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

By Matteo Barigozzi, Haeran Cho, Piotr Fryzlewicz. Journal of Econometrics, 2016

106 citations recorded by the source corpus.

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.

Subfields: Applied Mathematics, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/466fba4e996b8069aadba235998d5d1d9cb73727

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Citations in the corpus

Selective review of offline change point detection methods (2020, background): “For instance, the trans970 formation can be based on local periodograms [153], or more recently, on PCA 971 and wavelet transforms [154, 155].”
Finite sample change point inference and identification for high‐dimensional mean vectors (2017, background): “In practical applications, those restrictive assumptions can be easily violated such as in the problem of detecting structural breaks in financial data with latent factors [5].”
State-Varying Factor Models of Large Dimensions (2018, methodology): “11In contrast to other time-varying factor models, for example, Bates et al. (2013), Cheng, Liao, and Schorfheide (2016), and Su and Wang (2017), our model directly incorporates the driving forces for the changes in loadings.”