FORECASTING US INFLATION USING BAYESIAN NONPARAMETRIC MODELS
成果类型：
Article
署名作者：
Clark, Todd E.; Huber, Florian; Koop, Gary; Marcellino, Massimiliano
署名单位：
Federal Reserve System - USA; Federal Reserve Bank - Cleveland; Salzburg University; University of Strathclyde; Bocconi University; Centre for Economic Policy Research - UK
刊物名称：
ANNALS OF APPLIED STATISTICS
ISSN/ISSBN：
1932-6157
DOI：
10.1214/23-AOAS1841
发表日期：
2024
页码：
1421-1444
关键词：
摘要：
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.
来源URL：