[R] sandwich package: HAC estimators

[T.Riedle]

I understood. But how do I get the R2 an Chi2 of my logistic regression under HAC standard errors? I would like to create a table with HAC SE via e.g. stargazer(). 

Do I get these information by using the functions

bread.lrm <- function(x, ...) vcov(x) * nobs(x)
estfun.lrm <- function(x, ...) residuals(x, "score")?
 
Do I need to use the coeftest() in this case?
________________________________________
From: R-help <r-help-bounces at r-project.org> on behalf of Achim Zeileis <Achim.Zeileis at uibk.ac.at>
Sent: 31 May 2016 08:36
To: Leonardo Ferreira Fontenelle
Cc: r-help at r-project.org
Subject: Re: [R] sandwich package: HAC estimators

On Mon, 30 May 2016, Leonardo Ferreira Fontenelle wrote:

> Em Sáb 28 mai. 2016, às 15:50, Achim Zeileis escreveu:
>> On Sat, 28 May 2016, T.Riedle wrote:
>> > I thought it would be useful to incorporate the HAC consistent
>> > covariance matrix into the logistic regression directly and generate an
>> > output of coefficients and the corresponding standard errors. Is there
>> > such a function in R?
>>
>> Not with HAC standard errors, I think.
>
> Don't glmrob() and summary.glmrob(), from robustbase, do that?

No, they implement a different concept of robustness. See also
https://CRAN.R-project.org/view=Robust

glmrob() implements GLMs that are "robust" or rather "resistant" to
outliers and other observations that do not come from the main model
equation. Instead of maximum likelihood (ML) estimation other estimation
techniques (along with corresponding covariances/standard errors) are
used.

In contrast, the OP asked for HAC standard errors. The motivation for
these is that the main model equation does hold for all observations but
that the observations might be heteroskedastic and/or autocorrelated. In
this situation, ML estimation is still consistent (albeit not efficient)
but the covariance matrix estimate needs to be adjusted.

>
> Leonardo Ferreira Fontenelle, MD, MPH
>
> ______________________________________________
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