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Smooth marginalized particle filters for dynamic network effect models

Author

Listed:
  • Dieter Wang

    (Vrije Universiteit Amsterdam)

  • Julia Schaumburg

    (Vrije Universiteit Amsterdam)

Abstract

We propose a dynamic network model for the study of high-dimensional panel data. Crosssectional dependencies between units are captured via one or multiple observed networks and a low-dimensional vector of latent stochastic network intensity parameters. The parameterdriven, nonlinear structure of the model requires simulation-based filtering and estimation, for which we suggest to use the smooth marginalized particle filter (SMPF). In a Monte Carlo simulation study, we demonstrate the SMPF’s good performance relative to benchmarks, particularly when the cross-section dimension is large and the network is dense. An empirical application on the propagation of COVID-19 through international travel networks illustrates the usefulness of our method.

Suggested Citation

  • Dieter Wang & Julia Schaumburg, 2020. "Smooth marginalized particle filters for dynamic network effect models," Tinbergen Institute Discussion Papers 20-023/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20200023
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    File URL: https://papers.tinbergen.nl/20023.pdf
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    References listed on IDEAS

    as
    1. Hannes Böhm & Julia Schaumburg & Lena Tonzer, 2022. "Financial Linkages and Sectoral Business Cycle Synchronization: Evidence from Europe," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(4), pages 698-734, December.
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    3. Blasques, Francisco & Koopman, Siem Jan & Lucas, Andre & Schaumburg, Julia, 2016. "Spillover dynamics for systemic risk measurement using spatial financial time series models," Journal of Econometrics, Elsevier, vol. 195(2), pages 211-223.
    4. Name 1 Dieter Wang Email 1 & Iman (I.P.P.) van Lelyveld & Julia (J.) Schaumburg, 2018. "Do information contagion and business model similarities explain bank credit risk commonalities?," Tinbergen Institute Discussion Papers 18-100/IV, Tinbergen Institute.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
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    7. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    8. Aït-Sahalia, Yacine & Laeven, Roger J.A. & Pelizzon, Loriana, 2014. "Mutual excitation in Eurozone sovereign CDS," Journal of Econometrics, Elsevier, vol. 183(2), pages 151-167.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Dynamic network effects; Multiple networks; Nonlinear state-space model; Smooth marginalized particle filter; COVID-19;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • 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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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