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Estimating and Testing a Quantile Regression Model with Interactive Effects

  • Harding, Matthew

    ()

    (Stanford University)

  • Lamarche, Carlos

    ()

    (University of Kentucky)

Registered author(s):

    This paper proposes a quantile regression estimator for a panel data model with interactive effects potentially correlated with the independent variables. We provide conditions under which the slope parameter estimator is asymptotically Gaussian. Monte Carlo studies are carried out to investigate the finite sample performance of the proposed method in comparison with other candidate methods. We discuss an approach to testing the model specification against a competing fixed effects specification. The paper presents an empirical application of the method to study the effect of class size and class composition on educational attainment. The findings show that (i) a change in the gender composition of a class impacts differently low- and high-performing students; (ii) while smaller classes are beneficial for low performers, larger classes are beneficial for high performers; (iii) reductions in class size do not seem to impact mean and median student performance; (iv) the fixed effects specification is rejected in favor of the interactive effects specification.

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    File URL: http://ftp.iza.org/dp6802.pdf
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    Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 6802.

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    Length: 32 pages
    Date of creation: Aug 2012
    Date of revision:
    Publication status: published in: Journal of Econometrics, 2014, 178, 101-113
    Handle: RePEc:iza:izadps:dp6802
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    1. Oriana Bandiera & Valentino Larcinese & Imran Rasul, 2010. "Heterogeneous Class Size Effects: New Evidence from a Panel of University Students," Economic Journal, Royal Economic Society, vol. 120(549), pages 1365-1398, December.
    2. V. Chernozhukov & Ivan Fernandez-Val, . "Quantile and Average Effects in Nonseparable Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-011, Boston University - Department of Economics.
    3. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    4. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    5. repec:cup:cbooks:9780521522717 is not listed on IDEAS
    6. 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.
    7. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    8. repec:cup:cbooks:9780521818551 is not listed on IDEAS
    9. 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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