Department of Economics Working Papers,
Vienna University of Economics and Business, Department of Economics

Abstract: This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging techniques both in terms of in-sample predictive performance and computational efficiency.

Keywords: spatial autoregressive model, variable selection, model uncertainty, Markov chain Monte Carlo methods

JEL-codes: C18; C21; C52 July 2015

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