Predictive Density Combination Using a Tree-Based Synthesis Function

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

No 23-30, Working Papers from Federal Reserve Bank of Cleveland

Abstract: Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. 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: Forecast density combination; Bayesian nonparametrics; Bayesian predictive synthesis (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 60
Date: 2023-11-21
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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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DOI: 10.26509/frbc-wp-202330

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