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Using Quantile Regression for Duration Analysis

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  • Fitzenberger, Bernd
  • Wilke, Ralf A.

Abstract

Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the functional relationship and of the error distribution. Censored quantile regression address the issue of right censoring of the response variable which is common in duration analysis. We compare quantile regression to standard duration models. Quantile regression do not impose a proportional effect of the covariates on the hazard over the duration time. However, the method can not take account of time{varying covariates and it has not been extended so far to allow for unobserved heterogeneity and competing risks. We also discuss how hazard rates can be estimated using quantile regression methods. A small application with German register data on unemployment duration for younger workers demonstrates the applicability and the usefulness of quantile regression for empirical duration analysis. --

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Bibliographic Info

Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 05-65.

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Date of creation: 2005
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Handle: RePEc:zbw:zewdip:4548

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Keywords: censored quantile regression; unemployment duration; unobserved heterogeneity; hazard rate;

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References

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  1. Yannis Bilias & Roger Koenker, 2001. "Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments," Empirical Economics, Springer, vol. 26(1), pages 199-220.
  2. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, April.
  3. Fitzenberger, Bernd, 1998. "The moving blocks bootstrap and robust inference for linear least squares and quantile regressions," Journal of Econometrics, Elsevier, vol. 82(2), pages 235-287, February.
  4. Wilke, Ralf A. & Fitzenberger, Bernd & Zhang, Xuan, 2004. "A Note on Implementing Box-Cox Quantile Regression," ZEW Discussion Papers 04-61, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  5. Hong H. & Chernozhukov V., 2002. "Three-Step Censored Quantile Regression and Extramarital Affairs," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 872-882, September.
  6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  7. Biewen, Martin & Wilke, Ralf A., 2005. "Unemployment Duration and the Length of Entitlement Periods for Unemployment Benefits: Do the IAB Employment Subsample and the German Socio-Economic Panel Yield the Same Results?," ZEW Discussion Papers 05-05, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  8. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
  9. José A. F. Machado & Pedro Portugal, 2002. "Quantile Regression Methods: na Application to U.S. Unemployment Duration," Working Papers w200201, Banco de Portugal, Economics and Research Department.
  10. 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.
  11. Portnoy S., 2003. "Censored Regression Quantiles," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1001-1012, January.
  12. Arntz, Melanie, 2005. "The Geographical Mobility of Unemployed Workers: Evidence from West Germany," ZEW Discussion Papers 05-34, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  13. Juliana Guimarães & (Universidade NOVA de Lisboa, 2004. "Has long become longer or short become shorter? Evidence from a censored quantile regression analysis of the changes in the distribution of U.S. unemployment duration," Econometric Society 2004 Latin American Meetings 128, Econometric Society.
  14. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
  15. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
  16. Bilias, Yannis & Chen, Songnian & Ying, Zhiliang, 2000. "Simple resampling methods for censored regression quantiles," Journal of Econometrics, Elsevier, vol. 99(2), pages 373-386, December.
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Citations

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Cited by:
  1. Coad, Alex & Segarra Blasco, Agustí, 1958- & Teruel, Mercedes, 2013. "Innovation and firm growth: Does firm age play a role?," Working Papers 2072/211886, Universitat Rovira i Virgili, Department of Economics.
  2. Melanie Arntz & Ralf Wilke, 2009. "Unemployment Duration in Germany: Individual and Regional Determinants of Local Job Finding, Migration and Subsidized Employment," Regional Studies, Taylor & Francis Journals, vol. 43(1), pages 43-61.
  3. Eva Mueller & Ralf A. Wilke & Philipp Zahn, 2007. "Beschäftigung und Arbeitslosigkeit aelterer Arbeitnehmer, Eine mikrooekonometrische Evaluation der Arbeitslosengeldreform von 1997," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 227(1), pages 65-86, February.
  4. Wichert, Laura & Wilke, Ralf A., 2005. "Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis," ZEW Discussion Papers 05-67, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  5. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
  6. De Silva, Dakshina G. & Kosmopoulou, Georgia & Lamarche, Carlos, 2007. "The Effect of Information on the Bidding and Survival of Entrants in Procurement Auctions," MPRA Paper 5236, University Library of Munich, Germany.
  7. Chen, Songnian, 2010. "An integrated maximum score estimator for a generalized censored quantile regression model," Journal of Econometrics, Elsevier, vol. 155(1), pages 90-98, March.
  8. Laura Wichert & Ralf A. Wilke, 2008. "Simple non-parametric estimators for unemployment duration analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(1), pages 117-126.
  9. Boockmann, Bernhard & Steffes, Susanne, 2007. "Seniority and Job Stability: A Quantile Regression Approach Using Matched Employer-Employee Data," ZEW Discussion Papers 07-014, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.

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