Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/234088
Authors: 
Dieppe, Alistair
Francis, Neville
Kindberg-Hanlon, Gene
Year of Publication: 
2021
Series/Report no.: 
ECB Working Paper No. 2534
Abstract: 
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.
Subjects: 
Identification
Long-Horizon and Business-Cycle Shocks
Confounding Shocks
JEL: 
C11
C30
E32
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4534-9
Document Type: 
Working Paper

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