Identifying shocks via time-varying volatility

Daniel Lewis

No 871, Staff Reports from Federal Reserve Bank of New York

Abstract: 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.

Keywords: heteroskedasticity; SVAR; fiscal multipliers; time-varying volatility; identification; impulse response functions; structural shocks (search for similar items in EconPapers)
JEL-codes: C32 C58 E20 E62 H30 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2018-10-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac, nep-ore and nep-rmg
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Citations: View citations in EconPapers (8)

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Journal Article: Identifying Shocks via Time-Varying Volatility (2021)
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