NETS: Network Estimation for Time Series

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