IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v47y2020i9p1562-1586.html
   My bibliography  Save this article

Modified likelihood ratio tests for unit gamma regressions

Author

Listed:
  • Ana C. Guedes
  • Francisco Cribari-Neto
  • Patrícia L. Espinheira

Abstract

Regression analyses are commonly performed with doubly limited continuous dependent variables; for instance, when modeling the behavior of rates, proportions and income concentration indices. Several models are available in the literature for use with such variables, one of them being the unit gamma regression model. In all such models, parameter estimation is typically performed using the maximum likelihood method and testing inferences on the model's parameters are usually based on the likelihood ratio test. Such a test can, however, deliver quite imprecise inferences when the sample size is small. In this paper, we propose two modified likelihood ratio test statistics for use with the unit gamma regressions that deliver much more accurate inferences when the number of data points in small. Numerical (i.e. simulation) evidence is presented for both fixed dispersion and varying dispersion models, and also for tests that involve nonnested models. We also present and discuss two empirical applications.

Suggested Citation

  • Ana C. Guedes & Francisco Cribari-Neto & Patrícia L. Espinheira, 2020. "Modified likelihood ratio tests for unit gamma regressions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(9), pages 1562-1586, June.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1562-1586
    DOI: 10.1080/02664763.2019.1683152
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2019.1683152
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2019.1683152?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.

    Citations

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


    Cited by:

    1. Tiago M. Magalhães & Gustavo H. A. Pereira & Denise A. Botter & Mônica C. Sandoval, 2024. "Bartlett corrections for zero-adjusted generalized linear models," Statistical Papers, Springer, vol. 65(4), pages 2191-2209, June.
    2. Suelena S. Rocha & Patrícia L. Espinheira & Francisco Cribari‐Neto, 2021. "Residual and local influence analyses for unit gamma regressions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(2), pages 137-160, May.

    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:japsta:v:47:y:2020:i:9:p:1562-1586. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.