relation: https://pure.iiasa.ac.at/id/eprint/13930/ title: Bayesian Variable Selection in Spatial Autoregressive Models creator: Piribauer, P. creator: Crespo Cuaresma, J. description: This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. It presents two alternative approaches that can be implemented using Gibbs sampling methods in a straightforward way and which allow one to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. A simulation study shows that the variable selection approaches tend to outperform existing Bayesian model averaging techniques in terms of both in-sample predictive performance and computational efficiency. The alternative approaches are compared in an empirical application using data on economic growth for European NUTS-2 regions. publisher: Routledge date: 2016-10-01 type: Article type: PeerReviewed identifier: Piribauer, P. & Crespo Cuaresma, J. (2016). Bayesian Variable Selection in Spatial Autoregressive Models. Spatial Economic Analysis 11 (4) 457-479. 10.1080/17421772.2016.1227468 . relation: 10.1080/17421772.2016.1227468 identifier: 10.1080/17421772.2016.1227468 doi: 10.1080/17421772.2016.1227468