USING MACRO DATA TO OBTAIN BETTER MICRO FORECASTS
提出一种结合宏观和微观层面预测的方法，仅需预测值和精度，无需完整数据，通过蒙特卡洛模拟和欧洲零利率预测验证其稳健性和准确性提升。
We consider the problem of combining forecasts from two different levels (called “macro” and “micro”), where we have access to the forecasts and their precisions but not to the full data set. We develop a theoretical framework and provide Monte Carlo evidence in the cases of both perfect and imperfect aggregation. Our proposed procedure is simple and robust. We also extend the procedure to time series and propose a forecast model for the European zero rates, combining quarterly and monthly observations. We show that forecast accuracy is improved at both levels.We are grateful to the editors of this special issue and to two referees for their constructive comments, which greatly helped improve the paper. Earlier versions of this paper were presented at the NAKE workshop in Amsterdam, at Tilburg University, and at ESEM 2006 in Vienna. We thank the participants for their useful comments. In addition, we thank Steffan Berridge, John Einmahl, and Hans Schumacher for their constructive comments.