plm (version 2.2-5)

vcovBK: Beck and Katz Robust Covariance Matrix Estimators

Description

Unconditional Robust covariance matrix estimators a la Beck and Katz for panel models (a.k.a. Panel Corrected Standard Errors (PCSE)).

Usage

vcovBK(x, ...)
# S3 method for plm
vcovBK(
  x,
  type = c("HC0", "HC1", "HC2", "HC3", "HC4"),
  cluster = c("group", "time"),
  diagonal = FALSE,
  ...
)

Arguments

x

an object of class "plm",

…

further arguments.

type

the weighting scheme used, one of "HC0", "HC1", "HC2", "HC3", "HC4", see Details,

cluster

one of "group", "time",

diagonal

a logical value specifying whether to force nondiagonal elements to zero,

Value

An object of class "matrix" containing the estimate of the covariance matrix of coefficients.

Details

vcovBK is a function for estimating a robust covariance matrix of parameters for a panel model according to the BECK:KATZ:95;textualplm method, a.k.a. Panel Corrected Standard Errors (PCSE), which uses an unconditional estimate of the error covariance across time periods (groups) inside the standard formula for coefficient covariance. Observations may be clustered either by "group" to account for timewise heteroskedasticity and serial correlation or by "time" to account for cross-sectional heteroskedasticity and correlation. It must be borne in mind that the Beck and Katz formula is based on N- (T-) asymptotics and will not be appropriate elsewhere.

The diagonal logical argument can be used, if set to TRUE, to force to zero all nondiagonal elements in the estimated error covariances; this is appropriate if both serial and cross--sectional correlation are assumed out, and yields a timewise- (groupwise-) heteroskedasticity--consistent estimator.

Weighting schemes specified by type are analogous to those in sandwich::vcovHC() in package sandwich and are justified theoretically (although in the context of the standard linear model) by MACK:WHIT:85;textualplm and CRIB:04;textualplm @see @ZEIL:04plm.

The main use of vcovBK is to be an argument to other functions, e.g. for Wald--type testing: argument vcov. to coeftest(), argument vcov to waldtest() and other methods in the lmtest package; and argument vcov. to linearHypothesis() in the car package (see the examples). Notice that the vcov and vcov. arguments allow to supply a function (which is the safest) or a matrix @see @ZEIL:04, 4.1-2 and examples belowplm.

References

BECK:KATZ:95plm

CRIB:04plm

GREE:03plm

MACK:WHIT:85plm

ZEIL:04plm

See Also

sandwich::vcovHC() from the sandwich package for weighting schemes (type argument).

Examples

# NOT RUN {
library(lmtest)
library(car)
data("Produc", package="plm")
zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="random")
## standard coefficient significance test
coeftest(zz)
## robust significance test, cluster by group
## (robust vs. serial correlation), default arguments
coeftest(zz, vcov.=vcovBK)
## idem with parameters, pass vcov as a function argument
coeftest(zz, vcov.=function(x) vcovBK(x, type="HC1"))
## idem, cluster by time period
## (robust vs. cross-sectional correlation)
coeftest(zz, vcov.=function(x) vcovBK(x,
 type="HC1", cluster="time"))
## idem with parameters, pass vcov as a matrix argument
coeftest(zz, vcov.=vcovBK(zz, type="HC1"))
## joint restriction test
waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovBK)
## test of hyp.: 2*log(pc)=log(emp)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovBK)

# }

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