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转换结构向量自回归模型，该模型具有数据驱动的时变识别功能。该模型随时间推移选择替代性排除约束，并作为搜索条件，允许通过各体制内的异方差性验证识别结果。基于四种替代性货币政策规则，我们证明月度六变量系统支持美国货币政策冲击识别的时变性。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，有效抑制通胀。在2000年后出现且在全球金融危机和新冠疫情后持续性增强的第二体制中，该规则表现为货币增广型泰勒规则。这一体制的非传统货币政策提供经济刺激，具有流动性效应，并由纯期限利差冲击作为补充。若缺失第二体制的特定货币政策，2008年后的平均通胀率将高出超过一个百分点。