Bayesian model uncertainty in smooth transition autoregressions

Journal Article

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 AkaikesÌ€ information criteria, the Bayesian information criteria (BIC) and the deviance information criteria. Our methodology is extensively studied against simulated and real-time series. © 2005 Blackwell Publishing Ltd.,.

Full Text

Cited Authors

  • Lopes, HF; Salazar, E

Published Date

  • 2006

Published In

Volume / Issue

  • 27 / 1

Start / End Page

  • 99 - 117

International Standard Serial Number (ISSN)

  • 0143-9782

Digital Object Identifier (DOI)

  • 10.1111/j.1467-9892.2005.00455.x