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 -20.0724 96.9576 -0.207 0.83599
UTS.MA -58.1814 19.3533 -3.006 0.00264 **
UPR.MA -337.5798 64.3078 -5.249 1.53e-07 ***
PPI.MA 729.3769 73.0529 9.984 < 2e-16 ***
RV.MA 116.0011 16.5456 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) -5.26088 0.89269 -5.8933 3.786e-09 ***
crash.MA 0.49219 0.49662 0.9911 0.321652
bubble.MA 12.12868 1.37357 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.MA 116.00106 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) -5.26088 5.01706 -1.0486 0.29436
crash.MA 0.49219 2.41688 0.2036 0.83863
bubble.MA 12.12868 5.85228 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.MA 116.00106 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)
1 698
2 705 -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)
1 698
2 705 -7 16.311 0.02242 *
---
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 <- 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 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.
>
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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