NETS: Network Estimation for Time Series

35 Pages Posted: 14 Apr 2013 Last revised: 19 Oct 2018

See all articles by Matteo Barigozzi

Matteo Barigozzi

London School of Economics and Political Science

Christian T. Brownlees

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences; Barcelona Graduate School of Economics (Barcelona GSE)

Date Written: October 15, 2018

Abstract

We model a large panel of time series as a VAR where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyse a panel of volatility measures of ninety bluechips. The model captures an important fraction of the overall variability of the time series and improves out-of-sample forecasting.

Keywords: Networks, Multivariate Time Series, Long Run Covariance, LASSO

JEL Classification: C01, C32, C52

Suggested Citation

Barigozzi, Matteo and Brownlees, Christian T., NETS: Network Estimation for Time Series (October 15, 2018). Available at SSRN: https://ssrn.com/abstract=2249909 or http://dx.doi.org/10.2139/ssrn.2249909

Matteo Barigozzi

London School of Economics and Political Science ( email )

Houghton Street
London, England WC2A 2AE
United Kingdom

Christian T. Brownlees (Contact Author)

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain

HOME PAGE: http://84.89.132.1/~cbrownlees/

Barcelona Graduate School of Economics (Barcelona GSE) ( email )

Ramon Trias Fargas 25-27
Barcelona, Catalonia 08014
Spain

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