NETS: Network estimation for time series
Matteo Barigozzi and
Christian Brownlees ()
Journal of Applied Econometrics, 2019, vol. 34, issue 3, 347-364
We model a large panel of time series as a vector autoregression 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 analyze a panel of volatility measures of 90 blue chips. The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out‐of‐sample forecasting.
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Working Paper: Nets: network estimation for time series (2018)
Working Paper: Nets: Network Estimation for Time Series (2013)
Working Paper: Nets: Network estimation for time series (2013)
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