Simultaneous multiple change-point and factor analysis for high-dimensional time series
Simultaneous multiple change-point and factor analysis for high-dimensional time series
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DOI：
10.1016/j.jeconom.2018.05.003
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发表时间：
2018-09-01
影响因子：
6.3
通讯作者：
Fryzlewicz, Piotr
中科院分区：
文献类型：
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作者：
Barigozzi, Matteo;Cho, Haeran;Fryzlewicz, Piotr
首次提出了具有多个变点的高维时间序列因子模型的二阶结构综合处理方法。我们在分段平稳性的最灵活定义下运作，一致地估计变点的数量和位置，并识别它们是起源于共同的组成部分还是特殊的组成部分。通过使用小波，我们将高维时间序列的二阶结构中的变点检测问题转化为(相对容易的)高维面板数据中的变点检测问题。此外，我们的方法通过采用筛选程序绕过了在存在多个变化点的情况下准确估计因素真实数量的困难问题。我们进一步表明，在由所提出的方法估计的变化点定义的每一段上实现了一致的因子分析。在广泛的模拟研究中，我们观察到在变点检测之前的因素分析提高了变点的可检测性，并识别和描述了一种有趣的‘溢出’效应，在这种效应中，特殊成分中的实质性突变自然地被识别为共同成分中的变点，这促使我们将相应的变点也视为一种形式的‘因素’。我们的方法是在CRAN提供的R包factorcpt中实现的。(C)2018年提交人(S)。爱思唯尔出版公司(Elsevier B.V.)
We propose the first comprehensive treatment of high-dimensional time series factor models with multiple change-points in their second-order structure. We operate under the most flexible definition of piecewise stationarity, and estimate the number and locations of change-points consistently as well as identifying whether they originate in the common or idiosyncratic components. Through the use of wavelets, we transform the problem of change-point detection in the second-order structure of a high-dimensional time series, into the (relatively easier) problem of change-point detection in the means of high-dimensional panel data. Also, our methodology circumvents the difficult issue of the accurate estimation of the true number of factors in the presence of multiple change-points by adopting a screening procedure. We further show that consistent factor analysis is achieved over each segment defined by the change-points estimated by the proposed methodology. In extensive simulation studies, we observe that factor analysis prior to change-point detection improves the detectability of change-points, and identify and describe an interesting 'spillover' effect in which substantial breaks in the idiosyncratic components get, naturally enough, identified as change-points in the common components, which prompts us to regard the corresponding change-points as also acting as a form of 'factors'. Our methodology is implemented in the R package factorcpt, available from CRAN. (C) 2018 The Author(s). Published by Elsevier B.V.