eprintid: 13930 rev_number: 6 eprint_status: archive userid: 5 dir: disk0/00/01/39/30 datestamp: 2016-11-14 08:09:12 lastmod: 2021-08-27 17:41:38 status_changed: 2016-11-14 08:09:12 type: article metadata_visibility: show item_issues_count: 1 creators_name: Piribauer, P. creators_name: Crespo Cuaresma, J. creators_id: 1838 title: Bayesian Variable Selection in Spatial Autoregressive Models ispublished: pub divisions: prog_pop keywords: determinants of economic growth; Markov chain Monte Carlo methods; model uncertainty; Spatial autoregressive model; variable selection abstract: 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. date: 2016-10-01 date_type: published publisher: Routledge id_number: 10.1080/17421772.2016.1227468 creators_browse_id: 58 full_text_status: none publication: Spatial Economic Analysis volume: 11 number: 4 pagerange: 457-479 refereed: TRUE issn: 1742-1772 coversheets_dirty: FALSE fp7_project: no fp7_type: info:eu-repo/semantics/article citation: 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 .