Newey and West (1987) Robust Covariance Matrix Estimator
Nonparametric robust covariance matrix estimators a la Newey and West for panel models with serial correlation.
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),
...
)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, |
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 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
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/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)
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.