The use of robust regression has gained popularity among applied econometricians.
Unfortunately, most practitioners who have used these estimators seem
to be unaware of the fact that their properties can be dramatically affected by
both heteroskedasticity and skewness of the errors. In this paper we reconsider
the interpretation of a specific robust regression estimator that has become popular
in applied econometrics, and conclude that its use in this context cannot
be generally recommended. Alternatively, quantile and mode regression could
be used when the researcher wants to estimate conditional location functions
that are robust to the presence of outliers.