Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171829
Authors: 
Burden, Sandy
Cressie, Noel
Steel, David G.
Year of Publication: 
2015
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 3 [Year:] 2015 [Issue:] 2 [Pages:] 317-338
Abstract: 
The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random effects (SRE) model, and in this article, we calibrate it to the SAR model of interest using a generalisation of the Moran operator that allows for heteroskedasticity and an asymmetric SAR spatial dependence matrix. In general, spatial data have a measurement-error component, which we model, and we use restricted maximum likelihood to estimate the SRE model covariance parameters; its required computational time is only the order of the size of the dataset. Our implementation is demonstrated using mean usual weekly income data from the 2011 Australian Census.
Subjects: 
asymmetric spatial dependence matrix
Australian census
heteroskedasticity
Moran operator
spatial autoregressive model
spatial basis functions
spatial random effects model
JEL: 
C21
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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