Long term economic relationships from cointegration maps

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T15:28:46Z
dc.date.accessioned2022-10-05T16:50:42Z
dc.date.available2014-05-20T15:28:46Z
dc.date.available2022-10-05T16:50:42Z
dc.date.created2014-05-20T15:28:46Z
dc.date.issued2007-07-01
dc.identifierPhysica A-statistical Mechanics and Its Applications. Amsterdam: Elsevier B.V., v. 380, p. 317-324, 2007.
dc.identifier0378-4371
dc.identifierhttp://hdl.handle.net/11449/38515
dc.identifier10.1016/j.physa.2007.02.067
dc.identifierWOS:000247243600027
dc.identifier0500034174785796
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3909832
dc.description.abstractWe employ the Bayesian framework to define a cointegration measure aimed to represent long term relationships between time series. For visualization of these relationships we introduce a dissimilarity matrix and a map based on the sorting points into neighborhoods (SPIN) technique, which has been previously used to analyze large data sets from DNA arrays. We exemplify the technique in three data sets: US interest rates (USIR), monthly inflation rates and gross domestic product (GDP) growth rates. (c) 2007 Elsevier B.V. All rights reserved.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationPhysica A: Statistical Mechanics and Its Applications
dc.relation2.132
dc.relation0,773
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectcomplex systems
dc.subjecteconophysics
dc.subjectcointegration
dc.subjectclustering
dc.subjectBayesian inference
dc.titleLong term economic relationships from cointegration maps
dc.typeArtigo


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