vcovNW {plm} | R Documentation |
Newey and West (1987) Robust Covariance Matrix Estimator
Description
Nonparametric robust covariance matrix estimators a la Newey and West for panel models with serial correlation.
Usage
vcovNW(x, ...)
## S3 method for class 'plm'
vcovNW(
x,
type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
maxlag = NULL,
wj = function(j, maxlag) 1 - j/(maxlag + 1),
...
)
## S3 method for class 'pcce'
vcovNW(
x,
type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
maxlag = NULL,
wj = function(j, maxlag) 1 - j/(maxlag + 1),
...
)
Arguments
x |
an object of class |
... |
further arguments |
type |
the weighting scheme used, one of |
maxlag |
either |
wj |
weighting function to be applied to lagged terms, |
Details
vcovNW
is a function for estimating a robust covariance matrix of
parameters for a panel model according to the
Newey and West (1987) method. The function works
as a restriction of the Driscoll and Kraay (1998) covariance (see
vcovSCC()
) to no 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) (see Zeileis 2004).
The main use of vcovNW
(and the other variance-covariance estimators
provided in the package vcovHC
, vcovBK
, vcovDC
, 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
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.
MacKinnon JG, White H (1985). “Some Heteroskedasticity–Consistent Covariance Matrix Estimators With Improved Finite Sample Properties.” Journal of Econometrics, 29, 305–325.
Newey WK, West KD (1987). “A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica, 55(3), 703–08.
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 = vcovNW)
summary(zz, vcov = function(x) vcovNW(x, method="arellano", type="HC1"))
## standard coefficient significance test
library(lmtest)
coeftest(zz)
## NW robust significance test, default
coeftest(zz, vcov.=vcovNW)
## idem with parameters, pass vcov as a function argument
coeftest(zz, vcov.=function(x) vcovNW(x, type="HC1", maxlag=4))
## joint restriction test
waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovNW)
## Not run:
## test of hyp.: 2*log(pc)=log(emp)
library(car)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovNW)
## End(Not run)