Abstract
Markov switching autoregressive models (MSARMs) are efficient tools to analyse nonlinear
and non-Gaussian time series. A special MSARM with two harmonic components is proposed
to analyse periodic time series. We present a full Bayesian analysis based on Gibbs sampling
algorithm for model choice and the estimations of the unknown parameters, missing data and
predictive distributions. The implementation and modelling steps are developed by tackling the
problem of the hidden states labeling by means of random permutation sampling and constrained
permutation sampling. We apply MSARMs to study a data set about air pollution which presents
periodicities since the hourly mean concentration of carbon monoxide varies according to the
dynamics of the 24 day-hours and of the year. Hence, we introduce in the model both a hidden
state-dependent daily component and a state-independent yearly component, giving rise to periodic
MSARMs.
Year
2004
Category
Refereed journal