Averaging and the Optimal Combination of Forecasts
Averaging and the Optimal Combination of Forecasts
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平均和预测的最佳组合
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
G. Elliott
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文献类型：
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作者：
G. Elliott
贝茨和格兰杰（1969）详细阐述的最优预测组合，在实践中往往被简单平均数所掩盖。为什么平均在实践中可能比构建最优组合更好的解释集中在估计误差上，并且数据生成过程的影响变化对该误差有影响。这种解释的另一面是，增益的大小必须足够小，以便被估计误差所抵消。本文研究了最优组合的理论增益的大小，提供了限制参数空间的增益的界限，并在此条件下平均和最优组合是等价的。该论文还提出了一种新的方法来选择模型，似乎与SPF数据很好地工作。
The optimal combination of forecasts, detailed in Bates and Granger (1969), has empirically often been overshadowed in practice by using the simple average instead. Explanations of why averaging might in practice work better than constructing the optimal combination have centered on estimation error and the eects variations of the data generating process have on this error. The ‡ip side of this explanation is that the size of the gains must be small enough to be outweighed by the estimation error. This paper examines the sizes of the theoretical gains to optimal combination, providing bounds for the gains for restricted parameter spaces and also conditions under which averaging and optimal combination are equivalent. The paper also suggests a new method for selecting between models that appears to work well with SPF data.