Abstract
We consider Markov switching autoregressive models to tackle the prediction problem
of nonlinear time series with missing data. We obtain the maximum likelihood estimators of
the parameters of the model by the EM algorithm and we reconstruct the sequence of the hidden
states by a Monte Carlo procedure. Then we restore the missing observations and forecast the
future values of the observed variable through a new multistep forecasting procedure based on
the reconstructed chain. This method is applied to analyse a time series of daily mean concentrations
of sulphur dioxide; the different levels of the pollution are described by the dynamics
of the hidden states.
Year
2002
Category
Refereed journal