[R] sandwich package: HAC estimators

[Achim Zeileis]

On Tue, 31 May 2016, T.Riedle wrote:

> 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?

The bread()/estfun() methods enable application of vcovHAC(), kernHAC(), 
NeweyWest(). This in turn enables the application of coeftest(),
waldtest(), or linearHypothesis() with a suitable vcov argument.

All of these give you different kinds of Wald tests with HAC covariances 
including marginal tests of individual coefficients (coeftest) or global 
tests of nested models (waldtest/linearHypothesis). The latter can serve 
as replacement for the "chi-squared test". For pseudo-R-squared values I'm 
not familiar with HAC-adjusted variants.

And I'm not sure whether there is a LaTeX export solution that encompasses 
all of these aspects simultaneously.

> ________________________________________
> 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
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>