Testing a linear ARMA Model against threshold-ARMA models : a Bayesian approach
Liang, Rubing and Xia, Qiang and Pan, Jiazhu and Liu, Jinshan (2017) Testing a linear ARMA Model against threshold-ARMA models : a Bayesian approach. Communications in Statistics - Simulation and Computation, 46 (2). pp. 1302-1317. ISSN 0361-0918 (https://doi.org/10.1080/03610918.2014.1002616)
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Abstract
We introduce a Bayesian approach to test linear autoregressive moving-average (ARMA) models against threshold autoregressive moving-average (TARMA) models. Firstly, the marginal posterior densities of all parameters, including the threshold and delay, of a TARMA model are obtained by using Gibbs sampler with Metropolis-Hastings algorithm. Secondly, reversible-jump Markov chain Monte Carlo (RJMCMC) method is adopted to calculate the posterior probabilities for ARMA and TARMA models: Posterior evidence in favor of TARMA models indicates threshold nonlinearity. Finally, based on RJMCMC scheme and Akaike information criterion (AIC) or Bayesian information criterion (BIC), the procedure for modeling TARMA models is exploited. Simulation experiments and a real data example show that our method works well for distinguishing a ARMA from a TARMA model and for building TARMA models.
ORCID iDs
Liang, Rubing, Xia, Qiang, Pan, Jiazhu ORCID: https://orcid.org/0000-0001-7346-2052 and Liu, Jinshan;-
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Item type: Article ID code: 56444 Dates: DateEvent28 February 2017Published25 March 2015Published Online18 December 2014AcceptedNotes: This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Simulation and Computation on 25/03/2015, available online: http://www.tandfonline.com/10.1080/03610918.2014.1002616 Subjects: Science > Mathematics > Probabilities. Mathematical statistics Department: Faculty of Science > Mathematics and Statistics Depositing user: Pure Administrator Date deposited: 19 May 2016 13:32 Last modified: 28 Oct 2024 01:32 URI: https://strathprints.strath.ac.uk/id/eprint/56444