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Using quantile regression for duration analysis

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  • Bernd Fitzenberger
  • Ralf Wilke

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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Suggested Citation

  • Bernd Fitzenberger & Ralf Wilke, 2006. "Using quantile regression for duration analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 105-120, March.
    • Handle: RePEc:spr:alstar:v:90:y:2006:i:1:p:105-120
    DOI: 10.1007/s10182-006-0224-2
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    Cited by:

    1. 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.
    2. Xavier D’Haultfoeuille & Pauline Givord, 2014. "La régression quantile en pratique," Économie et Statistique, Programme National Persée, vol. 471(1), pages 85-111.
    3. repec:jns:jbstat:v:227:y:2007:i:1:p:65-86 is not listed on IDEAS
    4. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    5. 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.
    6. repec:iab:iabfme:200709(en is not listed on IDEAS
    7. 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.
    8. 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.
    9. Müller Eva & Zahn Philipp & Wilke Ralf A., 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.
    10. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Alona Zharova & Andrija Mihoci & Wolfgang Karl Härdle, 2016. "Academic Ranking Scales in Economics: Prediction and Imputation," SFB 649 Discussion Papers SFB649DP2016-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    19. 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].

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    More about this item

    Keywords

    Censored quantile regression; hazard rate; unobserved heterogeneity. JEL C13; C14;
    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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