IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v27y2008i4-6p526-541.html
   My bibliography  Save this article

Finite Sample Evidence Suggesting a Heavy Tail Problem of the Generalized Empirical Likelihood Estimator

Author

Listed:
  • Patrik Guggenberger

Abstract

Comprehensive Monte Carlo evidence is provided that compares the finite sample properties of generalized empirical likelihood (GEL) estimators to the ones of k-class estimators in the linear instrumental variables (IV) model. We focus on sample median, mean, mean squared error, and on the coverage probability and length of confidence intervals obtained from inverting a t-statistic based on the various estimators. The results indicate that in terms of the above criteria, all the GEL estimators and the limited information maximum likelihood (LIML) estimator behave very similarly. This suggests that GEL estimators might also share the “no-moment” problem of LIML. At sample sizes as in our Monte Carlo study, there is no systematic bias advantage of GEL estimators over k-class estimators. On the other hand, the standard deviation of GEL estimators is pronouncedly higher than for some of the k-class estimators. Therefore, if mean squared error is used as the underlying loss function, our study suggests the use of computationally simple estimators, such as two-stage least squares, in the linear IV model rather than GEL. Based on the properties of confidence intervals, we cannot recommend the use of GEL estimators either in the linear IV model.

Suggested Citation

  • Patrik Guggenberger, 2008. "Finite Sample Evidence Suggesting a Heavy Tail Problem of the Generalized Empirical Likelihood Estimator," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 526-541.
  • Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:526-541
    DOI: 10.1080/07474930801960410
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930801960410
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/07474930801960410?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.

    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:emetrv:v:27:y:2008:i:4-6:p:526-541. 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.

    We have no bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.