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-boun...@r-project.org> on behalf of Achim Zeileis
<achim.zeil...@uibk.ac.at>
Sent: 31 May 2016 08:36
To: Leonardo Ferreira Fontenelle
Cc: r-help@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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