Boundary Value Models for the Combination of Forecasts
研究了预测组合的边界值模型，当难以判断单个预测模型的相对准确性和误差项相关性时，这些模型可作为最优模型的合理替代，有助于降低预测误差。
The combination of forecasts model (which is usually expressed as a linear combination of individual forecasting models) has received increasing attention in the literature. The principal advantage of forecast combination models is that they yield lower forecast errors than their individual constituent models under appropriate conditions. Two conditions of particular importance are (1) the relative accuracy of the individual forecasting models and (2) the correlations among the models’ disturbance terms. For marketing forecasting the author demonstrates that a variety of boundary value models for the forecast combination represent reasonable model alternatives to an optimal model when it is difficult to judge the relative accuracy and disturbance term intercorrelations of the individual forecasting models.