Nonparametric Instrumental Regression With Right Censored Duration Outcomes

Jad Beyhum, Jean-Pierre Florens and Ingrid van Keilegom
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Jad Beyhum: KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
Jean-Pierre Florens: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Ingrid van Keilegom: KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven

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Abstract: This article analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global identification properties that rely on a nonlinear system of equations. We propose an estimation procedure to solve this system and derive rates of convergence and conditions under which the estimator is asymptotically normal. When censoring makes identification fail, we develop partial identification results. Our estimators exhibit good finite sample properties in simulations. We also apply our methodology to the Illinois Reemployment Bonus Experiment.

Keywords: Duration Models; Endogeneity; Instrumental variable; Nonseparability; Partial identification (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

Published in Journal of Business and Economic Statistics, 2022, 40 (3), pp.1034-1045. ⟨10.1080/07350015.2021.1895814⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04042903

DOI: 10.1080/07350015.2021.1895814

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