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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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File URL: http://www1.vwa.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
Date of revision:
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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  2. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-90, March.
  3. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
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  11. 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.
  12. Pedro Carneiro & Sokbae 'Simon' Lee, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," CeMMAP working papers CWP01/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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  23. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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Cited by:

  1. Bargain, Olivier & Kwenda, Prudence, 2009. "The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data," IZA Discussion Papers 4286, Institute for the Study of Labor (IZA).
  2. 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.
  3. 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.
  4. Van Kerm, Philippe, 2009. "Generalized measures of wage differentials," IRISS Working Paper Series 2009-08, IRISS at CEPS/INSTEAD.
  5. SELEZNEVA Ekaterina & VAN KERM Philippe, 2013. "Inequality-adjusted gender wage differentials in Germany," CEPS/INSTEAD Working Paper Series 2013-18, CEPS/INSTEAD.
  6. 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.
  7. 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.
  8. Louis N. Christofides & Alexandros Polycarpou & Konstantinos Vrachimis, 2013. "Gender Wage Gaps, 'Sticky Floors' and 'Glass Ceilings' in Europe," Working Papers 1301, University of Guelph, Department of Economics and Finance.

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