plm (version 2.6-4)

vcovNW: Newey and West (1987) Robust Covariance Matrix Estimator

Description

Nonparametric robust covariance matrix estimators a la Newey and West for panel models with serial correlation.

Usage

vcovNW(x, ...)
# S3 method for plm
vcovNW(
  x,
  type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
  maxlag = NULL,
  wj = function(j, maxlag) 1 - j/(maxlag + 1),
  ...
)
# S3 method for pcce
vcovNW(
  x,
  type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
  maxlag = NULL,
  wj = function(j, maxlag) 1 - j/(maxlag + 1),
  ...
)

Value

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

Arguments

x

an object of class "plm" or "pcce"

...

further arguments

type

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

maxlag

either NULL or a positive integer specifying the maximum lag order before truncation

wj

weighting function to be applied to lagged terms,

Author

Giovanni Millo

Details

vcovNW is a function for estimating a robust covariance matrix of parameters for a panel model according to the NEWE:WEST:87;textualplm method. The function works as a restriction of the DRIS:KRAA:98;textualplm covariance (see vcovSCC()) to no cross--sectional correlation.

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 vcovNW (and the other variance-covariance estimators provided in the package vcovHC, vcovBK, vcovDC, vcovSCC) is to pass it to plm's own functions like summary, pwaldtest, and phtest or together with testing functions from the lmtest and car packages. All of these typically allow passing the vcov or vcov. parameter either as a matrix or as a function, 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), see @see also @ZEIL:04plm, 4.1-2, and examples below.

References

CRIB:04plm

DRIS:KRAA:98plm

MACK:WHIT:85plm

NEWE:WEST:87plm

ZEIL:04plm

See Also

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

Examples

data("Produc", package="plm")
zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling")
## as function input to plm's summary method (with and without additional arguments):
summary(zz, vcov = vcovNW)
summary(zz, vcov = function(x) vcovNW(x, method="arellano", type="HC1"))
## standard coefficient significance test
library(lmtest)
coeftest(zz)
## NW robust significance test, default
coeftest(zz, vcov.=vcovNW)
## idem with parameters, pass vcov as a function argument
coeftest(zz, vcov.=function(x) vcovNW(x, type="HC1", maxlag=4))
## joint restriction test
waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovNW)
if (FALSE) {
## test of hyp.: 2*log(pc)=log(emp)
library(car)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovNW)
}