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Quantile regression with censoring and sample selection

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  • Chen, Songnian
  • Wang, Qian

Abstract

Arellano and Bonhomme (2017) considered nonparametric identification and semiparametric estimation of a quantile selection model, and Arellano and Bonhomme (2017s) extended the estimation approach to the case with censoring. However, there are some major drawbacks associated with the approach in Arellano and Bonhomme (2017s). In this paper we consider nonparametric and semiparametric identification of the quantile selection model with censoring, and we further propose a semiparametric estimation procedure by making some major adjustments to Arellano and Bonhomme’s (2017, 2017s) approaches to overcome the above mentioned drawbacks. Our estimator is shown to be consistent and asymptotically normal. A Monte Carlo study indicates that our estimator performs well in finite samples. Our method is illustrated with a CPS data to study wage inequality.

Suggested Citation

  • Chen, Songnian & Wang, Qian, 2023. "Quantile regression with censoring and sample selection," Journal of Econometrics, Elsevier, vol. 234(1), pages 205-226.
    • Handle: RePEc:eee:econom:v:234:y:2023:i:1:p:205-226
    DOI: 10.1016/j.jeconom.2021.11.018
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    More about this item

    Keywords

    Quantile regression; Selection; Censoring;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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