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Artificial regression based mis-specification tests for discrete choice models

Author

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  • Anthony Murphy

Abstract

LM tests for omitted variables, neglected heteroscedasticity and other mis-specifications in general discrete choice models may be simply and conveniently calculated using an artificial regression. This artificial regression approach is likely to have better small sample properties than the more common outer product gradient (OPG) form of LM test.

Suggested Citation

  • Anthony Murphy, 1994. "Artificial regression based mis-specification tests for discrete choice models," Working Papers 199416, School of Economics, University College Dublin.
    • Handle: RePEc:ucn:wpaper:199416
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    File URL: http://hdl.handle.net/10197/1760
    File Function: First version, 1994
    Download Restriction: no
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    Citations

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    Cited by:

    1. Murphy, Anthony, 1996. "Simple LM tests of mis-specification for ordered logit models," Economics Letters, Elsevier, vol. 52(2), pages 137-141, August.
    2. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.

    More about this item

    Keywords

    Discrete choice; LM mis-specification tests; Artificial regressions; Regression analysis; Econometrics--Mathematical models;
    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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