Xavier de Luna, Marc G. Genton

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We analyze spatio-temporal data on U.S. unemployment rates. For this purpose, we present a family of models designed for the analysis and time-forward prediction of spatio-temporal econometric data. Our model is aimed at applications with spatially sparse but temporally rich data, i.e. for observations collected at few spatial regions, but at many regular time intervals. The family of models utilized does not make spatial stationarity assumptions and consists in a vector autoregressive (VAR) specification, where there are as many time series as spatial regions. A model building strategy is used that takes into account the spatial dependence structure of the data. Model building may be performed either by displaying sample partial correlation functions, or automatically with an information criterion. Monthly data on unemployment rates in the nine census divisions of the U.S. are analyzed. We show with a residual analysis that our autoregressive model captures the dependence structure of the data better than with univariate time series modeling.

Original languageEnglish (US)
Title of host publicationSpatial and Spatiotemporal Econometrics
Number of pages16
StatePublished - Dec 1 2004
Externally publishedYes

Publication series

NameAdvances in Econometrics
ISSN (Print)0731-9053

ASJC Scopus subject areas

  • Economics and Econometrics

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