Simultaneous multiple change-point and factor analysis for high-dimensional time series. 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. Our methodology circumvents the difficult issue of the accurate estimation of the true number of factors by adopting a screening procedure. In extensive simulation studies, we show 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’. We introduce a simple graphical tool for visualising the piecewise stationary evolution of the factor structure over time. Our methodology is implemented in the R package factorcpt, available from CRAN.
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References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Barigozzi, Matteo; Hallin, Marc; Soccorsi, Stefano; von Sachs, Rainer: Time-varying general dynamic factor models and the measurement of financial connectedness (2021)
- Wang, Daren; Yu, Yi; Rinaldo, Alessandro: Optimal covariance change point localization in high dimensions (2021)
- Wang, Hanchao; Peng, Bin; Li, Degui; Leng, Chenlei: Nonparametric estimation of large covariance matrices with conditional sparsity (2021)
- Barigozzi, Matteo; Cho, Haeran: Consistent estimation of high-dimensional factor models when the factor number is over-estimated (2020)
- Barigozzi, Matteo; Trapani, Lorenzo: Sequential testing for structural stability in approximate factor models (2020)
- Yang, Qing; Li, Yu-Ning; Zhang, Yi: Change point detection for nonparametric regression under strongly mixing process (2020)
- Barigozzi, Matteo; Cho, Haeran; Fryzlewicz, Piotr: Simultaneous multiple change-point and factor analysis for high-dimensional time series (2018)
- Matteo Barigozzi, Haeran Cho, Piotr Fryzlewicz: Simultaneous multiple change-point and factor analysis for high-dimensional time series (2016) arXiv