Simultaneous multiple change-point and factor analysis with applications Seminar
- Time:
- 14:00 - 15:00
- Date:
- 16 February 2017
- Venue:
- University of Southampton, Highfield Campus, Building 54, Seminar Room 5027
For more information regarding this seminar, please email Professor Dankmar Bohning at D.A.Bohning@soton.ac.uk .
Event details
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. 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. Applications to S&P100 stock returns, US macroeconomic data and exchange rates against British Sterling are demonstrated. This is a joint work with Haeran Cho and Piotr Fryzlewicz.
Speaker information
Dr Matteo Barigozzi , London School of Economics. Assistant Professor, Department of Statistics