Double-Clustering Robust Covariance Matrix Estimator
High-level convenience wrapper for double-clustering robust covariance matrix estimators a la Thompson (2011) and Cameron et al. (2011) for panel models.
vcovDC(x, ...)
## S3 method for class 'plm'
vcovDC(x, type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"), ...)x |
an object of class |
... |
further arguments |
type |
the weighting scheme used, one of |
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 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).
An object of class "matrix" containing the estimate of
the covariance matrix of coefficients.
Giovanni Millo
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/v11/i10/.
sandwich::vcovHC() from the sandwich
package for weighting schemes (type argument).
library(lmtest)
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)
## Not run:
## test of hyp.: 2*log(pc)=log(emp)
library(car)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovDC)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.