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Backfitting and smooth backfitting in varying coefficient quantile regression

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  • Young K. Lee
  • Enno Mammen
  • Byeong U. Park

Abstract

In this paper, we study ordinary backfitting and smooth backfitting as methods of fitting varying coefficient quantile models. We do this in a unified framework that accommodates various types of varying coefficient models. Our framework also covers the additive quantile model as a special case. Under a set of weak conditions, we derive the asymptotic distributions of the backfitting estimators. We also briefly report on the results of a simulation study.

Suggested Citation

  • Young K. Lee & Enno Mammen & Byeong U. Park, 2014. "Backfitting and smooth backfitting in varying coefficient quantile regression," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 20-38, June.
    • Handle: RePEc:wly:emjrnl:v:17:y:2014:i:2:p:s20-s38
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    File URL: http://hdl.handle.net/10.1111/ectj.12017
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    Cited by:

    1. Zehua Li & Xiaola Wu & Xicheng Wang & Haimin Zhong & Jiongtao Chen & Xu Ma, 2022. "Measurement and Analysis of Contribution Rate for China Rice Input Factors via a Varying-Coefficient Production Function Model," Agriculture, MDPI, vol. 12(9), pages 1-19, September.

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