Bayesian Local Projections

• 成果类型: Article
• 署名作者: Ferreira, Leonardo N.; Miranda-Agrippino, Silvia; Ricco, Giovanni
• 署名单位: Central Bank of Brazil; Federal Reserve System - USA; Federal Reserve Bank - New York; Centre for Economic Policy Research - UK; Institut Polytechnique de Paris; Ecole Polytechnique; University of Warwick
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01334
• 发表日期: 2025-09
• 页码: 1424-1438
• 关键词: vector autoregressions; impulse responses; inference; models; vars

摘要

We propose a Bayesian approach to Local Projections (LPs) that optimally addresses the empirical bias-variance trade-off intrinsic in the choice between direct and iterative methods. Bayesian Local Projections (BLPs) regularize LP regressions via informative priors and estimate impulse response functions that capture the properties of the data more accurately than iterative VARs. BLPs preserve the flexibility of LPs while retaining a degree of estimation uncertainty comparable to Bayesian VARs with standard macroeconomic priors. As regularized direct forecasts, BLPs are also a valuable alternative to BVARs for multivariate out-of-sample projections.