RT Journal Article
SR 00
ID 10.1080/17421772.2016.1227468
A1 Piribauer, P.
A1 Crespo Cuaresma, J.
T1 Bayesian Variable Selection in Spatial Autoregressive Models
JF Spatial Economic Analysis
YR 2016
FD 2016-10-01
VO 11
IS 4
SP 457
OP 479
K1 determinants of economic growth; Markov chain Monte Carlo methods; model uncertainty; Spatial autoregressive model; variable selection
AB 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.
PB Routledge
SN 1742-1772
LK https://pure.iiasa.ac.at/id/eprint/13930/