Abstract This paper proposes a three-step method for detecting multiple structural breaks for piecewise stationary vector autoregressive processes. The number of structural breaks can be large and unknown with the locations of the breaks being different among different components. The proposed method is established via a link between a structural break problem and a high-dimensional regression problem. By means of this connection, a group orthogonal greedy algorithm, originated from the high-dimensional variable selection context, is developed for efficiently screening out potential break-points in the first step. A high-dimensional information criterion is proposed for consistent structural breaks estimation in the second step. In the third step, the information criterion further determines the specific components in which structural breaks occur. Monte Carlo experiments are conducted to demonstrate the finite sample performance, and applications to stock data are provided to illustrate the proposed method.

中文翻译：

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多元时间序列变点估计的群正交贪婪算法

摘要 本文提出了一种用于检测分段平稳向量自回归过程的多个结构断裂的三步法。结构断裂的数量可能很大而且是未知的，断裂的位置在不同的部件之间是不同的。所提出的方法是通过结构断裂问题和高维回归问题之间的联系建立的。通过这种联系，开发了一种源自高维变量选择上下文的群正交贪婪算法，用于在第一步中有效地筛选出潜在的断点。在第二步中，提出了一种高维信息标准，用于一致的结构断裂估计。第三步，信息准则进一步确定发生结构断裂的具体部件。