IDEAS home Printed from
   My bibliography  Save this paper

A Partially Linear Censored Quantile Regression Model for Unemployment Duration


  • Neocleous, Tereza

    (University of Glasgow)

  • Portnoy, Stephen

    (University of Illinois)


Censored Regression Quantile (CRQ) methods provide a powerful and flexible approach for the analysis of censored survival data when standard linear models are felt to be appropriate. In many cases however, greater flexibility is desired to go beyond the usual multiple regression paradigm. One area of common interest is that of partially linear models, where one (or more) of the explanatory variables are assumed to act on the response through a non-linear function. Here the CRQ approach (Portnoy, 2003) is extended to such partially linear setting. Basic consistency results are presented. A simulation experiment and analysis of unemployment data example justify the use of the partially linear approach over methods based on the Cox proportional hazards regression model and methods not permitting nonlinearity.

Suggested Citation

  • Neocleous, Tereza & Portnoy, Stephen, 2008. "A Partially Linear Censored Quantile Regression Model for Unemployment Duration," IRISS Working Paper Series 2008-07, IRISS at CEPS/INSTEAD.
  • Handle: RePEc:irs:iriswp:2008-07

    Download full text from publisher

    File URL:
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Sohn, Christophe & Reitel, Bernard & Walther, Olivier, 2009. "Cross-border metropolitan integration in Europe (Luxembourg, Basel and Geneva)," IRISS Working Paper Series 2009-02, IRISS at CEPS/INSTEAD.

    More about this item


    quantile regression ; partially linear models ; B-splines ; censored data ; unemployment duration;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:irs:iriswp:2008-07. 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: (Philippe Van Kerm). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.