eprintid: 10091972
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/09/19/72
datestamp: 2021-08-24 15:51:01
lastmod: 2021-09-29 22:17:04
status_changed: 2021-08-24 15:51:01
type: working_paper
metadata_visibility: show
creators_name: Sakaguchi, S
title: Partial Identification and Inference in Duration Models with Endogenous Censoring
ispublished: pub
divisions: UCL
divisions: B03
divisions: C03
divisions: F24
keywords: partial identification, duration models, transformation models, censoring, conditional moment inequality
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2019-08-30
publisher: Social Science Research Network (SSRN)
official_url: https://dx.doi.org/10.2139/ssrn.3443101
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1766289
doi: 10.2139/ssrn.3443101
lyricists_name: Sakaguchi, Shosei
lyricists_id: SSAKA28
actors_name: Allington-Smith, Dominic
actors_id: DAALL44
actors_role: owner
full_text_status: public
place_of_pub: Amsterdam, Netherlands
pages: 38
issn: 1556-5068
citation:        Sakaguchi, S;      (2019)    Partial Identification and Inference in Duration Models with Endogenous Censoring.                    Social Science Research Network (SSRN): Amsterdam, Netherlands.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10091972/8/Sakaguchi_SSRN-id3443101.pdf