vcovSCC {plm} | R Documentation |
Nonparametric robust covariance matrix estimators a la Driscoll and Kraay for panel models with cross-sectional and serial correlation.
## 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), ...)
x |
an object of class |
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, |
... |
further arguments |
.
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 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(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/.
vcovHC
from the sandwich package for weighting schemes (type
argument).
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)