RICHARD KLEIJN AND HERMAN K. VAN DIJK

SUMMARY
A flexible decomposition of a time series into stochastic cycles under possible non-stationarity is specified,
providing both a useful data analysis tool and a very wide model class. A Bayes procedure using Markov
Chain Monte Carlo (MCMC) is introduced with a model averaging approach which explicitly deals with the
uncertainty on the appropriate number of cycles. The convergence of the MCMC method is substantially
accelerated through a convenient reparametrization based on a hierarchical structure of variances in a state
space model. The model and corresponding inferential procedure are applied to simulated data and to cyclical
economic time series like US industrial production and unemployment. We derive the implied posterior
distributions of model parameters and some relevant functions thereof, shedding light on several key features
of economic time series. Copyright  2006 John Wiley & Sons, Ltd.