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Non-crossing convex quantile regression

Author

Listed:
  • Dai, Sheng
  • Kuosmanen, Timo
  • Zhou, Xun

Abstract

Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A direct approach to address this problem is to impose non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression approach that can circumvent quantile crossing while maintaining the quantile property. A Monte Carlo study demonstrates the superiority of the proposed penalized approach in addressing the quantile crossing problem.

Suggested Citation

  • Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Non-crossing convex quantile regression," Economics Letters, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:ecolet:v:233:y:2023:i:c:s0165176523004226
    DOI: 10.1016/j.econlet.2023.111396
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    Cited by:

    1. Timo Kuosmanen & Sheng Dai, 2023. "Modeling economies of scope in joint production: Convex regression of input distance function," Papers 2311.11637, arXiv.org.
    2. Kuosmanen, Natalia & Kuosmanen, Timo & Maczulskij, Terhi & Zhou, Xun, 2024. "Least-cost Decarbonization Pathways for Electricity Generation in Finland: A Convex Quantile Regression Approach," ETLA Working Papers 114, The Research Institute of the Finnish Economy.

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