Predictive Density Combination Using a Tree-Based Synthesis Function

Tony Chernis, Niko Hauzenberger, Florian Huber, Gary Koop and James Mitchell

Staff Working Papers from Bank of Canada

Abstract: Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in 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 US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits—in terms of improved forecast accuracy and interpretability—of modeling the synthesis function nonparametrically.

Keywords: Econometric; and; Statistical; Methods (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2023-12
New Economics Papers: this item is included in nep-ets and nep-for
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Related works:
Working Paper: Predictive Density Combination Using a Tree-Based Synthesis Function (2023)
Working Paper: Predictive Density Combination Using a Tree-Based Synthesis Function (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:23-61

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