A 2-STAGE ESTIMATOR FOR PROBIT MODELS WITH STRUCTURAL GROUP EFFECTS

This paper outlines a two-stage technique for estimation and inference in probit models with structural group effects. The structural group specification belongs to a broader class of random components models. In particular, individuals in a given group share a common component in the specification of the conditional mean of a latent variable. For a number of computational reasons, existing random effects models are impractical for estimation and inference in this type of problem. Our two-stage estimator provides an easily estimable alternative to the random effects specification. In addition, we conduct a Monte Carlo simulation comparing the performance of alternative estimators, and find that the two-stage estimator is superior - both in terms of estimation and inference - to traditional estimators.