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This is in contrast to linear or count data regression where there may be heteroskedasticity, overdispersion, etc.In this research, we present the inferential statistics for Cronbach’s Coefficient Alpha based on the standard statistical assumption of multivariate normality. However, in a binary regression there is no room for misspecification because the model equation just consists of the mean (= probability) and the likelihood is the mean and 1 - mean, respectively. Because the basic assumption for the sandwich standard errors to work is that the model equation (or more precisely the corresponding score function) is correctly specified while the rest of the model may be misspecified. Provided that the model is correctly specified, they are consistent and it's ok to use them but they don't guard against any misspecification in the model. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Īnd just for the record: In the binary response case, these "robust" standard errors are not robust against anything. So to obtain the same results as in Stata you can do do: sandwich1 |z|) At least not to the best of my knowledge. And except for a few special cases (e.g., OLS linear regression) there is no argument for 1/(n - k) or 1/(n - 1) to work "correctly" in finite samples (e.g., unbiasedness). Of course, asymptotically these do not differ at all. Alternatively, sandwich(., adjust = TRUE) can be used which divides by 1/(n - k) where k is the number of regressors.
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In the sandwich(.) function no finite-sample adjustment is done at all by default, i.e., the sandwich is divided by 1/n where n is the number of observations. The only difference is how the finite-sample adjustment is done. The default so-called "robust" standard errors in Stata correspond to what sandwich() from the package of the same name computes. Myfit<-glm(admit~gre+gpa+rank,data=mydata,family=binomial(link="logit"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC0"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC3"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC1"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC2"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "const"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC4"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC4m"))Ĭoeftest(myfit, vcov = vcovHC(myfit, "HC5")) Does anybody know how Stata calculate the sandwich estimator for non-linear regression, in my case the logit regression? I have tried some OLS linear regression examples it seems like the sandwich estimators of R and Stata give me the same robust standard error for OLS. I am able to replicate the exactly same coefficients from Stata, but I am not able to have the same robust standard error with the package "sandwich". In Stata I use the option "robust" to have the robust standard error (heteroscedasticity-consistent standard error). I am trying to replicate a logit regression from Stata to R.