Selection of spillover channels in spatial dynamic panel models using heterogeneous shrinkage on spatial parameters

This paper proposes a Bayesian approach to estimating heterogeneous spatial dynamic panel models, subject to possible shrinkage on spatial dependence parameters. This amounts to heterogeneous selection of candidate spatial weight matrices that represent different spillover channels. The shrinkage methods include both the traditional and more flexible ones that allow the shrinkage strength to vary across spatial parameters. Monte Carlo results indicate that when the true model has a relatively low proportion of nonzero spatial parameters, flexible shrinkage in general leads to lower average root mean squared errors in estimating these parameters. An empirical study using this approach shows that there exists substantial heterogeneity in spillover channels across counties that determine the correlation patterns of county COVID-19 vaccination rates in four states in the United States.