Driscoll and Kraay (1998) Robust Covariance Matrix Estimator
Nonparametric robust covariance matrix estimators a la Driscoll and Kraay for panel models with cross-sectional and serial correlation.
vcovSCC(x, ...)
## S3 method for class 'plm'
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),
...
)
## S3 method for class 'pcce'
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),
...
)x |
an object of class |
... |
further arguments |
type |
the weighting scheme used, one of |
cluster |
switch for vcovG; set at |
maxlag |
either |
inner |
the function to be applied to the residuals inside the
sandwich: |
wj |
weighting function to be applied to lagged terms, |
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 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 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).
An object of class "matrix" containing the estimate of
the covariance matrix of coefficients.
Giovanni Millo, partially ported from Daniel Hoechle's (2007) Stata code
Cribari–Neto F (2004). “Asymptotic Inference Under Heteroskedasticity of Unknown Form.” Computational Statistics \& Data Analysis, 45, 215–233.
Driscoll JC, Kraay AC (1998). “Consistent covariance matrix estimation with spatially dependent panel data.” Review of economics and statistics, 80(4), 549–560.
Hoechle D (2007). “Robust standard errors for panel regressions with cross-sectional dependence.” Stata Journal, 7(3), 281–312. https://ideas.repec.org/a/tsj/stataj/v7y2007i3p281-312.html.
MacKinnon JG, White H (1985). “Some Heteroskedasticity–Consistent Covariance Matrix Estimators With Improved Finite Sample Properties.” Journal of Econometrics, 29, 305–325.
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)
## 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)
## Not run:
## test of hyp.: 2*log(pc)=log(emp)
library(car)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovSCC)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.