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

dc.contributor.authorGefang, Deborah
dc.contributor.authorHall, Stephen George
dc.contributor.authorTavlas, George S.
dc.date.accessioned2025-04-24T07:45:24Z
dc.date.available2025-04-24T07:45:24Z
dc.date.issued2025
dc.descriptionDATA 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.en_US
dc.description.abstractWe 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.en_US
dc.description.departmentEconomicsen_US
dc.description.librarianhj2025en_US
dc.description.sdgSDG-08:Decent work and economic growthen_US
dc.description.urihttps://www.tandfonline.com/journals/rsea20en_US
dc.identifier.citationDeborah 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.en_US
dc.identifier.issn1742-1772 (print)
dc.identifier.issn1742-1780 (online)
dc.identifier.other10.1080/17421772.2025.2482071
dc.identifier.urihttp://hdl.handle.net/2263/102201
dc.language.isoenen_US
dc.publisherTaylor and Francisen_US
dc.rights© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http:// creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.subjectVariational inferenceen_US
dc.subjectSpatial panel data modelsen_US
dc.subjectSimultaneous equationsen_US
dc.subjectLarge datasetsen_US
dc.subjectSDG-08: Decent work and economic growthen_US
dc.titleFast two-stage variational Bayesian approach to estimating panel spatial autoregressive models with unrestricted spatial weights matricesen_US
dc.typeArticleen_US

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