期刊名称:Euro Area Balance of Payments and International Investment Position Statistics
印刷版ISSN:1830-3420
电子版ISSN:1830-3439
出版年度:2021
卷号:2021
语种:English
出版社:European Central Bank
摘要:We address the identification of low-frequency macroeconomic shocks, such as technology, in Structural Vector Autoregressions. Whilst identification issues with long-run restrictions are well documented, we demonstrate that the recent attempt to overcome said issues using the Max-Share approach of Francis et al. (2014) and Barsky and Sims (2011) has its own shortcomings, primarily that they are vulnerable to bias from confounding non-technology shocks, although less so than long-run specifications. We offer a new spectral methodology to improve empirical identification. This new preferred methodology offers equivalent or improved identification in a wide range of data generating processes and when applied to US data. Our findings on the bias generated by confounding shocks also importantly extends to the identification of dominant business-cycle shocks, which will be a combination of shocks rather than a single structural driver. This can result in a mis-characterization of the business cycle anatomy.