IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v38y2006i3p301-305.html
   My bibliography  Save this article

An approach to nonparametric smoothing techniques for regressions with discrete data

Author

Listed:
  • Kajal Mukhopadhyay
  • Lawrence Marsh

Abstract

This paper proposes nonparametric regression estimation techniques for small samples in situations where the dependent variable involves count data. Often the form of a kernel will not matter asymptotically. However, in small samples the kernel structure may play a more important role in approximating the small sample distribution especially for discrete random variables. In particular for count data we introduce a Poisson kernel regression estimator and a binomial kernel regression estimator. These new regression methods are applied to coal mine wildcat strike data. We use cross validation to evaluate out-of-sample performance.

Suggested Citation

  • Kajal Mukhopadhyay & Lawrence Marsh, 2006. "An approach to nonparametric smoothing techniques for regressions with discrete data," Applied Economics, Taylor & Francis Journals, vol. 38(3), pages 301-305.
    • Handle: RePEc:taf:applec:v:38:y:2006:i:3:p:301-305
    DOI: 10.1080/00036840500368581
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840500368581
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036840500368581?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ullah, A. & Vinod, H.D., 1992. ""General Nonparametric Regression Estimation and Testing in Econometrics"," The A. Gary Anderson Graduate School of Management 92-34, The A. Gary Anderson Graduate School of Management. University of California Riverside.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. Dominitz & C. F. Manski, "undated". "Using expectations data to study subjective income expectations," Institute for Research on Poverty Discussion Papers 1050-94, University of Wisconsin Institute for Research on Poverty.
    2. Fan, Yanqin & Ullah, Aman, 1999. "Asymptotic Normality of a Combined Regression Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 191-240, November.

    More about this item

    Statistics

    Access and download statistics

    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:taf:applec:v:38:y:2006:i:3:p:301-305. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.