Fast two-stage variational Bayesian approach to estimating panel spatial autoregressive models with unrestricted spatial weights matrices
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Date
Authors
Gefang, Deborah
Hall, Stephen George
Tavlas, George S.
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor and Francis
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.
Description
DATA AVAILABILITY STATEMENT : Data sources are provided in Appendix E in the online supplemental data. The Matlab codes can
be found at: https://github.com/DBayesian/GHT2025_HS and https://github.com/DBayesian/GHT2025_DL.
Keywords
Variational inference, Spatial panel data models, Simultaneous equations, Large datasets, SDG-08: Decent work and economic growth
Sustainable Development Goals
SDG-08:Decent work and economic growth
Citation
Deborah Gefang, Stephen G. Hall & George S. Tavlas (17 Apr 2025):
Fast two-stage variational Bayesian approach to estimating panel spatial autoregressive models with unrestricted spatial weights matrices, Spatial Economic Analysis, DOI: 10.1080/17421772.2025.2482071.