Helix123 - 1 year ago 102

R Question

With package

`plm`

`summary()`

`summery()`

`waldtest()`

`require(plm)`

require(lmtest)

data("Grunfeld")

gp <- plm(inv ~ value + capital,data=Grunfeld,model="pooling")

# summary() and waldtest() yield same F statistic [w/o user supplied covariance matrix]

summary(gp)

waldtest(gp, test="F")

# summary() and waldtest() yield different F statistic [w/ user supplied covariance matrix]

summary(gp, .vcov = plm::vcovHC(gp, "white2"))

waldtest(gp, test="F", vcov=plm::vcovHC(gp, "white2"))

Considering this post about Stata's robust standard erros and comparing the output for the F statistic w/ and w/o robust standard errors there, I feel like the F statistic should change.

This was with plm 1.4 (then stable release).

`.vcov`

`plm:::Ftest`

`plm:::Ftest`

Here is a good reference for robust inference for practitioners: Cameron/Miller, "A Practitioner's Guide to Cluster-Robust Inference", Journal of Human Resources, Spring 2015, Vol.50, No. 2, pp.317-373. http://cameron.econ.ucdavis.edu/research/papers.html

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Answer Source

If you look at the source code of `plm:::summary.plm`

then you see that the first line is: `object$fstatistic <- Ftest(object, test = "F")`

. Thus, the `.vcov`

argument is not passed on to `plm:::Ftest()`

and hence the F-statistic is not affected at all. You could contact the `plm`

maintainers and ask that this should either be improved or at least pointed out on the manual page. Currently, `.vcov`

is only used for the partial Wald tests of each coefficients, i.e., corresponding to what `lmtest`

computes via `coeftest(gp, vcov = vcovHC(gp, "white2"))`

.

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