Discrepancy Risk Model Selection Test theory for comparing possibly misspecified or nonnested models
 R. M. Golden
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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 goodnessoffit measures (which include loglikelihood 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 goodnessoffit 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 timeseries models using correlated observations as well as data consisting of independent and identically distributed observations.
 Title
 Discrepancy Risk Model Selection Test theory for comparing possibly misspecified or nonnested models
 Journal

Psychometrika
Volume 68, Issue 2 , pp 229249
 Cover Date
 200306
 DOI
 10.1007/BF02294799
 Print ISSN
 00333123
 Online ISSN
 18600980
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 asymptotic statistical theory
 model selection
 hypothesistesting
 model misspecification
 timeseries
 mestimation
 Industry Sectors
 Authors

 R. M. Golden ^{(1)}
 Author Affiliations

 1. Applied Cognition and Neuroscience Program (GR4.1), University of Texas at Dallas, Box 830688, 750830688, Richardson, TX