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Nets: Network estimation for time series

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.

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Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 1391.

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Date of creation: Oct 2013
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Handle: RePEc:upf:upfgen:1391
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  8. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2015. "Financial Network Systemic Risk Contributions," Review of Finance, European Finance Association, vol. 19(2), pages 685-738.
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  11. Daron Acemoglu & Vasco Carvalho & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2011. "The network origins of aggregate fluctuations," Economics Working Papers 1291, Department of Economics and Business, Universitat Pompeu Fabra.
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