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

With Haeran Cho (Bristol)

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 .

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

Add to your calendar or Include in your list

How can mathematics help us to understand the behaviour of ants? Read more about the fanscinating work being carri… https://t.co/iCODvvxqE6 View on Twitter