  A literature search shows that robust regression techniques are rarely used
in applied econometrics. We list several misconceptions about robustness which
lead to this situation. We show that most data sets are not normal, least
squares performs very poorly even in large data sets with small numbers of
outliers, and that commonly used techniques for achieving robustness fail to do
so. We then provide newly developed techniques from the statistics literature
which are easy to understand, and achieve robustness. We show the practical use
of these techniques by re-analyzing three regression models from recent
literature, and arriving at different conclusions from those reached by the
authors.
