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    Bayesian estimation and model selection of a multivariate smooth transition autoregressive model

    Access Status
    Fulltext not available
    Authors
    Livingston, G.
    Nur, Darfiana
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Livingston, G. and Nur, D. 2019. Bayesian estimation and model selection of a multivariate smooth transition autoregressive model. Environmetrics.
    Source Title
    Environmetrics
    DOI
    10.1002/env.2615
    ISSN
    1180-4009
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/79611
    Collection
    • Curtin Research Publications
    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.

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