Spatial Effects in Dynamic Conditional Correlations
M. Mucciardi and
Edoardo Otranto ()
Working Paper CRENoS from Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia
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: weight matrix; time-varying correlation; space-time correlation; gaussian kernel (search for similar items in EconPapers)
JEL-codes: C13 C33 J13 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ure
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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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