European Business Schools Librarian's Group

Department of Economics Working Papers,
Vienna University of Economics and Business, Department of Economics

Bayesian Variable Selection in Spatial Autoregressive Models

Jesus Crespo Cuaresma () and Philipp Piribauer ()
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Jesus Crespo Cuaresma: Department of Economics, Vienna University of Economics and Business
Philipp Piribauer: Department of Economics, Vienna University of Economics and Business

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

JEL-codes: C18; C21; C52 July 2015

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