| pvcm {plm} | R Documentation |
Estimators for random and fixed effects models with variable coefficients.
pvcm(formula, data, subset, na.action, effect = "individual", model,
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, |
object, x |
an object of class "pvcm", |
data |
a data.frame, |
subset |
see lm, |
na.action |
see lm, |
effect |
the effects introduced in the model: one of
"individual", "time", |
model |
one of "within", "random", |
index |
the indexes, see plm.data, |
digits |
digits, |
width |
the maximum length of the lines in the print output, |
... |
further arguments. |
pvcm estimates variable coefficients models.
Time or individual effects are introduced, respectively, if
effect="time" or effect="individual"
(the default value).
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 use the results of the previous model.
an object of class c("pvcm","panelmodel"), which has the following elements :
coefficients |
the vector (or the list for fixed effects) 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, |
Delta |
the estimation of the covariance matrix of the coefficients (random effect models only), |
std.error |
the standard errors for all the coefficients for each individual (within models only), |
pvcm objects have print, summary and print.summary methods.
Yves Croissant
Swamy, P.A.V.B. (1970) Efficient Inference in a Random Coefficient Regression Model, Econometrica, 38(2), pp.311–323.
data("Produc", package="Ecdat")
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")