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Using the Reversible Jump MCMC Procedure for Identifying and Estimating Univariate TAR Models

2012; Taylor & Francis; Volume: 42; Issue: 4 Linguagem: Inglês

10.1080/03610918.2012.655827

1532-4141

Fabio H. Nieto, Hanwen Zhang, Wen Li,

Statistical Methods and Inference

One way that has been used for identifying and estimating threshold autoregressive (TAR) models for nonlinear time series follows the Markov chain Monte Carlo (MCMC) approach via the Gibbs sampler. This route has major computational difficulties, specifically, in getting convergence to the parameter distributions. In this article, a new procedure for identifying a TAR model and for estimating its parameters is developed by following the reversible jump MCMC procedure. It is found that the proposed procedure conveys a Markov chain with convergence properties.