FORECASTING LIMITED DEPENDENT VARIABLES: BETTER STATISTICS FOR BETTER STEAKS

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.


Issue Date:
2004
Publication Type:
Conference Paper/ Presentation
DOI and Other Identifiers:
Record Identifier:
https://ageconsearch.umn.edu/record/34612
PURL Identifier:
http://purl.umn.edu/34612
Total Pages:
20
Series Statement:
Selected Paper




 Record created 2017-04-01, last modified 2019-08-26

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