Bayesian forecast combination using time-varying features
Yanfei Kang and
Feng Li ()
Papers from arXiv.org
In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecasting combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to add weight to the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point and density forecasts.
Date: 2021-08, Revised 2022-06
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-isf
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2108.02082
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