Cyclical Components in Economic Time Series: a Bayesian Approach
Harvey, Andrew C.
van Dijk, Herman
Cambridge Working Papers in Economics
Faculty of Economics
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Harvey, A. C., Trimbur, T., & van Dijk, H. (2004). Cyclical Components in Economic Time Series: a Bayesian Approach. https://doi.org/10.17863/CAM.5196
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 allow relatively smooth cycles to be extracted. Posterior densities of parameters and smoothed cycles are obtained using Markov chain Monte Carlo methods. An application to estimating business cycles in macroeconomic series illustrates the viability of the procedure for both univariate and bivariate models.
Gibbs sampler, Kalman filter, Markov chain Monte Carlo, state space, unobserved components, Classification-JEL: C11, C32, E32, band pass filter
This record's DOI: https://doi.org/10.17863/CAM.5196
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