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Identifiability of regular and singular multivariate autoregressive models from mixed frequency data

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Anderson, Brian; Deistler, Manfred; Felsenstein, Elisabeth; Funovits, Bernd; Zadrozny, Peter; Eichler, Michael; Chen, Weitien; Zamani, Mohsen

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This paper is concerned with identifiability of an underlying high frequency multivariate AR system from mixed frequency observations. Such problems arise for instance in economics when some variables are observed monthly whereas others are observed quarterly. If we have identifiability, the system and noise parameters and thus all second moments of the output process can be estimated consistently from mixed frequency data. Then linear least squares methods for forecasting and interpolating...[Show more]

dc.contributor.authorAnderson, Brian
dc.contributor.authorDeistler, Manfred
dc.contributor.authorFelsenstein, Elisabeth
dc.contributor.authorFunovits, Bernd
dc.contributor.authorZadrozny, Peter
dc.contributor.authorEichler, Michael
dc.contributor.authorChen, Weitien
dc.contributor.authorZamani, Mohsen
dc.coverage.spatialMaui USA
dc.date.accessioned2015-12-10T23:16:28Z
dc.date.createdDecember 10-13 2012
dc.identifier.isbn9781467320665
dc.identifier.urihttp://hdl.handle.net/1885/65075
dc.description.abstractThis paper is concerned with identifiability of an underlying high frequency multivariate AR system from mixed frequency observations. Such problems arise for instance in economics when some variables are observed monthly whereas others are observed quarterly. If we have identifiability, the system and noise parameters and thus all second moments of the output process can be estimated consistently from mixed frequency data. Then linear least squares methods for forecasting and interpolating nonobserved output variables can be applied. Two ways for guaranteeing generic identifiability are discussed.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE Conference on Decision and Control (CDC 2012)
dc.sourceProceedings of the IEEE Conference on Decision and Control
dc.subjectKeywords: AR system; Frequency data; High frequency; Identifiability; Linear least-squares method; Multivariate autoregressive models; Noise parameters; Output variables; Second moments; Two ways; Control
dc.titleIdentifiability of regular and singular multivariate autoregressive models from mixed frequency data
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2012
local.identifier.absfor090602 - Control Systems, Robotics and Automation
local.identifier.ariespublicationu4334215xPUB1047
local.type.statusPublished Version
local.contributor.affiliationAnderson, Brian, College of Engineering and Computer Science, ANU
local.contributor.affiliationDeistler, Manfred, Vienna University of Technology
local.contributor.affiliationFelsenstein, Elisabeth, Technical University of Vienna
local.contributor.affiliationFunovits, Bernd, Vienna University of Technology
local.contributor.affiliationZadrozny, Peter, Bureau of Labor Statistics
local.contributor.affiliationEichler, Michael, Maastricht University
local.contributor.affiliationChen, Weitien, University of Windsor
local.contributor.affiliationZamani, Mohsen, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage184
local.bibliographicCitation.lastpage189
local.identifier.doi10.1109/CDC.2012.6426713
local.identifier.absseo910199 - Macroeconomics not elsewhere classified
dc.date.updated2016-02-24T10:57:04Z
local.identifier.scopusID2-s2.0-84874222474
CollectionsANU Research Publications

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