The authors apply a Hidden Markov Model to identify regimes of shifting inflation and then employ an attribution technique based on the Mahalanobis distance to identify the economic variables that determine the trajectory of inflation. Their analysis...
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ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
Inter-library loan:
No inter-library loan
The authors apply a Hidden Markov Model to identify regimes of shifting inflation and then employ an attribution technique based on the Mahalanobis distance to identify the economic variables that determine the trajectory of inflation. Their analysis enables policymakers to focus on the most effective tools to manage inflation, and it offers guidance to investors whose strategies might benefit from knowledge of the prevailing determinants of inflation. Their analysis reveals that as of February 2022, the most important determinant of the recent spike in inflation was spending by the federal government
Publisher:
European Central Bank, Frankfurt am Main, Germany
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...
more
ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
Signature:
DS 534
Inter-library loan:
No inter-library loan
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.
