Censored Regression Estimation Under Unobserved Heterogeneity: A Stochastic Parameter Approach
提出一种处理删失样本回归中未观测异质性的随机参数方法，通过模拟和实证表明，控制异质性后模型表现显著优于忽略该问题的传统方法。
This paper presents a methodology for addressing the problem of unobserved heterogeneity in the context of regression models based on censored samples. The effectiveness, feasibility, and usefulness of the proposed approach is illustrated by means of an empirical application as well as a simulation experiment. The paper demonstrates that censored regressions that control for the presence of unobserved heterogeneity perform substantially better in comparison to their counterparts in which the problem of unobserved heterogeneity is ignored.