A0346
Title: Nonparametric data segmentation in multivariate time series via joint characteristic functions
Authors: Haeran Cho - University of Bristol (United Kingdom)
Euan McGonigle - University of Southampton (United Kingdom) [presenting]
Abstract: In time series analysis, many data sets of practical interest contain abrupt changes in structure, such as the mean level or serial dependence. Nonparametric change point detection is a flexible approach that aims to find general distributional changes in the data. A method is proposed for nonparametric detection of multiple change points in serially dependent multivariate time series. A notion of distributional change is defined using joint characteristic functions of the time series and its lagged values. This is used in combination with a moving sum-type procedure to identify multiple change points by finding local maximizers of a test statistic calculated in a rolling fashion over the data. This enables the detection of changes in both the marginal and pairwise joint distributions of the time series. The theoretical properties of the procedure are examined, and the flexibility of the method is illustrated by applying it to a data example from economics.
