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. DOI: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 Mar 2017 06:53
URI: http://pure.iiasa.ac.at/13930

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