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Quantile Regression in the Presence of Sample Selection

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Listed author(s):
  • 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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File URL: http://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-1109.pdf
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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
Handle: RePEc:usg:econwp:2011:09
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Keywords: Sample selection; quantile regression; independence; test;

Find related papers by JEL classification:

  • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
  • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
  • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
  • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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  13. Carneiro, Pedro & Lee, Sokbae, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," Journal of Econometrics, Elsevier, vol. 149(2), pages 191-208, April.
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Cited by:

  1. Ekaterina Selezneva & Philippe Van Kerm, 2013. "Inequality-adjusted gender wage differentials in Germany," Working Papers 334, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
  2. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, 02.
  3. Olivier Bargain & Prudence Kwenda, 2014. "The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data," Economic Development and Cultural Change, University of Chicago Press, vol. 63(1), pages 117-153.
  4. Stefan Hoderlein & Bettina Siflinger & Joachim Winter, 2015. "Identification of structural models in the presence of measurement error due to rounding in survey responses," Boston College Working Papers in Economics 869, Boston College Department of Economics.
  5. Christofides, Louis N. & Polycarpou, Alexandros & Vrachimis, Konstantinos, 2013. "Gender wage gaps, ‘sticky floors’ and ‘glass ceilings’ in Europe," Labour Economics, Elsevier, vol. 21(C), pages 86-102.
  6. Schwiebert, Jörg, 2012. "Semiparametric Estimation of a Sample Selection Model in the Presence of Endogeneity," Hannover Economic Papers (HEP) dp-504, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  7. Ekaterina Selezneva & Philippe Van Kerm, 2016. "A distribution-sensitive examination of the gender wage gap in Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(1), pages 21-40, March.
  8. Philippe Van Kerm, 2013. "Generalized measures of wage differentials," Empirical Economics, Springer, vol. 45(1), pages 465-482, August.
  9. 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.
  10. DOORLEY Karina & SIERMINSKA Eva, 2011. "Beauty and the beast in the labor market: Evidence from a distribution regression approach," LISER Working Paper Series 2011-62, LISER.

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