Variable Coefficients Models for Panel Data
Estimators for random and fixed effects models with variable coefficients.
pvcm( formula, data, subset, na.action, effect = c("individual", "time"), model = c("within", "random"), index = NULL, ... ) ## S3 method for class 'pvcm' summary(object, ...) ## S3 method for class 'summary.pvcm' print( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... )
formula |
a symbolic description for 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, |
pvcm
estimates variable coefficients models. Individual or time
effects are introduced, respectively, if effect = "individual"
(default) or effect = "time"
.
Coefficients are assumed to be fixed if model = "within"
and
random if model = "random"
. In the first case, a different model
is estimated for each individual (or time period). In the second
case, the Swamy (1970) model is estimated. It
is a generalized least squares model which uses the results of the
previous model.
An object of class c("pvcm", "panelmodel")
, which has the
following elements:
coefficients |
the vector (or the data frame for fixed effects) of coefficients, |
residuals |
the vector of residuals, |
fitted.values |
the vector of fitted values, |
vcov |
the covariance matrix of the coefficients (a list for fixed effects), |
df.residual |
degrees of freedom of the residuals, |
model |
a data frame containing the variables used for the estimation, |
call |
the call, |
Delta |
the estimation of the covariance matrix of the coefficients (random effect models only), |
std.error |
a data frame containing standard errors for all coefficients for each individual (within models only). |
pvcm
objects have print
, summary
and print.summary
methods.
Yves Croissant
Swamy PAVB (1970). “Efficient Inference in a Random Coefficient Regression Model.” Econometrica, 38, 311–323.
data("Produc", package = "plm") zw <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "within") zr <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "random") ## replicate Greene (2012), p. 419, table 11.14 summary(pvcm(log(gsp) ~ log(pc) + log(hwy) + log(water) + log(util) + log(emp) + unemp, data = Produc, model = "random")) ## Not run: # replicate Swamy (1970), p. 166, table 5.2 data(Grunfeld, package = "AER") # 11 firm Grunfeld data needed from package AER gw <- pvcm(invest ~ value + capital, data = Grunfeld, index = c("firm", "year")) ## End(Not run)
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