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

Author

Listed:
  • 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.

Suggested Citation

  • Norwood, F. Bailey & Ferrier, Peyton Michael & Lusk, Jayson L., 2001. "Model Selection Criteria Using Likelihood Functions And Out-Of-Sample Performance," 2001 Conference, April 23-24, 2001, St. Louis, Missouri 18947, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    • Handle: RePEc:ags:ncrone:18947
    DOI: 10.22004/ag.econ.18947
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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(3), pages 1-16, 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.
    4. Bhat, Chandra R. & Castro, Marisol & Pinjari, Abdul Rawoof, 2015. "Allowing for complementarity and rich substitution patterns in multiple discrete–continuous models," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 59-77.

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    Keywords

    Research Methods/ Statistical Methods;

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