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

Bruce L. Dixon and Larry J. Martin

American Journal of Agricultural Economics, 1982, vol. 64, issue 3, 530-538

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.

Date: 1982
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:64:y:1982:i:3:p:530-538.

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