文章基本信息

标题：MODEL SELECTION CRITERIA USING LIKELIHOOD FUNCTIONS AND OUT-OF-SAMPLE PERFORMANCE
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作者：Norwood, F. Bailey ; Ferrier, Peyton Michael ; Lusk, Jayson L. 等
期刊名称：Journal of Food Distribution Research
印刷版ISSN：0047-245X
出版年度：2001
期号：SUPPL
出版社：Food Distribution Research Society
摘要：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.
关键词：Model selection; forecasting; heteroskedasticity.