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Instrumental variable quantile regression under random right censoring

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  • Jad Beyhum
  • Lorenzo Tedesco
  • Ingrid Van Keilegom

Abstract

SummaryThis paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile regression coefficients are a solution. Identification is obtained when this system of equations has a unique solution. Our estimation procedure solves an empirical analogue of the system of equations. We derive conditions under which the estimator is asymptotically normal and prove the validity of a bootstrap procedure for inference. The finite sample performance of the approach is evaluated through numerical simulations. An application to the national Job Training Partnership Act study illustrates the method.

Suggested Citation

  • Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2024. "Instrumental variable quantile regression under random right censoring," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 21-36.
    • Handle: RePEc:oup:emjrnl:v:27:y:2024:i:1:p:21-36.
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    File URL: http://hdl.handle.net/10.1093/ectj/utad015
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    Cited by:

    1. Gilles Crommen & Jad Beyhum & Ingrid Van Keilegom, 2024. "An instrumental variable approach under dependent censoring," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(2), pages 473-495, June.
    2. Gilles Crommen & Jean-Pierre Florens & Ingrid Van Keilegom, 2025. "Tests of exogeneity in duration models with censored data," Papers 2510.26613, arXiv.org, revised Dec 2025.
    3. Wei Miao & Jad Beyhum & Jonas Striaukas & Ingrid Van Keilegom, 2025. "High-dimensional censored MIDAS logistic regression for corporate survival forecasting," Papers 2502.09740, arXiv.org.
    4. Lorenzo Tedesco & Jad Beyhum & Ingrid Van Keilegom, 2023. "Instrumental variable estimation of the proportional hazards model by presmoothing," Papers 2309.02183, arXiv.org.
    5. Gilles Crommen & Jad Beyhum & Ingrid Van Keilegom, 2025. "Estimation of the complier causal hazard ratio under dependent censoring," Papers 2504.02096, arXiv.org.

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