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Smoothed quantile regression for panel data

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  • Galvao, Antonio F.
  • Kato, Kengo

Abstract

This paper studies fixed effects estimation of quantile regression models for panel data. Under an asymptotic framework where both the numbers of individuals and time periods grow at the same rate, we show that the fixed-effects estimator for the smoothed objective function has a limiting normal distribution with a bias in the mean, and provide the analytic form of the asymptotic bias. We propose a one-step bias correction estimator based on the analytic bias formula obtained from the asymptotic analysis. Importantly, our results cover the case that observations are dependent over time. We illustrate the effects of the bias correction through simulations.

Suggested Citation

  • Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
  • Handle: RePEc:eee:econom:v:193:y:2016:i:1:p:92-112
    DOI: 10.1016/j.jeconom.2016.01.008
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    Cited by:

    1. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    2. Hang, Yin & Xue, Wenjun, 2020. "The asymmetric effects of monetary policy on the business cycle: Evidence from the panel smoothed quantile regression model," Economics Letters, Elsevier, vol. 195(C).
    3. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2021. "Smoothing Quantile Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 338-357, January.
    4. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    5. Fernandes, Marcelo & Guerre, Emmanuel & Horta, Eduardo, 2017. "Smoothing quantile regressions," Textos para discussão 457, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Victor Chernozhukov & Ivan Fernandez-Val & Martin Weidner, 2018. "Network and panel quantile effects via distribution regression," CeMMAP working papers CWP21/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Alexandre Belloni & Mingli Chen & Oscar Hernan Madrid Padilla & Zixuan & Wang, 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," Papers 1912.02151, arXiv.org.
    8. Theodore Panagiotidis & Panagiotis Printzis, 2021. "Investment and Uncertainty: Are large firms different from small ones?," Discussion Paper Series 2021_06, Department of Economics, University of Macedonia, revised Feb 2021.
    9. Carolina Castagnetti & Luisa Rosti & Marina Töpfer, 2019. "The Public-Private Sector Wage Differential Across Gender in Italy: a New Quantile-Based Decomposition Approach," Economics Bulletin, AccessEcon, vol. 39(4), pages 2533-2539.
    10. Liang Chen & Yulong Huo, 2019. "A Simple Estimator for Quantile Panel Data Models Using Smoothed Quantile Regressions," Papers 1911.04729, arXiv.org.
    11. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
    12. Liang Chen, 2019. "Nonparametric Quantile Regressions for Panel Data Models with Large T," Papers 1911.01824, arXiv.org, revised Sep 2020.
    13. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    14. Castagnetti, Carolina & Giorgetti, Maria Letizia, 2019. "Understanding the gender wage-gap differential between the public and private sectors in Italy: A quantile approach," Economic Modelling, Elsevier, vol. 78(C), pages 240-261.
    15. Dogan, Eyup & Altinoz, Buket & Tzeremes, Panayiotis, 2020. "The analysis of ‘Financial Resource Curse’ hypothesis for developed countries: Evidence from asymmetric effects with quantile regression," Resources Policy, Elsevier, vol. 68(C).
    16. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    17. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    18. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    19. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.

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    More about this item

    Keywords

    Bias correction; Incidental parameters problem; Panel data; Quantile regression; Smoothing;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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