Lag length estimation in large dimensional systems

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dc.contributor.author Gonzalo, Jesús
dc.contributor.author Pitarakis, Jean-Yves
dc.date.accessioned 2008-10-16T12:25:15Z
dc.date.available 2008-10-16T12:25:15Z
dc.date.issued 2002-07
dc.identifier.bibliographicCitation Journal of Time Series Analysis, July 2002, Vol. 23, nº 4, p. 401-423
dc.identifier.issn 0143-9782
dc.identifier.uri http://hdl.handle.net/10016/765
dc.description.abstract We study the impact of the system dimension on commonly used model selection criteria (AIC, BIC, HQ) and LR based general to specific testing strategies for lag length estimation in VARs. We show that AIC?s well known overparameterization feature becomes quickly irrelevant as we move away from univariate models, with the criterion leading to consistent estimates under sufficiently large system dimensions. Unless the sample size is unrealistically small, all model selection criteria will tend to point towards low orders as the system dimension increases, with the AIC remaining by far the best performing criterion. This latter point is also illustrated via the use of an analytical power function for model selection criteria. The comparison between the model selection and general to specific testing strategy is discussed within the context of a new penalty term leading to the same choice of lag length under both approaches.
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso eng
dc.language.iso eng
dc.publisher Blackwell
dc.subject.other Dimensionality
dc.subject.other Information criteria
dc.subject.other Lag length selection
dc.subject.other VAR
dc.title Lag length estimation in large dimensional systems
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=10170210&site=ehost-live
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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