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 logoORCID: https://orcid.org/0000-0001-7346-2052 and Liu, Jinshan;
  • Item type:Article
    ID code:56444
    Dates:
    Date
    Event
    28 February 2017
    Published
    25 March 2015
    Published Online
    18 December 2014
    Accepted
    Notes: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:11 Nov 2024 11:17
    URI:https://strathprints.strath.ac.uk/id/eprint/56444
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