@article{iiasa13930,
          volume = {11},
          number = {4},
           month = {October},
           title = {Bayesian Variable Selection in Spatial Autoregressive Models},
       publisher = {Routledge},
            year = {2016},
         journal = {Spatial Economic Analysis},
             doi = {10.1080/17421772.2016.1227468},
           pages = {457--479},
        keywords = {determinants of economic growth; Markov chain Monte Carlo methods; model uncertainty; Spatial autoregressive model; variable selection},
             url = {https://pure.iiasa.ac.at/id/eprint/13930/},
            issn = {1742-1772},
        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.},
          author = {Piribauer, P. and Crespo Cuaresma, J.}
}