Robust Covariance Matrix Estimators
Robust covariance matrix estimators a la White for panel models.
## S3 method for class 'plm'
vcovHC(
x,
method = c("arellano", "white1", "white2"),
type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
cluster = c("group", "time"),
...
)
## S3 method for class 'pcce'
vcovHC(
x,
method = c("arellano", "white1", "white2"),
type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
cluster = c("group", "time"),
...
)
## S3 method for class 'pgmm'
vcovHC(x, ...)x |
an object of class |
method |
one of |
type |
the weighting scheme used, one of |
cluster |
one of |
... |
further arguments. |
vcovHC is a function for estimating a robust covariance matrix of
parameters for a fixed effects or random effects panel model
according to the White method
(White 1980, 1984; Arellano 1987). Observations may be
clustered by "group" ("time") to account for serial
(cross-sectional) correlation.
All types assume no intragroup (serial) correlation between errors
and allow for heteroskedasticity across groups (time periods). As
for the error covariance matrix of every single group of
observations, "white1" allows for general heteroskedasticity but
no serial (cross–sectional) correlation; "white2" is "white1"
restricted to a common variance inside every group (time period)
(see Greene 2003, Sec. 13.7.1-2, Greene 2012, Sec. 11.6.1-2
and Wooldridge 2002, Sec. 10.7.2); "arellano" (see
ibid. and the original ref. Arellano 1987) allows a fully general
structure w.r.t. heteroskedasticity and serial (cross–sectional)
correlation.
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)
(Zeileis 2004). type = "sss" employs the small sample
correction as used by Stata.
The main use of vcovHC 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
(ZEIL:04, 4.1-2 and examples below).
A special procedure for pgmm objects, proposed by
Windmeijer (2005), is also provided.
An object of class "matrix" containing the estimate of
the asymptotic covariance matrix of coefficients.
The function pvcovHC is deprecated. Use vcovHC for the
same functionality.
Giovanni Millo & Yves Croissant
Arellano M (1987). “Computing Robust Standard Errors for Within-groups Estimators.” Oxford bulletin of Economics and Statistics, 49(4), 431–434.
Cribari–Neto F (2004). “Asymptotic Inference Under Heteroskedasticity of Unknown Form.” Computational Statistics \& Data Analysis, 45, 215–233.
Greene WH (2003). Econometric Analysis, 5th edition. Prentice Hall.
Greene WH (2012). Econometric Analysis, 7th edition. Prentice Hall.
MacKinnon JG, White H (1985). “Some Heteroskedasticity–Consistent Covariance Matrix Estimators With Improved Finite Sample Properties.” Journal of Econometrics, 29, 305–325.
Windmeijer F (2005). “A Finite Sample Correction for the Variance of Linear Efficient Two–Steps GMM Estimators.” Journal of Econometrics, 126, 25–51.
White H (1984). Asymptotic Theory for Econometricians. New York: Academic press. chap. 6
White H (1980). “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity.” Econometrica, 48(4), 817–838.
Wooldridge JM (2002). Econometric Analysis of Cross–Section and Panel Data. MIT press.
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 = "random")
## standard coefficient significance test
coeftest(zz)
## robust significance test, cluster by group
## (robust vs. serial correlation)
coeftest(zz, vcov.=vcovHC)
## idem with parameters, pass vcov as a function argument
coeftest(zz, vcov.=function(x) vcovHC(x, method="arellano", type="HC1"))
## idem, cluster by time period
## (robust vs. cross-sectional correlation)
coeftest(zz, vcov.=function(x) vcovHC(x, method="arellano",
type="HC1", cluster="group"))
## idem with parameters, pass vcov as a matrix argument
coeftest(zz, vcov.=vcovHC(zz, method="arellano", type="HC1"))
## joint restriction test
waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovHC)
## Not run:
## test of hyp.: 2*log(pc)=log(emp)
library(car)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovHC)
## End(Not run)
## Robust inference for CCE models
data("Produc", package = "plm")
ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p")
## IGNORE_RDIFF_BEGIN
summary(ccepmod, vcov = vcovHC)
## IGNORE_RDIFF_END
## Robust inference for GMM models
data("EmplUK", package="plm")
ar <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1)
+ log(capital) + lag(log(capital), 2) + log(output)
+ lag(log(output),2) | lag(log(emp), 2:99),
data = EmplUK, effect = "twoways", model = "twosteps")
rv <- vcovHC(ar)
mtest(ar, order = 2, vcov = rv)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.