- Title
- Bayesian estimation and model selection of a multivariate smooth transition autoregressive model
- Creator
- Livingston, Glen; Nur, Darfiana
- Relation
- Environmetrics Vol. 31, Issue 6, no. e2615
- Publisher Link
- http://dx.doi.org/10.1002/env.2615
- Publisher
- John Wiley & Sons
- Resource Type
- journal article
- Date
- 2020
- Description
- 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.
- Subject
- Bayesian; Icelandic river flow; multivariate time series; paleoclimate; eversible jump MCMC; smooth transition AR
- Identifier
- http://hdl.handle.net/1959.13/1447100
- Identifier
- uon:43051
- Identifier
- ISSN:1180-4009
- Language
- eng
- Reviewed
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