Working Paper: CEPR ID: DP18244
Authors: Todd Clark; Florian Huber; Gary Koop; Massimiliano Marcellino
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.
Keywords: Nonparametric Regression; Gaussian Process; Dirichlet Process; Mixture; Inflation Forecasting
JEL Codes: C11; C32; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nonparametric modeling (C52) | accuracy of inflation forecasts (E31) |
| Gaussian processes (C45) | accuracy of inflation forecasts (E31) |
| Dirichlet process mixtures (C11) | accuracy of inflation forecasts (E31) |
| nonparametric modeling (C52) | mean squared forecast error (MSE) (C53) |
| nonparametric modeling (C52) | log predictive likelihood (LPL) (C51) |
| nonparametric modeling (C52) | point and density forecasts (C53) |
| nonparametric modeling (C52) | predictive distribution (C46) |