A1565
Title: Estimating time-varying networks for high-dimensional time series
Authors: Yuning Li - University of York (United Kingdom) [presenting]
Abstract: Time-varying networks for high-dimensional locally stationary time series are explored using the large VAR model framework with both transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear estimation with time-varying weighted group LASSO to jointly estimate the transition matrices and identify their significant entries, and a time-varying CLIME method to estimate the precision matrix. The estimated transition and precision matrices are then used to determine the time-varying network structures. Under some mild conditions, we derive the theoretical properties of the proposed estimates, including the consistency and oracle properties. Extensive simulation studies and an empirical application are provided to illustrate the finite-sample performance of the developed methods.
