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Model Selection Criteria Using Likelihood Functions And Out-Of-Sample Performance

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Author Info

  • Norwood, F. Bailey
  • Ferrier, Peyton Michael
  • Lusk, Jayson L.

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.

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File URL: http://purl.umn.edu/18947
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Bibliographic Info

Paper 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.

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Date of creation: 2001
Date of revision:
Handle: RePEc:ags:ncrone:18947

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Web page: http://www.agebb.missouri.edu/ncrext/ncr134/

Related research

Keywords: Research Methods/ Statistical Methods;

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Cited by:
  1. Norwood, F. Bailey & Roberts, Matthew C. & Lusk, Jayson L., 2002. "How Are Crop Yields Distributed?," 2002 Annual meeting, July 28-31, Long Beach, CA 19733, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  2. 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.
  3. 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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