EVALUATING FORECASTS OF DISCRETE VARIABLES: PREDICTING CATTLE QUALITY GRADES

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dc.creatorBrorsen, B. Wade
dc.creatorLusk, Jayson L.
dc.creatorNorwood, F. Bailey
dc.date2017-04-01T19:30:47Z
dc.date.accessioned2026-07-09T03:26:19Z
dc.descriptionLittle 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.
dc.identifierdoi:10.22004/ag.econ.19017
dc.identifierhttps://ageconsearch.umn.edu/record/19017/files/cp04no01.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/19017
dc.identifier.urihttp://hdl.handle.net/123456789/532547
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/19017
dc.titleEVALUATING FORECASTS OF DISCRETE VARIABLES: PREDICTING CATTLE QUALITY GRADES
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