A hierarchical Bayesian model for spatial panel data is proposed. The idea behind the proposed method is to analyze spatially dependent panel data by means of a separable covariance matrix. Let us indicate the observations as yit, i = 1, ... ,N regions and t = 1,... , T time, var(y), the...

We model network formation and interactions under a unified framework by considering that individuals anticipate the effect of network structure on the utility of network interactions when choosing links. There are two advantages of this modeling approach: first, we can evaluate whether network...

This paper brings forward a novel Bayesian estimation and model selection method for spatial dynamic panel data model with convex combinations of different spatial weight matrices. We reparameterize the model as a high order spatial panel model, and recover the importance weights by a...