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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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File URL: http://www.econ.upf.edu/docs/papers/downloads/1391.pdf
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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
Contact details of provider: Web page: http://www.econ.upf.edu/

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  1. Monica Billio & Mila Getmansky & Andrew W. Lo & Loriana Pelizzon, 2011. "Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors," Working Papers 2011_21, Department of Economics, University of Venice "Ca' Foscari".
  2. 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.
  3. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?," CEPR Discussion Papers 5829, C.E.P.R. Discussion Papers.
  4. repec:dgr:uvatin:2012115 is not listed on IDEAS
  5. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  6. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, 09.
  7. 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.
  8. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
  9. 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.
  10. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-66, July.
  11. Freixas, Xavier & Parigi, Bruno M & Rochet, Jean-Charles, 2000. "Systemic Risk, Interbank Relations, and Liquidity Provision by the Central Bank," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(3), pages 611-38, August.
  12. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  13. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2013. "Financial network systemic risk contributions," CFS Working Paper Series 2013/20, Center for Financial Studies (CFS).
  14. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2013. "Forecasting systemic impact in financial networks," SFB 649 Discussion Papers SFB649DP2013-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  15. MArcelo C. Medeiros & Eduardo F.Mendes, 2012. "Estimating High-Dimensional Time Series Models," Textos para discussão 602, Department of Economics PUC-Rio (Brazil).
  16. 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.
  17. Bai, Jushan & Liao, Yuan, 2012. "Efficient Estimation of Approximate Factor Models," MPRA Paper 41558, University Library of Munich, Germany.
  18. 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.
  19. Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.
  20. Dungey, Mardi & Luciani, Matteo & Veredas, David, 2012. "Ranking systemically important financial institutions," Working Papers 15473, University of Tasmania, School of Economics and Finance, revised 21 Nov 2012.
  21. Anders Bredahl Kock, 2012. "On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions," CREATES Research Papers 2012-05, School of Economics and Management, University of Aarhus.
  22. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
  23. Mardi Dungey & Matteo Luciani & David Veredas, 2012. "Ranking Systemically Important Financial Institutions," Tinbergen Institute Discussion Papers 12-115/IV/DSF44, Tinbergen Institute.
  24. Markus K. Brunnermeier & Martin Oehmke, 2012. "Bubbles, Financial Crises, and Systemic Risk," NBER Working Papers 18398, National Bureau of Economic Research, Inc.
  25. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
  26. repec:eca:wpaper:2013/130530 is not listed on IDEAS
  27. repec:dgr:uvatin:20120115 is not listed on IDEAS
  28. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Inequalities for High Dimensional Vector Autoregressions," CREATES Research Papers 2012-16, School of Economics and Management, University of Aarhus.
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