pvcm {plm} | R Documentation |
Estimators for random and fixed effect 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" or "time" , |
model |
one of "within" or "random" , |
index |
the indexes, see plm.data , |
digits |
digits, |
width |
the maximum length of the lines in the print output, |
... |
further arguments. |
The pvcm
function enables the estimation of variable
coefficients models. Time or individual effects are introduced if
effect
is fixed to "time"
or "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")