Inflation Transmission Diagnostics via a Bayesian Graph Vector Autoregressive Model with Markov Switching

Inflation Transmission Diagnostics via a Bayesian Graph Vector Autoregressive Model with Markov Switching | 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 Inflation Transmission Diagnostics via a Bayesian Graph Vector Autoregressive Model with Markov Switching Jiali Fu, Fengjing Cai, Jinran Wu, Shangrui Zhao, You-Gan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3222276/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The transmission of inflation is a widespread occurrence, and managing inflationary pres-sures is a crucial macroeconomic challenge. Although inflation is a typical macroeconomic variable, its contemporaneous and lagged causal relationships have not been thoroughly investigated, which could result in missing important policy insights. The Bayesian graph vector autoregression (BGVAR) model can identify contemporaneous and lagged causal relationships among economic variables, but it lacks practical research on inflationary inflation. To account for the structural transformation in the inflation transmission process, we propose a Bayesian graph vector autoregressive model with Markov switching (MS-BGVAR), which considers both regime switching and contemporaneous causality among macroe-conomic variables. Our study focuses on analyzing the dynamics of inflation transmission relationships among G7 countries under different regimes, as these countries represent developed nations. We use inflation data from 1971-2019, which shows two distinct inflation regimes within the sample period. We conduct simulation experiments to generate moderately dimensional simulated data for both regimes and indicators, demonstrating the theoretical reliability of our model in accurately identifying graph structures. Finally, we apply the proposed model to identify structural breaks and causal transmission relationships in the inflation transmission process of G7 economies, demonstrating that the proposed model has significant economic significance and good explanatory power in the selected target countries. Econometrics Inflation transmission Markov Switching Contemporaneous causality G7 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3222276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":223661348,"identity":"2d9b7b4e-5af4-429d-ad2f-17cbd49cf80d","order_by":0,"name":"Jiali Fu","email":"","orcid":"","institution":"Wenzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiali","middleName":"","lastName":"Fu","suffix":""},{"id":223661349,"identity":"8d17dccf-c8ff-4555-a24a-467d008aa5ca","order_by":1,"name":"Fengjing 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