IDEAS home Printed from https://ideas.repec.org/p/gen/geneem/2009.07.html
   My bibliography  Save this paper

A Robust Version o f the Hurdle Model

Author

Listed:
  • Eva Cantoni
  • Asma Zedini

Abstract

The excess of zeros is a not a rare feature in count data. Statisticians advocate the Poisson-type hurdle model (among other techniques) as an interesting approach to handle this data peculiarity. However, the frequency of gross errors and the complexity intrinsic to some considered phenomena may render this classical model unreliable and too limiting. In this paper, we develop a robust version of the Poisson hurdle model by extending the robust procedure for GLM (Cantoni and Ronchetti, 2001) to the truncated Poisson regression model. The performance of the new robust approach is then investigated via a simulation study, a real data application and a sensitivity analysis. The results show the reliability of the new technique in the neighborhood of the truncated Poissonmodel. This robustmodelling approach is therefore a valuable complement to the classical one, providing a tool for reliable statistical conclusions and to take more effective decisions.

Suggested Citation

  • Eva Cantoni & Asma Zedini, 2009. "A Robust Version o f the Hurdle Model," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2009.07, Institut d'Economie et Econométrie, Université de Genève.
  • Handle: RePEc:gen:geneem:2009.07
    as

    Download full text from publisher

    File URL: http://www.unige.ch/ses/dsec/repec/files/2009_07.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Winkelmann, Rainer & Zimmermann, Klaus F, 1995. " Recent Developments in Count Data Modelling: Theory and Application," Journal of Economic Surveys, Wiley Blackwell, vol. 9(1), pages 1-24, March.
    2. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    3. Winfried Pohlmeier & Volker Ulrich, 1995. "An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care," Journal of Human Resources, University of Wisconsin Press, vol. 30(2), pages 339-361.
    4. Cantoni, Eva & Ronchetti, Elvezio, 2006. "A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures," Journal of Health Economics, Elsevier, vol. 25(2), pages 198-213, March.
    Full references (including those not matched with items on IDEAS)

    Corrections

    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:gen:geneem:2009.07. 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: (). General contact details of provider: http://www.unige.ch/gsem/dsec/index.html .

    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 CitEc recognized a reference but did not link an item in RePEc 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 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.