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Testing normality in bivariate probit models : a simple artificial regression based LM test

Author

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  • Anthony Murphy

Abstract

A simple and convenient LM test of normality in the bivariate probit model is derived. The alternative hypothesis is based on a form of truncated Gram Charlier Type series. The LM test may be calculated as an artificial regression. However, the proposed artificial regression does not use the outer product gradient form. Thus it is likely to perform reasonably well in small samples.

Suggested Citation

  • Anthony Murphy, 1994. "Testing normality in bivariate probit models : a simple artificial regression based LM test," Working Papers 199427, School of Economics, University College Dublin.
    • Handle: RePEc:ucn:wpaper:199427
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    File URL: http://hdl.handle.net/10197/1768
    File Function: First version, 1994
    Download Restriction: no
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    Cited by:

    1. Mosconi, Rocco & Seri, Raffaello, 2006. "Non-causality in bivariate binary time series," Journal of Econometrics, Elsevier, vol. 132(2), pages 379-407, June.

    More about this item

    Keywords

    Bivariate probit; Normality; Truncated Gram Charlier series; LM test; Artificial regression; 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

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