Spatial effects in dynamic conditional correlations

Edoardo Otranto, Massimo Mucciardi and Pietro Bertuccelli

Journal of Applied Statistics, 2016, vol. 43, issue 4, 604-626

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 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.

Date: 2016
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Citations: View citations in EconPapers (4)

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Working Paper: Spatial Effects in Dynamic Conditional Correlations (2014)
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DOI: 10.1080/02664763.2015.1071343

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