| pggls {plm} | R Documentation |
General FGLS estimators for panel data (balanced or unbalanced)
pggls(formula, data, subset, na.action, effect = c("individual","time"), model = c("within","random","pooling","fd"),
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"),...)
formula |
a symbolic description of the model to be estimated, |
object, x |
an object of class |
data |
a |
subset |
see |
na.action |
see |
effect |
the effects introduced in the model, one of |
model |
one of |
index |
the indexes, see |
digits |
digits, |
width |
the maximum length of the lines in the print output, |
... |
further arguments. |
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. 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 T(T+1)/2 variance
parameters, thus efficiency requires N > > T (if
effect="individual", else the opposite).
The model="random" and model="pooling" arguments both
produce an unrestricted FGLS model as in Wooldridge, Ch.10. If
model="within" (the default) then a FEGLS (fixed effects GLS, see
ibid.) is estimated; if model="fd" a FDGLS (first-difference GLS).
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 |
Giovanni Millo
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.
Im, K. S. and Ahn, S. C. and Schmidt, P. and Wooldridge, J. M. (1999) Efficient Estimation of Panel Data Models with Strictly Exogenous Explanatory Variables, Journal of Econometrics, 93, 177-201.
Wooldridge, J. M. (2002) Econometric Analysis of Cross Section and Panel Data, MIT Press.
data("Produc", package = "plm")
zz <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "pooling")
summary(zz)