In this paper we compare the relative efficiency of different forecasting methods of space-time series when variables are spatially and temporally correlated. We consider the case of a space-time series aggregated into a single time series and the more general instance of a space-time series aggregated into a coarser spatial partition. We extend in various directions the outcomes found in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. The outcomes obtained provide operational suggestions on how to choose between alternative forecasting methods in empirical circumstances.
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