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Forecasting U.S. Pork Production Using a Random Coefficient Model

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  • Bruce L. Dixon
  • Larry J. Martin

Abstract

A random coefficient regression model is found to be superior to a fixed coefficient model for short- and intermediate-term forecasting of quarterly U.S. pork production. The random coefficient model portrays some regression parameters as the sum of a systematically changing component and random error. Use of such models is discussed. Pork supply is hypothesized as a function of seasonal shifters with geometric lags on hog and feed prices. Results show seasonal effects declining, feed price not being a significant explanatory variable, and pork production adjusting faster to lagged price conditions than indicated by the constant coefficient model.

Suggested Citation

  • Bruce L. Dixon & Larry J. Martin, 1982. "Forecasting U.S. Pork Production Using a Random Coefficient Model," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 64(3), pages 530-538.
    • Handle: RePEc:oup:ajagec:v:64:y:1982:i:3:p:530-538.
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    File URL: http://hdl.handle.net/10.2307/1240645
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    Cited by:

    1. Nyankori, James Cyprian Okuk & Hammig, Michael D., 1983. "Empirical Evaluation of the Relative Forecasting Performances of Fixed and Varying Coefficient Demand Models," Working Papers 117654, Clemson University, Department of Agricultural and Applied Economics.
    2. Mingyu Xu & Xin Lai & Yuying Zhang & Zongjun Li & Bohan Ouyang & Jingmiao Shen & Shiming Deng, 2024. "An Integrated Hog Supply Forecasting Framework Incorporating the Time-Lagged Piglet Feature: Sustainable Insights from the Hog Industry in China," Sustainability, MDPI, vol. 16(19), pages 1-24, September.

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