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Robust penalized quantile regression estimation for panel data

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  • Lamarche, Carlos
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    Abstract

    This paper investigates a class of penalized quantile regression estimators for panel data. The penalty serves to shrink a vector of individual specific effects toward a common value. The degree of this shrinkage is controlled by a tuning parameter [lambda]. It is shown that the class of estimators is asymptotically unbiased and Gaussian, when the individual effects are drawn from a class of zero-median distribution functions. The tuning parameter, [lambda], can thus be selected to minimize estimated asymptotic variance. Monte Carlo evidence reveals that the estimator can significantly reduce the variability of the fixed-effect version of the estimator without introducing bias.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 157 (2010)
    Issue (Month): 2 (August)
    Pages: 396-408

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    Handle: RePEc:eee:econom:v:157:y:2010:i:2:p:396-408

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

    Related research

    Keywords: Shrinkage Robust Quantile regression Panel data Individual effects;

    References

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    1. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    3. Joshua Angrist & Eric Bettinger & Erik Bloom & Elizabeth King & Michael Kremer, 2002. "Vouchers for Private Schooling in Colombia: Evidence from a Randomized Natural Experiment," American Economic Review, American Economic Association, vol. 92(5), pages 1535-1558, December.
    4. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    5. Marc Nerlove, 1968. "Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross-Sections," Cowles Foundation Discussion Papers 257, Cowles Foundation for Research in Economics, Yale University.
    6. Baltagi, Badi H., 1981. "Pooling : An experimental study of alternative testing and estimation procedures in a two-way error component model," Journal of Econometrics, Elsevier, vol. 17(1), pages 21-49, September.
    7. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275.
    8. Horowitz, Joel L & Markatou, Marianthi, 1996. "Semiparametric Estimation of Regression Models for Panel Data," Review of Economic Studies, Wiley Blackwell, vol. 63(1), pages 145-68, January.
    9. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(02), pages 186-199, June.
    10. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    11. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
    12. Kooperberg, Charles & Stone, Charles J., 1991. "A study of logspline density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 12(3), pages 327-347, November.
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    Citations

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    Cited by:
    1. Adam Rosen, 2009. "Set identification via quantile restrictions in short panels," CeMMAP working papers CWP26/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Guilherme Resende Oliveira & Benjamin Miranda Tabak & José Guilherme de Lara Resende & Daniel Oliveira Cajueiro, 2012. "Determinantes da Estrutura de Capital das Empresas Brasileiras: uma abordagem em regress˜ao quantílica," Working Papers Series 272, Central Bank of Brazil, Research Department.
    3. Javier Alejo, 2013. "Relación de Kuznets en América Latina. Explorando más allá de la media condicional," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 59, pages 3-55, January-D.
    4. Salima BOUAYAD AGHA & Nadine TURPIN & Lionel VEDRINE, 2013. "Au-Delà De La Moyenne : Les Effets Par Quantile De La Politique De Cohésion De L’Union Européenne," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 38, pages 27-48.
    5. Covas, Francisco B. & Rump, Ben & Zakrajšek, Egon, 2014. "Stress-testing US bank holding companies: A dynamic panel quantile regression approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 691-713.
    6. Y. Andriyana & I. Gijbels & A. Verhasselt, 2014. "P-splines quantile regression estimation in varying coefficient models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 23(1), pages 153-194, March.
    7. Fernando João Alexandre Parmagnani & Fabiana Fontes Rocha, 2013. "Evaluating the impact of SUS transfers on municipalities' health expenditures," Working Papers, Department of Economics 2013_23, University of São Paulo (FEA-USP), revised 15 Jan 2014.
    8. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    9. Javier Alejo, 2012. "Relación de Kuznets en América Latina. Explorando más allá de la media condicional," CEDLAS, Working Papers 0129, CEDLAS, Universidad Nacional de La Plata.
    10. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    11. Lamarche, Carlos, 2011. "Measuring the incentives to learn in Colombia using new quantile regression approaches," Journal of Development Economics, Elsevier, vol. 96(2), pages 278-288, November.
    12. Harding, Matthew & Lamarche, Carlos, 2012. "Estimating and Testing a Quantile Regression Model with Interactive Effects," IZA Discussion Papers 6802, Institute for the Study of Labor (IZA).
    13. Harding, Matthew & Lamarche, Carlos, 2013. "Penalized Quantile Regression with Semiparametric Correlated Effects: Applications with Heterogeneous Preferences," IZA Discussion Papers 7741, Institute for the Study of Labor (IZA).
    14. 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.
    15. Sula, Ozan, 2011. "Demand for international reserves in developing nations: A quantile regression approach," Journal of International Money and Finance, Elsevier, vol. 30(5), pages 764-777, September.
    16. 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.

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