The package estimates linear models on panel data structures in the presence of AR(1)-type autocorrelation as well as panel heteroskedasticity and/or contemporaneous correlation. First, AR(1)-type autocorrelation is addressed via a two-step Prais-Winsten feasible generalized least squares (FGLS) procedure, where the autocorrelation coefficients may be panel-specific. A number of common estimators for the autocorrelation coefficient are supported. In case of panel heteroskedasticty, one can choose to use a sandwich-type robust standard error estimator with OLS or a panel weighted least squares estimator after the two-step Prais-Winsten estimator. Alternatively, if panels are both heteroskedastic and contemporaneously correlated, the package supports panel-corrected standard errors (PCSEs) as well as the Parks-Kmenta FGLS estimator.
|Author||Konstantin Kashin <email@example.com>|
|Date of publication||2014-02-27 16:15:29|
|Maintainer||Konstantin Kashin <firstname.lastname@example.org>|
|License||GPL (>= 2)|
BrooksKurtz: Brooks and Kurtz (2012) Replication Data
LupPon: Lupu and Pontusson (2011) Replication Data
panelAR: Estimation of Linear AR(1) Panel Data Models with...
plot.panelAR: Plot Panel Structure
predict.panelAR: Predict method for fitted objects of class '"panelAR"'.
Rehm: Rehm (2011) Replication Data
run.analysis: Run Analysis for Panel Data
summary.panelAR: Summary method for fitted objects of class '"panelAR"'
vcov.panelAR: Variance-covariance method for fitted objects of class...
WhittenWilliams: Whitten and Williams (2011) Replication Data