Spatial Time-Series Modeling: A review of the proposed methodologies

Yiannis Kamarianakis () and Poulicos Prastacos ()
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Poulicos Prastacos: Regional Analysis Division, Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas

No 604, Working Papers from University of Crete, Department of Economics

Abstract: This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better suited for applications of large spatial and temporal dimension whereas the latter can be realistically performed when the number of locations of the study is rather small. Next, we consider models that aim to describe relationships between variables with a spatio-temporal reference and discuss the general class of dynamic space-time models in the framework presented by Elhorst (2001). Each model class is introduced through a motivating application.

Keywords: spatial time-series; space-time models; STARIMA; Bayesian Vector Autoregressions (search for similar items in EconPapers)
Pages: 10 pages
Date: 2006-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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