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Estimating and testing a quantile regression model with interactive effects

Listed author(s):
  • 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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File URL: http://www.sciencedirect.com/science/article/pii/S0304407613001607
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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
DOI: 10.1016/j.jeconom.2013.08.010
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  1. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
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  8. DiNardo, John & Lee, David S., 2011. "Program Evaluation and Research Designs," Handbook of Labor Economics, Elsevier.
  9. De Giorgi, Giacomo & Pellizzari, Michele & Woolston, William Gui, 2009. "Class Size and Class Heterogeneity," IZA Discussion Papers 4443, Institute for the Study of Labor (IZA).
  10. 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.
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  12. Hausman, Jerry A, 1978. "Specification Tests in Econometrics," Econometrica, Econometric Society, vol. 46(6), pages 1251-1271, November.
  13. Bandiera, Oriana & Larcinese, Valentino & Rasul, Imran, 2009. "Heterogeneous Class Size Effects: New Evidence from a Panel of University Students," CEPR Discussion Papers 7512, C.E.P.R. Discussion Papers.
  14. Newey, Whitney K, 1991. "Uniform Convergence in Probability and Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 59(4), pages 1161-1167, July.
  15. Alan B. Krueger, 1999. "Experimental Estimates of Education Production Functions," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 497-532.
  16. V. Chernozhukov & Ivan Fernandez-Val, "undated". "Quantile and Average Effects in Nonseparable Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-011, Boston University - Department of Economics.
  17. 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.
  18. Edward Vytlacil & James J. Heckman, 2001. "Policy-Relevant Treatment Effects," American Economic Review, American Economic Association, vol. 91(2), pages 107-111, May.
  19. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, 03.
  20. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, 07.
  21. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
  22. 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.
  23. Tomohiro Ando & Ruey S. Tsay, 2011. "Quantile regression models with factor‐augmented predictors and information criterion," Econometrics Journal, Royal Economic Society, vol. 14, pages 1-24, 02.
  24. 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.
  25. 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.
  26. 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.
  27. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
  28. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
  29. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
  30. 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.
  31. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
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