Nonparametric identification and estimation of polychotomous choice models

In this paper we provide conditions guaranteeing the identification of nonparametric polychotomous choice models. In these models, neither the subutility function of observable attributes nor the distribution of the unobservable random terms is specified parametrically. Sets of nonparametric functions that possess properties that are often implied by economic theory and satisfy the restrictions required to identify the models are described. We use the identification results to develop nonparametric strongly-consistent estimators for the subutility function of observable attributes. The results concern models in which the distribution of the unobservable random terms both depend and do not depend on the observable characteristics.