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Predictions with dynamic Bayesian predictive synthesis are exact minimax

K\=osaku Takanashi and Kenichiro McAlinn

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Abstract: We analyze the combination of multiple predictive distributions for time series data when all forecasts are misspecified. We show that a specific dynamic form of Bayesian predictive synthesis -- a general and coherent Bayesian framework for ensemble methods -- produces exact minimax predictive densities with regard to Kullback-Leibler loss, providing theoretical support for finite sample predictive performance over existing ensemble methods. A simulation study that highlights this theoretical result is presented, showing that dynamic Bayesian predictive synthesis is superior to other ensemble methods using multiple metrics.

Date: 2019-11, Revised 2021-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Handle: RePEc:arx:papers:1911.08662