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Price Density Forecasts in the U.S. Hog Markets: Composite Procedures

Author

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  • Andres Trujillo-Barrera
  • Philip Garcia
  • Mindy L. Mallory

Abstract

We develop and evaluate quarterly out-of-sample individual and composite density forecasts for U.S. hog prices. Individual density forecasts are generated using time series models and the implied distributions of USDA and Iowa State University outlook forecasts. Composite density forecasts are constructed using linear and logarithmic combinations of the individual forecasts and several weighting schemes. Density forecasts are evaluated on predictive accuracy (sharpness), goodness of fit (calibration), and their economic value in a hedging simulation. Logarithmic combinations using equal and mean square error weights outperform all individual density forecasts and are modestly better than linear composites. Comparison of the outlook forecasts to the best composite demonstrates the usefulness of the composite procedure, and identifies the economic value that more accurate expected price probability distributions can provide to producers.

Suggested Citation

  • Andres Trujillo-Barrera & Philip Garcia & Mindy L. Mallory, 2016. "Price Density Forecasts in the U.S. Hog Markets: Composite Procedures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(5), pages 1529-1544.
    • Handle: RePEc:oup:ajagec:v:98:y:2016:i:5:p:1529-1544.
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    File URL: http://hdl.handle.net/10.1093/ajae/aaw050
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    Cited by:

    1. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    2. Dabin Zhang & Qian Li & Amin W. Mugera & Liwen Ling, 2020. "A hybrid model considering cointegration for interval‐valued pork price forecasting in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1324-1341, December.

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