{"id":443327,"date":"2026-03-17T16:24:01","date_gmt":"2026-03-17T15:24:01","guid":{"rendered":"https:\/\/www.wifo.ac.at\/publication\/443327\/"},"modified":"2026-03-17T16:24:01","modified_gmt":"2026-03-17T15:24:01","slug":"estimatew-an-r-package-for-bayesian-estimation-of-weight-matrices-in-spatial-econometric-panels","status":"publish","type":"publication","link":"https:\/\/www.wifo.ac.at\/en\/publication\/443327\/","title":{"rendered":"estimateW: An R Package for Bayesian Estimation of Weight Matrices in Spatial Econometric Panels"},"content":{"rendered":"","protected":false},"featured_media":0,"template":"","class_list":["post-443327","publication","type-publication","status-publish","hentry"],"acf":{"subtitle":"","text":"This document introduces the R library estimateW to estimate spatial weight matrices for Bayesian spatial econometric panel models. The approach focuses on spatial weights that are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori assumptions on (socio-)economic distances between the spatial units. The estimation approach relies on efficient Bayesian Gibbs sampling techniques and the library supports a variety of the most common spatial econometric panel specifications. estimateW moreover supports to elicit flexible shrinkage priors, which allow to estimate spatial spillovers even in settings where the number of time period is small relative to number of cross-sectional units. 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The approach focuses on spatial weights that are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori assumptions on (socio-)economic distances between the spatial units. The estimation approach relies on efficient Bayesian Gibbs sampling techniques and the library supports a variety of the most common spatial econometric panel specifications. estimateW moreover supports to elicit flexible shrinkage priors, which allow to estimate spatial spillovers even in settings where the number of time period is small relative to number of cross-sectional units. An empirical illustration for European NUTS-1 regions demonstrates that the method recovers plausible spatial dependence patterns, interpretable spillover effects, and meaningful clustering in the estimated network structure.\\\",<\\\/div><div>  keywords = \\\"Bayesian spatial econometrics, spatial weight matrix estimation, regional economic growth, R\\\",<\\\/div><div>  author   = \\\"Tam{\\\\'a}s Krisztin and Philipp Piribauer\\\",<\\\/div><div>  year     = \\\"2026\\\",<\\\/div><div>  month    = mar,<\\\/div><div>  day      = \\\"17\\\",<\\\/div><div>  language = \\\"English\\\",<\\\/div><div>  journal  = \\\"WIFO Working Papers\\\",<\\\/div><p>}<\\\/p><\\\/div>\",\"ris\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_ris rendering_contributiontoperiodical rendering_ris rendering_contributiontoperiodical_ris\\\"><p>TY  - GEN<\\\/p><p>T1  - estimateW: An R Package for Bayesian Estimation of Weight Matrices in Spatial Econometric Panels<\\\/p><p>AU  - Krisztin, Tam\u00e1s<\\\/p><p>AU  - Piribauer, Philipp<\\\/p><p>PY  - 2026\\\/3\\\/17<\\\/p><p>Y1  - 2026\\\/3\\\/17<\\\/p><p>N2  - This document introduces the R library estimateW to estimate spatial weight matrices for Bayesian spatial econometric panel models. The approach focuses on spatial weights that are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori assumptions on (socio-)economic distances between the spatial units. The estimation approach relies on efficient Bayesian Gibbs sampling techniques and the library supports a variety of the most common spatial econometric panel specifications. estimateW moreover supports to elicit flexible shrinkage priors, which allow to estimate spatial spillovers even in settings where the number of time period is small relative to number of cross-sectional units. An empirical illustration for European NUTS-1 regions demonstrates that the method recovers plausible spatial dependence patterns, interpretable spillover effects, and meaningful clustering in the estimated network structure.<\\\/p><p>AB  - This document introduces the R library estimateW to estimate spatial weight matrices for Bayesian spatial econometric panel models. 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An empirical illustration for European NUTS-1 regions demonstrates that the method recovers plausible spatial dependence patterns, interpretable spillover effects, and meaningful clustering in the estimated network structure.<\\\/p><p>KW  - Bayesian spatial econometrics<\\\/p><p>KW  - spatial weight matrix estimation<\\\/p><p>KW  - regional economic growth<\\\/p><p>KW  - R<\\\/p><p>M3  - WIFO series<\\\/p><p>JO  - WIFO Working Papers<\\\/p><p>JF  - WIFO Working Papers<\\\/p><p>ER  - <\\\/p><\\\/div>\"}","scientific_assistance":"[]","scientific_review":"[]","version":"","release_date":null,"expiration_date":null,"surveyor":"","research_assistance":"","edv":"","additional_info_de":"","additional_info_en":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>estimateW: An R Package for Bayesian Estimation of Weight Matrices in Spatial Econometric Panels - WIFO<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wifo.ac.at\/en\/publication\/443327\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"estimateW: An R Package for Bayesian Estimation of Weight Matrices in Spatial Econometric Panels\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wifo.ac.at\/en\/publication\/443327\/\" \/>\n<meta property=\"og:site_name\" content=\"WIFO\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/WIFOat\/\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.wifo.ac.at\/wp-content\/uploads\/2024\/05\/WIFO-Gebaeude-FotoAlexanderMueller-www.alexandermueller.at-IMG_6326-Bearbeitet-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1280\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@WIFOat\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.wifo.ac.at\\\/en\\\/publication\\\/443327\\\/\",\"url\":\"https:\\\/\\\/www.wifo.ac.at\\\/en\\\/publication\\\/443327\\\/\",\"name\":\"estimateW: An R Package for Bayesian Estimation of Weight Matrices in Spatial Econometric Panels - 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