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Instrumental variable quantile regression under random right censoring

Author

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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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