Spatial Effects in Dynamic Conditional Correlations

P. Bertuccelli, M. Mucciardi and Edoardo Otranto

Working Paper CRENoS from Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia

Abstract: 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 are omitted. We propose to extend a time-varying conditional correlation model (following an ARMA 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 (GKM), 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.

Keywords: time-varying correlation; space-time correlation; gaussian kernel; weight matrix (search for similar items in EconPapers)
JEL-codes: C13 C33 J13 (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ure
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https://crenos.unica.it/crenos/sites/default/files/WP14-06.pdf (application/pdf)

Related works:
Journal Article: Spatial effects in dynamic conditional correlations (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:cns:cnscwp:201406

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