Forecast Combination Across Estimation Windows
研究了将同一模型在不同估计窗口下生成的预测进行组合的方法，发现相比单一窗口，平均预测能降低偏差和均方根预测误差，尤其适用于存在结构突变的情形，并在20个股指期货周收益率数据中验证了有效性。
In this article we consider combining forecasts generated from the same model but over different estimation windows. We develop theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error (RMSFE) compared with forecasts based on a single estimation window for all but the smallest breaks. An application to weekly returns on 20 equity index futures shows that averaging forecasts over estimation windows leads to a smaller RMSFE than some competing methods.