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 economic time series like industrial production, unemployment and real exchange rates. We derive the implied posterior distributions of model parameters and some relevant functions thereof, shedding light on a wide range of key features of each economic time series.

Fourier analysis, Markov Chain Monte Carlo, model averaging, state space models, time series decomposition
Econometric Institute Research Papers
Erasmus School of Economics

Kleijn, R.H, & van Dijk, H.K. (2003). Bayes model averaging of cyclical decompositions in economic time series (No. EI 2003-48). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/1080