Crowdsourcing of Economic Forecast – Combination of Forecasts using Bayesian Model Averaging

Dongkoo Kim, Tae-hwan Rhee, K. Ryu, Changmock Shin
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Abstract

Economic forecasts are quite essential in our daily lives, which is why many research institutions periodically make and publish forecasts of main economic indicators. We ask (1) whether we can consistently have a better prediction when we combine multiple forecasts of the same variable and (2) if we can, what will be the optimal method of combination. We linearly combine multiple linear combinations of existing forecasts to form a new forecast ('combination of combinations'), and the weights are given by Bayesian model averaging. In the case of forecasts on Germany's real GDP growth rate, this new forecast dominates any single forecast in terms of root-mean-square prediction errors.
经济预测的众包——使用贝叶斯模型平均的预测组合
经济预测在我们的日常生活中是非常重要的,这就是为什么许多研究机构定期对主要经济指标进行预测和发布。我们的问题是(1)当我们对同一变量的多个预测进行组合时,我们是否能够始终如一地得到更好的预测;(2)如果可以,那么最优的组合方法是什么?我们将现有预测的多个线性组合进行线性组合,形成一个新的预测(“组合的组合”),权重由贝叶斯模型平均给出。在对德国实际GDP增长率的预测中,这一新预测在均方根预测误差方面优于任何单一预测。
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