针对短时间序列与超短时间序列的聚类局部投影——一种分层贝叶斯框架
Clustered Local Projections for Short and Ultra-Short Time Series -- A Hierarchical Bayesian Framework
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中文总结 AI 辅助
本文提出适配非平衡面板的分层贝叶斯聚类局部投影方法，可提升短时间序列的LP估计精度，通过美国价格数据集验证了供应链与石油冲击会引发不同价格指标的异质反应。
中文摘要 AI 辅助
在短样本和超短样本中估计经济冲击的动态效应时，会因自由度不足而受阻。本文提出一种解决方案，基于贝叶斯分层框架来估计一组相关时间序列面板的局部投影（LP）脉冲响应函数。该框架明确适配非平衡面板，其中部分序列的长度显著短于其他序列，允许短序列在自身数据极少或无数据的时间范围，从较长序列借用信息。由于序列可能呈现异质动态，本文开发了一种稀疏有限混合池，按脉冲响应轮廓的相似性对单元进行聚类。模拟结果显示，若时间序列较短，本文方法相较标准方法可大幅提升LP估计精度，而对于较长时间序列则生成相近的LP。利用美国价格数据集（补充了调查回复），本文发现供应链冲击与石油冲击会触发不同价格指标的异质反应：总体价格指数的反应比核心价格指数更剧烈，商品价格的变动幅度大于服务价格。
英文摘要
Estimating the dynamic effects of economic shocks in short and very short samples is impeded by a lack of degrees of freedom. We offer a solution based on a Bayesian hierarchical framework for estimating local projection (LP) impulse response functions across a panel of related time series. The framework explicitly accommodates unbalanced panels in which some series are substantially shorter than others, allowing the short series to borrow information from longer ones at horizons where the short series carry little or no own data. Since series might exhibit heterogeneous dynamics, we develop a sparse finite mixture pool that clusters units by similarity of their impulse response profiles. We show in simulations that our approach substantially improves LP estimation accuracy relative to the standard approach if the time series are short while producing similar LPs for longer time series. Using a US price dataset, augmented with survey responses, we find that supply-chain and oil shocks trigger heterogeneous reactions of different price measures, with headline price indices responding more sharply than their core counterparts and goods prices changing more than services prices.
