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

Bailey Norwood (), Peyton Ferrier () and Jayson Lusk

No 18947, 2001 Conference, April 23-24, 2001, St. Louis, Missouri from NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management

Abstract: Model 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.

Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
Pages: 20
Date: 2001
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:ags:ncrone:18947

DOI: 10.22004/ag.econ.18947

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