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Economic Criteria for Evaluating Commodity Price Forecasts

Author

Listed:
  • Dorfman, Jeffrey H.
  • Mcintosh, Christopher S.

Abstract

Forecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values for forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.

Suggested Citation

  • Dorfman, Jeffrey H. & Mcintosh, Christopher S., 1997. "Economic Criteria for Evaluating Commodity Price Forecasts," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 29(2), pages 337-345, December.
    • Handle: RePEc:cup:jagaec:v:29:y:1997:i:02:p:337-345_00
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    Cited by:

    1. Bingzi Jin & Xiaojie Xu, 2025. "Steel price index forecasts through machine learning for northwest China," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(4), pages 811-833, December.
    2. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2019. "Statistical and economic evaluation of time series models for forecasting arrivals at call centers," Empirical Economics, Springer, vol. 57(3), pages 923-955, September.
    3. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.
    4. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2011. "Forecast Evaluation in Call Centers: Combined Forecasts, Flexible Loss Functions and Economic Criteria," Working Papers 20110301, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    5. Bingzi Jin & Xiaojie Xu, 2026. "Machine learning wholesale white wheat price index forecasts," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 277-305, February.

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