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Nets: network estimation for time series

Matteo Barigozzi and Christian T. Brownlees

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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 total variability, on top of what is explained by volatility factors, and improves out-of-sample forecasting.

Keywords: networks; multivariate time series; var; lasso; forecasting (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-net and nep-ore
Date: 2018-12-05
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Published in Journal of Applied Econometrics, 5, December, 2018. ISSN: 1099-1255

Downloads: (external link)
http://eprints.lse.ac.uk/90493/ Open access version. (application/pdf)

Related works:
Journal Article: NETS: Network estimation for time series (2019) 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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