NETS: Network Estimation for Time Series
35 Pages Posted: 14 Apr 2013 Last revised: 19 Oct 2018
Date Written: October 15, 2018
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: Suggested Citation