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Estimating Quantile Regressions with Multiple Fixed Effects through Method of Moments

Author

Listed:
  • Rios-Avila, Fernando

    (Levy Economics Institute)

  • Siles, Leonardo

    (Universidad de Chile)

  • Canavire Bacarreza, Gustavo J.

    (World Bank)

Abstract

This paper proposes a new method to estimate quantile regressions with multiple fixed effects. The method, which expands on the strategy proposed by Machado and Santos Silva (2019), allows for the inclusion of multiple fixed effects and provides various alternatives for estimating standard errors. We provide Monte Carlo simulations to show the finite sample properties of the proposed method in the presence of two sets of fixed effects. Finally, we apply the proposed method to two different examples using macroeconomic and microeconomic data and allowing for multiple fixed effects with robust results.

Suggested Citation

  • Rios-Avila, Fernando & Siles, Leonardo & Canavire Bacarreza, Gustavo J., 2024. "Estimating Quantile Regressions with Multiple Fixed Effects through Method of Moments," IZA Discussion Papers 17262, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17262
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    References listed on IDEAS

    as
    1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    2. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    3. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    4. John M. Abowd & Francis Kramarz & Sébastien Roux, 2006. "Wages, Mobility and Firm Performance: Advantages and Insights from Using Matched Worker-Firm Data," Economic Journal, Royal Economic Society, vol. 116(512), pages 245-285, June.
    5. Fernando Rios-Avila, 2015. "Feasible fitting of linear models with N fixed effects," Stata Journal, StataCorp LP, vol. 15(3), pages 881-898, September.
    6. Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
    7. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    11. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    12. Gaure, Simen, 2013. "OLS with multiple high dimensional category variables," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 8-18.
    13. Paolo Frumento & Matteo Bottai, 2016. "Parametric modeling of quantile regression coefficient functions," Biometrics, The International Biometric Society, vol. 72(1), pages 74-84, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    fixed effects; linear heteroskedasticity; location-scale model;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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