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

François Bourguignon, Martin Fournier, Marc Gurgand, Journal of Economic Surveys, vol. 21, n°1, 2007

Abstract: 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) as well as the semi-parametric alternative recently proposed by Dahl (2002) are to be preferred to the most commonly used Lee (1983) method. We also find that a restriction imposed in the original Dubin and MacFadden paper can be waved to achieve more robust estimators. 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.