Identifying shocks via time-varying volatility

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Authors and Corporations:	Lewis, Daniel J. (Author)
Type of Resource:	E-Book
Language:	English
published:	New York, NY Federal Reserve Bank of New York [2018]
Series:	Federal Reserve Bank of New York: Staff reports ; no. 871 (October 2018)
Source:	Verbunddaten SWB Lizenzfreie Online-Ressourcen

Description
Summary:	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 is available only 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. I offer intuitive criteria to select among shock orderings; this selection does not impact inference asymptotically. The identification scheme performs well in simulations. I apply it to the debate on fiscal multipliers. I obtain estimates that are lower than those of Blanchard and Perotti (2002) and Mertens and Ravn (2014), but in line with those of more recent studies.
Physical Description:	1 Online-Ressource (circa 46 Seiten); Illustrationen