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          Volume 29, Number 03, December 2004 >

Please use this identifier to cite or link to this item: http://purl.umn.edu/30912

Title: Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks
Authors: Norwood, F. Bailey
Lusk, Jayson L.
Brorsen, B. Wade
Keywords: discrete dependent variables
forecasting
likelihood functions
model selection
out-of-sample
quality grades
receiver-operator curves
Issue Date: 2004-12
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.
URI: http://purl.umn.edu/30912
Institution/Association: Journal of Agricultural and Resource Economics>Volume 29, Number 03, December 2004
Total Pages: 16
Language: English
From Page: 404
To Page: 419
Collections:Volume 29, Number 03, December 2004

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