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Title: Fast two-stage variational Bayesian approach to estimating panel SAR models with unrestricted spatial weights matrices Authors:  Deborah Gefang - University of Leicester (United Kingdom) [presenting]
Stephen Hall - University of Leicester (United Kingdom)
George Tavlas - Bank of Greece (Greece)
Abstract: A fast two-stage variational Bayesian algorithm is proposed to estimate panel spatial autoregressive models with unknown spatial weights matrices. Using Dirichlet-Laplace global-local shrinkage priors, we are able to uncover the spatial impacts 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. The method is important because of its relevance to other popular large data models, such as Bayesian vector autoregressions. Matlab code is provided.