Fast two-stage variational Bayesian approach to estimating panel spatial autoregressive models with unrestricted spatial weights matrices

Abstract

We propose a fast two-stage variational Bayesian (VB) algorithm to estimate unrestricted panel spatial autoregressive models. Using Dirichlet–Laplace shrinkage priors, we uncover the spatial relationships between cross-sectional units without imposing any a priori restrictions. Monte Carlo experiments show that our approach works well for both long and short panels. We are also the first in the literature to develop VB methods to estimate large covariance matrices with unrestricted sparsity patterns, which are useful for popular large data models such as Bayesian vector autoregressions. In empirical applications, we examine the spatial interdependence between euro area sovereign bond ratings and spreads.