A Statistically Identified Structural Vector Autoregression with Endogenously Switching Volatility Regime
提出一种条件协方差矩阵可内生切换的结构向量自回归模型，通过同时对角化误差协方差矩阵识别结构性冲击，并给出在统计识别条件不满足时结合符号与零约束识别部分冲击的方法。应用于美国货币政策冲击分析，发现紧缩性政策显著且持久地降低产出，价格永久下降但短期存在惯性。
We introduce a structural vector autoregressive model with endogenously switching conditional covariance matrix. The structural shocks are identified by simultaneously diagonalizing the reduced form error covariance matrices. It is not, however, always clear whether the condition for the full statistical identification is satisfied, and its validity is difficult to justify formally. Therefore, we provide general sets of conditions, that allow to combine sign and zero restrictions on the impact matrix, for identifying a subset of the shocks when the condition for statistical identification of the model fails. In an empirical application to the effects of the U.S. monetary policy shock, we find that a contractionary monetary policy shock significantly decreases output in a persistent hump-shaped pattern. Prices decrease permanently, but there is short-run inertia in their response. The accompanying R package gmvarkit provides a comprehensive set of tools for numerical analysis of the model.