Forecasting with shadow rate VARs

• 成果类型: Article
• 署名作者: Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano; Mertens, Elmar
• 署名单位: University of London; Queen Mary University London; Federal Reserve System - USA; Federal Reserve Bank - Cleveland; Bocconi University; Centre for Economic Policy Research - UK; Deutsche Bundesbank
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2547
• 发表日期: 2025
• 页码: 795-822
• 关键词: Macroeconomic forecasting; effective lower bound; term structure; censored observations; C34; E17; E43

摘要

Vector autoregressions (VARs) are popular for forecasting, but ill-suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We examine reduced-form shadow rate VARs that model interest rates as censored observations of a latent shadow rate process and develop an efficient Bayesian estimation algorithm that accommodates large models. When compared to a standard VAR, our better-performing shadow rate VARs generate superior predictions for interest rates and broadly similar predictions for macroeconomic variables. We obtain this result for shadow rate VARs in which the federal funds rate is the only interest rate and in models including additional interest rates. Our shadow rate VARs also deliver notable gains in forecast accuracy relative to a VAR that omits shorter-term interest rate data in order to avoid modeling the lower bound.