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

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

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.

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File URL: http://purl.umn.edu/30912
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Article provided by Western Agricultural Economics Association in its journal Journal of Agricultural and Resource Economics.

Volume (Year): 29 (2004)
Issue (Month): 03 (December)
Pages:

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Handle: RePEc:ags:jlaare:30912
Contact details of provider: Web page: http://waeaonline.org/

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  1. 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(03), December.
  2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  3. 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.
  4. 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.
  5. John B. Loomis & Lucas S. Bair & Armando Gonz�lez-Cab�n, 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
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