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Spatial effects in dynamic conditional correlations

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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, respectively.
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Keywords: 62H11; 62M10; 62M30; C13; C33; C58; Gaussian kernel; J13; space–time correlation; time-varying correlation; weight matrix

Document Type: Research Article

Affiliations: 1: Department of Cognitive Sciences, Educational and Cultural Studies and CRENoS, Università di Messina, Via Concezione, 6, 98121, Messina, Italy 2: Department of Economics, Business, Environmental Sciences and Quantitative Methods, University of Messina, Via Tommaso Cannizzaro, 278, 98122, Messina, Italy

Publication date: March 11, 2016

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