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Vector Autoregressive based Network Models

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SNAW04 - Dynamic Networks

Vector autoregressions represent a popular class of time series models that aim to capture temporal interconnections between temporally evolving
entities.  They have been widely used in macroeconomic and financial modeling and more recently they have found novel applications in functional genomics and neuroscience. In this presentation, we discuss modeling and estimation issues in the high dimensional setting under different constrains
on the transition matrices – sparsity, low rankness. We also provide extensions to multi-layer networks and illustrate the results with application​s
to financial stability monitoring and biological regulation.

This talk is part of the Isaac Newton Institute Seminar Series series.

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