On the Forecast Combination Puzzle
On the Forecast Combination Puzzle
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DOI：
10.3390/econometrics7030039
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发表时间：
2015-05
期刊：
影响因子：
1.5
通讯作者：
W. Qian;Craig Rolling;Gang Cheng;Yuhong Yang
中科院分区：
文献类型：
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作者：
W. Qian;Craig Rolling;Gang Cheng;Yuhong Yang
在预测组合文献中经常报告，候选预测的简单平均值比复杂的组合方法更稳健。这种现象通常被称为“预测组合难题”。出于这个难题，我们探索其可能的解释，包括估计目标最佳权重的高方差（估计误差），无效的加权公式，以及组合前的模型/候选筛选。我们表明，现有的理解的难题，应补充不同的预测组合方案的区别，结合适应和结合改进。应用组合方法而不考虑潜在的场景本身就可能导致困惑。基于我们的新认识，模拟和真实的数据评估进行说明的困惑的原因。我们进一步提出了一个多层次的AFTER策略，可以集成不同的组合方法的优势，并智能地适应底层场景。特别是，通过将简单平均值视为候选预测，所提出的策略可以降低估计误差的沉重代价，并在很大程度上减轻了困惑。
It is often reported in the forecast combination literature that a simple average of candidate forecasts is more robust than sophisticated combining methods. This phenomenon is usually referred to as the “forecast combination puzzle”. Motivated by this puzzle, we explore its possible explanations, including high variance in estimating the target optimal weights (estimation error), invalid weighting formulas, and model/candidate screening before combination. We show that the existing understanding of the puzzle should be complemented by the distinction of different forecast combination scenarios known as combining for adaptation and combining for improvement. Applying combining methods without considering the underlying scenario can itself cause the puzzle. Based on our new understandings, both simulations and real data evaluations are conducted to illustrate the causes of the puzzle. We further propose a multi-level AFTER strategy that can integrate the strengths of different combining methods and adapt intelligently to the underlying scenario. In particular, by treating the simple average as a candidate forecast, the proposed strategy is shown to reduce the heavy cost of estimation error and, to a large extent, mitigate the puzzle.