pvcm: Variable Coefficients Models for Panel Data

In plm: Linear Models for Panel Data

Description

Estimators for random and fixed effects models with variable coefficients.

Usage

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"),
  ...
)

Arguments

formula	a symbolic description for the model to be estimated,
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 pdata.frame(),
...	further arguments.
object, x	an object of class "pvcm",
digits	digits,
width	the maximum length of the lines in the print output,

Details

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", i.e., separate pooled OLS models are estimated per individual (effect = "individual") or per time period (effect = "time"). Coefficients are assumed to be random if model = "random" and the model by \insertCiteSWAM:70;textualplm is estimated. It is a generalized least squares model which uses the results of the previous model.

Value

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 model (model = "within")),
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.

Author(s)

Yves Croissant

References

\insertRef

SWAM:70plm

Examples

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)
             

plm documentation built on Sept. 21, 2021, 3:01 p.m.