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Using Quantile Regression for Duration Analysis

In: Modern Econometric Analysis

Author

Listed:
  • Bernd Fitzenberger

    (J.W. Goethe University Frankfurt)

  • Ralf A. Wilke

    (Centre for European Economic Research (ZEW))

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 addresses 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 does not impose a proportional effect of the covariates on the hazard over the duration time. However, the method cannot 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.

Suggested Citation

  • Bernd Fitzenberger & Ralf A. Wilke, 2006. "Using Quantile Regression for Duration Analysis," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 8, pages 103-118, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-32693-9_8
    DOI: 10.1007/3-540-32693-6_8
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    References listed on IDEAS

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    9. Martin Biewen & Ralf Wilke, 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?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 89(2), pages 209-236, June.
    10. 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.
    11. Wilke, Ralf A. & Fitzenberger, Bernd & Zhang, Xuan, 2004. "A Note on Implementing Box-Cox Quantile Regression," ZEW Discussion Papers 04-61, ZEW - Leibniz Centre for European Economic Research.
    12. 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.
    13. 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.
    14. Koenker R. & Geling O., 2001. "Reappraising Medfly Longevity: A Quantile Regression Survival Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 458-468, June.
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    Citations

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    Cited by:

    1. 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.
    2. repec:jns:jbstat:v:227:y:2007:i:1:p:65-86 is not listed on IDEAS
    3. repec:iab:iabfme:200709(en is not listed on IDEAS
    4. Debajyoti Sinha & Piyali Basak & Stuart R. Lipsitz, 2022. "Median regression models for clustered, interval-censored survival data - An application to prostate surgery study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 723-743, October.
    5. Wilke, Ralf A. & Wichert, Laura, 2005. "Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis," ZEW Discussion Papers 05-67 [rev.], ZEW - Leibniz Centre for European Economic Research.
    6. repec:hum:wpaper:sfb649dp2016-020 is not listed on IDEAS
    7. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    8. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    9. Coad, Alex & Segarra, Agustí & Teruel, Mercedes, 2016. "Innovation and firm growth: Does firm age play a role?," Research Policy, Elsevier, vol. 45(2), pages 387-400.
    10. Wichert, Laura & Wilke, Ralf A., 2007. "Simple nonparametric estimators for unemployment duration analysis," FDZ Methodenreport 200709_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    11. De Silva, Dakshina G. & Kosmopoulou, Georgia & Lamarche, Carlos, 2009. "The effect of information on the bidding and survival of entrants in procurement auctions," Journal of Public Economics, Elsevier, vol. 93(1-2), pages 56-72, February.
    12. Härdle, Wolfgang Karl & Spokoiny, Vladimir & Wang, Weining, 2010. "Local quantile regression," SFB 649 Discussion Papers 2011-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Xavier D’Haultfoeuille & Pauline Givord, 2014. "La régression quantile en pratique," Économie et Statistique, Programme National Persée, vol. 471(1), pages 85-111.
    14. Müller Eva & Wilke Ralf A. & Zahn Philipp, 2007. "Beschäftigung und Arbeitslosigkeit älterer Arbeitnehmer / Employment and Unemployment of the Elderly: Eine mikroökonometrische Evaluation der Arbeitslosengeldreform von 1997 / A Microeconometric Evalu," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 227(1), pages 65-86, February.
    15. Huiping Li & Yunxuan Li, 2023. "A Novel Explanatory Tabular Neural Network to Predicting Traffic Incident Duration Using Traffic Safety Big Data," Mathematics, MDPI, vol. 11(13), pages 1-24, June.
    16. De Silva, Dakshina G. & Kosmopoulou, Georgia & Lamarche, Carlos, 2017. "Subcontracting and the survival of plants in the road construction industry: A panel quantile regression analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 137(C), pages 113-131.
    17. Zharova, Alona & Mihoci, Andrija & Härdle, Wolfgang Karl, 2016. "Academic ranking scales in economics: Prediction and imputation," SFB 649 Discussion Papers 2016-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. 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.
    19. Chen, Songnian, 2023. "Two-step estimation of censored quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 235(2), pages 1310-1336.
    20. Bernd Fitzenberger & Ralf A. Wilke, 2010. "New Insights into Unemployment Duration and Post Unemployment Earnings in Germany," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(6), pages 794-826, December.
    21. Boockmann, Bernhard & Steffes, Susanne, 2007. "Seniority and Job Stability: A Quantile Regression Approach Using Matched Employer-Employee Data," ZEW Discussion Papers 07-014, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    Keywords

    Hazard Rate; Unobserved Heterogeneity; Duration Data; Unemployment Duration; Duration Analysis;
    All these keywords.

    JEL classification:

    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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