发表机构
University of North Florida（北佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于标准与自适应融合组套索的方法检测高维非平稳时间序列中的多个同时变点，证明了其L_2和L_0一致性，并通过美国50年经济金融数据验证了性能。
AI 中文摘要
本文研究了高维非平稳经济和金融时间序列数据中多个同时（系统性）变点的检测问题。所使用的分析框架基于标准与自适应融合组套索方法，其中混合L_{2,1}惩罚项要么是均匀的，要么由数据依赖的权重重新加权。本文表明，在适当条件下，该方法具有L_2一致性，并且通过采用数据依赖的权重，能够以趋近于1的概率正确选择变点（L_0一致性）。本文量化了结构变化的平均最小幅度、变点数量与观测数量之间相互作用的条件，以一致地发现变点。通过对过去50年美国大型经济和金融时间序列数据面板的分析，展示了该方法的性能。
英文摘要
This paper studies the detection of multiple simultaneous (systematic) change points for high-dimensional nonstantionary economic and financial time series data. The analytic framework used is based on the standard and adaptive fused group lasso method, where the mixed L_{2,1} penalty is either uniform or re-weighted by data-dependent weights. This paper shows that, under appropriate conditions, this approach is L_2 consistent and, by adopting the data-dependent weights, could correctly select the change points with probability approaching unity (L_0 consis- tency). It quantifies the conditions on the interplay among the averaged minimum magnitude of structural changes, the number of change points and the number of observations for consistently discovering the change points. The performance of this approach is illustrated via an analysis of a large panel of U.S. economic and financial time series data over the past 50 years.
