1% Generated by roxygen2: do not edit by hand
2% Please edit documentation in R/est_ggls.R
3\name{pggls}
4\alias{pggls}
5\alias{summary.pggls}
6\alias{print.summary.pggls}
7\alias{residuals.pggls}
8\title{General FGLS Estimators}
9\usage{
10pggls(
11  formula,
12  data,
13  subset,
14  na.action,
15  effect = c("individual", "time"),
16  model = c("within", "random", "pooling", "fd"),
17  index = NULL,
18  ...
19)
20
21\method{summary}{pggls}(object, ...)
22
23\method{print}{summary.pggls}(
24  x,
25  digits = max(3, getOption("digits") - 2),
26  width = getOption("width"),
27  ...
28)
29
30\method{residuals}{pggls}(object, ...)
31}
32\arguments{
33\item{formula}{a symbolic description of the model to be estimated,}
34
35\item{data}{a \code{data.frame},}
36
37\item{subset}{see \code{\link[=lm]{lm()}},}
38
39\item{na.action}{see \code{\link[=lm]{lm()}},}
40
41\item{effect}{the effects introduced in the model, one of
42\code{"individual"} or \code{"time"},}
43
44\item{model}{one of \code{"within"}, \code{"pooling"}, \code{"random"} or \code{"fd"},}
45
46\item{index}{the indexes, see \code{\link[=pdata.frame]{pdata.frame()}},}
47
48\item{\dots}{further arguments.}
49
50\item{object, x}{an object of class \code{pggls},}
51
52\item{digits}{digits,}
53
54\item{width}{the maximum length of the lines in the print output,}
55}
56\value{
57An object of class \code{c("pggls","panelmodel")} containing:
58\item{coefficients}{the vector of coefficients,}
59\item{residuals}{the vector of residuals,}
60\item{fitted.values}{the vector of fitted values,}
61\item{vcov}{the covariance matrix of the coefficients,}
62\item{df.residual}{degrees of freedom of the residuals,}
63\item{model}{a data.frame containing the variables used for the
64estimation,}
65\item{call}{the call,}
66\item{sigma}{the estimated intragroup (or cross-sectional, if
67\code{effect = "time"}) covariance of errors,}
68}
69\description{
70General FGLS estimators for panel data (balanced or unbalanced)
71}
72\details{
73\code{pggls} is a function for the estimation of linear panel models by
74general feasible generalized least squares, either with or without
75fixed effects. General FGLS is based on a two-step estimation
76process: first a model is estimated by OLS (\code{model = "pooling"}),
77fixed effects (\code{model = "within"}) or first differences (\code{model = "fd"}), then its residuals are used to estimate an error covariance
78matrix for use in a feasible-GLS analysis. This framework allows
79the error covariance structure inside every group (if \code{effect = "individual"}, else symmetric) of observations to be fully
80unrestricted and is therefore robust against any type of intragroup
81heteroskedasticity and serial correlation. Conversely, this
82structure is assumed identical across groups and thus general FGLS
83estimation is inefficient under groupwise heteroskedasticity. Note
84also that this method requires estimation of \eqn{T(T+1)/2}
85variance parameters, thus efficiency requires N >> T (if \code{effect = "individual"}, else the opposite). Setting \code{model = "random"} or
86\code{model = "pooling"}, both produce an unrestricted FGLS model as in
87Wooldridge, Ch. 10.5, although the former is deprecated and
88included only for retro--compatibility reasons. If \code{model = "within"} (the default) then a FEGLS (fixed effects GLS, see ibid.)
89is estimated; if \code{model = "fd"} a FDGLS (first-difference GLS).
90}
91\examples{
92
93data("Produc", package = "plm")
94zz_wi <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
95               data = Produc, model = "within")
96summary(zz_wi)
97
98zz_pool <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
99                 data = Produc, model = "pooling")
100summary(zz_pool)
101
102zz_fd <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
103               data = Produc, model = "fd")
104summary(zz_fd)
105
106
107}
108\references{
109\insertRef{IM:SEUN:SCHM:WOOL:99}{plm}
110
111\insertRef{KIEF:80}{plm}
112
113\insertRef{WOOL:02}{plm}
114
115\insertRef{WOOL:10}{plm}
116}
117\author{
118Giovanni Millo
119}
120\keyword{regression}
121