Bayesian Estimation of Spatial Autoregressive Models

James LeSage

International Regional Science Review, 1997, vol. 20, issue 1-2, 113-129

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.

Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:sae:inrsre:v:20:y:1997:i:1-2:p:113-129

DOI: 10.1177/016001769702000107

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