Trends and cyclical components in economic time series are modeled in a Bayesian framework. This enables prior notions about the duration of cycles to be used, while the generalized class of stochastic cycles employed allows the possibility of relatively smooth cycles being extracted. The posterior distributions of such underlying cycles can be very informative for policy makers, particularly with regard to the size and direction of the output gap and potential turning points. From the technical point of view a contribution is made in investigating the most appropriate prior distributions for the parameters in the cyclical components and in developing Markov chain Monte Carlo methods for both univariate and multivariate models. Applications to US macroeconomic series are presented.

Kalman filter, Markov chain Monte Carlo, output gap, real time estimation, turning points, unobserved components
Bayesian Analysis (jel C11), Time-Series Models; Dynamic Quantile Regressions (jel C32), Business Fluctuations; Cycles (jel E32)
Econometric Institute Research Papers
Report / Econometric Institute, Erasmus University Rotterdam
Erasmus School of Economics

Harvey, A.C, Trimbur, T.M, & van Dijk, H.K. (2005). Trends and cycles in economic time series: A Bayesian approach (No. EI 2005-27). Report / Econometric Institute, Erasmus University Rotterdam. Retrieved from