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Estimating and testing a quantile regression model with interactive effects

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  • Harding, Matthew
  • Lamarche, Carlos
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    Abstract

    This paper proposes a quantile regression estimator for a model with interactive effects potentially correlated with covariates. We provide conditions under which the estimator is asymptotically Gaussian and we investigate the finite sample performance of the method. An approach to testing the specification against a competing fixed effects specification is introduced. The paper presents an application to study the effect of class size and composition on educational attainment. The evidence suggests that while smaller classes are beneficial for low performers, larger classes are beneficial for high performers. The fixed effects specification is rejected in favor of the interactive effects specification.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0304407613001607
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 178 (2014)
    Issue (Month): P1 ()
    Pages: 101-113

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    Handle: RePEc:eee:econom:v:178:y:2014:i:p1:p:101-113

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

    Related research

    Keywords: Quantile regression; Panel data; Interactive effects; Instrumental variables;

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    8. Harding, Matthew & Lamarche, Carlos, 2009. "A quantile regression approach for estimating panel data models using instrumental variables," Economics Letters, Elsevier, vol. 104(3), pages 133-135, September.
    9. Eric A. Hanushek & John F. Kain & Jacob M. Markman & Steven G. Rivkin, 2001. "Does Peer Ability Affect Student Achievement?," NBER Working Papers 8502, National Bureau of Economic Research, Inc.
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    Cited by:
    1. Ding, Weili & Lehrer, Steven F., 2014. "Understanding the role of time-varying unobserved ability heterogeneity in education production," Economics of Education Review, Elsevier, vol. 40(C), pages 55-75.

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