Modeling Dependence in Spatio-Temporal Econometrics

Noel Cressie () and Christopher K. Wikle ()
Additional contact information
Noel Cressie: University of Wollongong
Christopher K. Wikle: University of Missouri

A chapter in Advances in Contemporary Statistics and Econometrics, 2021, pp 363-383 from Springer

Abstract: 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).

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-73249-3_19

Ordering information: This item can be ordered from
http://www.springer.com/9783030732493

DOI: 10.1007/978-3-030-73249-3_19

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().