Nonparametric identification and estimation of random coefficients in multinomial choice models
提出非参数方法识别多项选择模型中消费者异质性的随机系数分布，给出一般识别条件并验证其适用于多项选择模型，进而证明非参数估计量的一致性。
We show how to nonparametrically identify the distribution of unobservables, such as random coefficients, that characterizes the heterogeneity among consumers in multinomial choice models. We provide general identification conditions for a class of nonlinear models and then verify these conditions using the primitives of the multinomial choice model. We require that the distribution of unobservables lie in the class of all distributions with finite support, which under our most general assumptions, resembles a product space where some of the product members are function spaces. We show how identification leads to the consistency of a nonparametric estimator.