Bayesian Variable Selection in Spatial Autoregressive Models

Jesus Crespo Cuaresma, Philipp Piribauer

Publication: Working/Discussion PaperWU Working Paper

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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.
Original languageEnglish
Place of PublicationVienna
PublisherWU Vienna University of Economics and Business
Publication statusPublished - 1 Jul 2015

Publication series

NameDepartment of Economics Working Paper Series
No.199

WU Working Paper Series

  • Department of Economics Working Paper Series

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