Robust Inference in Time-Varying Structural VAR Models: The DC-Cholesky Multivariate Stochastic Volatility Model

Benny Hartwig

VfS Annual Conference 2020 (Virtual Conference): Gender Economics from Verein für Socialpolitik / German Economic Association

Abstract: This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are imposed when the ratio of volatilities is time-varying. Simulations demonstrate that estimated covariance matrices become more divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy and in ation-gap persistence indicate that conclusions may critically hinge on a selected ordering of variables. The dynamic correlation Cholesky multivariate stochastic volatility model is proposed as a robust alternative.

Keywords: Model uncertainty; Multivariate stochastic volatility; Dynamic correlations; Monetary policy; Structural VAR (search for similar items in EconPapers)
JEL-codes: C11 C32 E32 E52 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-mac, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Robust inference intime-varying structural VAR models: The DC-Cholesky multivariate stochasticvolatility model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc20:224528

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