Whether to use robust linear regression or bootstrapping when there is heteroscedasticity?

I have a dataset where I need to do linear regression. Unfortunately there is a problem with heteroscedasticity. I´ve rerun the analysis using robust regression with the HC3 estimator for the variance and also done bootstrapping with the bootcov function in Hmisc for R. The results are quite close. What is generally recommended?

• What R package did you use for HC3 estimation? sandwich, contrast?
– chl
Sep 15 '10 at 19:46
• Another question while we are in: What is the design you are considering, I mean is there any clustering or multiple predictors, or is it a simple linear regression? This may help the reader to better understand the context of your study.
– chl
Sep 15 '10 at 20:47
• Have you tried re-expressing the dependent variable to stabilize variance?
– whuber
Sep 15 '10 at 21:34
• I´m using the sandwich package for the HC3 estimator. I´m using just simple linear regression. Intuitively I feel more comfortable with the bootstrap version and I guess I´ll stick with that. //thx for the input Sep 16 '10 at 5:19