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Fast Algorithms for Quantile Regression with Selection

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  • Pereda-Fernández Santiago

    (Departamento de Economía, 16761 Universidad de Cantabria , Avenida de los Castros, s/n, 39005 Santander, Spain)

Abstract

The estimation of Quantile Regression with Selection (QRS) requires the estimation of the entire quantile process several times to estimate the parameters that model self-selection. Moreover, closed-form expressions of the asymptotic variance are too cumbersome, making the bootstrap more convenient to perform inference. I propose streamlined algorithms for the QRS estimator that significantly reduce computation time through preprocessing techniques and quantile grid reduction for the estimation of the parameters. I show the optimization enhancements and how they can improve the precision of the estimates without sacrificing computational efficiency with some simulations.

Suggested Citation

  • Pereda-Fernández Santiago, 2025. "Fast Algorithms for Quantile Regression with Selection," Journal of Econometric Methods, De Gruyter, vol. 14(1), pages 35-47.
    • Handle: RePEc:bpj:jecome:v:14:y:2025:i:1:p:35-47:n:1002
    DOI: 10.1515/jem-2024-0022
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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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