June 2024 Forecasting U.S. inflation using Bayesian nonparametric models
Todd E. Clark, Florian Huber, Gary Koop, Massimiliano Marcellino
Author Affiliations +
Ann. Appl. Stat. 18(2): 1421-1444 (June 2024). DOI: 10.1214/23-AOAS1841


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).


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.


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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


Received: 1 December 2022; Revised: 1 October 2023; Published: June 2024
First available in Project Euclid: 5 April 2024

Digital Object Identifier: 10.1214/23-AOAS1841

Keywords: Dirichlet process mixture , Gaussian process , inflation forecasting , Nonparametric regression

Rights: Copyright © 2024 Institute of Mathematical Statistics


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Vol.18 • No. 2 • June 2024
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