Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-15T19:27:56.876Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Appelbaum, E. 1982. “The Estimation of the Degree of Oligopoly Power.” Journal of Econometrics 19: 287–99.Google Scholar
Arnade, C., and Pick, D. 1996. “Effects of Seasonality on Market Behavior: The Case of U.S. Fresh Fruit Market.” Paper presented at AAEA meetings, San Antonio, TX. July.Google Scholar
Azzam, A.M., and Schroeter, J.R. 1991. “Implications of Increased Regional Concentration and Oligopsonistic Coordination in the Beef Packing Industry.” Western Journal of Agricultural Economics 16: 374–81.Google Scholar
Bansal, A., Kauffman, R.J., and Weitz, R.R. 1993. “Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach.” Journal of Management Information Systems 10: 11–32.CrossRefGoogle Scholar
Beltratti, A., Margarita, S., and Terna, P. 1996. Neural Networks for Economic and Financial Modelling. London: International Thompson Computer Press.Google Scholar
Box, G., and Cox, D. 1964. “An Analysis of Transformations.” Journal of the Royal Statistical Society, series B, 26: 211–64.Google Scholar
Bresnahan, T. 1989. “Empirical Studies of Industries with Market Power.” In Handbook of Industrial Organization, Vol. 2, ed. Schmalansee, R. and Willig, R.D. New York: North-Holland.Google Scholar
Brester, G.W., and Musick, D.C. 1995. “The Effect of Market Concentration on Lamb Marketing Margins.” Journal of Agricultural and Applied Economics 27: 172–83.Google Scholar
Brorsen, B.W., Chavas, J.-P., Grant, W.R., and Schnake, L.D. 1985. “Marketing Margins and Price Uncertainty: The Case of the U.S. Wheat Market.” American Journal of Agricultural Economics 67: 521–28.Google Scholar
Chakraborty, K., Mehrotra, K., Mohan, C.K., and Ranka, S. 1992. “Forecasting the Behavior of Multivariate Time Series Using Neural Networks.” Neural Networks 5: 961–70.Google Scholar
Cheng, B., and Titterington, D.M. 1994. “Neural Networks: A Review from a Statistical Perspective.” Statistical Science 9: 2–54.Google Scholar
Cotterill, R.W. 1986. “Market Power in the Retail Food Industry: Evidence from Vermont.” Review of Economics and Statistics 68: 379–86.Google Scholar
Davidson, R., and MacKinnon, J. 1981. “Several Tests for Model Specification in the Presence of Alternative Hypotheses.” Econometrica 19: 781–93.Google Scholar
Dorfman, J.H., and McIntosh, C.S. 1990. “Results of a Price-Forecasting Competition.” American Journal of Agricultural Economics 72: 804–10.CrossRefGoogle Scholar
Durham, C.A., and Sexton, R.J. 1992. “Oligopsony Potential in Agriculture: Residual Supply Estimation in California's Processing Tomato Market.” American Journal of Agricultural Economics 74: 962–72.Google Scholar
Faminow, M.D., and Laubscher, J.M. 1991. “Empirical Testing of Alternative Price Spread Models in the South African Maize Market.” Agricultural Economics 6: 49–66.Google Scholar
Gardner, B. 1975. “The Farm-Retail Price Spread in a Competitive Food Industry.” American Journal of Agricultural Economics 57: 399–409.Google Scholar
Goldberger, A.S. 1968. “The Interpretation and Estimation of Cobb-Douglas Functions.” Econometrica 35: 464–72.Google Scholar
Gorr, W.L., Naglin, D., and Szczypula, J. 1994. “Comparative Study of Artificial Neural Network and Statistical Models for Predicting Student Grade Point Averages.” International Journal of Forecasting 10: 17–34.Google Scholar
Grudnitski, G., and Osburn, L. 1993. “Forecasting S&P and Gold Futures Prices: An Application of Neural Networks.” Journal of Futures Markets 13: 631–43.Google Scholar
Heien, D.M. 1980. “Markup Pricing in a Dynamic Model of the Food Industry.” American Journal of Agricultural Economics 62: 10–18.Google Scholar
Henriksson, R.D., and Merton, R.C. 1981. “On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills.” Journal of Business 54: 513–33.Google Scholar
Hiemstra, Y. 1996. “Linear Regression versus Backpropagation Networks to Predict Quarterly Stock Market Excess Returns.” Computational Economics 9: 67–76.Google Scholar
Holloway, G.J. 1991. “The Farm-Retail Price Spread in an Imperfectly Competitive Food Industry.” American Journal of Agricultural Economics 73: 979–89.Google Scholar
Holt, M.T. 1993. “Risk Responses in the Beef Marketing Channel: A Multivariate Generalized ARCH-M Approach.” American Journal of Agricultural Economics 75: 559–71.Google Scholar
Joerding, W.H., Li, Y., and Young, D.L. 1994. “Feedforward Neural Network Estimation of a Crop Yield Response Function.” Journal of Agricultural and Applied Economics 26: 252–63.Google Scholar
Jordan, K., and VanSickle, J. 1995. “Market Structure and Behavior in the U.S. Winter Market for Fresh Tomatoes.” Paper presented at the AAEA meetings, Indianapolis. August.CrossRefGoogle Scholar
Kastens, T., and Brester, G.W. 1996. “Model Selection and Forecasting Ability of Theory-Constrained Food Demand Systems.” American Journal of Agricultural Economics 78: 301–12.Google Scholar
Kastens, T., and Featherstone, A. 1996. “Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences.” American Journal of Agricultural Economics 78: 400–415.Google Scholar
Kastens, T., Featherstone, A., and Biere, A.W. 1995. “A Neural Networks Primer for Agricultural Economists.” Agricultural Finance Review 55: 54–73.Google Scholar
Kinnucan, H.W., and Forker, O.D. 1987. “Asymmetry in Farm-Retail Price Transmission for Major Dairy Products.” American Journal of Agricultural Economics 69: 285–92.Google Scholar
Kinnucan, H.W., and Nelson, R.G. 1993. “Vertical Control and the Farm-Retail Price Spread for Eggs.” Review of Agricultural Economics 15: 473–82.Google Scholar
Kohzadi, N., Boyd, M.S., Kaastra, I., Kermanshahi, B.S., and Scuse, D. 1995. “Neural Networks for Forecasting: An Introduction.” Canadian Journal of Agricultural Economics 43: 463–74.Google Scholar
Kosko, B. 1992. Neural Networks and Fuzzy Systems. Englewood Cliffs, N.J.: Prentice-Hall.Google Scholar
Lucier, G., Plummer, C.S., Johnson, D., and Love, J. 1996. “Vegetables and Specialties: Situation and Outlook.” Washington, D.C.: Economic Research Service, USDA. November. Google Scholar
Lyon, G.D., and Thompson, G.D. 1993. “Temporal and Spatial Aggregation: Alternative Marketing Margin Models.” American Journal of Agricultural Economics 75: 523–36.Google Scholar
Mendelsohn, L., and Stein, J. 1991. “Fundamental Analysis Meets the Neural Network.” Futures 20: 22–24.Google Scholar
Moody, J. 1995. “Economic Forecasting: Challenges and Neural Network Solutions.” Paper presented at the International Symposium on Artificial Neural Networks, Hsinchu, Taiwan. December.Google Scholar
NeuralWare Inc. 1993. Using NeuralWorks: A Tutorial for NeuralWorks Professional II/PLUS and NeuralWorks Explorer. Pittsburgh, Pa.Google Scholar
Packer, The. 1996–97. Various issues. Lenexa, Kansas.Google Scholar
Parker, D.D., and Zilberman, D. 1993. “Hedonic Estimation of Quality Factors Affecting the Farm-Retail Margin.” American Journal of Agricultural Economics 75: 458–66.Google Scholar
Powers, N.J. 1995. “Sticky Short-Run Prices and Vertical Pricing: Evidence from the Market for Iceberg Lettuce.” Agribusiness 11: 57–75.Google Scholar
Ramsey, J. 1969. “Tests for Specification Errors in Classical Linear Least Squares Regression Analysis.” Journal of the Royal Statistical Society, series B, 31: 350–71.Google Scholar
Refenes, A.N., Zapranis, A., and Francis, G. 1994. “Stock Performance Modeling Using Neural Networks: A Comparative Study With Regression Models.” Neural Networks 7: 375–88.Google Scholar
Salchenberger, L.M., Cinar, E.M., and Lash, N.A. 1993. “Neural Networks: A New Tool for Predicting Thrift Failures.” In Neural Networks in Finance and Investing, ed. Trippi, R.R., and Turban, E. Chicago: Probus.Google Scholar
Schroeter, J., and Azzam, A. 1991. “Marketing Margins, Market Power, and Price Uncertainty.” American Journal of Agricultural Economics 73: 990–99.Google Scholar
Stiegert, K.W., Azzam, A., and Brorsen, B.W. 1993. “Mark-down Pricing and Cattle Supply in the Beef Packing Industry.” American Journal of Agricultural Economics 75: 549–58.Google Scholar
Theil, H. 1961. Economic Forecasts and Policy. Amsterdam: North-Holland.Google Scholar
Thompson, G.D., and Lyon, C.C. 1989. “Marketing Order Impacts on Farm-Retail Price Spreads: The Suspension of Prorates on California-Arizona Navel Oranges.” American Journal of Agricultural Economics 71: 647–60.CrossRefGoogle Scholar
Trippi, R.R., and DeSieno, D. 1992. “Trading Equity Index Futures with a Neural Network.” Journal of Portfolio Management 19: 27–33.Google Scholar
U.S. Department of Agriculture, National Agricultural Statistics Service. 1995. Tomato Statistics: 1960–94. Washington, D.C.Google Scholar
U.S. Department of Labor, Bureau of Labor Statistics (BLS). 1997. Consumer Price Index: Average Price Data. Washington, D.C.Google Scholar
Ward, R.W. 1982. “Asymmetry in Retail, Wholesale, and Shipping Point Pricing for Fresh Vegetables.” American Journal of Agricultural Economics 64: 205–12.CrossRefGoogle Scholar
Ward Systems Group, Inc. 1996. NeuroShell 2. Frederick, Md.Google Scholar
Waugh, F.V. 1964. “Demand and Price Analysis: Some Examples from Agriculture.” USDA Technical Bulletin no. 1316.Google Scholar
WEFA Group. 1997. Long Term U.S. Economic Outlook. Eddystone, Pa.Google Scholar
Wohlgenant, M.K. 1985. “Competitive Storage, Rational Expectations, and Short-run Food Price Determination.” American Journal of Agricultural Economics 67: 739–48.CrossRefGoogle Scholar
Wohlgenant, M.K. 1989. “Demand for Farm Output in a Complete System of Demand Functions.” American Journal of Agricultural Economics 71: 241–52.Google Scholar
Wohlgenant, M.K., and Mullen, J.D. 1987. “Modeling the Farm-Retail Price Spread for Beef.” Western Journal of Agricultural Economics 12: 119–25.Google Scholar