The recent literature on time series has developed a lot of models for the analysis of the dynamic conditional correlation, involving the same variable observed in different locations; very often, in this framework, the consideration of the spatial interactions is omitted. We propose
to extend a time-varying conditional correlation model (following an autoregressive moving average dynamics) to include the spatial effects, with a specification depending on the local spatial interactions. The spatial part is based on a fixed symmetric weight matrix, called Gaussian kernel
matrix, but its effect will vary along the time depending on the degree of time correlation in a certain period. We show the theoretical aspects, with the support of simulation experiments, and apply this methodology to two space–time data sets, in a demographic and a financial framework,
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Document Type: Research Article
Department of Cognitive Sciences, Educational and Cultural Studies and CRENoS, Università di Messina, Via Concezione, 6, 98121, Messina, Italy
Department of Economics, Business, Environmental Sciences and Quantitative Methods, University of Messina, Via Tommaso Cannizzaro, 278, 98122, Messina, Italy
March 11, 2016