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Nets: Network Estimation for Time Series

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  • Matteo Barigozzi
  • Christian Brownlees

Abstract

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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Bibliographic Info

Paper provided by Barcelona Graduate School of Economics in its series Working Papers with number 723.

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Date of creation: Oct 2013
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Handle: RePEc:bge:wpaper:723

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Keywords: Networks; Multivariate Time Series; Long Run Covariance; Lasso;

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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. NBER/NSF Time-Series Conference: Retrospect and Prospect
    by Francis Diebold in No Hesitations on 2013-10-25 13:14:00
  2. Network Estimation for Time Series
    by Francis Diebold in No Hesitations on 2013-10-16 11:41:00
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Cited by:
  1. Sessi Tokpavi, 2013. "Testing for the Systemically Important Financial Institutions: a Conditional Approach," EconomiX Working Papers 2013-27, University of Paris West - Nanterre la Défense, EconomiX.
  2. Kamil Yilmaz, 2014. "Volatility Connectedness of Bank Stocks Across the Atlantic," Koç University-TUSIAD Economic Research Forum Working Papers 1402, Koc University-TUSIAD Economic Research Forum.
  3. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2014. "A Two Stage Approach to Spatiotemporal Analysis with Strong and Weak Cross-Sectional Dependence," CESifo Working Paper Series 4592, CESifo Group Munich.
  4. Aldasoro, Iñaki & Angeloni, Ignazio, 2013. "Input-Output-based Measures of Systemic Importance," MPRA Paper 49557, University Library of Munich, Germany.
  5. Paolo Giudici & Alessandro Spelta, 2013. "Graphical network models for international financial flows," DEM Working Papers Series 052, University of Pavia, Department of Economics and Management.

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