New Approaches to the Identification of Low-Frequency Drivers: An Application to Technology Shocks

Alistair Dieppe (), Francis Neville and Gene Joseph Kindberg Hanlon

No 9047, Policy Research Working Paper Series from The World Bank

Abstract: 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.

Keywords: Labor Markets; Macroeconomic Management; National Governance; Social Analysis; Quality of Life&Leisure; Youth and Governance; Government Policies; Public Finance Decentralization and Poverty Reduction; Macro-Fiscal Policy; Taxation&Subsidies; Public Sector Economics; Economic Adjustment and Lending (search for similar items in EconPapers)
Date: 2019-10-24
New Economics Papers: this item is included in nep-ets and nep-mac
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