Model Selection Criteria Using Likelihood Functions And Out-Of-Sample Performance
AbstractModel 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.
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Bibliographic InfoPaper provided by NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management in its series 2001 Conference, April 23-24, 2001, St. Louis, Missouri with number 18947.
Date of creation: 2001
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Web page: http://www.agebb.missouri.edu/ncrext/ncr134/
Research Methods/ Statistical Methods;
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- Norwood, F. Bailey & Lusk, Jayson L. & Brorsen, B. Wade, 2004. "Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(03), December.
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- Lusk, Jayson L. & Norwood, F. Bailey & Brorsen, B. Wade, 2004. "Forecasting Limited Dependent Variables: Better Statistics For Better Steaks," 2004 Annual Meeting, February 14-18, 2004, Tulsa, Oklahoma 34612, Southern Agricultural Economics Association.
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