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A quantile regression approach for estimating panel data models using instrumental variables

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  • Harding, Matthew
  • Lamarche, Carlos
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    Abstract

    We introduce a quantile regression approach to panel data models with endogenous variables and individual effects correlated with the independent variables. We find newly developed quantile regression methods can be easily adapted to estimate this class of models efficiently.

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    File URL: http://www.sciencedirect.com/science/article/B6V84-4W741VF-1/2/c5a09b3045d8c6585570b2eecae3d360
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    Bibliographic Info

    Article provided by Elsevier in its journal Economics Letters.

    Volume (Year): 104 (2009)
    Issue (Month): 3 (September)
    Pages: 133-135

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    Handle: RePEc:eee:ecolet:v:104:y:2009:i:3:p:133-135

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

    Related research

    Keywords: Quantile regression Instrumental Variables Individual Effects;

    References

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    1. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    2. Hausman Jerry A. & Sidak J. Gregory, 2004. "Why Do the Poor and the Less-Educated Pay More for Long-Distance Calls?," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 3(1), pages 1-29, April.
    3. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    4. Lamarche, Carlos, 2008. "Private school vouchers and student achievement: A fixed effects quantile regression evaluation," Labour Economics, Elsevier, vol. 15(4), pages 575-590, August.
    5. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    6. Cecilia Elena Rouse, 1998. "Private School Vouchers And Student Achievement: An Evaluation Of The Milwaukee Parental Choice Program," The Quarterly Journal of Economics, MIT Press, vol. 113(2), pages 553-602, May.
    7. Victor Chernozhukov & Christian Hansen, 2004. "The Effects of 401(K) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 735-751, August.
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    Citations

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    Cited by:
    1. Matano, Alessia & Naticchioni, Paolo, 2013. "Rent sharing as a driver of the glass ceiling effect," Economics Letters, Elsevier, vol. 118(1), pages 55-59.
    2. Sieger, Philip, 2013. "Job Losses and Criminal Gains: Analyzing the Effect of Unemployment on Criminal Activity," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79929, Verein für Socialpolitik / German Economic Association.
    3. Gustavo A. Crespi & Alessandro Maffioli & Pierre Mohnen & Gonzalo Vázquez, 2011. "Evaluating the Impact of Science, Technology and Innovation Programs: a Methodological Toolkit," SPD Working Papers 1104, Inter-American Development Bank, Office of Strategic Planning and Development Effectiveness (SPD).
    4. David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers 816, RAND Corporation Publications Department.
    5. 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.
    6. David Powell, 2010. "Unconditional Quantile Regression for Panel Data with Exogenous or Endogenous Regressors," Working Papers 710-1, RAND Corporation Publications Department.
    7. Alessia Matano & Paolo Naticchioni, 2009. "Wage distribution and the spatial sorting of workers and firms," Working Papers - Dipartimento di Economia 8-DEISFOL, Dipartimento di Economia, Sapienza University of Rome, revised 2009.
    8. Weili Ding & Steven F. Lehrer, 2014. "Understanding the Role of Time-Varying Unobserved Ability Heterogeneity in Education Production," NBER Working Papers 19937, National Bureau of Economic Research, Inc.
    9. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    10. Lamarche, Carlos, 2013. "Industry-Wide Work Rules and Productivity: Evidence from Argentine Union Contract Data," IZA Discussion Papers 7673, Institute for the Study of Labor (IZA).
    11. Hyungsik Roger Moon & Martin Weidner, 2013. "Dynamic linear panel regression models with interactive fixed effects," CeMMAP working papers CWP63/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. 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.
    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. 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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