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Forecasting Limited Dependent Variables: Better Statistics For Better Steaks

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

Abstract

Little research has been conducted on evaluating out-of-sample forecasts of limited dependent variables. This study describes the large and small sample properties of two forecast evaluation techniques for limited 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 slightly better at detecting forecast accuracy in small samples, while receiver-operator curves are better at comparing forecasts across different data. 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

  • 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.
    • Handle: RePEc:ags:saeaft:34612
    DOI: 10.22004/ag.econ.34612
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    References listed on IDEAS

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    1. 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.
    2. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    4. Ted C. Schroeder & Jennifer L. Graff, 2000. "Estimated Value of Increased Pricing Accuracy for Fed Cattle," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 22(1), pages 89-101.
    5. 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).
    6. Loureiro, Maria L. & Hine, Susan E., 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, Southern Agricultural Economics Association, vol. 34(3), pages 1-11, December.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    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.
    13. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-1291, November.
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