Bayesian Variable Selection in Spatial Autoregressive Models

Jesus Crespo Cuaresma, Philipp Piribauer

Publikation: Working/Discussion PaperWU Working Paper

7 Downloads (Pure)

Abstract

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.
OriginalspracheEnglisch
ErscheinungsortVienna
HerausgeberWU Vienna University of Economics and Business
PublikationsstatusVeröffentlicht - 1 Juli 2015

Publikationsreihe

NameDepartment of Economics Working Paper Series
Nr.199

WU Working Paper Reihe

  • Department of Economics Working Paper Series

Dieses zitieren