Heteroscedastic Transformation Models With Covariate Dependent Censoring
提出了一种处理误差项条件异方差的变换模型推断方法，该方法对任意形式的协变量依赖删失具有稳健性，并在出口持续时间实证中展示了优势。
In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form. We provide sufficient conditions for point identification. We then propose an estimator and show that it is √ n -consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We also use the estimator in an empirical illustration of export duration, where we find advantages of the proposed method over existing ones.