Abstract
Markov switching autoregressive models (MSARMs) are efficient tools to analyse nonlinear
and non-normal time series. A special MSARM with a hidden state-dependent seasonal
component is proposed here to analyse periodic time series. We present a complete
Metropolis-within-Gibbs algorithm for constraint identification, for model choice and for
the estimation of the unknown parameters and the latent data. These three consecutive
steps are developed tackling the problem of the hidden states labeling, by means of random
permutation sampling and constrained permutation sampling. The missing observations
occurring within the observed series and the future values are respectively estimated
and forecasted considering them as unknown parameters. We illustrate our methodology
with an example about the dynamics of an air pollutant.
Year
2002
Category
Refereed journal