Forecasting US Inflation Using Bayesian Nonparametric Models

TL;DR

This paper introduces a Bayesian nonparametric model for US inflation forecasting that captures nonlinear relationships and asymmetric shocks, improving prediction accuracy especially in tail events.

Contribution

It develops a novel nonparametric approach using Gaussian and Dirichlet processes for inflation prediction, addressing nonlinearities and asymmetric errors.

Findings

Enhanced forecast accuracy overall

Significant improvements in tail risk prediction

Nonparametric mean modeling is particularly effective

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.