Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/177669 
Year of Publication: 
2017
Series/Report no.: 
Tinbergen Institute Discussion Paper No. 17-101/II
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
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.
Subjects: 
network dynamics
latent position model
interbank network
Bayesian inference
JEL: 
C11
D85
G21
Document Type: 
Working Paper

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