MODEL SELECTION CRITERIA USING LIKELIHOOD FUNCTIONS AND OUT-OF-SAMPLE PERFORMANCE

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dc.creatorNorwood, F. Bailey
dc.creatorFerrier, Peyton Michael
dc.creatorLusk, Jayson L.
dc.date2017-04-01T19:38:07Z
dc.date.accessioned2026-07-09T03:26:02Z
dc.descriptionModel selection is often conducted by ranking models by their out-of-sample forecast error. Such criteria only incorporate information about the expected value, whereas models usually describe the entire probability distribution. Hence, researchers may desire a criteria evaluating the performance of the entire probability distribution. Such a method is proposed and is found to increase the likelihood of selecting the true model relative to conventional model ranking techniques.
dc.identifierdoi:10.22004/ag.econ.18947
dc.identifierhttps://ageconsearch.umn.edu/record/18947/files/cp01no01.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/18947
dc.identifier.urihttp://hdl.handle.net/123456789/532477
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/18947
dc.titleMODEL SELECTION CRITERIA USING LIKELIHOOD FUNCTIONS AND OUT-OF-SAMPLE PERFORMANCE
dc.typeText

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