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 | |
【 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.
【 预 览 】
Files | Size | Format | View |
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RO202003190012940ZK.pdf | 7847KB | download |