Simultaneous multiple change-point and factor analysis for high-dimensional time series
Matteo Barigozzi,
Haeran Cho and
Piotr Fryzlewicz
LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library
Abstract:
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.
Keywords: piecewise stationary factor model; change-point detection; principal component analysis; wavelet transformation; Double CUSUM Binary Segmentation (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2018-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (37)
Published in Journal of Econometrics, September, 2018, 206(1), pp. 187-225. ISSN: 0304-4076
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:88110
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