Comparing Forecasting Models for Predicting Infant Mortality: VECM vs VAR and BVAR Specifications

Comparing Forecasting Models for Predicting Infant Mortality: VECM vs VAR and BVAR Specifications | 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 Comparing Forecasting Models for Predicting Infant Mortality: VECM vs VAR and BVAR Specifications Benard Odur, Tom Etil, Bosco Opio, Memon Shaheen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8656051/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 17 You are reading this latest preprint version Abstract This study investigates infant mortality in Uganda, a persistent public health challenge in many developing countries where economic disparities limit access to healthcare. It compares the forecasting performance of three econometric models; Vector Error Correction Model (VECM), Vector Autoregressive (VAR), and Bayesian VAR (BVAR) using annual data on infant mortality rates (IMR), neonatal mortality rates (NMR), GDP, and GDP per capita (GDPP) from 1954 to 2016. Model accuracy was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Theil’s U-statistic. The results show strong long-term relationships among IMR, NMR, GDP, and GDPP. VECM provides the most reliable long-term forecasts, with an adjusted R-squared of 97.7%. Impulse response analysis indicates that GDP increases IMR in the short run, while GDPP exerts a stronger long-term reducing effect. For NMR, GDP has a negative impact, whereas GDPP shows a gradual positive response over time. Granger causality tests reveal bidirectional causality between GDPP and IMR, and a unidirectional influence of IMR on GDP. Uganda’s IMR is projected to fall to about 17 deaths per 1,000 live births by 2035, though NMR declines will slow. Policymakers should use VECM for long-term planning due to its superior accuracy and the strong cointegration among IMR, NMR, GDP, and GDPP, while VAR/BVAR can guide short-term monitoring. Because GDPP most strongly reduces mortality, welfare-enhancing strategies such as social protection and employment are crucial. GDP gains should fund maternal and neonatal health, and systems must be strengthened to withstand economic shocks. Infant Mortality Rate (IMR) Neonatal Mortality Rate (NMR) Vector Error Correction Model (VECM) Gross Domestic Product per Capita (GDPP) Time Series Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 28 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor invited by journal 05 Feb, 2026 Editor assigned by journal 01 Feb, 2026 Submission checks completed at journal 01 Feb, 2026 First submitted to journal 21 Jan, 2026 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. 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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-8656051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604262287,"identity":"68608b1a-86f6-41ae-ba9c-6d648279f8fa","order_by":0,"name":"Benard Odur","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFCCBAZmCCP5AANjA2la0hJI1pJjQJwWfvYcs88FFXfkzdtzvkn83GEjx8B++OgGfFoke94Yz55x5pnhnDNvt0n2nkkzZuBJS7uBT4vBjRxjZt62w4wzJHK3SQAZiQ0SPGZEaPl32H6GRM4zyb/Ea2k4nAjUwiZNlC2SPc+KmWccO5w8g+eZsbVsW5oxGyG/8LMnb2YuqDlsO4M9+eHNt202cvzsh4/h1YIMWCRAJBuxykGA+QMpqkfBKBgFo2DkAACEJ0oCVxgNsgAAAABJRU5ErkJggg==","orcid":"","institution":"Makerere University","correspondingAuthor":true,"prefix":"","firstName":"Benard","middleName":"","lastName":"Odur","suffix":""},{"id":604262288,"identity":"cc4da1f1-79b1-4e05-b5e9-d476ed3feb23","order_by":1,"name":"Tom Etil","email":"","orcid":"","institution":"Lira University","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Etil","suffix":""},{"id":604262289,"identity":"acf55464-d137-459c-b8a4-d9000207c04a","order_by":2,"name":"Bosco Opio","email":"","orcid":"","institution":"Lira University","correspondingAuthor":false,"prefix":"","firstName":"Bosco","middleName":"","lastName":"Opio","suffix":""},{"id":604262290,"identity":"655ea0ca-a2fb-41c5-b86a-d4c1a23bb8bf","order_by":3,"name":"Memon Shaheen","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Memon","middleName":"","lastName":"Shaheen","suffix":""}],"badges":[],"createdAt":"2026-01-21 06:40:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8656051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8656051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780811,"identity":"c8c8f8cf-b211-45b1-803d-16f17269f461","added_by":"auto","created_at":"2026-03-17 07:54:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":703629,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptForecastingmodels2026Revised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8656051/v1_covered_887e7212-15f8-4a3a-9c08-4f7c0365651e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparing Forecasting Models for Predicting Infant Mortality: VECM vs VAR and BVAR Specifications","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Infant Mortality Rate (IMR), Neonatal Mortality Rate (NMR), Vector Error Correction Model (VECM), Gross Domestic Product per Capita (GDPP), Time Series Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8656051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8656051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates infant mortality in Uganda, a persistent public health challenge in many developing countries where economic disparities limit access to healthcare. It compares the forecasting performance of three econometric models; Vector Error Correction Model (VECM), Vector Autoregressive (VAR), and Bayesian VAR (BVAR) using annual data on infant mortality rates (IMR), neonatal mortality rates (NMR), GDP, and GDP per capita (GDPP) from 1954 to 2016. Model accuracy was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Theil\u0026rsquo;s U-statistic.\u003c/p\u003e \u003cp\u003eThe results show strong long-term relationships among IMR, NMR, GDP, and GDPP. VECM provides the most reliable long-term forecasts, with an adjusted R-squared of 97.7%. Impulse response analysis indicates that GDP increases IMR in the short run, while GDPP exerts a stronger long-term reducing effect. For NMR, GDP has a negative impact, whereas GDPP shows a gradual positive response over time. 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