{"id":55309,"date":"2023-12-01T18:44:03","date_gmt":"2023-12-01T17:44:03","guid":{"rendered":"https:\/\/www.wifo.ac.at\/publication\/macroeconometric-forecasting-using-a-cluster-of-dynamic-factor-models\/"},"modified":"2024-11-07T06:22:33","modified_gmt":"2024-11-07T05:22:33","slug":"macroeconometric-forecasting-using-a-cluster-of-dynamic-factor-models","status":"publish","type":"publication","link":"https:\/\/www.wifo.ac.at\/en\/publication\/55309\/","title":{"rendered":"Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models"},"content":{"rendered":"","protected":false},"featured_media":0,"template":"","class_list":["post-55309","publication","type-publication","status-publish","hentry"],"acf":{"subtitle":"","text":"We propose a modelling approach involving a series of small-scale factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. GDP forecasts are established across the production, income and expenditure accounts within a disaggregated approach. This method merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent forecasting on a large scale. While the CDFM has a simple structure, its forecasts outperform those of a wide range of competing models and of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.","onlinedate":"2020-10-27 12:00:00","lang":"English","publication_series":"","publication_date":"20201001","publication_date_year":"","publication_date_full":false,"publication_num_pages":"42","keywords":"[]","jelcodes":"[]","related_publications":"[]","pdf":385223,"zip":null,"link":"","monthly_report":true,"monthly_report_main":false,"monthly_report_volume":"","monthly_report_pages":"","monthly_report_pages_sort":"0","issue":"614","journal":"WIFO Working Papers","publisher":"","invisible":false,"external":false,"embargo_date":"2020-10-27 12:00:00","types":[44410],"research_groups":[3500],"collaboration":"","persons":[3543,3641],"persons_data":"[{\"personId\":3543,\"personName\":\"Christian Glocker\",\"organisationNames\":[]},{\"personId\":3641,\"personName\":\"Serguei Kaniovski\",\"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\\\"><span>Glocker, C.<\\\/span><span>, &amp; Kaniovski, S.<\\\/span> (2020). <span>Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models<\\\/span>. <span><em>WIFO Working Papers<\\\/em><\\\/span>, (614). <a onclick=\\\"window.open(this.href, '_blank','noopener,noreferrer'); return false;\\\" href=\\\"https:\\\/\\\/www.wifo.ac.at\\\/wwa\\\/pubid\\\/66533\\\" class=\\\"link\\\"><span>https:\\\/\\\/www.wifo.ac.at\\\/wwa\\\/pubid\\\/66533<\\\/span><\\\/a><\\\/div>\",\"vancouver\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_vancouver rendering_contributiontoperiodical rendering_vancouver rendering_contributiontoperiodical_vancouver\\\"><span>Glocker C<\\\/span><span>, Kaniovski S<\\\/span>. <span class=\\\"title\\\"><span>Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models<\\\/span><\\\/span>. <span>WIFO Working Papers<\\\/span>. 2020 Oct;(614).<\\\/div>\",\"bibtex\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_bibtex rendering_contributiontoperiodical rendering_bibtex rendering_contributiontoperiodical_bibtex\\\"><div>@misc{a0837381997e49e4a0e129e53b40b51e,<\\\/div><div>  title    = \\\"Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models\\\",<\\\/div><div>  abstract = \\\"We propose a modelling approach involving a series of small-scale factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. GDP forecasts are established across the production, income and expenditure accounts within a disaggregated approach. This method merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent forecasting on a large scale. While the CDFM has a simple structure, its forecasts outperform those of a wide range of competing models and of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.\\\",<\\\/div><div>  keywords = \\\"Forecasting, Dynamic factor model, Granger causality, Structural modeling\\\",<\\\/div><div>  author   = \\\"Christian Glocker and Serguei Kaniovski\\\",<\\\/div><div>  note     = \\\"Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models\\\",<\\\/div><div>  year     = \\\"2020\\\",<\\\/div><div>  month    = oct,<\\\/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  - Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models<\\\/p><p>AU  - Glocker, Christian<\\\/p><p>AU  - Kaniovski, Serguei<\\\/p><p>N1  - Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models<\\\/p><p>PY  - 2020\\\/10<\\\/p><p>Y1  - 2020\\\/10<\\\/p><p>N2  - We propose a modelling approach involving a series of small-scale factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. GDP forecasts are established across the production, income and expenditure accounts within a disaggregated approach. This method merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent forecasting on a large scale. While the CDFM has a simple structure, its forecasts outperform those of a wide range of competing models and of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.<\\\/p><p>AB  - We propose a modelling approach involving a series of small-scale factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. GDP forecasts are established across the production, income and expenditure accounts within a disaggregated approach. This method merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent forecasting on a large scale. While the CDFM has a simple structure, its forecasts outperform those of a wide range of competing models and of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.<\\\/p><p>KW  - Forecasting<\\\/p><p>KW  -  Dynamic factor model<\\\/p><p>KW  -  Granger causality<\\\/p><p>KW  -  Structural modeling<\\\/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>Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models - 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\/55309\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wifo.ac.at\/en\/publication\/55309\/\" \/>\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=\"2024-11-07T05:22:33+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\\\/55309\\\/\",\"url\":\"https:\\\/\\\/www.wifo.ac.at\\\/en\\\/publication\\\/55309\\\/\",\"name\":\"Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models - 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