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dc.contributor.authorLivingston, G.
dc.contributor.authorNur, Darfiana
dc.date.accessioned2020-06-12T05:06:25Z
dc.date.available2020-06-12T05:06:25Z
dc.date.issued2019
dc.identifier.citationLivingston, G. and Nur, D. 2019. Bayesian estimation and model selection of a multivariate smooth transition autoregressive model. Environmetrics.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/79611
dc.identifier.doi10.1002/env.2615
dc.description.abstract

The multivariate smooth transition autoregressive model with order k (M-STAR)(k) is a nonlinear multivariate time series model able to capture regime changes in the conditional mean. The main aim of this paper is to develop a Bayesian estimation scheme for the M-STAR(k) model that includes the coefficient parameter matrix, transition function parameters, covariance parameter matrix, and the model order k as parameters to estimate. To achieve this aim, the joint posterior distribution of the parameters for the M-STAR(k) model is derived. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Markov chain Monte Carlo (MCMC) algorithms that includes the Metropolis-Hastings, Gibbs sampler, and reversible jump MCMC algorithms. Following this, extensive simulation studies, as well as case studies, are detailed at the end.

dc.titleBayesian estimation and model selection of a multivariate smooth transition autoregressive model
dc.typeJournal Article
dcterms.source.issn1180-4009
dcterms.source.titleEnvironmetrics
dc.date.updated2020-06-12T05:06:24Z
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidNur, Darfiana [0000-0002-7690-1097]
dcterms.source.eissn1099-095X
curtin.contributor.scopusauthoridNur, Darfiana [8921799600]


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