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Quantile regression for dynamic panel data with fixed effects

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  • Galvao Jr., Antonio F.
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    Abstract

    This paper studies a quantile regression dynamic panel model with fixed effects. Panel data fixed effects estimators are typically biased in the presence of lagged dependent variables as regressors. To reduce the dynamic bias, we suggest the use of the instrumental variables quantile regression method of Chernozhukov and Hansen (2006) along with lagged regressors as instruments. In addition, we describe how to employ the estimated models for prediction. Monte Carlo simulations show evidence that the instrumental variables approach sharply reduces the dynamic bias, and the empirical levels for prediction intervals are very close to nominal levels. Finally, we illustrate the procedures with an application to forecasting output growth rates for 18 OECD countries.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 164 (2011)
    Issue (Month): 1 (September)
    Pages: 142-157

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    Handle: RePEc:eee:econom:v:164:y:2011:i:1:p:142-157

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    Web page: http://www.elsevier.com/locate/jeconom

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    Keywords: Quantile regression Dynamic panel Fixed effects Instrumental variables;

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    Cited by:
    1. Alessia Matano & Paolo Naticchioni, 2011. "Rent Sharing as a Driver of the Glass Ceiling Effect," Working Papers - Dipartimento di Economia 12-DEISFOL, Dipartimento di Economia, Sapienza University of Rome, revised 2011.
    2. Coad, Alex & Segarra Blasco, Agustí, 1958- & Teruel, Mercedes, 2013. "Innovation and firm growth: Does firm age play a role?," Working Papers 2072/211886, Universitat Rovira i Virgili, Department of Economics.
    3. David Powell, 2010. "Unconditional Quantile Regression for Panel Data with Exogenous or Endogenous Regressors," Working Papers 710-1, RAND Corporation Publications Department.
    4. Daunfeldt, Sven-Olov & Halvarsson, Daniel, 2012. "Are high-growth firms one-hit wonders? Evidence from Sweden," HUI Working Papers 73, HUI Research.
    5. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    6. Klomp, Jeroen, 2013. "Government interventions and default risk: Does one size fit all?," Journal of Financial Stability, Elsevier, vol. 9(4), pages 641-653.
    7. Lee, Jen-Sin & Huang, Gow-Liang & Kuo, Chin-Tai & Lee, Liang-Chien, 2012. "The momentum effect on Chinese real estate stocks: Evidence from firm performance levels," Economic Modelling, Elsevier, vol. 29(6), pages 2392-2406.
    8. Nadine Levratto & Aziza Garsaa & Luc Tessier, 2013. "La Corse est-elle soluble dans le modèle méditerranéen ? Une analyse à partir d’une régression quantile sur données d’entreprises en panel entre 2004 et 2010. Is the Corsican economy a part ," EconomiX Working Papers 2013-20, University of Paris West - Nanterre la Défense, EconomiX.
    9. Nadine Levratto & Aziza Garsaa & Luc Tessier, 2013. "La Corse est-elle soluble dans le modèle méditerranéen ?," Working Papers hal-00842059, HAL.
    10. Genya Kobayashi & Hideo Kozumi, 2012. "Bayesian analysis of quantile regression for censored dynamic panel data," Computational Statistics, Springer, vol. 27(2), pages 359-380, June.
    11. Andini, Corrado, 2014. "Persistence Bias and Schooling Returns," IZA Discussion Papers 8143, Institute for the Study of Labor (IZA).
    12. Michele Aquaro & Pavel Čížek, 2014. "Robust estimation of dynamic fixed-effects panel data models," Statistical Papers, Springer, vol. 55(1), pages 169-186, February.
    13. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.

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