Bayesian Variable Selection in Spatial Autoregressive Models. Issue 4 (1st October 2016)

• Record Type: Journal Article
• Authors: Piribauer, Philipp; Crespo Cuaresma, Jesús
• Is Part Of: Spatial economic analysis. Volume 11:Issue 4(2016)
• Page Start: 457
• Page End: 479
• Publication Date: 2016-10-01
• Subjects: Spatial autoregressive model -- variable selection -- model uncertainty -- Markov chain Monte Carlo methods -- determinants of economic growth; C18 -- C21 -- C52 -- O47; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2016.1227468
• Languages: English
• ISSNs: 1742-1772

Abstract

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.