SEMIPARAMETRIC STRUCTURAL MODELS OF BINARY RESPONSE: SHAPE RESTRICTIONS AND PARTIAL IDENTIFICATION
研究了当解释变量可能内生时，结构单方程阈值交叉模型对二元响应的部分识别能力，推导了离散解释变量下阈值函数的尖锐识别集，并特别关注单调性形状约束带来的计算优势。
I study the partial identifying power of structural single-equation threshold-crossing models for binary responses when explanatory variables may be endogenous. The sharp identified set of threshold functions is derived for the case in which explanatory variables are discrete, and I provide a constructive proof of sharpness. There is special attention to a widely employed semiparametric shape restriction, which requires the threshold-crossing function to be a monotone function of a linear index involving the observable explanatory variables. The restriction brings great computational benefits, allowing calculation of the identified set of index coefficients without calculating the nonparametrically specified threshold function. With the restriction in place, the methods of the paper can be applied to produce identified sets in a class of binary response models with mismeasured explanatory variables.