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

vcovNW {plm} | R Documentation

## 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

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

### Arguments

`x` | an object of class `"plm"` or `"pcce"`
`type` | one of `"HC0"`, `"sss"`, `"HC1"`, `"HC2"`, `"HC3"`,`"HC4"`,
`maxlag` | either `NULL` or a positive integer specifying the maximum lag order before truncation
`wj` | weighting function to be applied to lagged terms,
`...` | further arguments

.

### Details

`vcovNW` is a function for estimating a robust covariance matrix of parameters for a panel model according to the Newey and West (1987) method. The function works as a restriction of the Driscoll and Kraay (1998) covariance to no cross–sectional correlation.

Weighting schemes are analogous to those in `vcovHC` in package sandwich and are justified theoretically (although in the context of the standard linear model) by MacKinnon and White (1985) and Cribari-Neto (2004) (see Zeileis (2004)).

The main use of `vcovNW` 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 Zeileis (2004), 4.1-2 and examples below).

### Value

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

### Author(s)

Giovanni Millo

### References

Cribari-Neto, F. (2004) Asymptotic inference under heteroskedasticity of unknown form. Computational Statistics & Data Analysis 45(2), pp. 215–233.

MacKinnon, J. G. and White, H. (1985) Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics 29(3), pp. 305–325.

Newey, W.K. & West, K.D. (1986) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55(3), pp. 703–708.

Zeileis, A. (2004) Econometric Computing with HC and HAC Covariance Matrix Estimators. Journal of Statistical Software, 11(10), pp. 1–17. URL http://www.jstatsoft.org/v11/i10/.

### See Also

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

### Examples

    library(lmtest)
    library(car)
    data("Produc", package="plm")
    zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling")
    ## standard coefficient significance test
    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)
    ## test of hyp.: 2*log(pc)=log(emp)
    linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovNW)

[Package plm version 1.6-5 Index]