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Quantile regression for duration models with time-varying regressors

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  • Chen, Songnian

Abstract

Since the seminal work of Koenker and Bassett (1978), quantile regression has become a widely used tool in duration analysis. The existing literature, however, has focused on time-invariant regressors, even though time-varying regressors are common in practice. In this paper, we introduce a quantile regression framework with time-varying regressors and develop quantile regression estimators. Our estimators are motivated by Manski’s (1975, 1985) maximum score estimator and Chen’s (2010) integrated maximum score estimator. Our estimators are consistent and asymptotically normal under some regularity conditions, and perform well in finite samples. Our method is illustrated with an unemployment duration data set.

Suggested Citation

  • Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    • Handle: RePEc:eee:econom:v:209:y:2019:i:1:p:1-17
    DOI: 10.1016/j.jeconom.2018.11.015
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    5. Geert Ridder, 1990. "The Non-Parametric Identification of Generalized Accelerated Failure-Time Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(2), pages 167-181.
    6. Bijwaard, G.E., 2002. "Instrumental variable estimation for duration data," Econometric Institute Research Papers EI 2002-39, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. 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.
    8. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    9. 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.
    10. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    11. Bo E. HonorÈ & Luojia Hu, 2010. "Estimation of a transformation model with truncation, interval observation and time-varying covariates," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 127-144, February.
    12. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    13. Hausman, Jerry A. & Woutersen, Tiemen, 2014. "Estimating a semi-parametric duration model without specifying heterogeneity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 114-131.
    14. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521424318, November.
    15. Bijwaard, G.E., 2007. "Instrumental variable estimation of treatment effects for duration outcomes," Econometric Institute Research Papers EI 2007-20, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. Horváth, Lajos, 1983. "The rate of strong uniform consistency for the multivariate product-limit estimator," Journal of Multivariate Analysis, Elsevier, vol. 13(1), pages 202-209, March.
    17. Thomas, Laine & Reyes, Eric M., 2014. "Tutorial: Survival Estimation for Cox Regression Models with Time-Varying Coe?cients Using SAS and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(c01).
    18. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    19. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    20. Portnoy S., 2003. "Censored Regression Quantiles," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1001-1012, January.
    21. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
    22. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    23. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    24. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521370905, November.
    25. 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.
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    Cited by:

    1. 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.
    2. Zamanzadeh, Akbar & Chan, Marc K. & Ehsani, Mohammad Ali & Ganjali, Mojtaba, 2020. "Unemployment duration, Fiscal and monetary policies, and the output gap: How do the quantile relationships look like?," Economic Modelling, Elsevier, vol. 91(C), pages 613-632.

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    More about this item

    Keywords

    Quantile regression; Time-varying regressors; Duration analysis;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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