Competing risks quantile regression at work: in-depth exploration of the role of public child support for the duration of maternity leave. (English) Zbl 1516.62254
Summary: Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We use large-scale maternity duration data with multiple competing risks derived from German linked social security records to analyse how public policies are related to the length of economic inactivity of young mothers after giving birth. Our results show that the model delivers detailed insights into the distribution of transitions out of maternity leave. It is found that cumulative incidences implied by the quantile regression model differ from those implied by a proportional hazards model. To foster the use of the model, we make an R-package (cmprskQR) available.
MSC:
| 62-XX | Statistics |
References:
| [1] | M. Arntz, S. Dlugosz, and R.A. Wilke, The sorting of female careers after first birth: A competing risks analysis of out of work duration, ZEW Discussion Paper (2014), pp. 14-125. |
| [2] | V. Chernozhukov, I. Fernández-Val, and A. Galichon, Quantile and probability curves without crossing, Econometrica 78 (2010), pp. 1093-1125. doi: 10.3982/ECTA7880 · Zbl 1192.62255 · doi:10.3982/ECTA7880 |
| [3] | J.P. Fine and R.J. Gray, A proportional hazards model for the subdistribution of a competing risk, J. Amer. Statist. Assoc. 94 (1999), pp. 496-509. doi: 10.1080/01621459.1999.10474144 · Zbl 0999.62077 |
| [4] | B. Fitzenberger and R.A. Wilke, Using quantile regression for duration analysis, Allgemeines Statistisches Archiv 90 (2006), pp. 103-118. doi: 10.1007/s10182-006-0224-2 · Zbl 1103.62094 · doi:10.1007/s10182-006-0224-2 |
| [5] | D. Hochfellner, D. Müller, and A. Wurdack, Biographical data of social insurance agencies in Germany – improving the content of administrative data, Schmollers Jahrbuch 132 (2012), pp. 443-451. doi: 10.3790/schm.132.3.443 · doi:10.3790/schm.132.3.443 |
| [6] | J. J. Hsieh, A. A. Ding, W. Wang, and Y. L. Chi, Quantile regression based on semi-competing risks data, Open J. Stat. 3 (2013), pp. 12-26. doi: 10.4236/ojs.2013.31003 · doi:10.4236/ojs.2013.31003 |
| [7] | R. Koenker, Quantile Regression, Cambridge University Press, New York, 2005. · Zbl 1111.62037 · doi:10.1017/CBO9780511754098 |
| [8] | R. Koenker, Censored quantile regression redux, J. Statist. Softw. 27 (2008), pp. 1-24. doi: 10.18637/jss.v027.i06 · doi:10.18637/jss.v027.i06 |
| [9] | R. Koenker and G. Bassett, Regression quantiles, Econometrica 46 (1978), pp. 33-50. doi: 10.2307/1913643 · Zbl 0373.62038 · doi:10.2307/1913643 |
| [10] | R. Koenker and O. Geling, Reappraising medfly longevity: A quantile regression survival analysis, J. Amer. Statist. Assoc. 96 (2001), pp. 458-468. doi: 10.1198/016214501753168172 · Zbl 1019.62100 |
| [11] | L. Peng and J.P. Fine, Competing risks quantile regression, J. Amer. Statist. Assoc. 104 (2009), pp. 1440-1453. doi: 10.1198/jasa.2009.tm08228 · Zbl 1205.62090 |
| [12] | J.L. Powell, Censored regression quantiles, J. Economet. 32 (1986), pp. 143-155. doi: 10.1016/0304-4076(86)90016-3 · Zbl 0605.62139 · doi:10.1016/0304-4076(86)90016-3 |
| [13] | J. Qian and L. Peng, Censored quantile regression with partially functional effects, Biometrika 97 (2010), pp. 839-850. doi: 10.1093/biomet/asq050 · Zbl 1204.62071 · doi:10.1093/biomet/asq050 |
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