Endogeneity Corrections in Binary Outcome Models With Nonlinear Transformations: Identification and Inference
针对二元结果模型，提出一种基于非线性秩变换的内生性校正方法，无需外部工具变量即可实现识别，并通过德国破产数据验证了其有效性。
ABSTRACT For binary outcome models, an endogeneity correction based on nonlinear rank‐based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: Either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the dependence between the endogenous and exogenous regressors is nonlinear. Under these conditions, we prove consistency and asymptotic normality. Monte Carlo simulations and an application to German insolvency data illustrate the usefulness of the method.