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Dynamic Bayesian predictive synthesis in time series forecasting

Kenichiro McAlinn and Mike West

Journal of Econometrics, 2019, vol. 210, issue 1, 155-169

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.

Keywords: Agent opinion analysis; Bayesian forecasting; Density forecast combination; Dynamic latent factors models; Macroeconomic forecasting (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 E37 (search for similar items in EconPapers)
Date: 2019
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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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Handle: RePEc:eee:econom:v:210:y:2019:i:1:p:155-169