pggls: General FGLS Estimators

In plm: Linear Models for Panel Data

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", "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, ...)

Arguments

formula	a symbolic description of the model to be estimated,
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", "pooling", "fd",
index	the indexes, see pdata.frame(),
...	further arguments.
object, x	an object of class pggls,
digits	digits,
width	the maximum length of the lines in the print output,

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 (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).

If model = "within" (the default) then a FEGLS (fixed effects GLS, see Wooldridge, Ch. 10.5) is estimated; if model = "fd" a FDGLS (first-difference GLS). Setting model = "pooling" produces an unrestricted FGLS model (see ibid.) (model = "random" does the same, but using this value is deprecated and included only for retro–compatibility reasons).

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

\insertRef

IM:SEUN:SCHM:WOOL:99plm

\insertRef

KIEF:80plm

\insertRef

WOOL:02plm

\insertRef

WOOL:10plm

Examples

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)

plm documentation built on June 22, 2024, 6:54 p.m.