A spatial prior for Bayesian vector autoregressive models

In this paper we develop a Bayesian prior motivated by cross-sectional spatial autoregressive models for use in time-series vector antoregressive forecasting involving spatial variables. We compare forecast accuracy of the proposed spatial prior to that from a vector autoregressive model relying on the Minnesota prior and find a significant improvement. In addition to a spatially motivated prior variance as in LeSage and Pan (1995) we develop a set of prior means based on spatial contiguity. A Theil-Goldberger estimator may be used for the proposed model making it easy to implement.