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Modeling Dependence in Spatio-Temporal Econometrics

In: Advances in Contemporary Statistics and Econometrics

Author

Listed:
  • Noel Cressie

    (University of Wollongong)

  • Christopher K. Wikle

    (University of Missouri)

Abstract

This chapter is concerned with lattice data that have a temporal label as well as a spatial label, where these spatio-temporal data appear in the “space-time cube” as a time series of spatial lattice (regular or irregular) processes. The spatio-temporal autoregressive (STAR) models have traditionally been used to model such data but, importantly, one should include a component of variation that models instantaneous spatial dependence as well. That is, the STAR model should include the spatial autoregressive (SAR) model as a subcomponent, for which we give a generic form. Perhaps more importantly, we illustrate how noisy and missing data can be accounted for by using the STAR-like models as process models, alongside a data model and potentially a parameter model, in a hierarchical statistical model (HM).

Suggested Citation

  • Noel Cressie & Christopher K. Wikle, 2021. "Modeling Dependence in Spatio-Temporal Econometrics," Springer Books, in: Abdelaati Daouia & Anne Ruiz-Gazen (ed.), Advances in Contemporary Statistics and Econometrics, pages 363-383, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-73249-3_19
    DOI: 10.1007/978-3-030-73249-3_19
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