Non-crossing convex quantile regression

Sheng Dai, Timo Kuosmanen, Xun Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number111396
Number of pages5
JournalEconomics Letters
Volume233
Early online date17 Oct 2023
DOIs
Publication statusPublished - Dec 2023

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© 2023 The Author(s)

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