plm (version 0.2-2)

pvcm: Variable coefficients models for panel data

Description

Estimators for random and fixed effect models with variable coefficients.

Usage

pvcm(formula,data,effect="individual",model,...)
## S3 method for class 'pvcm':
summary(object, ...)
## S3 method for class 'summary.pvcm':
print(x,digits=5,length.line=70, ...)

Arguments

formula

a symbolic description for the model to be estimated,

object,x

an object of class pvcm,

data

the data, must be an object of class pdata.frame and is mandatory,

effect

the effects introduced in the model, one of "individual" or "time",

model

one of "within" or "random",

digits

digits,

length.line

the maximum length of the lines in the print output,

...

further arguments.

Value

• 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.

Details

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.

References

Swamy, P.A.V.B. (1970), Efficient Inference in a Random Coefficient Regression Model, Econometrica, 38(2), pp.311--323.

Examples

library(Ecdat)
data(Produc)
pdata.frame(Produc,"state","year","pprod")
zw <- pvcm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=pprod,model="within")
zr <- pvcm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=pprod,model="random")