Dynamic Bayesian predictive synthesis in time series forecasting

Accepted

Journal Article

© 2018 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.

Full Text

Duke Authors

Cited Authors

  • McAlinn, K; West, M

Published Date

  • May 1, 2019

Published In

Volume / Issue

  • 210 / 1

Start / End Page

  • 155 - 169

Electronic International Standard Serial Number (EISSN)

  • 1872-6895

International Standard Serial Number (ISSN)

  • 0304-4076

Digital Object Identifier (DOI)

  • 10.1016/j.jeconom.2018.11.010

Citation Source

  • Scopus