Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We extend the heterogeneous coefficients spatial autoregressive panel model from Aquaro et al. (2015) to allow for Bayesian prior information. A Markov Chain Monte Carlo estimation methodology is set forth for the Bayesian model. Monte Carlo performance results mirror those from quasi maximum likelihood estimation set forth in Aquaro et al. (2015). Matrix expressions for marginal effects used to interpret these models are set forth. The heterogeneous coefficients spatial autoregressive panel model is capable of producing estimates of spillin and spillout effects for each region in the sample. Spillin effects reflect the impact of changes in neighboring region characteristics on own-region outcomes, while spillout effects show how changes in own-region characteristics impact neighboring region outcomes. We illustrate the model using a panel wage curve relationship for the contiguous US states over the 67 months from January 2011 to July 2016.