Hello together,

I have a question regarding the application of standard errors in case of heteroskedasticity and autocorrelation. I conduct three different regressions [(1), (2) and (3)]. All using the same dependent variable, but with variations in control variables (dummies, interactions etc.). For all three, I identify heteroskedasticity, therefore I will use robust standard errors. Furthermore, Reg (2) contains autocorrelation, which is not the case for (1) and (3). Thus, clustered errors for (2) are definitely necessary.

My question is, is it better to use robust standard errors for (1) and (3) and clustered for (2)? Or is it mathematically applicable to use clustered on all three, even though autocorrelation does not affect each model? I would be happy if a sensible answer could be shortly explained, so I can expand my knowledge.

Apreciate your help and thanks in advance.

/Kevin