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

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.


Issue Date:
2001
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/18947
Total Pages:
20
Series Statement:
2001 Conference, St. Louis, MO, April 23-24, 2001




 Record created 2017-04-01, last modified 2017-08-24

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