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.
Original language | English |
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Article number | e2615 |
Number of pages | 16 |
Journal | Environmetrics |
Volume | 31 |
Issue number | 6 |
Early online date | 26 Dec 2019 |
DOIs | |
Publication status | Published - 1 Sep 2020 |
Keywords
- Bayesian
- icelandic river flow
- multivariate time series
- paleoclimate
- reversible jump MCMC
- smooth transition AR