Arbeitspapier
Dynamic Interbank Network Analysis Using Latent Space Models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 17-101/II
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Network Formation and Analysis: Theory
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
- Thema
-
network dynamics
latent position model
interbank network
Bayesian inference
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Linardi, Fernando
Diks, Cees
van der Leij, Marco (M.J.)
Lazier, Iuri
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2017
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Linardi, Fernando
- Diks, Cees
- van der Leij, Marco (M.J.)
- Lazier, Iuri
- Tinbergen Institute
Entstanden
- 2017
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