pwaldtest {plm} | R Documentation |
Wald-style chi-square test and F test of slope coefficients being zero jointly, including robust versions of the tests.
pwaldtest(x, ...) ## S3 method for class 'plm' pwaldtest(x, test = c("Chisq", "F"), vcov = NULL, df2adj = (test == "F" && !is.null(vcov) && missing(.df2)), .df1, .df2, ...)
x |
an estimated model of which the coefficients should be tested (usually of class |
test |
a character, indicating the test to be performed, may be either |
vcov |
|
df2adj |
logical, only relevant for |
.df1 |
a numeric, used if one wants to overwrite the first degrees of freedom parameter in the performed test (usually not used), |
.df2 |
a numeric, used if one wants to overwrite the second degrees of freedom parameter for the F test (usually not used), |
... |
further arguments (currently none). |
pwaldtest
can be used stand–alone with a plm object. It is also used in
summary.plm
to produce the F statistic.
pwaldtest
performs the test if the slope coefficients of a panel regression are jointly zero.
It does not perform general purpose Wald-style tests (for those, see waldtest
(from package lmtest) or linearHypothesis
(from car)).
If a user specified variance-covariance matrix/function is given in
argument vcov
, the robust version of the tests are carried out.
In that case, if the F test is requested (test = "F"
) and no
overwriting of the second degrees of freedom parameter is given (by
supplying argument (.df2
)), the adjustment of the second degrees
of freedom parameter is performed by default. The second degrees of
freedom parameter is adjusted to be the number of unique elements of the
cluster variable - 1, e. g. the number of individuals - 1. For the degrees of
freedom adjustment of the F test in general, see e. g. Cameron/Miller (2015),
section VII; Andress/Golsch/Schmidt (2013), pp. 126, footnote 4.
The degrees of freedom adjustment requires the vcov object supplied or created by a supplied function
to carry an attribute called "cluster" with a known clustering described as a
character (for now this could be either "group"
or "time"
).
The vcovXX functions of the package plm provide such an attribute for their returned
variance–covariance matrices. No adjustment is done for unknown descriptions given
in the attribute "cluster" or when the attribute "cluster" is not present.
Robust vcov objects/functions from package clubSandwich work as inputs to
pwaldtest
's F test because a they are translated internally to match the
needs described above.
An object of class "htest"
.
Yves Croissant (initial implementation) and Kevin Tappe (extensions: vcov argument and F test's df2 adjustment)
Wooldridge, J.M. (2010) Econometric Analysis of Cross Section and Panel Data, 2nd ed., MIT Press, Sec. 4.2.3, pp. 60–62.
Andress, H.-J./Golsch, K./Schmidt, A. (2013), Applied Panel Data Analysis for Economic and Social Surveys, Springer, Heidelberg et al.
Cameron, A. C./Miller, D. L. (2015), "A Practitioner's Guide to Cluster-Robust Inference", Journal of Human Resources, 2015, 50(2), pp. 317–373; see also the supplements under http://cameron.econ.ucdavis.edu/research/papers.html.
vcovHC
for an example of the vcovXX functions, a robust
estimation for the variance–covariance matrix;
summary.plm
data("Grunfeld", package = "plm") mod_fe <- plm(inv ~ value + capital, data = Grunfeld, model = "within") mod_re <- plm(inv ~ value + capital, data = Grunfeld, model = "random") pwaldtest(mod_fe, test = "F") pwaldtest(mod_re, test = "Chisq") # with robust vcov (matrix, function) pwaldtest(mod_fe, vcov = vcovHC(mod_fe)) pwaldtest(mod_fe, vcov = function(x) vcovHC(x, type = "HC3")) pwaldtest(mod_fe, vcov = vcovHC(mod_fe), df2adj = FALSE) # w/o df2 adjustment # example without attribute "cluster" in the vcov vcov_mat <- vcovHC(mod_fe) attr(vcov_mat, "cluster") <- NULL # remove attribute pwaldtest(mod_fe, vcov = vcov_mat) # no df2 adjustment performed