English, Article, Journal or magazine article edition: Spatial Time-Series Modeling: A review of the proposed methodologies Yiannis Kamarianakis; Poulicos Prastacos

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Bookmark: http://trove.nla.gov.au/version/76947
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Spatial Time-Series Modeling: A review of the proposed methodologies
Author
  • Yiannis Kamarianakis
  • Poulicos Prastacos
Physical Description
  • preprint
Notes
  • 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.
  • spatial time-series, space-time models, STARIMA, Bayesian Vector Autoregressions
  • RePEc:crt:wpaper:0604
Language
  • English
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