Statistical Modelling 4 (2004), 1938
Periodic Markov switching autoregressive models for Bayesian
analysis and forecasting of air pollution
Luigi Spezia
Department of Statistics
Athens University of Economics and Business
Greece
Roberta Paroli
Istituto di Statistica
Università Cattolica S.C. di Milano
Italy
eMail:
roberta.paroli@unicatt.it
Petros Dellaportas
Department of Statistics
Athens University of Economics and Business
Greece
Abstract:
Markov switching autoregressive models (MSARMs) are eficient tools to
analyse linear and non-Gaussian time series. A special MSARM with two
harmonic components is proposed to analyse periodoc 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.
Keywords:
Hidden Markov chain; harmonic components; Gibbs sampling;
label switching; permutation sampling; carbon monoxide.
Downloads:
Data and
software in zipped archive
back