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Discrepancy Risk Model Selection Test Theory for Comparing Possibly Misspecified or Nonnested Models

Published online by Cambridge University Press:  01 January 2025

R. M. Golden
R. M. Golden*
Affiliation:
Applied Cognition and Neuroscience Program, University of Texas at Dallas
*
Requests for reprints should be sent to Richard M. Golden, Applied Cognition and Neuroscience Program (GR4.1), University of Texas at Dallas, Box 830688, Richardson, TX 75083-0688. E-Mail: [email protected]
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Abstract

A new model selection statistical test is proposed for testing the null hypothesis that two probability models equally effectively fit the underlying data generating process (DGP). The new model selection test, called the Discrepancy Risk Model Selection Test (DRMST), extends previous work (see Vuong, 1989) on this problem in four distinct ways. First, generalized goodness-of-fit measures (which include log-likelihood functions) can be used. Second, unlike the classical likelihood ratio test, the models are not required to be fully nested where the nesting concept is defined for generalized goodness-of-fit measures. The DRMST also differs from the likelihood ratio test by not requiring that either competing model provides a completely accurate representation of the DGP. And, fourth, the DRMST may be used to compare competing time-series models using correlated observations as well as data consisting of independent and identically distributed observations.

Keywords

asymptotic statistical theorymodel selectionhypothesis-testingmodel misspecificationtime-seriesm-estimation
Type
Articles
Information
Psychometrika , Volume 68 , Issue 2 , June 2003 , pp. 229 - 249
Copyright
Copyright © 2003 The Psychometric Society

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Footnotes

This research was supported in part by a University of Texas at Dallas Special Faculty Development Award and the NSF Information Technology Research Initiative through the Research on Learning and Education Program Award 0113369 to the author at the University of Texas at Dallas. This research was also supported by the National Institute on Alcohol Abuse and Alcoholism grant to Martingale Research Corporation (Grant No. R44AA11607). The author thanks Halbert White, Steven Henley, and the UTD A2N2 research group for discussions regarding issues related to this manuscript. The author also thanks three anonymous reviewers for feedback and comments regarding a previous version of this paper.

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