Testing Censoring Point Independence

Identification in censored regression analysis and hazard models of duration outcomes relies on the condition that censoring points are conditionally independent of latent outcomes, an assumption which may be questionable in many settings. This article proposes a test for this assumption based on a...

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Bibliographic Details
Published inJournal of business & economic statistics Vol. 37; no. 3; pp. 496 - 505
Main Author Frandsen, Brigham R.
Format Journal Article
LanguageEnglish
Published Alexandria Taylor & Francis 03.07.2019
Taylor & Francis Ltd
Subjects
Censored regression
Duration data
Hazard models
Kaplan-Meier estimator
Subsampling
Survival analysis
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Summary:Identification in censored regression analysis and hazard models of duration outcomes relies on the condition that censoring points are conditionally independent of latent outcomes, an assumption which may be questionable in many settings. This article proposes a test for this assumption based on a Cramer-von-Mises-like test statistic comparing two different nonparametric estimators for the latent outcome cdf: the Kaplan-Meier estimator, and the empirical cdf conditional on the censoring point exceeding (for right-censored data) the cdf evaluation point. The test is consistent and has power against a wide variety of alternatives. Applying the test to unemployment duration data from the NLSY, the SIPP, and the PSID suggests the assumption is frequently suspect.
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2017.1383261