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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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    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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