Dynamic Bayesian Predictive Synthesis in Time Series Forecasting

Kenichiro McAlinn; Mike West

doi:10.1016/j.jeconom.2018.11.010

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arXiv · 1601.07463

Dynamic Bayesian Predictive Synthesis in Time Series Forecasting

Kenichiro McAlinn·Mike West

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Abstract

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.

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BibTeX RIS

Kenichiro McAlinn, Mike West. 2016-01-27. Dynamic Bayesian Predictive Synthesis in Time Series Forecasting. https://doi.org/10.1016/j.jeconom.2018.11.010

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