# 17-101/II (2017-10-26)

Author(s)
Fernando Linardi, University of Amsterdam, The Netherlands; Central Bank of Brazil, Brazil; Cees (C.G.H.) Diks, University of Amsterdam, The Netherlands; Tinbergen Institute, The Netherlands; Marco (M.J.) van der Leij, University of Amsterdam, The Netherlands; De Nederlandsche Bank, The Netherlands; Iuri Lazier, Central Bank of Brazil, Brazil
Keywords:
network dynamics, latent position model, interbank network, Bayesian inference
JEL codes:
C11, D85, G21

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.