Bayesian model uncertainty in smooth transition autoregressions

Hedibert F. Lopes, Esther Salazar

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

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 languageEnglish (US)
Pages (from-to)99-117
Number of pages19
JournalJournal of Time Series Analysis
Volume27
Issue number1
DOIs
StatePublished - Jan 2006
Externally publishedYes

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

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