Bayesian spatial econometrics: a software architecture

Abstract

Bayesian approaches play an important role in the development of new spatial econometric methods, but are uncommon in applied work. This is partly due to a lack of accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with the specifics of spatial econometrics, while classical implementations do not harness the flexibility of Bayesian modelling. In this paper, I present a layered, objected-oriented software architecture that bridges this gap. An R implementation in the bsreg package allows quick and easy estimation of spatial econometric models, while remaining maintainable and extensible. I demonstrate the benefits of the Bayesian approach and using a well-known dataset on cigarette demand. First, I show that Bayesian posterior densities yield better insights into the uncertainty of non-linear models. Second, I find that earlier studies overestimate spillover effects for distance-based connectivities due to a scaling error, highlighting the need for tried and tested software.