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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
Date of revision:
Handle: RePEc:bge:wpaper:723
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  1. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
  2. Francis X. Diebold & Kamil Yilmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," NBER Working Papers 17490, National Bureau of Economic Research, Inc.
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  7. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2014. "Forecasting systemic impact in financial networks," International Journal of Forecasting, Elsevier, vol. 30(3), pages 781-794.
  8. X. Freixas & B. Parigi & J-C. Rochet, 2000. "Systemic Risk, Interbank Relations and Liquidity Provision by theCentral Bank," DNB Staff Reports (discontinued) 47, Netherlands Central Bank.
  9. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, 09.
  10. Marcelo C. Medeiros & Eduardo F. Mendes, 2012. "Estimating High-Dimensional Time Series Models," CREATES Research Papers 2012-37, School of Economics and Management, University of Aarhus.
  11. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2015. "Financial Network Systemic Risk Contributions," Review of Finance, European Finance Association, vol. 19(2), pages 685-738.
  12. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  13. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2015. "Systemic Risk and Stability in Financial Networks," American Economic Review, American Economic Association, vol. 105(2), pages 564-608, February.
  14. Bai, Jushan & Liao, Yuan, 2012. "Efficient Estimation of Approximate Factor Models," MPRA Paper 41558, University Library of Munich, Germany.
  15. 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.
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  18. 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.
  19. 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.
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  24. repec:eca:wpaper:2013/130530 is not listed on IDEAS
  25. 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.
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