Spatial Temporal Conditional Auto-Regressive Model: A New Autoregressive Matrix

Abstract

In the study of geographical patterns of disease, multivariate areal data models proposed so far in the literature (Ma and Carlin, 2007; Carlin and Banerjee, 2003; Knorr-Held and Best, 2001) have allowed to handle several features of a phenomenon at the same time. In this paper, we propose a new model for areal data, the Spatial Temporal Conditional Auto-Regressive (STCAR) model, that allows to handle the spatial dependence between sites as well as the temporal dependence among the realizations, in the presence ofmeasurements recorded at each spatial location in a time interval. Inspired by the Generalized Multivariate Conditional Auto-Regressive (GMCAR) model published by Jin, Carlin, and Banerjee (2005), the STCAR model reduces the unknown parameters to the single parameter of spatial association estimated at every period considered. Unlike the Vector Auto-Regressive (VAR) model proposed by Sims (1980), in addition, its space-time autoregressive matrix takes into account the spatial localization of the realizations sampled. Moreover, we already know that the main areas of application of these modelsrelate to disease mapping, disease clustering, ecological analysis (Lawson, Browne, and Vidal Rodeiro, 2003). In this work, however, the STCAR model is applied in business, exploiting the analogy between the danger of contracting a particular disease and the risk of falling into bankruptcy, in order to “reconstruct” the spatial temporal distribution of expected bankruptcies of small and medium enterprises of the province of Lecce (Italy).