NETS: Network estimation for time series. (11th January 2019)

• Record Type: Journal Article
• Authors: Barigozzi, Matteo; Brownlees, Christian
• Is Part Of: Journal of applied econometrics. Volume 34:Number 3(2019)
• Page Start: 347
• Page End: 364
• Publication Date: 2019-01-11
• Subjects: Econometrics -- Periodicals; 330.015195
• DOI: 10.1002/jae.2676
• Languages: English
• ISSNs: 0883-7252

Abstract

Summary: 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 calledNETS 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.