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

Author

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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(03), December.
  • Handle: RePEc:ags:jlaare:30912
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    File URL: http://purl.umn.edu/30912
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    References listed on IDEAS

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    1. Loureiro, Maria L. & Hine, Susan, 2002. "Discovering Niche Markets: A Comparison of Consumer Willingness to Pay for Local (Colorado Grown), Organic, and GMO-Free Products," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 34(03), pages 477-487, December.
    2. repec:oup:revage:v:25:y:2003:i:1:p:203-217. is not listed on IDEAS
    3. Jayson L. Lusk & Randall Little & Allen Williams & John Anderson & Blair McKinley, 2003. "Utilizing Ultrasound Technology to Improve Livestock Marketing Decisions," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 25(1), pages 203-217.
    4. John B. Loomis & Lucas S. Bair & Armando González-Cabán, 2002. "Language-Related Differences in a Contingent Valuation Study: English Versus Spanish," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(4), pages 1091-1102.
    5. Bailey Norwood & Matthew C. Roberts & Jayson L. Lusk, 2004. "Ranking Crop Yield Models Using Out-of-Sample Likelihood Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1032-1043.
    6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    7. M. K. Haener & P. C. Boxall & W. L. Adamowicz, 2001. "Modeling Recreation Site Choice: Do Hypothetical Choices Reflect Actual Behavior?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 629-642.
    8. Koontz, Stephen R. & Hoag, Dana L. & Walker, Jodine L. & Brethour, John R., 2000. "Returns To Market Timing And Sorting Of Fed Cattle," 2000 Conference, April 17-18 2000, Chicago, Illinois 18930, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    9. Jeffrey H. Dorfman, 1998. "Bayesian Composite Qualitative Forecasting: Hog Prices Again," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(3), pages 543-551.
    10. Ashley, Richard, 1998. "A new technique for postsample model selection and validation," Journal of Economic Dynamics and Control, Elsevier, vol. 22(5), pages 647-665, May.
    11. Terry L. Kastens & Gary W. Brester, 1996. "Model Selection and Forecasting Ability of Theory-Constrained Food Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 301-312.
    12. 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.
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    Citations

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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), April.
    2. 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, 2016, Boston, Massachusetts 235424, Agricultural and Applied Economics Association.
    3. Brorsen, B. Wade, 2009. "Research: Are We Valuing the Right Stuff?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(1), April.
    4. 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.
    5. 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.
    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. Norwood, F. Bailey, 2005. "Can Calibration Reconcile Stated and Observed Preferences?," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 37(01), April.
    8. 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.
    9. Drichoutis, Andreas & Lusk, Jayson, 2012. "Judging statistical models of individual decision making under risk using in- and out-of-sample criteria," MPRA Paper 38951, University Library of Munich, Germany.

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