Nonparametric and Districtuion-Free Estimation of the Binary Threshold Crossing and The Binary Choice Models
证明无需对可观测外生变量的系统函数或随机项分布施加参数结构，即可识别二元阈值交叉和二元选择模型，并开发了完全非参数的最大似然估计量，该估计量具有强一致性。
In this paper, it is shown that it is possible to identify binary threshold crossing models and binary choice models without imposing any parametric structure either on the systematic function of observable exogenous variables or on the distribution of the random term. This identification result is employed to develop a fully nonparametric maximum likelihood estimator for both the function of observable exogenous variables and the distribution of the random term. The estimator is shown to be strongly consistent, and a two step procedure for its calculation is developed. The paper also includes examples of economic models that satisfy the conditions that are necessary to apply the results.