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Partial Identification and Inference in Duration Models with Endogenous Censoring

Sakaguchi, S; (2019) Partial Identification and Inference in Duration Models with Endogenous Censoring. Social Science Research Network (SSRN): Amsterdam, Netherlands. Green open access

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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. I allow the censoring of duration outcome to be arbitrarily correlated with observed covariates and unobserved heterogeneity. I impose no parametric restrictions on the transformation function or the distribution function of the unobserved heterogeneity. In this setting, I partially identify the regression parameters and the transformation function, which are characterized by conditional moment inequalities of U-statistics. I provide an inference method for them by constructing an inference approach for the conditional moment inequality models of U-statistics. As an empirical illustration, I apply the proposed inference method to evaluate the effect of heart transplants on patients' survival time using data from the Stanford Heart Transplant Study.

Type: Working / discussion paper
Title: Partial Identification and Inference in Duration Models with Endogenous Censoring
Open access status: An open access version is available from UCL Discovery
DOI: 10.2139/ssrn.3443101
Publisher version: https://dx.doi.org/10.2139/ssrn.3443101
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: partial identification, duration models, transformation models, censoring, conditional moment inequality
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10091972
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