Forecasting U.S. Pork Production Using a Random Coefficient Model
发现随机系数回归模型比固定系数模型更适合预测美国季度猪肉产量，模型将部分回归参数分解为系统性变化和随机误差，并分析了季节效应、饲料价格和滞后价格的影响。
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.