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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.

Suggested Citation

  • Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona Graduate School of Economics.
  • Handle: RePEc:bge:wpaper:723
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    References listed on IDEAS

    as
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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 18:14:00
    2. Network Estimation for Time Series
      by Francis Diebold in No Hesitations on 2013-10-16 16:41:00

    Citations

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    Cited by:

    1. Abbassi, Puriya & Brownlees, Christian & Hans, Christina & Podlich, Natalia, 2016. "Credit risk interconnectedness: What does the market really know?," Discussion Papers 09/2016, Deutsche Bundesbank.
    2. Paolo Giudici & Laura Parisi, 2015. "Modeling Systemic Risk with Correlated Stochastic Processes," DEM Working Papers Series 110, University of Pavia, Department of Economics and Management.
    3. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2015. "Estimating Global Bank Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 1512, Koc University-TUSIAD Economic Research Forum.
    4. P. Giudici & A. Spelta, 2016. "Graphical Network Models for International Financial Flows," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 128-138.
    5. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    6. Paolo Giudici & Laura Parisi, 2016. "Bail in or Bail out? The Atlante example from a systemic risk perspective," DEM Working Papers Series 124, University of Pavia, Department of Economics and Management.
    7. repec:eee:finsta:v:29:y:2017:i:c:p:1-12 is not listed on IDEAS
    8. Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
    9. 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.
    10. Iñaki Aldasoro & Ignazio Angeloni, 2015. "Input-output-based measures of systemic importance," Quantitative Finance, Taylor & Francis Journals, pages 589-606.
    11. Ivan Alves & Stijn Ferrari & Pietro Franchini & Jean-Cyprien Heam & Pavol Jurca & Sam Langfield & Sebastiano Laviola & Franka Liedorp & Antonio Sánchez & Santiago Tavolaro & Guillaume Vuillemey, 2013. "The structure and resilience of the European interbank market," ESRB Occasional Paper Series 03, European Systemic Risk Board.
    12. Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, pages 255-271.
    13. Paolo Giudici & Laura Parisi, 2016. "CoRisk: measuring systemic risk through default probability contagion," DEM Working Papers Series 116, University of Pavia, Department of Economics and Management.
    14. Sessi Tokpavi, 2013. "Testing for the Systemically Important Financial Institutions: a Conditional Approach," EconomiX Working Papers 2013-27, University of Paris Nanterre, EconomiX.
    15. Paola Cerchiello & Paolo Giudici, 2014. "Conditional graphical models for systemic risk measurement," DEM Working Papers Series 087, University of Pavia, Department of Economics and Management.
    16. Holger Kraft & Claus Munk & Frank Thomas Seifried & Sebastian Wagner, 2017. "Consumption habits and humps," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), pages 305-330.
    17. Anufriev, Mikhail & Panchenko, Valentyn, 2015. "Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 241-255.
    18. P. Giudici & A. Spelta, 2016. "Graphical Network Models for International Financial Flows," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 128-138.
    19. Iñaki Aldasoro & Ignazio Angeloni, 2015. "Input-output-based measures of systemic importance," Quantitative Finance, Taylor & Francis Journals, pages 589-606.

    More about this item

    Keywords

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

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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