Bayesian Variable Selection in Spatial Autoregressive Models

Crespo Cuaresma, Jesus ORCID: and Piribauer, Philipp (2015) Bayesian Variable Selection in Spatial Autoregressive Models. Department of Economics Working Paper Series, 199. WU Vienna University of Economics and Business, Vienna.


Download (390kB)


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.

Item Type: Paper
Keywords: spatial autoregressive model / variable selection / model uncertainty / Markov chain Monte Carlo methods
Classification Codes: JEL C18, C21, C52
Divisions: Departments > Volkswirtschaft
Depositing User: Claudia Tering-Raunig
Date Deposited: 13 Jul 2015 14:04
Last Modified: 15 Oct 2021 14:36


View Item View Item


Downloads per month over past year

View more statistics