Description Usage Arguments Details Value Note Author(s) References See Also Examples
Robust covariance matrix estimators a la White for panel models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## 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
\insertCiteWHIT:80,WHIT:84b,AREL:87plm. 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)
\insertCite@see @GREE:03, Sec. 13.7.1-2, @GREE:12, Sec. 11.6.1-2
and @WOOL:02, Sec. 10.7.2plm; "arellano"
\insertCite@see
ibid. and the original ref. @AREL:87plm 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 \insertCiteMACK:WHIT:85;textualplm and
\insertCiteCRIB:04;textualplm
\insertCiteZEIL:04plm. type = "sss"
employs the small sample
correction as used by Stata.
The main use of vcovHC
(and the other variance-covariance estimators
provided in the package vcovBK
, vcovNW
, 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 \insertCite@ZEIL:04, 4.1-2 and examples belowplm.
A special procedure for pgmm
objects, proposed by
\insertCiteWIND:05;textualplm, 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
AREL:87plm
\insertRefCRIB:04plm
\insertRefGREE:03plm
\insertRefGREE:12plm
\insertRefMACK:WHIT:85plm
\insertRefWIND:05plm
\insertRefWHIT:84bplm chap. 6
\insertRefWHIT:80plm
\insertRefWOOL:02plm
\insertRefZEIL:04plm
sandwich::vcovHC()
from the sandwich
package for weighting schemes (type
argument).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | data("Produc", package = "plm")
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "random")
## as function input to plm's summary method (with and without additional arguments):
summary(zz, vcov = vcovHC)
summary(zz, vcov = function(x) vcovHC(x, method="arellano", type="HC1"))
## standard coefficient significance test
library(lmtest)
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)
|
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