ercomp: Estimation of the error components

In plm: Linear Models for Panel Data

Description

This function enables the estimation of the variance components of a panel model.

Usage

ercomp(object, ...)

## S3 method for class 'plm'
ercomp(object, ...)

## S3 method for class 'pdata.frame'
ercomp(
  object,
  effect = c("individual", "time", "twoways", "nested"),
  method = NULL,
  models = NULL,
  dfcor = NULL,
  index = NULL,
  ...
)

## S3 method for class 'formula'
ercomp(
  object,
  data,
  effect = c("individual", "time", "twoways", "nested"),
  method = NULL,
  models = NULL,
  dfcor = NULL,
  index = NULL,
  ...
)

## S3 method for class 'ercomp'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

object	a formula or a plm object,
...	further arguments.
effect	the effects introduced in the model, see plm() for details,
method	method of estimation for the variance components, see plm() for details,
models	the models used to estimate the variance components (an alternative to the previous argument),
dfcor	a numeric vector of length 2 indicating which degree of freedom should be used,
index	the indexes,
data	a data.frame,
x	an ercomp object,
digits	digits,

Value

An object of class "ercomp": a list containing

• sigma2 a named numeric with estimates of the variance components,

• theta contains the parameter(s) used for the transformation of the variables: For a one-way model, a numeric corresponding to the selected effect (individual or time); for a two-ways model a list of length 3 with the parameters. In case of a balanced model, the numeric has length 1 while for an unbalanced model, the numerics' length equal the number of observations.

Author(s)

Yves Croissant

References

\insertRef

AMEM:71plm

\insertRef

NERLO:71plm

\insertRef

SWAM:AROR:72plm

\insertRef

WALL:HUSS:69plm

See Also

plm() where the estimates of the variance components are used if a random effects model is estimated

Examples

data("Produc", package = "plm")
# an example of the formula method
ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc,
       method = "walhus", effect = "time")
# same with the plm method
z <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
         data = Produc, random.method = "walhus",
         effect = "time", model = "random")
ercomp(z)
# a two-ways model
ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc,
       method = "amemiya", effect = "twoways")

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