TY  - JOUR
ID  - iiasa13930
UR  - https://pure.iiasa.ac.at/id/eprint/13930/
IS  - 4
A1  - Piribauer, P.
A1  - Crespo Cuaresma, J.
N2  - 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.
VL  - 11
TI  - Bayesian Variable Selection in Spatial Autoregressive Models
AV  - none
EP  - 479
Y1  - 2016/10/01/
PB  - Routledge
JF  - Spatial Economic Analysis
KW  - determinants of economic growth; Markov chain Monte Carlo methods; model uncertainty; Spatial autoregressive model; variable selection
SN  - 1742-1772
SP  - 457
ER  -