Identification of Structural Vector Autoregressions by Stochastic Volatility
Dominik Bertsche () and
Robin Braun ()
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Dominik Bertsche: University of Konstanz, Department of Economics, Box 129, 78457 Konstanz, Germany
No 2017-11, Working Paper Series of the Department of Economics, University of Konstanz from Department of Economics, University of Konstanz
In Structural Vector Autoregressive (SVAR) models, heteroskedasticity can be exploited to identify structural parameters statistically. In this paper, we propose to capture time variation in the second moment of structural shocks by a stochastic volatility (SV) model, assuming that their log variances follow latent AR(1) processes. Estimation is performed by Gaussian Maximum Likelihood and an efficient Expectation Maximization algorithm is developed for that purpose. Since the smoothing distributions required in the algorithm are intractable, we propose to approximate them either by Gaussian distributions or with the help of Markov Chain Monte Carlo (MCMC) methods. We provide simulation evidence that the SV-SVAR model works well in estimating the structural parameters also under model misspecification. We use the proposed model to study the interdependence between monetary policy and the stock market. Based on monthly US data, we find that the SV specification provides the best fit and is favored by conventional information criteria if compared to other models of heteroskedasticity, including GARCH, Markov Switching, and Smooth Transition models. Since the structural shocks identified by heteroskedasticity have no economic interpretation, we test conventional exclusion restrictions as well as Proxy SVAR restrictions which are overidentifying in the heteroskedastic model.
Keywords: Structural Vector Autoregression (SVAR); Identification via heteroskedasticity; Stochastic Volatility; Proxy SVAR (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Working Paper: Identification of Structural Vector Autoregressions by Stochastic Volatility (2018)
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