Abstract
In this paper, we propose a fully Bayesian approach to the special class of nonlinear time-scries models called the logistic smooth transition autoregressive (LSTAR) model. Initially, a Gibbs sampler is proposed for the LSTAR where the lag length, k, is kept fixed. Then, uncertainty about k is taken into account and a novel reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is proposed. We compared our RJMCMC algorithm with well-known information criteria, such as the Akaikes̀ information criteria, the Bayesian information criteria (BIC) and the deviance information criteria. Our methodology is extensively studied against simulated and real-time series.
Original language | English (US) |
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Pages (from-to) | 99-117 |
Number of pages | 19 |
Journal | Journal of Time Series Analysis |
Volume | 27 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2006 |
Externally published | Yes |
Keywords
- Deviance information criterion
- Markov Chain Monte Carlo
- Model selection
- Nonlinear time-series model
- Reversible jump MCMC
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics