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Pinball boosting of regression quantiles

Author

Listed:
  • Bauer, Ida
  • Haupt, Harry
  • Linner, Stefan

Abstract

An algorithm for boosting regression quantiles using asymmetric least absolute deviations, better known as pinball loss, is proposed. Existing approaches for boosting regression quantiles are essentially equal to least squares boosting of regression means with the single difference that their working residuals are based on pinball loss. All steps of our boosting algorithm are embedded in the well-established framework of quantile regression, and its main components – sequential base learning, fitting, and updating – are based on consistent scoring rules for regression quantiles. The Monte Carlo simulations performed indicate that the pinball boosting algorithm is competitive with existing approaches for boosting regression quantiles in terms of estimation accuracy and variable selection, and that its application to the study of regression quantiles of hedonic price functions allows the estimation of previously infeasible high-dimensional specifications.

Suggested Citation

  • Bauer, Ida & Haupt, Harry & Linner, Stefan, 2024. "Pinball boosting of regression quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:csdana:v:200:y:2024:i:c:s0167947324001117
    DOI: 10.1016/j.csda.2024.108027
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    More about this item

    Keywords

    Quantile regression; Component-wise functional gradient boosting; Regression boosting; L1 loss; Consistent scoring rule;
    All these keywords.

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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