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) -5.26088 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
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
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