Authors: Daniel J. Lewis
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The New York Innovation Center bridges the worlds of finance, technology, and innovation and generates insights into high-value central bank-related opportunities.
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Staff Reports
Identifying Shocks via Time-Varying Volatility
Number 871
October 2018 Revised May 2019
October 2018 Revised May 2019
JEL classification: C32, C58, E20, E62, H30
An n-variable structural vector auto-regression (SVAR) can be identified (up to shock order) from the evolution of the residual covariance across time if the structural shocks exhibit heteroskedasticity (Rigobon (2003), Sentana and Fiorentini (2001)). However, the path of residual covariances can only be recovered from the data under specific parametric assumptions on the variance process. I propose a new identification argument that identifies the SVAR up to shock orderings using the autocovariance structure of second moments of the residuals, implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions like those required by existing approaches. The conditions required for identification can be tested using a simple procedure. The identification scheme performs well in simulations. I apply the approach to the debate on fiscal multipliers and obtain estimates lower than those of Blanchard and Perotti (2002) and Mertens and Ravn (2014), but in line with more recent studies.
Author Disclosure Statement(s)
Daniel J. Lewis
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
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