Forecasting US Inflation Using Bayesian Nonparametric Models

Todd Clark, Florian Huber, Gary Koop and Massimiliano Marcellino

No 18244, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Date: 2023-06
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Related works:
Working Paper: Forecasting US Inflation Using Bayesian Nonparametric Models (2022)
Working Paper: Forecasting US Inflation Using Bayesian Nonparametric Models (2022)
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