EBSLG

 

 
European Business Schools Librarian's Group
Home About Series Subject/JEL codes Advanced Search
Department of Economics, WU (Wirtschaftsuniversität Wien) Working papers, Department of Economics, WU (Wirtschaftsuniversität Wien)

No 199:
Bayesian Variable Selection in Spatial Autoregressive Models

Jesus Crespo Cuaresma () and Philipp Piribauer ()

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.

Keywords: spatial autoregressive model, variable selection, model uncertainty, Markov chain Monte Carlo methods; (follow links to similar papers)

JEL-Codes: C18,; C21,; C52; (follow links to similar papers)

July 2015

PDF Document

Before downloading any of the electronic versions below you should read our statement on copyright.
Download GhostScript for viewing Postscript files and the Acrobat Reader for viewing and printing pdf files.

Downloadable files:

wp199.pdf    PDF-file
Download Statistics


Report other problems with accessing this service to Sune Karlsson () or Helena Lundin ().

Programing by
Design Joakim Ekebom

Handle: RePEc:wiw:wiwwuw:wuwp199 This page was generated on 2017-09-12 21:40:54