Bayesian estimation and model selection of a multivariate smooth transition autoregressive model
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 coeff...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/79611 |
| _version_ | 1848764081639522304 |
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| author | Livingston, G. Nur, Darfiana |
| author_facet | Livingston, G. Nur, Darfiana |
| author_sort | Livingston, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:13:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-79611 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:13:41Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-796112020-06-15T00:30:01Z Bayesian estimation and model selection of a multivariate smooth transition autoregressive model Livingston, G. Nur, Darfiana 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. 2019 Journal Article http://hdl.handle.net/20.500.11937/79611 10.1002/env.2615 restricted |
| spellingShingle | Livingston, G. Nur, Darfiana Bayesian estimation and model selection of a multivariate smooth transition autoregressive model |
| title | Bayesian estimation and model selection of a multivariate smooth transition autoregressive model |
| title_full | Bayesian estimation and model selection of a multivariate smooth transition autoregressive model |
| title_fullStr | Bayesian estimation and model selection of a multivariate smooth transition autoregressive model |
| title_full_unstemmed | Bayesian estimation and model selection of a multivariate smooth transition autoregressive model |
| title_short | Bayesian estimation and model selection of a multivariate smooth transition autoregressive model |
| title_sort | bayesian estimation and model selection of a multivariate smooth transition autoregressive model |
| url | http://hdl.handle.net/20.500.11937/79611 |