Bayesian Estimation of Spatial Autoregressive Models
- 1 April 1997
- journal article
- research article
- Published by SAGE Publications in International Regional Science Review
- Vol. 20 (1-2) , 113-129
- https://doi.org/10.1177/016001769702000107
Abstract
Spatial econometrics has relied extensively on spatial autoregressive models. Anselin (1988) developed a taxonomy of these models using a regression model framework and maximum likelihood estimation methods. A Bayesian approach to estimating these models based on Gibbs sampling is introduced here. It allows for non-constant variance over space taking an unspecified form and outliers in the sample data. In addition, estimates of the non-constant variance at each point in space allow inferences regarding the spatial nature of heteroskedasticity and the position of outliers.Keywords
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