Partial identification and inference in duration models with endogenous censoring
研究了内生删失下变换模型（如加速失效时间模型、比例风险模型）的识别与推断，允许删失与协变量和未观测异质性任意相关，通过条件矩不等式推导回归参数和变换函数的边界，并基于U统计量提供推断方法，应用于评估失业保险对失业持续时间的影响。
Abstract This paper studies identification and inference in transformation models with endogenous censoring. Many kinds of duration models, such as the accelerated failure time model, proportional hazard model, and mixed proportional hazard model, can be viewed as transformation models. We allow the censoring of a duration outcome to be arbitrarily correlated with observed covariates and unobserved heterogeneity. We impose no parametric restrictions on either the transformation function or the distribution function of the unobserved heterogeneity. In this setting, we develop bounds on the regression parameters and the transformation function, which are characterized by conditional moment inequalities involving U‐statistics. Subsequently, we provide inference methods for them by constructing an inference approach for conditional moment inequality models in which the sample analogs of moments are U‐statistics. We apply the proposed inference methods to evaluate the effect of unemployment insurance on duration of joblessness using data from the Current Population Survey's Displaced Workers Supplements.