Partial Identification and Inference in Duration Models with Endogenous Censoring

TL;DR

This paper develops bounds and inference methods for duration models with endogenous censoring, allowing arbitrary correlation with covariates and heterogeneity, without parametric assumptions, and applies it to heart transplant data.

Contribution

It introduces a nonparametric bounding and inference framework for transformation models with endogenous censoring, accommodating complex dependencies.

Findings

Bounds on regression parameters and transformation functions derived

Inference methods based on U-statistics for conditional moment inequalities

Applied to real data on heart transplants to assess survival effects

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. 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 heart transplants on patients' survival time using data from the Stanford Heart Transplant Study.