BAYESIAN DYNAMIC VARIABLE SELECTION IN HIGH DIMENSIONS

• 成果类型: Article
• 署名作者: Koop, Gary; Korobilis, Dimitris
• 署名单位: University of Strathclyde; University of Glasgow; University of Glasgow
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12623
• 发表日期: 2023
• 页码: 1047-1074
• 关键词: model selection; shrinkage; inference; inflation

摘要

This article addresses the issue of inference in time-varying parameter regression models in the presence of many predictors and develops a novel dynamic variable selection strategy. The proposed variational Bayes dynamic variable selection algorithm allows for assessing at each time period in the sample which predictors are relevant (or not) for forecasting the dependent variable. The algorithm is used to forecast inflation using over 400 macroeconomic, financial, and global predictors, many of which are potentially irrelevant or short-lived. The new methodology is able to ensure parsimonious solutions to this high-dimensional estimation problem, which translate into excellent forecast performance.