Seasonal autoregressions with regime switching

R. Paroli & L. Spezia
Markov switching autoregressive models (MSARMs) are efficient tools to analyse non-linear 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...