COMPOSITE FORECASTING METHODS: AN APPLICATION TO SPANISH MAIZE PRICES
研究了在观测数据有限的情况下，如何通过组合预测方法提高预测精度，并以西班牙加入欧共体后的月度玉米价格为例进行验证。
This paper investigates alternative forecasting methods when few observations are available. An illustration is provided by Spanish monthly maize prices after Spanish accession into the EC. Sophisticated multiple‐equation models are difficult to specify in situations of limited data, and simpler models have to be considered. In this paper, several individual and composite forecasting methods are compared, based on 24 one‐period‐ahead forecasts generated from these models. Results based on different quantitative and qualitative measures show that composite forecasting methods are more accurate. In situations where severe multicollinearity exists, forecasting performance is improved by modelling this problem explicitly.