Detection of Multiple Structural Breaks in Multivariate Time Series

By Philip Preuss, R. Puchstein, Holger Dette. 2013

120 citations recorded by the source corpus.

We propose a new nonparametric procedure (referred to as MuBreD) for the detection and estimation of multiple structural breaks in the autocovariance function of a multivariate (second-order) piecewise stationary process, which also identifies the components of the series where the breaks occur. MuBreD is based on a comparison of the estimated spectral distribution on different segments of the observed time series and consists of three steps: it starts with a consistent test, which allows us to prove the existence of structural breaks at a controlled Type I error. Second, it estimates sets containing possible break points and finally these sets are reduced to identify the relevant structural breaks and corresponding components which are responsible for the changes in the autocovariance structure. In contrast to all other methods proposed in the literature, our approach does not make any parametric assumptions, is not especially designed for detecting one single change point, and addresses the problem of multiple structural breaks in the autocovariance function directly with no use of the binary segmentation algorithm. We prove that the new procedure detects all components and the corresponding locations where structural breaks occur with probability converging to one as the sample size increases and provide data-driven rules for the selection of all regularization parameters. The results are illustrated by analyzing financial asset returns, and in a simulation study it is demonstrated that MuBreD outperforms the currently available nonparametric methods for detecting breaks in the dependency structure of multivariate time series. Supplementary materials for this article are available online.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a8f83e67431c431d6d5035ca4cacb49bed61dff2

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Citations in the corpus

Detecting relevant changes in time series models (2014, background): “ regression models [see Andrews (1993) and Bai and Perron (1998)] and to change-point inference on the second order characteristics of a time series [see Berkes et al. (2009a), Wied et al. (2012) and Preuss et al. (2014)]. A rather extensive list of references can be found in the recent work of Aue ”
A MOSUM procedure for the estimation of multiple random change points (2018, unknown): “More recently, [38] use moving sum statistics in the frequency domain to detect changes in the autocovariance structure of multivariate time series.”