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Simultaneous multiple change-point and factor analysis for high-dimensional time series

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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 .

Joint work with Matteo Barigozzi and Piotr Fryzlewicz (LSE).

This talk is part of the Statistics series.

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