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Quantile Regression in the Presence of Sample Selection

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  • Huber, Martin

    ()

  • Melly, Blaise

    ()

Abstract

Most sample selection models assume that the errors are independent of the regressors. Under this assumption, all quantile and mean functions are parallel, which implies that quantile estimators cannot reveal any (per definition non-existing) heterogeneity. However, quantile estimators are useful for testing the independence assumption, because they are consistent under the null hypothesis. We propose tests for this crucial restriction that are based on the entire conditional quantile regression process after correcting for sample selection bias. Monte Carlo simulations demonstrate that they are powerful and two empirical illustrations indicate that violations of this assumption are likely to be ubiquitous in labor economics.

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Bibliographic Info

Paper provided by University of St. Gallen, School of Economics and Political Science in its series Economics Working Paper Series with number 1109.

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Length: 33 pages
Date of creation: Mar 2011
Date of revision:
Handle: RePEc:usg:econwp:2011:09

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Keywords: Sample selection; quantile regression; independence; test;

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References

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  1. Angrist, Joshua D., 1997. "Conditional independence in sample selection models," Economics Letters, Elsevier, vol. 54(2), pages 103-112, February.
  2. Chen, Songnian & Khan, Shakeeb, 2003. "Semiparametric Estimation Of A Heteroskedastic Sample Selection Model," Econometric Theory, Cambridge University Press, vol. 19(06), pages 1040-1064, December.
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  5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, November.
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  10. Moshe Buchinsky, 1998. "The dynamics of changes in the female wage distribution in the USA: a quantile regression approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 1-30.
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  12. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.
  13. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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  17. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
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  19. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
  20. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
  21. Michael Lechner & Blaise Melly, 2010. "Partial Idendification of Wage Effects of Training Programs," Working Papers 2010-8, Brown University, Department of Economics.
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Citations

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Cited by:
  1. SELEZNEVA Ekaterina & VAN KERM Philippe, 2013. "Inequality-adjusted gender wage differentials in Germany," CEPS/INSTEAD Working Paper Series 2013-18, CEPS/INSTEAD.
  2. Schwiebert, Jörg, 2012. "Semiparametric Estimation of a Sample Selection Model in the Presence of Endogeneity," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover dp-504, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  3. Louis N. Christofides & Alexandros Polycarpou & Konstantinos Vrachimis, 2013. "Gender Wage Gaps, ‘Sticky Floors’ and ‘Glass Ceilings’ in Europe," University of Cyprus Working Papers in Economics 02-2013, University of Cyprus Department of Economics.
  4. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.
  5. Philippe Van Kerm, 2013. "Generalized measures of wage differentials," Empirical Economics, Springer, vol. 45(1), pages 465-482, August.
  6. DOORLEY Karina & SIERMINSKA Eva, 2011. "Beauty and the beast in the labor market: Evidence from a distribution regression approach," CEPS/INSTEAD Working Paper Series 2011-62, CEPS/INSTEAD.
  7. Martin Huber & Blaise Melly, 2012. "A test of the conditional independence assumption in sample selection models," Working Papers 2012-11, Brown University, Department of Economics.

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