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：我们提出了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，该模型采用数据驱动的时变识别方法。该模型能够随时间选择不同的排除性约束，并作为搜索条件，允许通过每个区制内的异方差性来验证识别。基于四种备选货币政策规则，我们表明，一个包含六个变量的月度系统支持美国货币政策冲击识别的时变特性。在样本主导的第一区制中，系统性货币政策遵循由期限利差扩展的泰勒规则，有效抑制了通货膨胀。在2000年后出现、并在全球金融危机和新冠疫情后持续增强的第二区制中，其特点为货币增广泰勒规则。该区制的非常规货币政策提供了经济刺激，呈现流动性效应，并辅以纯粹的期限利差冲击。若缺失第二区制的特定货币政策，2008年后的平均通胀率将高出超过一个百分点。