Robust regression in Stata

Least-squares regression is a major workhorse in applied research. Yet its estimates may be deemed nonrobust under various conditions. One example is heavy-tailed error distributions, in which least-squares estimation may lose its cutting edge with respect to efficiency. More importantly, ordinary regression methods can produce biased results if the data are contaminated by a set of observations stemming from an alternative process. Various robust regression estimators have been proposed in the literature to address these problems, but they do not seem to be employed much in practical research. One reason for this underutilization may be a lack of convenient software implementations, as is exemplified by a close-to-complete absence of robust estimators from official Stata. In this talk, I will therefore present a number of user-written commands geared toward robust estimation of regression models.