New Approaches to the Identification of Low-Frequency Drivers : An Application to Technology Shocks
This paper addresses the identification of low-frequency macroeconomic shocks, such as technology, in Structural Vector Autoregressions. Whilst identification issues with long-run restricted VARs are well documented, the recent attempt to overcome...
| Main Authors: | , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2019
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/133781571930814658/New-Approaches-to-the-Identification-of-Low-Frequency-Drivers-An-Application-to-Technology-Shocks http://hdl.handle.net/10986/32656 |
| Summary: | This paper addresses the identification
of low-frequency macroeconomic shocks, such as technology,
in Structural Vector Autoregressions. Whilst identification
issues with long-run restricted VARs are well documented,
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. A modification to the Max-Share approach and two
further spectral methods are proposed to improve empirical
identification. Performance directly hinges on whether these
confounding shocks are of high or low frequency. Applied to
US and emerging market data, spectral identifications are
most robust across specifications, and non-technology shocks
appear to be biasing traditional methods of identifying
technology shocks. These findings also extend to the SVAR
identification of dominant business-cycle shocks, which are
shown will be a variance-weighted combination of shocks
rather than a single structural driver. |
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