Predictive Density Combination Using a Tree-Based Synthesis Function

RePEc Short ID: pch1679
ORCID iD: 0000-0001-9860-9241

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

doi:10.26509/frbc-wp-202330

Working Paper

Predictive Density Combination Using a Tree-Based Synthesis Function

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;

JEL Classification: C11; C32; C53;

https://doi.org/10.26509/frbc-wp-202330