We introduce a new method for dynamic clustering of panel data with dynamics for cluster location and shape, cluster composition, and for the number of clusters. Whereas current techniques typically result in (economically) too many switches, our method results in economically more meaningful...

We propose a new unified approach to identifying and estimating spatio-temporal dependence structures in large panels. The model accommodates global cross-sectional dependence due to global dynamic factors as well as local cross-sectional dependence, which may arise from local network...

We introduce a new dynamic clustering method for multivariate panel data char- acterized by time-variation in cluster locations and shapes, cluster compositions, and, possibly, the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional...

We propose a dynamic clustering model for studying time-varying group structures in multivariate panel data. The model is dynamic in three ways: First, the cluster means and covariance matrices are time-varying to track gradual changes in cluster characteristics over time. Second, the units of...

We propose a dynamic clustering model for studying time-varying group structures in multivariate panel data. The model is dynamic in three ways: First, the cluster means and covariance matrices are time-varying to track gradual changes in cluster characteristics over time. Second, the units of...

We propose a new unified approach to identifying and estimating spatio-temporal dependence structures in large panels. The model accommodates global crosssectional dependence due to global dynamic factors as well as local cross-sectional dependence, which may arise from local network structures....

We introduce a new method for dynamic clustering of panel data with dynamics for cluster location and shape, cluster composition, and for the number of clusters. Whereas current techniques typically result in (economically) too many switches, our method results in economically more meaningful...

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...

We analyze a large panel of units grouped by shared extreme value indices (EVIs) and aim to identify these unknown groups. To achieve this, we order the Hill estimates of individual EVIs and segment them by minimizing the total squared distance between each estimate and its corresponding group...