Selection Bias Corrections Based on the Multinomial Logit Model: Monte-Carlo Comparisons.

This survey presents the set of methods available in the literature on selection bias correction, when selection is specified as a multinomial logit model. It contrasts the underlying assumptions made by the different methods and shows results from a set of Monte-Carlo experiments. We find that, in many cases, the approach initiated by Dubin and MacFadden (1984) is to be preferred to the most commonly used Lee (1984) method, as well as to the semi-parametric alternative method recently proposed by Dahl (2002), even in the presence of high non-linearity in the selection term. Monte-Carlo experiments also show that selection bias correction based on the multinomial logit model can provide fairly good correction for the outcome equation, even when the IIA hypothesis is violated.