TY - BOOK ID - 138025776 TI - New Approaches to the Identification of Low-Frequency Drivers : An Application to Technology Shocks AU - Dieppe, Alistair. AU - Kindberg-Hanlon, Gene. AU - Neville, Francis. PY - 2019 PB - Washington, D.C. : The World Bank, DB - UniCat KW - Business Cycle KW - Business Cycles and Stabilization Policies KW - Economic Conditions and Volatility KW - Economic Growth KW - Economic Shocks KW - Economic Theory and Research KW - Macroeconomics and Economic Growth KW - Productivity KW - Science and Technology Development KW - Structural Vector Autoregression KW - Technology Innovation KW - Technology Shock UR - https://www.unicat.be/uniCat?func=search&query=sysid:138025776 AB - 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 and others (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. ER -