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Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model

Author

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  • Karen X. Yan

    (Department of Economics, Texas A&M University, College Station, TX 77845, USA)

  • Qi Li

    (Department of Economics, Texas A&M University, College Station, TX 77845, USA
    International School of Economics and Management (ISEM), Capital University of Economics and Business, Beijing 100070, China)

Abstract

This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction. Monte Carlo simulations show that the proposed estimator performs well in finite samples.

Suggested Citation

  • Karen X. Yan & Qi Li, 2018. "Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model," JRFM, MDPI, vol. 11(3), pages 1-10, August.
    • Handle: RePEc:gam:jjrfmx:v:11:y:2018:i:3:p:44-:d:161849
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    References listed on IDEAS

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    Cited by:

    1. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2024. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1026-1040, July.
    2. Liang Chen, 2019. "Nonparametric Quantile Regressions for Panel Data Models with Large T," Papers 1911.01824, arXiv.org, revised Sep 2020.
    3. Thanasis Stengos, 2019. "Nonparametric Econometric Methods and Applications," JRFM, MDPI, vol. 12(4), pages 1-3, November.

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