{"id":436981,"date":"2025-10-28T11:37:26","date_gmt":"2025-10-28T10:37:26","guid":{"rendered":"https:\/\/www.wifo.ac.at\/publication\/436981\/"},"modified":"2025-10-28T11:37:27","modified_gmt":"2025-10-28T10:37:27","slug":"coarsened-bayesian-vars","status":"publish","type":"publication","link":"https:\/\/www.wifo.ac.at\/en\/publication\/436981\/","title":{"rendered":"Coarsened Bayesian VARs"},"content":{"rendered":"","protected":false},"featured_media":0,"template":"","class_list":["post-436981","publication","type-publication","status-publish","hentry"],"acf":{"subtitle":"Correcting BVARs for Incorrect Specification","text":"Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be misspecified along important but unknown dimensions. Since endogenous variables in a VAR can feature different degrees of misspecification, our model allows for this and automatically detects the degree of misspecification. The resulting cBVARs perform well in simulations for several types of misspecification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs.","onlinedate":"2025-10-28 10:37:27","lang":"English","publication_series":"","publication_date":"20251028","publication_date_year":"","publication_date_full":true,"publication_num_pages":"44","keywords":"[]","jelcodes":"[]","related_publications":"[]","pdf":436984,"zip":null,"link":"","monthly_report":true,"monthly_report_main":false,"monthly_report_volume":"","monthly_report_pages":"","monthly_report_pages_sort":"0","issue":"714","journal":"WIFO Working Papers","publisher":"","invisible":false,"external":false,"embargo_date":null,"types":[44410],"research_groups":[3500],"collaboration":"","persons":[11879,285051,5108],"persons_data":"[{\"personId\":11879,\"personName\":\"Florian Huber\",\"organisationNames\":[\"University of Salzburg\"]},{\"personId\":285051,\"personName\":\"Massimiliano Marcellino\",\"organisationNames\":[\"Bocconi University\"]},{\"personId\":5108,\"personName\":\"Tobias Scheckel\",\"organisationNames\":[]}]","clients":[],"subclients":[],"partners":[],"topics":[],"host_publication_title":"","host_publication_subtitle":"","place_of_publication":"","host_publication_editors":"[]","type_description":"","output_media":"","chapter":"","article_number":"","citations":"{\"apa\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_apa rendering_contributiontoperiodical rendering_apa rendering_contributiontoperiodical_apa\\\">Huber, F., Marcellino, M.<span>, &amp; Scheckel, T.<\\\/span> (2025). <span>Coarsened Bayesian VARs: Correcting BVARs for Incorrect Specification<\\\/span>. <span><em>WIFO Working Papers<\\\/em><\\\/span>, (714).<\\\/div>\",\"vancouver\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_vancouver rendering_contributiontoperiodical rendering_vancouver rendering_contributiontoperiodical_vancouver\\\">Huber F, Marcellino M<span>, Scheckel T<\\\/span>. <span class=\\\"title\\\"><span>Coarsened Bayesian VARs: <span class=\\\"subtitle\\\">Correcting BVARs for Incorrect Specification<\\\/span><\\\/span><\\\/span>. <span>WIFO Working Papers<\\\/span>. 2025 Oct 28;(714).<\\\/div>\",\"bibtex\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_bibtex rendering_contributiontoperiodical rendering_bibtex rendering_contributiontoperiodical_bibtex\\\"><div>@misc{df9f4b2882a541b3b70ac3d9c9fe90ab,<\\\/div><div>  title    = \\\"Coarsened Bayesian VARs: Correcting BVARs for Incorrect Specification\\\",<\\\/div><div>  abstract = \\\"Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be misspecified along important but unknown dimensions. Since endogenous variables in a VAR can feature different degrees of misspecification, our model allows for this and automatically detects the degree of misspecification. The resulting cBVARs perform well in simulations for several types of misspecification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs.\\\",<\\\/div><div>  author   = \\\"Florian Huber and Massimiliano Marcellino and Tobias Scheckel\\\",<\\\/div><div>  year     = \\\"2025\\\",<\\\/div><div>  month    = oct,<\\\/div><div>  day      = \\\"28\\\",<\\\/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  - Coarsened Bayesian VARs<\\\/p><p>T2  - Correcting BVARs for Incorrect Specification<\\\/p><p>AU  - Huber, Florian<\\\/p><p>AU  - Marcellino, Massimiliano<\\\/p><p>AU  - Scheckel, Tobias<\\\/p><p>PY  - 2025\\\/10\\\/28<\\\/p><p>Y1  - 2025\\\/10\\\/28<\\\/p><p>N2  - Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be misspecified along important but unknown dimensions. Since endogenous variables in a VAR can feature different degrees of misspecification, our model allows for this and automatically detects the degree of misspecification. The resulting cBVARs perform well in simulations for several types of misspecification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs.<\\\/p><p>AB  - Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be misspecified along important but unknown dimensions. Since endogenous variables in a VAR can feature different degrees of misspecification, our model allows for this and automatically detects the degree of misspecification. The resulting cBVARs perform well in simulations for several types of misspecification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs.<\\\/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>Coarsened Bayesian VARs - 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\/436981\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Coarsened Bayesian VARs\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wifo.ac.at\/en\/publication\/436981\/\" \/>\n<meta property=\"og:site_name\" content=\"WIFO\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/WIFOat\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-10-28T10:37:27+00:00\" \/>\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\\\/436981\\\/\",\"url\":\"https:\\\/\\\/www.wifo.ac.at\\\/en\\\/publication\\\/436981\\\/\",\"name\":\"Coarsened Bayesian VARs - 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