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.
(authors' abstract) / Series: Department of Economics Working Paper Series
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4584 |
| Date | 07 1900 |
| Creators | Crespo Cuaresma, Jesus, Piribauer, Philipp |
| Publisher | WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://www.wu.ac.at/economics/forschung/wp/, http://epub.wu.ac.at/4584/ |
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