Description Usage Arguments Details Value Author(s) References Examples
General FGLS estimators for panel data (balanced or unbalanced)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  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 twostep 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 feasibleGLS 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 retroâ€“compatibility reasons. If model = "within"
(the default) then a FEGLS (fixed effects GLS, see ibid.)
is estimated; if model = "fd"
a FDGLS (firstdifference 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 crosssectional, if

Giovanni Millo
IM:SEUN:SCHM:WOOL:99plm
\insertRefKIEF:80plm
\insertRefWOOL:02plm
\insertRefWOOL:10plm
1 2 3 4 5 6 7 8 9 10 11 12  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)

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