Dynamic Interbank Network Analysis Using Latent Space Models
Tinbergen Institute Discussoin Paper 2017-101/II
38 Pages Posted: 27 Oct 2017
Date Written: September 14, 2017
Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model, each node has an unobserved temporal trajectory in a low-dimensional Euclidean space. Model parameters and latent banks' positions are estimated within a Bayesian framework. We apply this methodology to analyze two different datasets: the unsecured and the secured (repo) interbank lending networks. We show that the model that incorporates a latent space performs much better than the model in which the probability of a tie depends only on observed characteristics; the latent space model is able to capture some features of the dyadic data such as transitivity that the model without a latent space is not able to.
Keywords: network dynamics, latent position model, interbank network, Bayesian inference
JEL Classification: C11, D85, G21
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