Clustering and classification of spatio-temporal data using spatial dynamic panel data models

The class of Spatial Dynamic Panel Data models has been proposed in the socio-econometric literature to analyze spatio-temporal data. In this paper we consider a particular variant of such models, where the set of spatial units is assumed to be partitioned into clusters and the parameters of the model are assumed to be homogeneous within clusters and heterogeneous across clusters. For this model, assuming that the true partition is unknown, we propose a new clustering procedure and a validation test, based on a multiple testing approach, that help to choose the best configuration of model, for a given observed dataset, by estimating the optimal number of clusters and the best partition of units. The validity of the proposed procedures has been shown both theoretically and empirically, on simulated and real data, also compared to alternative methods.