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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.

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