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

Author

Listed:
  • 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.

Suggested Citation

  • Yiannis Kamarianakis & Poulicos Prastacos, 2006. "Spatial Time-Series Modeling: A review of the proposed methodologies," Working Papers 0604, University of Crete, Department of Economics.
    • Handle: RePEc:crt:wpaper:0604
    as

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    File URL: http://economics.soc.uoc.gr/wpa/docs/103_YK_AGILE_05.pdf
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    References listed on IDEAS

    as
    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 509-533, 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, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    4. Elhorst, J.P., 2000. "Dynamic models in space and time," Research Report 00C16, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Lambert, Dayton M. & Malzer, Gary L. & Lowenberg-DeBoer, James, 2004. "General Moment And Quasi-Maximum Likelihood Estimation Of A Spatially Autocorrelated System Of Equations: An Empirical Example Using On-Farm Precision Agriculture Data," Staff Papers 28667, Purdue University, Department of Agricultural Economics.
    2. Laranjeiro, Patrícia F. & Merchán, Daniel & Godoy, Leonardo A. & Giannotti, Mariana & Yoshizaki, Hugo T.Y. & Winkenbach, Matthias & Cunha, Claudio B., 2019. "Using GPS data to explore speed patterns and temporal fluctuations in urban logistics: The case of São Paulo, Brazil," Journal of Transport Geography, Elsevier, vol. 76(C), pages 114-129.

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    More about this item

    Keywords

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