IDEAS home Printed from https://ideas.repec.org/p/ucn/wpaper/199422.html
   My bibliography  Save this paper

A simple artificial regression based Lagrange multiplier test of normality in the probit model

Author

Listed:
  • Anthony Murphy

Abstract

A convenient artifical regression based LM test of non-normality in the probit model is derived using a Gram Charlier type A alternative. The test is simply derived and may be extended to the bivariate probit case. The outer product gradient form of LM test is not used so the proposed test is likely to perform reasonably well in small samples. The test is compared with two other existing tests.

Suggested Citation

  • Anthony Murphy, 1994. "A simple artificial regression based Lagrange multiplier test of normality in the probit model," Working Papers 199422, School of Economics, University College Dublin.
    • Handle: RePEc:ucn:wpaper:199422
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10197/1951
    File Function: First version, 1994
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Albuquerque, Paula C., 2003. "The Traditional Brokers: What are their Chances in the Forex?," Journal of Applied Economics, Universidad del CEMA, vol. 6(2), pages 1-16, November.

    More about this item

    Keywords

    Probit; Lagrange multiplier test; Non-normality; Gram Charlier series; Artificial regression; Outer product gradient; Probits; Econometrics--Mathematical models; Regression analysis;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    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:ucn:wpaper:199422. 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: Nicolas Clifton (email available below). General contact details of provider: https://edirc.repec.org/data/educdie.html .

    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.