Time Series Gaussian Chain Graph Models
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AI 根据所提供的论文材料归类：摘要的核心是多变量时间序列的谱密度、滞后依赖和图结构估计，直接对应时间序列方法。
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时间序列高斯链图模型
- 研究问题
- 多变量时间序列具有分块依赖时，如何同时表示块间当期与滞后方向关系、块内条件依赖，并识别和恢复相应图结构？
- 方法与思路
- 作者构造高斯链图模型，利用逆谱密度的跨频率组稀疏与组低秩分解，结合正则化 Whittle 似然设计三阶段学习程序。
- 材料陈述的贡献
- 摘要称，该框架建立链图结构的可识别性与精确恢复一致性，并通过模拟及美国宏观数据应用展示经验表现和货币政策传导关系。
- 阅读关注点
- 应核对稀疏与低秩分解唯一性的条件、三阶段估计之间的依赖、惩罚参数选择，以及图中方向关系获得因果解释所需的条件。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核验。摘要未列出识别与一致性的具体条件，经验优势及政策机制解释仍需全文证据支持。
原文摘要
Time series graphical models have recently received considerable attention for characterizing (conditional) dependence structures in multivariate time series. In many applications, the multivariate series exhibit variable-partitioned blockwise dependence, with distinct patterns within and across blocks. In this paper, we introduce a new class of time series Gaussian chain graph models that represent contemporaneous and lagged causal relations via directed edges across blocks, while capturing within-block conditional dependencies through undirected edges. In the frequency domain, this formulation induces a cross-frequency shared group sparse plus group low-rank decomposition of the inverse spectral density matrices, which we exploit to establish identifiability of the time series chain graph structure. Building on this, we then propose a three-stage learning procedure for estimating the undirected and directed edge sets, which involves optimizing a regularized Whittle likelihood with a group lasso penalty to encourage group sparsity and a novel tensor-unfolding nuclear norm penalty to enforce group low-rank structure. We investigate the asymptotic properties of the proposed method, ensuring its consistency for exact recovery of the chain graph structure. The superior empirical performance of the proposed method is demonstrated through both extensive simulation studies and an application to U.S. macroeconomic data that highlights key monetary policy transmission mechanisms.