Evaluating Forecasts Of Discrete Variables: Predicting Cattle Quality Grades
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.
|Date of creation:||2004|
|Contact details of provider:|| Web page: http://www.agebb.missouri.edu/ncrext/ncr134/|
When requesting a correction, please mention this item's handle: RePEc:ags:ncrfou:19017. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search)
If references are entirely missing, you can add them using this form.