vcovDC {plm}

Double-Clustering Robust Covariance Matrix Estimator

Description

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

Usage

vcovDC(x, ...)

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

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,

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 sandwich::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 (and the other variance-covariance estimators provided in the package vcovHC, vcovBK, vcovNW, 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 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 AC, Gelbach JB, Miller DL (2011). “Robust inference with multiway clustering.” Journal of Business & Economic Statistics, 29(2).

Cribari–Neto F (2004). “Asymptotic Inference Under Heteroskedasticity of Unknown Form.” Computational Statistics & Data Analysis, 45, 215–233.

MacKinnon JG, White H (1985). “Some Heteroskedasticity–Consistent Covariance Matrix Estimators With Improved Finite Sample Properties.” Journal of Econometrics, 29, 305–325.

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

Zeileis A (2004). “Econometric Computing With HC and HAC Covariance Matrix Estimators.” Journal of Statistical Software, 11(10), 1–17. https://www.jstatsoft.org/article/view/v011i10.

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 = vcovDC)
summary(zz, vcov = function(x) vcovDC(x, type="HC1", maxlag=4))
## standard coefficient significance test
library(lmtest)
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)
## Not run: 
## test of hyp.: 2*log(pc)=log(emp)
library(car)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovDC)

## End(Not run)