  This paper introduces a fully Bayesian analysis of mixture autoregressive
models with Student t components. With the capacity of capturing the behaviour
in the tails of the distribution, the Student t MAR model provides a more
flexible modelling framework than its Gaussian counterpart, leading to fitted
models with fewer parameters and of easier interpretation. The degrees of
freedom are also treated as random variables, and hence are included in the
estimation process.
