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Forecasting Hog Prices with a Neural Network

Author

Listed:
  • Hamm, Lonnie
  • Brorsen, B. Wade

Abstract

Neural network models were compared to traditional forecasting methods in forecasting the quarterly and monthly farm price of hogs. A quarterly neural network model forecasted poorly in comparison to a quarterly econometric model. A monthly neural network model outperformed a monthly ARIMA model with respect to the mean square error criterion and performed similarly to the ARIMA model with respect to turning point accuracy. The more positive results of the monthly neural network model in comparison to the quarterly neural network model may be due to nonlinearities in the monthly data which are not in the quarterly data.

Suggested Citation

  • Hamm, Lonnie & Brorsen, B. Wade, 1997. "Forecasting Hog Prices with a Neural Network," Journal of Agribusiness, Agricultural Economics Association of Georgia, vol. 15(1), pages 1-18.
    • Handle: RePEc:ags:jloagb:90646
    DOI: 10.22004/ag.econ.90646
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    Cited by:

    1. Xiaohong Yu & Bin Liu & Yongzeng Lai, 2024. "Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications," Sustainability, MDPI, vol. 16(4), pages 1-26, February.

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