Using the reversible jump MCMC procedure for identifying and estimating univariate TAR models

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dc.contributor.authorNieto, Fabio H.
dc.contributor.authorZhang, Hanwen
dc.coverage.campusCRAI-USTA Bogotáspa
dc.date.accessioned2020-01-22T18:00:03Z
dc.date.available2020-01-22T18:00:03Z
dc.date.issued2012-12-21
dc.description.abstractOne way that has been used for identifying and estimating threshold autoregressive (TAR) models for nonlinear time series follows the Markov chain Monte Carlo (MCMC) approach via the Gibbs sampler. This route has major computational difficulties, specifically, in getting convergence to the parameter distributions. In this article, a new procedure for identifying a TAR model and for estimating its parameters is developed by following the reversible jump MCMC procedure. It is found that the proposed procedure conveys a Markov chain with convergence properties.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.1080/03610918.2012.655827spa
dc.identifier.urihttp://hdl.handle.net/11634/21041
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dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/*
dc.subject.keywordBayesian model choicespa
dc.subject.keywordNonlinear time seriesspa
dc.subject.keywordRegime-switching modelsspa
dc.subject.keywordRJMCMCspa
dc.subject.keywordThreshold autoregressive (TAR) modelsspa
dc.titleUsing the reversible jump MCMC procedure for identifying and estimating univariate TAR modelsspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

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