Spatial Time-Series Modeling: A review of the proposed methodologies
AbstractThis 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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Bibliographic InfoPaper provided by University of Crete, Department of Economics in its series Working Papers with number 0604.
Length: 10 pages
Date of creation: Mar 2006
Date of revision:
spatial time-series; space-time models; STARIMA; Bayesian Vector Autoregressions;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-10-07 (All new papers)
- NEP-ECM-2006-10-07 (Econometrics)
- NEP-ETS-2006-10-07 (Econometric Time Series)
- NEP-GEO-2006-10-07 (Economic Geography)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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