Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2020-05-10
Number: 20-023/III
Author-Name: Dieter Wang
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Julia Schaumburg
Author-Workplace-Name: Vrije Universiteit Amsterdam
Title: Smooth marginalized particle filters for dynamic network effect models
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.
Classification-JEL: C63, C32, C33
Keywords: Dynamic network effects, Multiple networks, Nonlinear state-space model, Smooth marginalized particle filter, COVID-19
File-URL: https://papers.tinbergen.nl/20023.pdf
File-Format: application/pdf
File-Size: 7023983 bytes
Handle: RePEc:tin:wpaper:20200023