pggls {plm}R Documentation

General FGLS Estimators

Description

General FGLS estimators for panel data (balanced or unbalanced)

Usage

pggls(formula, data, subset, na.action, effect = c("individual","time"), model = c("within","random"),
index = NULL, ...)
## S3 method for class 'pggls':
summary(object, ...)
## S3 method for class 'summary.pggls':
print(x,digits = max(3, getOption("digits") -
2), width = getOption("width"),...)

Arguments

formula a symbolic description for the model to be estimated,
object, x an object of class pggls,
data a data.frame,
subset see lm,
na.action see lm,
effect the effects introduced in the model, one of "individual" or "time",
model one of "within" or "random",
index the indexes, see plm.data,
digits digits,
width the maximum length of the lines in the print output,
... further arguments.

Details

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 (random) or fixed effects (within), 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 effect="individual", else symmetric) of observations to be fully unrestricted and is therefore robust against any type of intragroup heteroskedasticity and serial correlation. This structure, by converse, is assumed identical across groups and thus general FGLS estimation is inefficient under groupwise heteroskedasticity. Care shall also be taken that this method requires estimation of T(T+1)/2 variance parameters, thus efficiency requires N > > T (if effect="individual", else the opposite).

Value

an object of class c("pggls","panelmodel") containing :

coefficients the vector of coefficients,
residuals the vector of residuals,
fitted.values the vector of fitted.values,
vcov the covariance matrix of the coefficients,
df.residual degrees of freedom of the residuals,
model a data.frame containing the variables used for the estimation,
call the call,
sigma the estimated intragroup (or cross-sectional, if effect="time") covariance of errors,

Author(s)

Giovanni Millo

References

Kiefer, N. M. (1980) Estimation of Fixed Effects Models for Time Series of Cross-Sections with Arbitrary Intertemporal Covariance, Journal of Econometrics, 14, 195–202.

Wooldridge J. M. (2003) Econometric Analysis of Cross Section and Panel Data, MIT Press

Examples


data("Produc", package="Ecdat")
zz <- pggls(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="random")
summary(zz)

[Package plm version 1.1-1 Index]