Intervention analysis for integer-valued autoregressive models

Intervention analysis for integer-valued autoregressive models | 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 Intervention analysis for integer-valued autoregressive models Xanthi Pedeli, Roland Fried This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2695330/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We study the problem of intervention effects generating various types of outliers in an integer-valued autoregressive model with Poisson innovations. We concentrate on outliers which enter the dynamics and can be seen as effects of extraordinary events. Weconsider three different scenarios, namely the detection of an intervention effect of a known type at a known time, the detection of an intervention effect of unknown type at a known time andthe detection of an intervention effect when both the type and the time are unknown. We develop \(F\) -tests and score tests for the first scenario. For the second and third scenarios we rely on the maximum of the different $ F $ -type or score statistics. The usefulness of the proposed approach is illustrated using monthly data on human brucellosis infections in Greece. count data time series innovation outlier level shift transient shift Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2695330","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":183886024,"identity":"8a6374ce-06bf-47a6-95bd-d2fdc844ffb0","order_by":0,"name":"Xanthi Pedeli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYJACCYYCCTkgzUaKFgMJY5K1MCQ2EK3F4HbzwRsfDCzS+2ckH3vA8OseYS2Sc44lW84wkMidcSMt3YCxr5iwFn6JHDNpHqCWhhs5ZhKMPQmEtbBJ5H+T/mMgkS5/I/8bcVqAtrBJA0MsweBGDpsEww8itAD9YmzZYyBhuPHMM3ODxAYitABD7OGNHxV18nLHk589+PCHCC3ASEECiW1E6EDVwvCHGC2jYBSMglEw0gAAMJk2mpzB0JEAAAAASUVORK5CYII=","orcid":"","institution":"Athens University of Economics and Business","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xanthi","middleName":"","lastName":"Pedeli","suffix":""},{"id":183886025,"identity":"9b8a992e-66d2-4882-9ed6-5edbda775fe6","order_by":1,"name":"Roland Fried","email":"","orcid":"","institution":"TU Dortmund University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Roland","middleName":"","lastName":"Fried","suffix":""}],"badges":[],"createdAt":"2023-03-15 08:44:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2695330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2695330/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34536422,"identity":"a7680052-42a5-40ff-9091-0bc1a396e606","added_by":"auto","created_at":"2023-03-20 17:10:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2905357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2695330/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intervention analysis for integer-valued autoregressive models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"count data, time series, innovation outlier, level shift, transient shift","lastPublishedDoi":"10.21203/rs.3.rs-2695330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2695330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e We study the problem of intervention effects generating various types of outliers in an integer-valued autoregressive model with Poisson innovations. 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