Bayesian Variable Selection in Spatial Autoregressive Models

Piribauer, P. & Crespo Cuaresma, J. (2016). Bayesian Variable Selection in Spatial Autoregressive Models. Spatial Economic Analysis 11 (4) 457-479. 10.1080/17421772.2016.1227468.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	determinants of economic growth; Markov chain Monte Carlo methods; model uncertainty; Spatial autoregressive model; variable selection
Research Programs:	World Population (POP)
Depositing User:	Romeo Molina
Date Deposited:	14 Nov 2016 08:09
Last Modified:	27 Aug 2021 17:41
URI:	https://pure.iiasa.ac.at/13930

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