Rodyti trumpą aprašą

dc.contributor.authorKrapavickaitė, Danutė
dc.date.accessioned2023-09-18T16:17:19Z
dc.date.available2023-09-18T16:17:19Z
dc.date.issued2022
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/112808
dc.description.abstractOne of the quality requirements in official statistics is coherence of statistical information across domains, in time, in national accounts, and internally. However, no measure of its strength is used. The concept of coherence is also met in signal processing, wave physics, and time series. In the current article, the definition of the coherence coefficient for a weakly stationary time series is recalled and discussed. The coherence coefficient is a correlation coefficient between two indi-cators in time indexed by the same frequency components of their Fourier transforms and shows a degree of synchronicity between the time series for each frequency. The usage of this coeffi-cient is illustrated through the coherence and Granger causality analysis of a collection of nu-merical economic and social statistical indicators. The coherence coefficient matrix-based non-metric multidimensional scaling for visualization of the time series in the frequency do-main is a newly suggested method. The aim of this article is to propose the use of this coherence coefficient and its applications in official statistics.eng
dc.formatPDF
dc.format.extentp. 1-20
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyJ-Gate
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://doi.org/10.3390/math10071159
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:126207338/datastreams/MAIN/content
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:126207338/datastreams/COVER/content
dc.titleCoherence coefficient for official statistics
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references48
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldN 001 - Matematika / Mathematics
dc.subject.studydirectionA03 - Statistika / Statistics
dc.subject.vgtuprioritizedfieldsFM0101 - Fizinių, technologinių ir ekonominių procesų matematiniai modeliai / Mathematical models of physical, technological and economic processes
dc.subject.ltspecializationsL103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society
dc.subject.enweakly stationary time series
dc.subject.enFourier transform
dc.subject.enperiodogram
dc.subject.enGranger causality
dc.subject.enmultidimensional scaling
dcterms.sourcetitleMathematics: Special issue: Time series analysis and econometrics with applications
dc.description.issueiss. 7
dc.description.volumevol. 10
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi000781942200001
dc.identifier.doi10.3390/math10071159
dc.identifier.elaba126207338


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