I have a question about a regression I am running. I believe it's a pretty basic question, although after a couple hours searching I couldn't find the answer.

I ran a regression in my software program (STATA) and saw that there are some concerns about heteroskedasticity in the model. As a result, I transformed my dependent variable to ln(y) and ran the regression that way. That seems to have solved the problem.

However, I also know that some programs (like STATA) also have options to use robust standard errors as a way to combat heteroskedasticity. My question is: Is it better to transform the variable or use the robust standard error option? Also, is it acceptable to use both at the same time (i.e. to run a regression with robust standard errors AND ln(y) as the DV)?

Thanks,

Jeffrey