Forecasting US Inflation Using Bayesian Nonparametric Models

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.

Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions

“…The presence of nonlinearities complicates our investigation since it is not clear on how to measure the effect of x t on a given quantile of y t in the presence of nonlinearities. As a simple solution, we follow Clark et al (2022a) and approximate the nonlinear, quantile-specific model using a linear posterior summary (see Woody et al, 2021). Specifically, we estimate the following regression model:…”

Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions

“…This issue is further intensified since priors such as the Ridge imply that most elements in x t have a small effect and thus the importance of a single variable is difficult to quantify. As a simple solution to both issues, we follow Clark et al (2022) and approximate the nonlinear, quantile-specific model using a linear posterior summary (see Woody et al, 2021). Specifically, we estimate the following regression model:…”