Abstract
The relationship between inflation and predictors, such as unemployment, is potentially nonlinear with a strength that varies over time, and prediction errors may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial benefits, both overall and in the left tail, with nonparametric modeling of the conditional mean being of particular importance.
Funding Statement
Huber gratefully acknowledges financial support from the Austrian Science Fund (FWF, grant no. ZK 35).
Acknowledgments
We are grateful to the Editor, two anonymous referees, Christiane Baumeister, Marta Banbura, Laurent Ferrara, Niko Hauzenberger, Liana Jacobi, Michael Pfarrhofer and participants of the WU Economics research seminar, the ECB research seminar, and the MacroFor seminar for useful comments and suggestions. The views expressed herein are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.
Citation
Todd E. Clark. Florian Huber. Gary Koop. Massimiliano Marcellino. "Forecasting U.S. inflation using Bayesian nonparametric models." Ann. Appl. Stat. 18 (2) 1421 - 1444, June 2024. https://doi.org/10.1214/23-AOAS1841
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