Maximum score estimation of preference parameters for a binary choice model under uncertainty

This paper develops maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectations. The preference parameters are estimated in two stages: we estimate conditional expectations nonparametrically in the first stage and then the preference parameters in the second stage based on Manski (1975, 1985)'s maximum score estimator using the choice data and first stage estimates. The paper establishes consistency and derives the rate of convergence of the corresponding two-stage estimator, which is of independent interest for maximum score estimation with generated regressors. The paper also provides results of some Monte Carlo experiments.