NETS: Network estimation for time series

By Matteo Barigozzi, Christian T. Brownlees. Journal of applied econometrics, 2019

189 citations recorded by the source corpus.

This work proposes novel network analysis techniques for multivariate time series. We define the network of a multivariate time series as a graph where vertices denote the components of the process and edges denote non zero long run partial correlations. We then introduce a two step LASSO procedure, called NETS, to estimate high dimensional sparse Long Run Partial Correlation networks. This approach is based on a VAR approximation of the process and allows to decompose the long run linkages into the contribution of the dynamic and contemporaneous dependence relations of the system. The large sample properties of the estimator are analysed and we establish conditions for consistent selection and estimation of the non zero long run partial correlations. The methodology is illustrated with an application to a panel of U.S. bluechips.

Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/890fbcd18c0a5fcca719e3768d06a2ba28fbeedf

Citations in the corpus

• Interconnectedness in the interbank market (2015, unknown): “Rejecting the null in (8) produces an edge between the returns of the two banks in .8 8 Barigozzi and Brownlees (2013) construct networks where edges are based on long run partial correlations.”
• Estimating Global Bank Network Connectedness (2017, unknown): “Recent theoretical work has therefore emphasized network connectedness and formation in financial and industry contexts, as in Jackson (2008), Easley and Kleinberg (2010), Acemoglu et al.”