IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

Using quantile regression for duration analysis

  • Bernd Fitzenberger

    ()

  • Ralf Wilke

    ()

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.

(This abstract was borrowed from another version of this item.)

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://hdl.handle.net/10.1007/s10182-006-0224-2
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Springer in its journal Allgemeines Statistisches Archiv.

Volume (Year): 90 (2006)
Issue (Month): 1 (March)
Pages: 105-120

as
in new window

Handle: RePEc:spr:alstar:v:90:y:2006:i:1:p:105-120
Contact details of provider: Web page: http://www.springerlink.com/link.asp?id=112915

Order Information: Web: http://link.springer.de/orders.htm

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. 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.
  2. Wilke, Ralf A. & Fitzenberger, Bernd & Zhang, Xuan, 2005. "A Note on Implementing Box-Cox Quantile Regression," ZEW Discussion Papers 04-61 [rev.], ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  3. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
  4. 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.
  5. 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.
  6. 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.
  7. 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, vol. 89(2), pages 209-236, June.
  8. Portnoy S., 2003. "Censored Regression Quantiles," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1001-1012, January.
  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. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
  11. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
  12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  13. 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.
  14. 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.
  15. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
  16. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:spr:alstar:v:90:y:2006:i:1:p:105-120. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn)

or (Christopher F Baum)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.