pvcm: Variable Coefficients Models for Panel Data

View source: R/est_vcm.R

pvcmR Documentation

Variable Coefficients 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 Aug. 16, 2022, 5:15 p.m.