Bayesian Spatial Econometrics and the Need for Software

Bayesian approaches to spatial econometric models are relatively uncommon in applied work, but play an important role in the development of new methods. This is partly due to a lack of easily accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with computational specifics of these models, while classical implementations cannot harness the flexibility of Bayesian modelling. In this paper, I present bsreg, an object-oriented R package, that bridges this gap. The package enables quick and easy estimation of spatial econometric models and is readily extensible. Using the package, I demonstrate the merits of the Bayesian approach by means of a well-known dataset on cigarette demand. Bayesian and frequentist point estimates coincide, but posterior inference affords better insights on uncertainty. I find that in previous works with distance-based connectivities the average spillover effects were overestimated considerably, highlighting the need for tried and tested software.