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Bayesian selection of threshold autoregressive models
Author(s) -
Campbell Edward P.
Publication year - 2004
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2004.01726.x
Subject(s) - reversible jump markov chain monte carlo , autoregressive model , setar , model selection , bayesian probability , markov chain monte carlo , mathematics , selection (genetic algorithm) , star model , bayesian information criterion , information criteria , bayesian inference , algorithm , econometrics , autoregressive integrated moving average , computer science , statistics , time series , artificial intelligence
. An approach to Bayesian model selection in self‐exciting threshold autoregressive (SETAR) models is developed within a reversible jump Markov chain Monte Carlo (RJMCMC) framework. Our approach is examined via a simulation study and analysis of the Zurich monthly sunspots series. We find that the method converges rapidly to the optimal model, whilst efficiently exploring suboptimal models to quantify model uncertainty. A key finding is that the parsimony of the model selected is influenced by the specification of prior information, which can be examined and subjected to criticism. This is a strength of the Bayesian approach, allowing physical understanding to constrain the model selection algorithm.