Estimating a Weighted Output Gap for Switzerland: Methodology and Application

Estimating a Weighted Output Gap for Switzerland: Methodology and Application | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimating a Weighted Output Gap for Switzerland: Methodology and Application Christian Glocker, Serguei Kaniovski, Philipp Wegmüller This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6266716/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2026 Read the published version in Swiss Journal of Economics and Statistics → Version 1 posted 9 You are reading this latest preprint version Abstract This paper introduces a novel weighted output gap measure for Switzerland, combining estimates from univariate filters, multivariate filters, and production function approaches. Published quarterly by SECO since 2019:Q4, the weighted output gap measure demonstrates significant advantages, including robustness, historical consistency, and superior predictive power for future inflation. Using a rigorous inflation forecasting framework, we show that the weighted output gap outperforms alternative methodologies in terms of Granger causality and achieves competitive mean absolute error (MAE) values. These findings emphasize the practical utility of the weighted output gap for business cycle analysis and forecasting, making it a valuable tool for researchers and policymakers alike. JEL class: E24, E32, E37 JEL class: E24, E32, E37 Output gap Inflation Business cycle Switzerland Revisions Forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2026 Read the published version in Swiss Journal of Economics and Statistics → Version 1 posted Editorial decision: Revision requested 16 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers invited by journal 04 Apr, 2025 Editor assigned by journal 28 Mar, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 20 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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