Dynamic clustering of multivariate panel data

Igor Custodio Joao, Andre Lucas, Julia Schaumburg and Bernd Schwaab

No 2577, Working Paper Series from European Central Bank

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. JEL Classification: G21, C33

Keywords: bank business models; dynamic clustering; Hidden Markov Model; panel data; score-driven dynamics (search for similar items in EconPapers)
Date: 2021-07
New Economics Papers: this item is included in nep-isf and nep-ore
Note: 955417
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Related works:
Journal Article: Dynamic clustering of multivariate panel data (2023)
Working Paper: Dynamic clustering of multivariate panel data (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212577

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