Nonparametric identification and estimation of polychotomous choice models
给出非参数多分类选择模型可识别的条件，不预设可观测属性的子效用函数或不可观测随机项的分布，并基于识别结果构造了子效用函数的非参数强一致估计量。
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.