General FGLS Estimators
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"),
...
)
## S3 method for class 'pggls'
residuals(object, ...)formula |
a symbolic description of the model to be estimated, |
data |
a |
subset |
see |
na.action |
see |
effect |
the effects introduced in the model, one of
|
model |
one of |
index |
the indexes, see |
... |
further arguments. |
object, x |
an object of class |
digits |
digits, |
width |
the maximum length of the lines in the print output, |
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 (model = "pooling"),
fixed effects (model = "within") or first differences (model = "fd"), 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). Setting model = "random" or
model = "pooling", both produce an unrestricted FGLS model as in
Wooldridge, Ch. 10.5, although the former is deprecated and
included only for retro–compatibility reasons. 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
Im KS, Ahn SC, Schmidt P, Wooldridge JM (1999). “Efficient estimation of panel data models with strictly exogenous explanatory variables.” Journal of Econometrics, 93(1), 177–201. ISSN 0304-4076, https://www.sciencedirect.com/science/article/pii/S0304407699000081.
Kiefer NM (1980). “Estimation of fixed effect models for time series of cross-sections with arbitrary intertemporal covariance.” Journal of Econometrics, 14(2), 195–202.
Wooldridge JM (2002). Econometric Analysis of Cross–Section and Panel Data. MIT press.
Wooldridge JM (2010). Econometric Analysis of Cross–Section and Panel Data. MIT press.
data("Produc", package = "plm")
zz_wi <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "within")
summary(zz_wi)
zz_pool <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "pooling")
summary(zz_pool)
zz_fd <- pggls(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "fd")
summary(zz_fd)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.