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Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences
Terry L. Kastens and Allen M. Featherstone
American Journal of Agricultural Economics
Vol. 78, No. 2 (May, 1996), pp. 400-415
Stable URL: http://www.jstor.org/stable/1243712
Page Count: 16
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An out-of-sample prediction of Kansas farmers' responses to five surveyed questions involving risk is used to compare ordered multinomial logistic regression models with feedforward backpropagation neural network models. Although the logistic models often predict more accurately than the neural network models in a mean-squared error sense, the neural network models are shown to be more accommodating of loss functions associated with a desire to predict certain combinations of categorical responses more accurately than others.
American Journal of Agricultural Economics © 1996 Agricultural & Applied Economics Association