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Predictive Density Combination Using Bayesian Machine Learning

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  • Tony Chernis
  • Niko Hauzenberger
  • Florian Huber
  • Gary Koop
  • James Mitchell

Abstract

Based on agent opinion analysis theory, Bayesian predictive synthesis (BPS) is a framework for combining predictive distributions in the face of model uncertainty. In this article, we generalize existing parametric implementations of BPS by showing how to combine competing probabilistic forecasts using interpretable Bayesian tree‐based machine learning methods. We demonstrate the advantages of our approach—in terms of improved forecast accuracy and interpretability—via two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area's Survey of Professional Forecasters. The second combines density forecasts of U.S. inflation produced by many simple regression models.

Suggested Citation

  • Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2025. "Predictive Density Combination Using Bayesian Machine Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 66(3), pages 1287-1315, August.
    • Handle: RePEc:wly:iecrev:v:66:y:2025:i:3:p:1287-1315
    DOI: 10.1111/iere.12759
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