PREDICTIVE DENSITY COMBINATION USING BAYESIAN MACHINE LEARNING

• 成果类型: Article
• 署名作者: Chernis, Tony; Hauzenberger, Niko; Huber, Florian; Koop, Gary; Mitchell, James
• 署名单位: Bank of Canada; University of Strathclyde; Salzburg University; Federal Reserve System - USA; Federal Reserve Bank - Cleveland
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12759
• 发表日期: 2025
• 页码: 1287-1315
• 关键词: combining density; inflation; forecasts; output; time

摘要

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.