Bayes estimates of the cyclical component in twentieth centruy US gross domestic product

Cyclical components in economic time series are analysed in a Bayesian framework, thereby allowing prior notions about periodicity to be used. The method is based on a general class of unobserved component models that encompasses a range of dynamics in the stochastic cycle. This allows for instance relatively smooth cycles to be extracted from time series. Posterior densities of parameters and estimated components are obtained using Markov chain Monte Carlo methods, which we develop for both univariate and multivariate models. Features such as time-varying amplitude may be studied by examining different functions of the posterior draws for the cyclical component and parameters. The empirical application illustrates the method for annual US real GDP over the last 130 years.