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dc.contributor.authorYaya, OlaOluwa S.
dc.contributor.authorOgbonna, Ahamuefula E.
dc.contributor.authorFuruoka, Fumitaka
dc.contributor.authorGil Alana, Luis A. 
dc.date.accessioned2022-06-13T10:18:16Z
dc.date.available2022-06-13T10:18:16Z
dc.date.issued2021
dc.identifier.issn0305-9049spa
dc.identifier.urihttp://hdl.handle.net/10641/3003
dc.description.abstractThis paper proposes a nonlinear unit root test based on the autoregressive neural network process for testing unemployment hysteresis. In this new unit root testing framework, the linear, quadratic and cubic components of the neural network process are used to capture the nonlinearity in a given time series data. The theoretical properties of the test are developed, while the size and the power properties are examined in a Monte Carlo simulation study. Various empirical applications with unemployment and inflation rates across a number of countries are carried out at the end of the article.spa
dc.language.isoengspa
dc.publisherOxford Bulletin of Economics and Statisticsspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectAutoregressive Neural Networkspa
dc.subjectFractional integrationspa
dc.subjectUnit root testspa
dc.subjectNon-linearityspa
dc.titleA New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network.spa
dc.typearticlespa
dc.description.versionpre-printspa
dc.rights.accessRightsopenAccessspa
dc.description.extent437 KBspa
dc.identifier.doi10.1111/obes.12422spa
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/abs/10.1111/obes.12422spa


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España