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

Loading...
Thumbnail Image

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.