Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/220060
Authors: 
Wang, Dieter
Schaumburg, Julia
Year of Publication: 
2020
Series/Report no.: 
Tinbergen Institute Discussion Paper TI 2020-023/III
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.
Subjects: 
Dynamic network effects
Multiple networks
Nonlinear state-space model
Smooth marginalized particle filter
COVID-19
JEL: 
C63
C32
C33
Document Type: 
Working Paper

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