Nonlinear Correlograms and Partial Autocorrelograms

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dc.contributor.authorAnderson, Heather
dc.contributor.authorVahid, Farshid
dc.date.accessioned2015-12-13T22:43:36Z
dc.date.issued2005
dc.date.updated2015-12-11T10:13:18Z
dc.description.abstractThis paper proposes neural network-based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely
dc.identifier.issn0305-9049
dc.identifier.urihttp://hdl.handle.net/1885/79271
dc.publisherBlackwell Publishing Ltd
dc.sourceOxford Bulletin of Economics and Statistics
dc.titleNonlinear Correlograms and Partial Autocorrelograms
dc.typeJournal article
local.bibliographicCitation.lastpage982
local.bibliographicCitation.startpage957
local.contributor.affiliationAnderson, Heather, College of Business and Economics, ANU
local.contributor.affiliationVahid, Farshid, College of Business and Economics, ANU
local.contributor.authoremailrepository.admin@anu.edu.au
local.contributor.authoruidAnderson, Heather, u3426640
local.contributor.authoruidVahid, Farshid, u4137903
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor140305 - Time-Series Analysis
local.identifier.absfor140302 - Econometric and Statistical Methods
local.identifier.absfor140303 - Economic Models and Forecasting
local.identifier.ariespublicationMigratedxPub7750
local.identifier.citationvolume67
local.identifier.doi10.1111/j.1468-0084.2005.00147.x
local.identifier.scopusID2-s2.0-29144475838
local.identifier.uidSubmittedByMigrated
local.type.statusPublished Version

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