A Bayesian Approach to Heavy-Tailed Finite Mixture Autoregressive Models

• Published in: Symmetry (Basel) Vol. 12; no. 6; p. 929
• Main Authors: Mahmoudi, Mohammad Reza, Maleki, Mohsen, Baleanu, Dumitru, Nguyen, Vu-Thanh, Pho, Kim-Hung
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.06.2020
• Subjects: finite mixture autoregressive models; Gibbs sampling; MCMC method; non-linear time series; SMSN distributions
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym12060929

In this paper, a Bayesian analysis of finite mixture autoregressive (MAR) models based on the assumption of scale mixtures of skew-normal (SMSN) innovations (called SMSN–MAR) is considered. This model is not simultaneously sensitive to outliers, as the celebrated SMSN distributions, because the proposed MAR model covers the lightly/heavily-tailed symmetric and asymmetric innovations. This model allows us to have robust inferences on some non-linear time series with skewness and heavy tails. Classical inferences about the mixture models have some problematic issues that can be solved using Bayesian approaches. The stochastic representation of the SMSN family allows us to develop a Bayesian analysis considering the informative prior distributions in the proposed model. Some simulations and real data are also presented to illustrate the usefulness of the proposed models.