Forecasting US Inflation Using Bayesian Nonparametric Models

Todd Clark, Florian Huber, Gary Koop and Massimiliano Marcellino

No 22-05, Working Papers from Federal Reserve Bank of Cleveland

Abstract: The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error 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.

Keywords: nonparametric regressions; Gaussian process; Dirichlet process mixture; inflation forecasting (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 39
Date: 2022-03-02
New Economics Papers: this item is included in nep-cba, nep-cwa, nep-ecm, nep-ets, nep-for, nep-mac, nep-mon and nep-ore
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Citations: View citations in EconPapers (6)

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Working Paper: Forecasting US Inflation Using Bayesian Nonparametric Models (2023)
Working Paper: Forecasting US Inflation Using Bayesian Nonparametric Models (2022)
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DOI: 10.26509/frbc-wp-202205

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