IDEAS home Printed from https://ideas.repec.org/p/nuf/econwp/9604.html
   My bibliography  Save this paper

An omnibus test for univariate and multivariate normalit

Author

Listed:
  • Jurgen A Doornik
  • Henrik Hansen

Abstract

We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based on Shenton and Bowman ["Journal of the American Statistical Association" (1977) Vol. 72, pp. 206-211], which controls well for size, for samples as low as 10 observations. A multivariate version is introduced. Size and power are investigated in comparison with four other tests for multivariate normality. The first power experiments consider the whole skewness-kurtosis plane; the second use a bivariate distribution which has normal marginals. It is concluded that the proposed test has the best size and power properties of the tests considered. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2008.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jurgen A Doornik & Henrik Hansen, "undated". "An omnibus test for univariate and multivariate normalit," Economics Papers W4&91., Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:9604
    as

    Download full text from publisher

    File URL: http://www.nuff.ox.ac.uk/economics/papers/1996/w4/normal2.ps
    Download Restriction: no

    File URL: http://www.nuff.ox.ac.uk/economics/papers/1996/w4/normal2.zip
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Srivastava, M. S. & Hui, T. K., 1987. "On assessing multivariate normality based on shapiro-wilk W statistic," Statistics & Probability Letters, Elsevier, vol. 5(1), pages 15-18, January.
    2. Srivastava, M. S., 1984. "A measure of skewness and kurtosis and a graphical method for assessing multivariate normality," Statistics & Probability Letters, Elsevier, vol. 2(5), pages 263-267, October.
    3. Jean-Marie Dufour & Abdeljelil Farhat & Lucien Gardiol & Lynda Khalaf, 1998. "Simulation-based finite sample normality tests in linear regressions," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 154-173.
    4. Bera, A. & John, S., 1983. "Tests for multivariate normality with Pearson alternatives," CORE Discussion Papers RP 534, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

    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:nuf:econwp:9604. 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: (Maxine Collett). General contact details of provider: https://www.nuffield.ox.ac.uk/economics/ .

    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.