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

Bailey Norwood (), Jayson Lusk and B Brorsen

Journal of Agricultural and Resource Economics, 2004, vol. 29, issue 3, 16

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.

Keywords: Agribusiness (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:ags:jlaare:30912

DOI: 10.22004/ag.econ.30912

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