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Estimating and testing a quantile regression model with interactive effects

  • Harding, Matthew
  • Lamarche, Carlos

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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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
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  10. Victor Chernozhukov & Ivan Fernandez-Val & Whitney Newey, 2009. "Quantile and average effects in nonseparable panel models," CeMMAP working papers CWP29/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  11. Graham, Bryan S. & Hahn, Jinyong & Powell, James L., 2009. "The incidental parameter problem in a non-differentiable panel data model," Economics Letters, Elsevier, vol. 105(2), pages 181-182, November.
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  19. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
  20. Edward Vytlacil & James J. Heckman, 2001. "Policy-Relevant Treatment Effects," American Economic Review, American Economic Association, vol. 91(2), pages 107-111, May.
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  22. 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.
  23. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
  24. Alan B. Krueger, 1999. "Experimental Estimates of Education Production Functions," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 497-532.
  25. Caroline M. Hoxby, 2000. "The Effects of Class Size on Student Achievement: New Evidence from Population Variation," The Quarterly Journal of Economics, Oxford University Press, vol. 115(4), pages 1239-1285.
  26. Tomohiro Ando & Ruey S. Tsay, 2011. "Quantile regression models with factor‐augmented predictors and information criterion," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 1-24, February.
  27. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 533-575.
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  29. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
  30. Lingjie Ma & Roger Koenker, 2004. "Quantile regression methods for recursive structural equation models," CeMMAP working papers CWP01/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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