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Testing for Consistency using Artificial Regressions

Published online by Cambridge University Press:  18 October 2010

Russell Davidson  and
James G. MacKinnon
Russell Davidson
Affiliation:
Queen's University, Ontario, Canada and GREQE, Ecole des Hautes Etudes en Sciences Sociales, Universités d'Aix-Marseille ll et ll Centre de la Vieille Charité, France
James G. MacKinnon
Affiliation:
Queen's University, Ontario, Canada
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Abstract

We consider several issues related to Durbin-Wu-Hausman tests; that is, tests based on the comparison of two sets of parameter estimates. We first review a number of results about these tests in linear regression models, discuss what determines their power, and propose a simple way to improve 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 of the implications of this result. In particular, we show how to create a variant of the information matrix test that tests for parameter consistency. We examine the conventional information matrix test and our new version in the context of binary-choice models, and provide a simple way to compute both tests using artificial regressions.

Type
Articles
Information
Econometric Theory , Volume 5 , Issue 3 , December 1989 , pp. 363 - 384
Copyright
Copyright © Cambridge University Press 1989

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