Bayesian analysis of spatial dynamic panel data model with convex combinations of different spatial weight matrices: A reparameterized approach

Abstract

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 computationally tractable Markov Chain Monte Carlo (MCMC) sampler. We also propose a nested model selection procedure to test which spillover channel exerts significant influence and whether one channel dominates the others. Simulation suggests that the reparameterized approach works well both for parameter estimation and model selection.