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Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences

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  • Terry L. Kastens
  • Allen M. Featherstone

Abstract

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. Copyright 1996, Oxford University Press.

Suggested Citation

  • Terry L. Kastens & Allen M. Featherstone, 1996. "Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 400-415.
    • Handle: RePEc:oup:ajagec:v:78:y:1996:i:2:p:400-415
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    Cited by:

    1. Odeh, Oluwarotimi O. & Featherstone, Allen M. & Sanjoy, Das, 2006. "Predicting Credit Default in an Agricultural Bank: Methods and Issues," 2006 Annual Meeting, February 5-8, 2006, Orlando, Florida 35359, Southern Agricultural Economics Association.
    2. Ross, Nicholas & Santos, Paulo & Capon, Timothy, 2012. "Risk, ambiguity and the adoption of new technologies: experimental evidence from a developing economy," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126492, International Association of Agricultural Economists.
    3. Steven M. Ramsey & Jason S. Bergtold, 2021. "Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1137-1165, December.
    4. Bergtold, Jason S. & Taylor, Daniel B. & Bosch, Darrell J., 2003. "Networking Your Way to a Better Prediction: Effectively Modeling Contingent Valuation Survey Data," 2003 Annual meeting, July 27-30, Montreal, Canada 22152, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    6. Sulewski, Piotr & Kłoczko-Gajewska, Anna, 2014. "Farmers’ risk perception, risk aversion and strategies to cope with production risk: an empirical study from Poland," Studies in Agricultural Economics, Research Institute for Agricultural Economics, vol. 116(3), pages 1-8, December.
    7. Bergtold, Jason S. & Ramsey, Steven M., 2015. "Neural Network Estimators of Binary Choice Processes: Estimation, Marginal Effects and WTP," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205649, Agricultural and Applied Economics Association.
    8. Richards, Timothy J. & Patterson, Paul M. & van Ispelen, Pieter, 1998. "Modeling Fresh Tomato Marketing Margins: Econometrics And Neural Networks," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 27(2), pages 1-14, October.

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