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VARMA versus VAR for Macroeconomic Forecasting

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dc.contributor.authorAthanasopoulos, George
dc.contributor.authorVahid, Farshid
dc.date.accessioned2015-12-07T22:22:45Z
dc.date.issued2008
dc.date.updated2015-12-07T09:08:52Z
dc.description.abstractIn this article, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to vector autoregressive (VAR) models, given the recent advances in vector autoregressive moving average (VARMA) modeling methodology and improvements in computing power. To support this claim, we use real macroeconomic data, and show that VARMA models forecast macroeconomic variables more accurately than VARs.
dc.identifier.issn0735-0015
dc.identifier.urihttp://hdl.handle.net/1885/20369
dc.publisherAmerican Statistical Association
dc.sourceJournal of Business and Economic Statistics
dc.subjectKeywords: Identification; Multivariate time series; Scalar components; VARMA models
dc.titleVARMA versus VAR for Macroeconomic Forecasting
dc.typeJournal article
local.bibliographicCitation.issue2
local.bibliographicCitation.lastpage252
local.bibliographicCitation.startpage237
local.contributor.affiliationAthanasopoulos, George, Monash University
local.contributor.affiliationVahid, Farshid, College of Business and Economics, ANU
local.contributor.authoruidVahid, Farshid, u4137903
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor140305 - Time-Series Analysis
local.identifier.ariespublicationu4137903xPUB12
local.identifier.citationvolume26
local.identifier.doi10.1198/073500107000000313
local.identifier.scopusID2-s2.0-41649104111
local.identifier.thomsonID000254056900008
local.type.statusPublished Version

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