vcovDC {plm} | R Documentation

## Double-Clustering Robust Covariance Matrix Estimator

### Description

High-level convenience wrapper for double-clustering robust covariance matrix estimators a la Thompson (2011) and Cameron, Gelbach and Miller (2011) for panel models.

### Usage

    ## S3 method for class 'plm'
    vcovDC(x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
                          ...)

### Arguments

`x` | an object of class `"plm"` or `"pcce"`
`type` | the weighting scheme used, one of `"HC0"`, `"sss"`, `"HC1"`, `"HC2"`, `"HC3"`, `"HC4"`, see Details,
`...` | further arguments

.

### Details

`vcovDC` is a function for estimating a robust covariance matrix of parameters for a panel model with errors clustering along both dimensions. The function is a convenience wrapper simply summing a group- and a time-clustered covariance matrix and subtracting a diagonal one a la White.

Weighting schemes specified by `type` 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 `vcovDC` 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

Cameron, A.C., Gelbach, J.B., & Miller, D.L. (2011) Robust inference with multiway clustering, Journal of Business and Economic Statistics 29(2), pp. 238–249.

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.

Thompson, S.B. (2011) Simple formulas for standard errors that cluster by both firm and time, Journal of Financial Economics 99(1), pp. 1–10.

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)
    ## DC robust significance test, default
    coeftest(zz, vcov.=vcovDC)
    ## idem with parameters, pass vcov as a function argument
    coeftest(zz, vcov.=function(x) vcovDC(x, type="HC1", maxlag=4))
    ## joint restriction test
    waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovDC)
    ## test of hyp.: 2*log(pc)=log(emp)
    linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovDC)

[Package plm version 1.6-6 Index]