An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series
高维时间序列中可解释且高效的无限阶向量自回归模型AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究高维时间序列的无限阶 VAR 与 VARMA 型动态，并包含宏观经济数据分析。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
高维时间序列中可解释且高效的无限阶向量自回归模型
- 研究问题
- 如何为高维时间序列保留 VARMA 的丰富动态模式，同时缓解其不可识别、计算困难及解释困难等问题？
- 方法与思路
- 作者提出稀疏无限阶 VAR，分别参数化时间与横截面结构，采用两种 ℓ₁ 正则化估计、分块坐标下降和 BIC 阶数选择。
- 材料陈述的贡献
- 摘要称该模型兼顾动态信息与解释性，给出非渐近误差界和一致阶数选择方法，并以模拟及宏观经济数据展示其表现。
- 阅读关注点
- 阅读时应核对模型可识别性、两类参数的分离方式、稀疏条件、误差界与阶数选择条件，以及模拟比较的基准和计算成本。
- 阅读边界
- 所据为 arXiv 2209.01172v4 预印本摘要，不能据此声称已经同行评审或期刊接收；摘要未提供具体条件、结果或刊本变化。
原文摘要
As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive moving average (VARMA) model can capture much richer temporal patterns than the widely used finite-order VAR model. However, its practicality has long been hindered by its non-identifiability, computational intractability, and difficulty of interpretation, especially for high-dimensional time series. This paper proposes a novel sparse infinite-order VAR model for high-dimensional time series, which avoids all above drawbacks while inheriting essential temporal patterns of the VARMA model. As another attractive feature, the temporal and cross-sectional structures of the VARMA-type dynamics captured by this model can be interpreted separately, since they are characterized by different sets of parameters. This separation naturally motivates the sparsity assumption on the parameters determining the cross-sectional dependence. As a result, greater statistical efficiency and interpretability can be achieved with little loss of temporal information. We introduce two $\ell_1$-regularized estimation methods for the proposed model, which can be efficiently implemented via block coordinate descent algorithms, and derive the corresponding nonasymptotic error bounds. A consistent model order selection method based on the Bayesian information criteria is also developed. The merit of the proposed approach is supported by simulation studies and a real-world macroeconomic data analysis.