Smooth marginalized particle filters for dynamic network effect models

Dieter Wang and Julia Schaumburg

No 20-023/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: Dynamic network effects; Multiple networks; Nonlinear state-space model; Smooth marginalized particle filter; COVID-19 (search for similar items in EconPapers)
JEL-codes: C32 C33 C63 (search for similar items in EconPapers)
Date: 2020-05-10
New Economics Papers: this item is included in nep-ecm, nep-net and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20200023

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