Bayesian Variable Selection in Spatial Autoregressive Models

Jesus Crespo Cuaresma and Philipp Piribauer ()

Department of Economics Working Papers from 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 (search for similar items in EconPapers)
JEL-codes: C18 C21 C52 (search for similar items in EconPapers)
Date: 2015-07
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-ure
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https://epub.wu.ac.at/4584/1/wp199.pdf (application/pdf)

Related works:
Journal Article: Bayesian Variable Selection in Spatial Autoregressive Models (2016)
Working Paper: Bayesian Variable Selection in Spatial Autoregressive Models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:wiw:wiwwuw:wuwp199

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