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Nonparametric Instrumental Regression With Right Censored Duration Outcomes

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  • Jad Beyhum
  • Jean-Pierre Florens
  • Ingrid Van Keilegom

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.

Suggested Citation

  • Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2022. "Nonparametric Instrumental Regression With Right Censored Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1034-1045, June.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:3:p:1034-1045
    DOI: 10.1080/07350015.2021.1895814
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    Cited by:

    1. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    2. Lorenzo Tedesco & Jad Beyhum & Ingrid Van Keilegom, 2023. "Instrumental variable estimation of the proportional hazards model by presmoothing," Papers 2309.02183, arXiv.org.

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