Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/237716 
Year of Publication: 
2021
Series/Report no.: 
ECB Working Paper No. 2577
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
Abstract: 
We propose a dynamic clustering model for uncovering latent time-varying group structures in multivariate panel data. The model is dynamic in three ways. First, the cluster location and scale matrices are time-varying to track gradual changes in cluster characteristics over time. Second, all units can transition between clusters based on a Hidden Markov model (HMM). Finally, the HMM's transition matrix can depend on lagged time-varying cluster distances as well as economic covariates. Monte Carlo experiments suggest that the units can be classified reliably in a variety of challenging settings. Incorporating dynamics in the cluster composition proves empirically important in an a study of 299 European banks between 2008Q1 and 2018Q2. We find that approximately 3% of banks transition per quarter on average. Transition probabilities are in part explained by differences in bank profitability, suggesting that low interest rates can lead to long-lasting changes in financial industry structure.
Subjects: 
dynamic clustering
panel data
Hidden Markov Model
score-driven dynamics
bank business models
JEL: 
G21
C33
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4763-3
Document Type: 
Working Paper

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