Nets: Network Estimation for Time Series
Matteo Barigozzi and
Christian Brownlees ()
No 723, Working Papers from Barcelona Graduate School of Economics
This work proposes novel network analysis techniques for multivariate time series. We define the network of a multivariate time series as a graph where vertices denote the components of the process and edges denote non-zero long run partial correlations. We then introduce a two step lasso procedure, called nets, to estimate high-dimensional sparse Long Run Partial Correlation networks. This approach is based on a var approximation of the process and allows to decompose the long run linkages into the contribution of the dynamic and contemporaneous dependence relations of the system. The large sample properties of the estimator are analysed and we establish conditions for consistent selection and estimation of the non-zero long run partial correlations. The methodology is illustrated with an application to a panel of U.S. bluechips.
Keywords: Networks; Multivariate Time Series; Long Run Covariance; Lasso (search for similar items in EconPapers)
JEL-codes: C01 C32 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-net and nep-ore
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Journal Article: NETS: Network estimation for time series (2019)
Working Paper: Nets: network estimation for time series (2018)
Working Paper: Nets: Network estimation for time series (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:723
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