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Measuring heterogeneity with fixed effect quantile regression: Long panels and short panels

Author

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  • Besstremyannaya, Galina

    (National Research University Higher School of Economics, Moscow;)

  • Golovan, Sergei

    (New Economic School, Moscow;)

Abstract

he desire to capture heterogeneity in the response of the dependent variable to covariates often forces empiricists to employ panel data quantile regression models. Very often practitioners forget the limitations of their datasets in terms of the sample size n and the length of panel T. Yet, quantile regression requires large samples, long panels and small value of the ratio n/T. So the estimator in quantile regression with short panels is biased. The paper reviews the approaches for estimating longitudinal models for quantile regression. We highlight the fact that a method of smoothed quantile regression may be viewed as a remedy for reducing the asymptotic bias of the estimator in short panels, both in case of quantile-dependent and quantile-independent fixed effect specifications.

Suggested Citation

  • Besstremyannaya, Galina & Golovan, Sergei, 2021. "Measuring heterogeneity with fixed effect quantile regression: Long panels and short panels," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 70-82.
    • Handle: RePEc:ris:apltrx:0433
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    References listed on IDEAS

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    1. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
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    7. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    8. Parente Paulo M.D.C. & Santos Silva João M.C., 2016. "Quantile Regression with Clustered Data," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 1-15, January.
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    11. Galina Besstremyannaya & Sergei Golovan, 2019. "Reconsideration of a simple approach to quantile regression for panel data," The Econometrics Journal, Royal Economic Society, vol. 22(3), pages 292-308.
    12. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    13. Besstremyannaya, Galina, 2017. "Heterogeneous effect of the global financial crisis and the Great East Japan Earthquake on costs of Japanese banks," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 66-89.
    14. Liang Chen & Yulong Huo, 2021. "A simple estimator for quantile panel data models using smoothed quantile regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 247-263.
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    Cited by:

    1. Besstremyannaya, Galina & Dasher, Richard & Golovan, Sergei, 2022. "Quantifying heterogeneity in the relationship between R&D intensity and growth at innovative Japanese firms: A quantile regression approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 27-45.
    2. Li Tao & Lingnan Tai & Manling Qian & Maozai Tian, 2023. "A New Instrumental-Type Estimator for Quantile Regression Models," Mathematics, MDPI, vol. 11(15), pages 1-26, August.

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    More about this item

    Keywords

    quantile regression; panel data;

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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