19 Pages Posted: 21 Feb 2006
In this paper, we propose a fully Bayesian approach to the special class of nonlinear time-series 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 Akaike information criteria, the Bayesian information criteria (BIC) and the deviance information criteria. Our methodology is extensively studied against simulated and real-time series.
Keywords: Markov Chain Monte Carlo, nonlinear time-series model, model selection, reversible jump MCMC; deviance information criterion
Suggested Citation: Suggested Citation
Lopes, Hedibert F. and Salazar, Esther, Bayesian Model Uncertainty in Smooth Transition Autoregressions. Journal of Time Series Analysis, Vol. 27, No. 1, pp. 99-117, January 2006. Available at SSRN: https://ssrn.com/abstract=875065 or http://dx.doi.org/10.1111/j.1467-9892.2005.00455.x
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