Identifying Structural Vector Autoregressions Via Non-Gaussianity of Potentially Dependent Structural Shocks

Abstract

We complement previous partial global identification results for the non-Gaussian SVAR model by showing that in the absence of co-skewness among the structural shocks, the skewed shocks are identified and in the absence of excess co-kurtosis, the shocks with with nonzero excess kurtosis are identified. The former case has the advantage that dependent conditional heteroskedasticity is allowed for. In each case, the remaining shocks are set identified, and these results can be combined to identify both skewed and non-mesokurtic shocks. To capture the non-Gaussian features of the data, versatile error distributions must be specified. We discuss the Bayesian implementation of an SVAR model with skewed t-distributed errors that exhibit dependent stochastic volatility, including the assessment of identification and checking the validity of exogenous instruments potentially used for identification. The methods are illustrated in an empirical application to U.S. monetary policy.

Keywords: Structural vector autoregression; Non-Gaussian time series; Identification; Instrumental variable; Bayesian inference, Bayesian inference, Identification, Instrumental variable, Non-Gaussian time series