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Title: A block segmentation scheme for structural break detection in large scale high-dimensional non-stationary VAR models Authors:  Abolfazl Safikhani - University of Florida (United States) [presenting]
George Michailidis - University of Florida (United States)
Abstract: Many real time series data sets exhibit structural changes over time. A popular model for capturing their temporal dependence is that of Vector Autoregressions (VAR), which can accommodate structural changes through time evolving transition matrices. The problem then becomes to both estimate the (unknown) number of structural break points, together with the VAR model parameters. An additional challenge emerges in the presence of very large data sets, namely on how to accomplish these two objectives in a computational efficient manner. In this paper, we propose a novel procedure which leverages a block segmentation scheme (BSS) that reduces the number of model parameters to be estimated through a regularized least squares criterion. Specifically, BSS examines appropriately defined blocks of the available data, which when combined with a fused lasso based estimation criterion, leads to significant computational gains without compromising on the statistical accuracy in identifying the number and location of the structural breaks. The procedure is scalable to large high-dimensional time series data sets with a computational complexity proportional to square root of sample size. Extensive numerical work on synthetic data supports the theoretical findings and illustrates the attractive properties of the procedure. Finally, an application to a neuroscience data set exhibits its usefulness in applications.