Testing for Consistency using Artificial RegressionsRussell Davidson and James MacKinnon () Working Papers from Queen's University, Department of Economics 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. Keywords: Durbin-Hausman tests; information matrix tests; binary choice models; Lagrange multiplier tests (search for similar items in EconPapers) Published in Econometric Theory, 5, 1989 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: http://EconPapers.repec.org/RePEc:qed:wpaper:687 Access Statistics for this paper More papers in Working Papers from Queen's University, Department of Economics |
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