Re: [R] sandwich package: HAC estimators
On Wed, 1 Jun 2016, T.Riedle wrote:
Thank you very much. I have applied the example to my case and get
following results:
crisis_bubble4<-glm(stock.market.crash~crash.MA+bubble.MA+MP.MA+UTS.MA+UPR.MA+PPI.MA+RV.MA,family=binomial("logit"),data=Data_logitregression_movingaverage)
summary(crisis_bubble4)
Call:
glm(formula = stock.market.crash ~ crash.MA + bubble.MA + MP.MA +
UTS.MA + UPR.MA + PPI.MA + RV.MA, family = binomial("logit"),
data = Data_logitregression_movingaverage)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7828 -0.6686 -0.3186 0.6497 2.4298
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.2609 0.8927 -5.893 3.79e-09 ***
crash.MA 0.4922 0.4966 0.991 0.32165
bubble.MA 12.1287 1.3736 8.830 < 2e-16 ***
MP.MA-20.072496.9576 -0.207 0.83599
UTS.MA -58.181419.3533 -3.006 0.00264 **
UPR.MA -337.579864.3078 -5.249 1.53e-07 ***
PPI.MA 729.376973.0529 9.984 < 2e-16 ***
RV.MA116.001116.5456 7.011 2.37e-12 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 869.54 on 705 degrees of freedom
Residual deviance: 606.91 on 698 degrees of freedom
AIC: 622.91
Number of Fisher Scoring iterations: 5
coeftest(crisis_bubble4)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.260880.89269 -5.8933 3.786e-09 ***
crash.MA 0.492190.49662 0.9911 0.321652
bubble.MA 12.128681.37357 8.8300 < 2.2e-16 ***
MP.MA-20.07238 96.95755 -0.2070 0.835992
UTS.MA -58.18142 19.35330 -3.0063 0.002645 **
UPR.MA -337.57985 64.30779 -5.2494 1.526e-07 ***
PPI.MA 729.37693 73.05288 9.9842 < 2.2e-16 ***
RV.MA116.00106 16.54560 7.0110 2.366e-12 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
coeftest(crisis_bubble4,vcov=NeweyWest)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.260885.01706 -1.0486 0.29436
crash.MA 0.492192.41688 0.2036 0.83863
bubble.MA 12.128685.85228 2.0725 0.03822 *
MP.MA-20.07238 499.37589 -0.0402 0.96794
UTS.MA -58.18142 77.08409 -0.7548 0.45038
UPR.MA -337.57985 395.35639 -0.8539 0.39318
PPI.MA 729.37693 358.60868 2.0339 0.04196 *
RV.MA116.00106 79.52421 1.4587 0.14465
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
waldtest(crisis_bubble4, vcov = NeweyWest,test="F")
Wald test
Model 1: stock.market.crash ~ crash.MA + bubble.MA + MP.MA + UTS.MA +
UPR.MA + PPI.MA + RV.MA
Model 2: stock.market.crash ~ 1
Res.Df Df F Pr(>F)
1698
2705 -7 2.3302 0.02351 *
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
waldtest(crisis_bubble4, vcov = NeweyWest,test="Chisq")
Wald test
Model 1: stock.market.crash ~ crash.MA + bubble.MA + MP.MA + UTS.MA +
UPR.MA + PPI.MA + RV.MA
Model 2: stock.market.crash ~ 1
Res.Df Df Chisq Pr(>Chisq)
1698
2705 -7 16.3110.02242 *
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Do you agree with the methodology?
Well, this is how you _can_ do what you _wanted_ to do. I already
expressed my doubts about several aspects. First, some coefficients and
their standard errors are very large which may (or may not) hint at
problems that are close to separation. Second, given the increase in the
standard errors, the autocorrelation appears to be substantial and it
might be good to try to capture these autocorrelations explicitly rather
than just correcting the standard errors.
I read in a book that it is also possible to use vcov=vcovHAC in the
coeftest() function.
Yes. (I also mentioned that in my e-mail yesterday, see below.)
Nevertheless, I am not sure what kind of HAC I generate with this
command. Which weights does this command apply, which bandwith and which
kernel?
Please consult vignette("sandwich", package = "sandwich") for the details.
In short: Both, vcovHAC and kernHAC use the quadratic spectral kernel with
Andrews' parametric bandwidth selection. The latter function uses
prewhitening by default while the latter does not. In contrast, NeweyWest
uses a Bartlett kernel with Newey & Wests nonparametric lag/bandwidth
selection and prewhitening by default.
Kind regards
From: Achim Zeileis <achim.zeil...@uibk.ac.at>
Sent: 31 May 2016 17:19
To: T.Riedle
Cc: r-help@r-project.org
Subject: Re: [R] sandwich package: HAC estimators
On Tue, 31 May 2016, T.Riedle wrote:
Many thanks for your feedback.
If I get the code for the waldtest right I can calculate the Chi2 and
the F statistic using waldtest().
Yes. In a logit model you w
Re: [R] sandwich package: HAC estimators
Thank you very much. I have applied the example to my case and get following
results:
crisis_bubble4<-glm(stock.market.crash~crash.MA+bubble.MA+MP.MA+UTS.MA+UPR.MA+PPI.MA+RV.MA,family=binomial("logit"),data=Data_logitregression_movingaverage)
> summary(crisis_bubble4)
Call:
glm(formula = stock.market.crash ~ crash.MA + bubble.MA + MP.MA +
UTS.MA + UPR.MA + PPI.MA + RV.MA, family = binomial("logit"),
data = Data_logitregression_movingaverage)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7828 -0.6686 -0.3186 0.6497 2.4298
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.2609 0.8927 -5.893 3.79e-09 ***
crash.MA 0.4922 0.4966 0.991 0.32165
bubble.MA 12.1287 1.3736 8.830 < 2e-16 ***
MP.MA-20.072496.9576 -0.207 0.83599
UTS.MA -58.181419.3533 -3.006 0.00264 **
UPR.MA -337.579864.3078 -5.249 1.53e-07 ***
PPI.MA 729.376973.0529 9.984 < 2e-16 ***
RV.MA116.001116.5456 7.011 2.37e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 869.54 on 705 degrees of freedom
Residual deviance: 606.91 on 698 degrees of freedom
AIC: 622.91
Number of Fisher Scoring iterations: 5
> coeftest(crisis_bubble4)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.260880.89269 -5.8933 3.786e-09 ***
crash.MA 0.492190.49662 0.9911 0.321652
bubble.MA 12.128681.37357 8.8300 < 2.2e-16 ***
MP.MA-20.07238 96.95755 -0.2070 0.835992
UTS.MA -58.18142 19.35330 -3.0063 0.002645 **
UPR.MA -337.57985 64.30779 -5.2494 1.526e-07 ***
PPI.MA 729.37693 73.05288 9.9842 < 2.2e-16 ***
RV.MA116.00106 16.54560 7.0110 2.366e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> coeftest(crisis_bubble4,vcov=NeweyWest)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.260885.01706 -1.0486 0.29436
crash.MA 0.492192.41688 0.2036 0.83863
bubble.MA 12.128685.85228 2.0725 0.03822 *
MP.MA-20.07238 499.37589 -0.0402 0.96794
UTS.MA -58.18142 77.08409 -0.7548 0.45038
UPR.MA -337.57985 395.35639 -0.8539 0.39318
PPI.MA 729.37693 358.60868 2.0339 0.04196 *
RV.MA116.00106 79.52421 1.4587 0.14465
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> waldtest(crisis_bubble4, vcov = NeweyWest,test="F")
Wald test
Model 1: stock.market.crash ~ crash.MA + bubble.MA + MP.MA + UTS.MA +
UPR.MA + PPI.MA + RV.MA
Model 2: stock.market.crash ~ 1
Res.Df Df F Pr(>F)
1698
2705 -7 2.3302 0.02351 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> waldtest(crisis_bubble4, vcov = NeweyWest,test="Chisq")
Wald test
Model 1: stock.market.crash ~ crash.MA + bubble.MA + MP.MA + UTS.MA +
UPR.MA + PPI.MA + RV.MA
Model 2: stock.market.crash ~ 1
Res.Df Df Chisq Pr(>Chisq)
1698
2705 -7 16.3110.02242 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Do you agree with the methodology? I read in a book that it is also possible to
use vcov=vcovHAC in the coeftest() function. Nevertheless, I am not sure what
kind of HAC I generate with this command. Which weights does this command
apply, which bandwith and which kernel?
Kind regards
From: Achim Zeileis <achim.zeil...@uibk.ac.at>
Sent: 31 May 2016 17:19
To: T.Riedle
Cc: r-help@r-project.org
Subject: Re: [R] sandwich package: HAC estimators
On Tue, 31 May 2016, T.Riedle wrote:
> Many thanks for your feedback.
>
> If I get the code for the waldtest right I can calculate the Chi2 and
> the F statistic using waldtest().
Yes. In a logit model you would usually use the chi-squared statistic.
> Can I use the waldtest() without using bread()/ estfun()? That is, I
> estimate the logit regression using glm() e.g. logit<-glm(...) and
> insert logit into the waldtest() function.
>
> Does that work to get chi2 under HAC standard errors?
I'm not sure what you mean here but I include a worked example. Caveat:
The data I use are cross-section data with an overly simplified set of
regressors. So none of this makes sense for the application - but it shows
how to use the commands.
## load AER package which provides the example data
## and automatically loads "lmtest" and "sandwich"
library("AER")
data("PSID1976", package = "AER")
## fit a simple logit model and obtain marginal Wald tests
## for the coefficients and an overall chi-squared statistic
m <-
Re: [R] sandwich package: HAC estimators
On Tue, 31 May 2016, T.Riedle wrote:
Many thanks for your feedback.
If I get the code for the waldtest right I can calculate the Chi2 and
the F statistic using waldtest().
Yes. In a logit model you would usually use the chi-squared statistic.
Can I use the waldtest() without using bread()/ estfun()? That is, I
estimate the logit regression using glm() e.g. logit<-glm(...) and
insert logit into the waldtest() function.
Does that work to get chi2 under HAC standard errors?
I'm not sure what you mean here but I include a worked example. Caveat:
The data I use are cross-section data with an overly simplified set of
regressors. So none of this makes sense for the application - but it shows
how to use the commands.
## load AER package which provides the example data
## and automatically loads "lmtest" and "sandwich"
library("AER")
data("PSID1976", package = "AER")
## fit a simple logit model and obtain marginal Wald tests
## for the coefficients and an overall chi-squared statistic
m <- glm(participation ~ education, data = PSID1976, family = binomial)
summary(m)
anova(m, test = "Chisq")
## replicate the same statistics with coeftest() and lrtest()
coeftest(m)
lrtest(m)
## the likelihood ratio test is asymptotically equivalent
## to the Wald test leading to a similar chi-squared test here
waldtest(m)
## obtain HAC-corrected (Newey-West) versions of the Wald tests
coeftest(m, vcov = NeweyWest)
waldtest(m, vcov = NeweyWest)
Instead of NeweyWest other covariance estimators (e.g., vcovHAC, kernHAC,
etc.) can also be plugged in.
hth,
Z
From: Achim Zeileis <achim.zeil...@uibk.ac.at>
Sent: 31 May 2016 13:18
To: T.Riedle
Cc: r-help@r-project.org
Subject: Re: [R] sandwich package: HAC estimators
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
__
R-help@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@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do re
Re: [R] sandwich package: HAC estimators
Many thanks for your feedback. If I get the code for the waldtest right I can calculate the Chi2 and the F statistic using waldtest(). Can I use the waldtest() without using bread()/ estfun()? That is, I estimate the logit regression using glm() e.g. logit<-glm(...) and insert logit into the waldtest() function. Does that work to get chi2 under HAC standard errors? From: Achim Zeileis <achim.zeil...@uibk.ac.at> Sent: 31 May 2016 13:18 To: T.Riedle Cc: r-help@r-project.org Subject: Re: [R] sandwich package: HAC estimators 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 >> >> __ >> R-help@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@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@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@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.
Re: [R] sandwich package: HAC estimators
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 __ R-help@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@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@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@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.
Re: [R] sandwich package: HAC estimators
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-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 > > __ > R-help@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@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@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.
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@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@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.
Re: [R] sandwich package: HAC estimators
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? Leonardo Ferreira Fontenelle, MD, MPH __ R-help@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.
Re: [R] sandwich package: HAC estimators
On Sat, 28 May 2016, T.Riedle wrote:
Dear R users,
I am running a logistic regression using the rms package and the code
looks as follows:
crisis_bubble4<-lrm(stock.market.crash~crash.MA+bubble.MA+MP.MA+UTS.MA+UPR.MA+PPI.MA+RV.MA,data=Data_logitregression_movingaverage)
Now, I would like to calculate HAC robust standard errors using the
sandwich package assuming the NeweyWest estimator which looks as
follows:
coeftest(crisis_bubble4,df=Inf,vcov=NeweyWest)
Error in match.arg(type) :
'arg' should be one of "li.shepherd", "ordinary", "score",
"score.binary", "pearson", "deviance", "pseudo.dep", "partial",
"dfbeta", "dfbetas", "dffit", "dffits", "hat", "gof", "lp1"
As you can see, it doesn't work.
Yes. The "sandwich" package relies on two methods being available: bread()
and estfun(). See vignette("sandwich-OOP", package = "sandwich") for the
background details.
For objects of class "lrm" no such methods are available. But as "lrm"
objects inherit from "glm" the corresponding methods are called. However,
"lrm" objects are actually too different from "glm" objects (despite the
inheritance) resulting in the error.
It is easy to add these methods, though, because "lrm" brings all the
necessary information:
bread.lrm <- function(x, ...) vcov(x) * nobs(x)
estfun.lrm <- function(x, ...) residuals(x, "score")
Therefore, I did the same using the glm() instead of lrm():
crisis_bubble4<-glm(stock.market.crash~crash.MA+bubble.MA+MP.MA+UTS.MA+UPR.MA+PPI.MA+RV.MA,family=binomial("logit"),data=Data_logitregression_movingaverage)
If I use the coeftest() function, I get following results.
coeftest(crisis_bubble4,df=Inf,vcov=NeweyWest)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.260885.01706 -1.0486 0.29436
crash.MA 0.492192.41688 0.2036 0.83863
bubble.MA 12.128685.85228 2.0725 0.03822 *
MP.MA-20.07238 499.37589 -0.0402 0.96794
UTS.MA -58.18142 77.08409 -0.7548 0.45038
UPR.MA -337.57985 395.35639 -0.8539 0.39318
PPI.MA 729.37693 358.60868 2.0339 0.04196 *
RV.MA116.00106 79.52421 1.4587 0.14465
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Some of these coefficients and standard errors are suspiciously large. It
might make sense to check for quasi-complete separation.
I am unsure whether the coeftest from the lmtest package is appropriate
in case of a logistic regression.
Yes, this is ok. (Whether or not the application of HAC standard errors is
the best way to go is a different matter though.)
Is there another function for logistic regressions? Furthermore, I would
like to present the regression coefficients, the F-statistic and the HAC
estimators in one single table. How can I do that?
Running first coeftest() and then lrtest() should get you close to what
you want - even though it is not a single table.
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.
Thanks for your support.
Kind regards
[[alternative HTML version deleted]]
__
R-help@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@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] sandwich package: HAC estimators
Dear R users,
I am running a logistic regression using the rms package and the code looks as
follows:
crisis_bubble4<-lrm(stock.market.crash~crash.MA+bubble.MA+MP.MA+UTS.MA+UPR.MA+PPI.MA+RV.MA,data=Data_logitregression_movingaverage)
Now, I would like to calculate HAC robust standard errors using the sandwich
package assuming the NeweyWest estimator which looks as follows:
coeftest(crisis_bubble4,df=Inf,vcov=NeweyWest)
Error in match.arg(type) :
'arg' should be one of "li.shepherd", "ordinary", "score", "score.binary",
"pearson", "deviance", "pseudo.dep", "partial", "dfbeta", "dfbetas", "dffit",
"dffits", "hat", "gof", "lp1"
As you can see, it doesn't work. Therefore, I did the same using the glm()
instead of lrm():
crisis_bubble4<-glm(stock.market.crash~crash.MA+bubble.MA+MP.MA+UTS.MA+UPR.MA+PPI.MA+RV.MA,family=binomial("logit"),data=Data_logitregression_movingaverage)
If I use the coeftest() function, I get following results.
coeftest(crisis_bubble4,df=Inf,vcov=NeweyWest)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.260885.01706 -1.0486 0.29436
crash.MA 0.492192.41688 0.2036 0.83863
bubble.MA 12.128685.85228 2.0725 0.03822 *
MP.MA-20.07238 499.37589 -0.0402 0.96794
UTS.MA -58.18142 77.08409 -0.7548 0.45038
UPR.MA -337.57985 395.35639 -0.8539 0.39318
PPI.MA 729.37693 358.60868 2.0339 0.04196 *
RV.MA116.00106 79.52421 1.4587 0.14465
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
I am unsure whether the coeftest from the lmtest package is appropriate in case
of a logistic regression. Is there another function for logistic regressions?
Furthermore, I would like to present the regression coefficients, the
F-statistic and the HAC estimators in one single table. How can I do that?
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?
Thanks for your support.
Kind regards
[[alternative HTML version deleted]]
__
R-help@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.
