2024/01/18 by Crespo Cuaresma, Jesus, Piribauer, Philipp
paper · doi:10.57938/aa3effd2-1312-4f27-bee3-5e8e74f45f99
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.