Smooth marginalized particle filters for dynamic network effect models - Dieter Wang; Julia Schaumburg (Tinbergen Institute, 2020)
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 SMPFâ exteuro™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: C63; C32; C33; 330; Dynamic network effects; Multiple networks; Nonlinear state-space model; Smooth marginalized particle filter; COVID-19