IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS*
研究在线性预测模型存在结构变化时，如何通过结合递归预测和滚动预测来提高预测精度，并给出最优观测窗口和组合权重的推导方法。
This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias–variance trade‐off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.