| 来源类型 | Article |
| 规范类型 | 其他 |
| DOI | 10.1080/17421772.2016.1227468 |
| Bayesian Variable Selection in Spatial Autoregressive Models. | |
| Piribauer P; Crespo Cuaresma J | |
| 发表日期 | 2016 |
| 出处 | Spatial Economic Analysis 11 (4): 457-479 |
| 出版年 | 2016 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | World Population (POP) |
| 关键词 | determinants of economic growth Markov chain Monte Carlo methods model uncertainty Spatial autoregressive model variable selection |
| URL | http://pure.iiasa.ac.at/id/eprint/13930/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/130791 |
| 推荐引用方式 GB/T 7714 | Piribauer P,Crespo Cuaresma J. Bayesian Variable Selection in Spatial Autoregressive Models.. 2016. |
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