期刊论文详细信息
Econometrics
The SAR Model for Very Large Datasets: A Reduced Rank Approach
Sandy Burden1  Noel Cressie2  David G. Steel2 
[1] National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW 2522, Australia;
关键词: asymmetric spatial dependence matrix;    Australian census;    heteroskedasticity;    Moran operator;    spatial autoregressive model;    spatial basis functions;    spatial random effects model;   
DOI  :  10.3390/econometrics3020317
来源: mdpi
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【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.

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