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Forecasting Agricultural Prices Using A Bayesian Composite Approach

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  • McIntosh, Christopher S.
  • Bessler, David A.

Abstract

Forecast users and market analysts need quality forecast information to improve their decision-making abilities. When more than one forecast is available, the analyst can improve forecast accuracy by using a composite forecast. One of several approaches to forming composite forecasts is a Bayesian approach using matrix beta priors. This paper explains the matrix beta approach and applies it to three individual forecasts of U.S. hog prices. The Bayesian composite forecast is evaluated relative to composites made from simple averages, restricted least squares, and an adaptive weighting technique.

Suggested Citation

  • McIntosh, Christopher S. & Bessler, David A., 1988. "Forecasting Agricultural Prices Using A Bayesian Composite Approach," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 20(2), pages 1-8, December.
    • Handle: RePEc:ags:sojoae:29269
    DOI: 10.22004/ag.econ.29269
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    References listed on IDEAS

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    1. Bessler, David A & Chamberlain, Peter J, 1987. "On Bayesian composite forecasting," Omega, Elsevier, vol. 15(1), pages 43-48.
    2. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
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    Cited by:

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    2. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2009. "Do Composite Procedures Really Improve the Accuracy of Outlook Forecasts?," 2009 Conference, April 20-21, 2009, St. Louis, Missouri 53052, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    3. Tronstad, Russell, 1991. "The Effects of Firm Size and Production Cost Levels on Dynamically Optimal After-Tax Cotton Storage and Hedging Decisions," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 23(1), pages 165-179, July.
    4. Xiaojie Xu & Yun Zhang, 2022. "Forecasting the total market value of a shares traded in the Shenzhen stock exchange via the neural network," Economics Bulletin, AccessEcon, vol. 42(3), pages 1266-1279.
    5. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip & Etienne, Xiaoli, 2012. "Composite and Outlook Forecast Accuracy," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-19, August.
    6. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    7. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.

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    Keywords

    Demand and Price Analysis;

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