  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.
