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Testing for Consistency using Artificial Regressions

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  • Russell Davidson
  • James G. MacKinnon

Abstract

We consider several issues related to what Hausman (1978) called "specification tests", namely tests designed to verify the consistency of parameter estimates. We first review a number of results about these tests in linear regression models, and present some new material on their distribution when the model being tested is false, and on a simple way to improve their power in certain cases. We then show how in a general nonlinear setting they may be computed as "score" tests by means of slightly modified versions of any artificial linear regression that can be used to calculate Lagrange multiplier tests, and explore some implications of this result. We show how to create a variant of the information matrix test to test for parameter consistency. We examine conventional tests and our new version in the context of binary choice models, and provide a simple way to compute both tests based on artificial regressions. Some Monte Carlo evidence suggests the most common form of information matrix test can be extremely badly behaved in samples of even quite large size.

Suggested Citation

  • Russell Davidson & James G. MacKinnon, 1987. "Testing for Consistency using Artificial Regressions," Working Paper 687, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:687
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    References listed on IDEAS

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    6. DAVIDSON, Russel & MACKINNON, James G., 1985. "Heteroskedastcity-robust tests in regressions directions," LIDAM Reprints CORE 678, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    23. Russell Davidson & James G. MacKinnon, 1985. "Testing Linear and Loglinear Regressions against Box-Cox Alternatives," Canadian Journal of Economics, Canadian Economics Association, vol. 18(3), pages 499-517, August.
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