Bayesian Model Uncertainty in Smooth Transition Autoregressions
Hedibert F. Lopes
University of Chicago - Booth School of Business
Duke University; Universidade Federal do Rio de Janeiro (UFRJ)
Journal of Time Series Analysis, Vol. 27, No. 1, pp. 99-117, January 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.
Number of Pages in PDF File: 19
Keywords: Markov Chain Monte Carlo, nonlinear time-series model, model selection, reversible jump MCMC; deviance information criterion
Date posted: February 21, 2006
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