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Dynamic Bayesian predictive synthesis in time series forecasting
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DOI：10.1016/j.jeconom.2018.11.010.png)
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Abstract
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We discuss model and forecast combination in time series forecasting. A foundational Bayesian perspective based on agent opinion analysis theory defines a new framework for density forecast combination, and encompasses several existing forecast pooling methods. We develop a novel class of dynamic latent factor models for time series forecast synthesis; simulation-based computation enables implementation. These models can dynamically adapt to time-varying biases, miscalibration and inter-dependencies among multiple models or forecasters. A macroeconomic forecasting study highlights the dynamic relationships among synthesized forecast densities, as well as the potential for improved forecast accuracy at multiple horizons. (C) 2018 Published by Elsevier B.V.
Keywords:
Agent opinion analysis
Bayesian forecasting
Density forecast combination
Dynamic latent factors models
Macroeconomic forecasting
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