code{lm},} 46 47\item{model}{one of \code{"mg"}, \code{"p"}, selects Mean Groups vs. Pooled 48CCE model,} 49 50\item{index}{the indexes, see \code{\link[=pdata.frame]{pdata.frame()}},} 51 52\item{trend}{logical specifying whether an individual-specific 53trend has to be included,} 54 55\item{\dots}{further arguments.} 56 57\item{object, x}{an object of class \code{"pcce"},} 58 59\item{vcov}{a variance-covariance matrix furnished by the user or a function to calculate one,} 60 61\item{digits}{digits,} 62 63\item{width}{the maximum length of the lines in the print output,} 64 65\item{type}{one of \code{"defactored"} or \code{"standard"},} 66} 67\value{ 68An object of class \code{c("pcce", "panelmodel")} containing: 69\item{coefficients}{the vector of coefficients,} 70\item{residuals}{the vector of (defactored) residuals,} 71\item{stdres}{the vector of (raw) residuals,} 72\item{tr.model}{the transformed data after projection on H,} 73\item{fitted.values}{the vector of fitted values,} 74\item{vcov}{the covariance matrix of the coefficients,} 75\item{df.residual}{degrees of freedom of the residuals,} 76\item{model}{a data.frame containing the variables used for the 77estimation,} 78\item{call}{the call,} 79\item{indcoef}{the matrix of individual coefficients from 80separate time series regressions,} 81\item{r.squared}{numeric, the R squared.} 82} 83\description{ 84Common Correlated Effects Mean Groups (CCEMG) and Pooled (CCEP) 85estimators for panel data with common factors (balanced or 86unbalanced) 87} 88\details{ 89\code{pcce} is a function for the estimation of linear panel models by 90the Common Correlated Effects Mean Groups or Pooled estimator, 91consistent under the hypothesis of unobserved common factors and 92idiosyncratic factor loadings. The CCE estimator works by 93augmenting the model by cross-sectional averages of the dependent 94variable and regressors in order to account for the common factors, 95and adding individual intercepts and possibly trends. 96} 97\examples{ 98 99data("Produc", package = "plm") 100ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p") 101summary(ccepmod) 102summary(ccepmod, vcov = vcovHC) # use argument vcov for robust std. errors 103 104ccemgmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="mg") 105summary(ccemgmod) 106 107} 108\references{ 109\insertRef{kappesyam11}{plm} 110} 111\author{ 112Giovanni Millo 113} 114\keyword{regression} 115