Evaluating Forecasts Of Discrete Variables: Predicting Cattle Quality Grades
AbstractLittle 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.
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Bibliographic InfoPaper provided by NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management in its series 2004 Conference, April 19-20, 2004, St. Louis, Missouri with number 19017.
Date of creation: 2004
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Web page: http://www.agebb.missouri.edu/ncrext/ncr134/
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