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Nets: Network Estimation for Time Series

  • Matteo Barigozzi
  • Christian Brownlees

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 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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  1. Dungey, Mardi & Luciani, Matteo & Veredas, David, 2012. "Ranking systemically important financial institutions," Working Papers 15473, University of Tasmania, Tasmanian School of Business and Economics, revised 21 Nov 2012.
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  9. Peng, Jie & Wang, Pei & Zhou, Nengfeng & Zhu, Ji, 2009. "Partial Correlation Estimation by Joint Sparse Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 735-746.
  10. Francis X. Diebold & Kamil Yılmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," PIER Working Paper Archive 11-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  11. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2012. "Financial Network Systemic Risk Contributions," SFB 649 Discussion Papers SFB649DP2012-053, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
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  16. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Inequalities for High Dimensional Vector Autoregressions," CREATES Research Papers 2012-16, Department of Economics and Business Economics, Aarhus University.
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  21. repec:eca:wpaper:2013/130530 is not listed on IDEAS
  22. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  23. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2013. "Systemic Risk and Stability in Financial Networks," NBER Working Papers 18727, National Bureau of Economic Research, Inc.
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  25. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
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