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NETS: Network estimation for time series

Matteo Barigozzi and Christian Brownlees ()

Journal of Applied Econometrics, 2019, vol. 34, issue 3, 347-364

Abstract: 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.

Date: 2019
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https://doi.org/10.1002/jae.2676

Related works:
Working Paper: Nets: network estimation for time series (2018) Downloads
Working Paper: Nets: Network Estimation for Time Series (2013) Downloads
Working Paper: Nets: Network estimation for time series (2013) Downloads
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