We propose a new Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects alternative exclusion restrictions over time and, as a condition for the search, allows to verify identification through heteroskedasticity within each regime. Based on four alternative monetary policy rules, we show that a monthly six-variable system supports time variation in US monetary policy shock identification. In the sample-dominating first regime, systematic monetary policy follows a Taylor rule extended by the term spread, effectively curbing inflation. In the second regime, occurring after 2000 and gaining more persistence after the global financial and COVID crises, it is characterized by a money-augmented Taylor rule. This regime's unconventional monetary policy provides economic stimulus, features the liquidity effect, and is complemented by a pure term spread shock. Absent the specific monetary policy of the second regime, inflation would be over one percentage point higher on average after 2008.

翻译：我们提出一种新的贝叶斯异方差马尔可夫切换结构向量自回归模型，该模型具有数据驱动的时变识别特性。该模型可随时间选择不同的排除性约束，并作为搜索条件，允许在每个体制内通过异方差性验证识别结果。基于四种替代性货币政策规则，我们证明一个包含六变量的月度系统能够支撑美国货币政策冲击识别的时变特征。在样本主导的第一体制中，系统性货币政策遵循由期限利差扩展的泰勒规则，有效抑制通货膨胀。而在2000年之后出现、并于全球金融危机及新冠疫情后更具持续性的第二体制中，货币政策则表现为货币增强型泰勒规则。该体制下的非常规货币政策提供经济刺激、体现流动性效应，并辅以纯粹的期限利差冲击。若无第二体制特有的货币政策，2008年后平均通胀率将超过一个百分点。