Identifying structural vector autoregressions via non-Gaussianity of potentially dependent shocks
证明在结构冲击无共偏度时偏态冲击可识别，无超额共峰度时非正态峰度冲击可识别，并允许依赖条件异方差，通过贝叶斯方法实现含偏态t分布和随机波动的SVAR模型，应用于美国货币政策分析。
Summary We complement previous partial global identification results for the non-Gaussian structural vector autoregressive (SVAR) model by showing that in the absence of co-skewness among the strucural shocks, the skewed shocks are identified and in the absence of excess co-kurtosis, the shocks with non-zero 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 US monetary policy.