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Modeling Fresh Tomato Marketing Margins: Econometrics and Neural Networks

Published online by Cambridge University Press:  15 September 2016

Timothy J. Richards ,
Paul M. Patterson  and
Pieter Van Ispelen
Timothy J. Richards
Affiliation:
School of Agribusiness and Resource Management and National Food and Agricultural Policy Project, Arizona State University
Paul M. Patterson
Affiliation:
School of Agribusiness and Resource Management and National Food and Agricultural Policy Project, Arizona State University
Pieter Van Ispelen
Affiliation:
School of Agribusiness and Resource Management and National Food and Agricultural Policy Project, Arizona State University
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Abstract

This study compares two methods of estimating a reduced form model of fresh tomato marketing margins: an econometric and an artificial neural network (ANN) approach. Model performance is evaluated by comparing out-of-sample forecasts for the period of January 1992 to December 1994. Parameter estimates using the econometric model fail to reject a dynamic, imperfectly competitive, uncertain relative price spread margin specification, but misspecification tests reject both linearity and log-linearity. This nonlinearity suggests that an inherently nonlinear method, such as a neural network, may be of some value. The neural network is able to forecast with approximately half the mean square error of the econometric model, but both are equally adept at predicting turning points in the time series.

Type
Articles
Information
Agricultural and Resource Economics Review , Volume 27 , Issue 2 , October 1998 , pp. 186 - 199
Copyright
Copyright © 1998 Northeastern Agricultural and Resource Economics Association 

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