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

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Author Info

  • Yiannis Kamarianakis

    ()
    (Department of Economics, University of Crete)

  • Poulicos Prastacos

    ()
    (Regional Analysis Division, Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas)

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.

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File URL: http://economics.soc.uoc.gr/wpa/docs/103_YK_AGILE_05.pdf
File Function: First version, 2006
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Bibliographic Info

Paper provided by University of Crete, Department of Economics in its series Working Papers with number 0604.

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Length: 10 pages
Date of creation: Mar 2006
Date of revision:
Handle: RePEc:crt:wpaper:0604

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Related research

Keywords: spatial time-series; space-time models; STARIMA; Bayesian Vector Autoregressions;

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References

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  1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  2. Kamarianakis, Yiannis & Prastacos, Poulicos, 2002. "Space-time modeling of traffic flow," ERSA conference papers ersa02p141, European Regional Science Association.
  3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
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