plm (version 1.6-5)

vcovSCC: Driscoll and Kraay (1998) Robust Covariance Matrix Estimator

Description

Nonparametric robust covariance matrix estimators a la Driscoll and Kraay for panel models with cross-sectional and serial correlation.

Usage

"vcovSCC"(x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), cluster="time", maxlag=NULL, inner=c("cluster","white","diagavg"), 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",

cluster

switch for vcovG; set at "time" here,

maxlag

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

inner

the function to be applied to the residuals inside the sandwich: "cluster" for SCC, "white" for Newey-West, ("diagavg" for compatibility reasons)

wj

weighting function to be applied to lagged terms,

...

further arguments

Value

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

Details

vcovSCC is a function for estimating a robust covariance matrix of parameters for a panel model according to the Driscoll and Kraay (1998) method, which is consistent with cross--sectional and serial correlation in a T-asymptotic setting and irrespective of the N dimension. The use with random effects models is undocumented.

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 vcovSCC 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).

References

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

Driscoll, J.C. and Kraay, A.C. (1998) Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. Review of Economics and Statistics 80(4), pp. 549--560.

Hoechle, D. (2007) Robust standard errors for panel regressions with cross-sectional dependence. Stata Journal, 7(3), pp. 281--312. 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. 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)
## SCC robust significance test, default
coeftest(zz, vcov.=vcovSCC)
## idem with parameters, pass vcov as a function argument
coeftest(zz, vcov.=function(x) vcovSCC(x, type="HC1", maxlag=4))
## joint restriction test
waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovSCC)
## test of hyp.: 2*log(pc)=log(emp)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovSCC)