标题： 货币货币政策冲击的时变识别 显示英文标题

标题： Time-Varying Identification of Monetary Policy Shocks

摘要： 我们提出了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，该模型采用数据驱动的时间变化识别方法。 该模型随时间选择不同的排除限制，并且作为搜索条件，允许通过每个状态内的异方差性来验证识别。 基于四种替代的货币政策规则，我们表明，一个每月六变量系统支持美国货币政策冲击识别的时间变化。 在占据样本主导的第一状态中，系统的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。 在第二状态中，该状态出现在2000年后，在全球金融危机和新冠疫情危机后表现出更大的持久性，其特点是货币增强型泰勒规则。 该状态的非常规货币政策提供了经济刺激，具有流动性效应，并由纯粹的期限利差冲击补充。 如果没有第二状态的特定货币政策，平均而言，2008年后通货膨胀率将高出超过一个百分点。 显示英文摘要

摘要： 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.