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Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks

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  • Norwood, F. Bailey
  • Lusk, Jayson L.
  • Brorsen, B. Wade

Abstract

Little research has been conducted on evaluating out-of sample forecasts of discrete dependent variables. This study describes the large and small sample properties of two forecast evaluation techniques for discrete dependent variables: receiver-operator curves and out-of-sample log-likelihood functions. The methods are shown to provide identical model rankings in large samples and similar rankings in small samples. The likelihood function method is better at detecting forecast accuracy in small samples. By improving forecasts of fed cattle quality grades, the forecast evaluation methods are shown to increase cattle marketing revenues by $2.59/head.

Suggested Citation

  • 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.
    • Handle: RePEc:ags:jlaare:30912
    DOI: 10.22004/ag.econ.30912
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    Cited by:

    1. Gallardo, R. Karina & Wang, Qianqian, 2013. "Willingness to Pay for Pesticides' Environmental Features and Social Desirability Bias: The Case of Apple and Pear Growers," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(1), pages 1-16, April.
    2. KIANI, Khurshid M., 2007. "Business Cycle Asymmetries In Stock Returns: Robust Evidence," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 4(2), pages 99-120.
    3. Andreas C. Drichoutis & Jayson L. Lusk, 2016. "What can multiple price lists really tell us about risk preferences?," Journal of Risk and Uncertainty, Springer, vol. 53(2), pages 89-106, December.
    4. Norwood, F. Bailey, 2005. "Can Calibration Reconcile Stated and Observed Preferences?," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 37(1), pages 237-248, April.
    5. Andreas C Drichoutis & Jayson L Lusk, 2014. "Judging Statistical Models of Individual Decision Making under Risk Using In- and Out-of-Sample Criteria," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    6. Chang, Jae Bong & Lusk, Jayson L., 2008. "Concerns for Fairness and Preferences for Organic Food," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6414, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Brorsen, B. Wade, 2009. "Research: Are We Valuing the Right Stuff?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(1), pages 1-10, April.
    8. Lusk, Jayson L. & Crespi, John M. & McFadden, Brandon R. & Cherry, J. Bradley C. & Martin, Laura & Bruce, Amanda, 2016. "Neural antecedents of a random utility model," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PA), pages 93-103.
    9. Dharmasena, Senarath & Bessler, David & Capps, Oral. Jr, 2016. "On the Evaluation of Probability Forecasts: An Application to Qualitative Choice Models," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235424, Agricultural and Applied Economics Association.

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