Cyclical components in economic time series: A Bayesian approach

Herman K. van Dijk, Andrew C. Harvey and Thomas Trimbur

No 105, Econometric Society 2004 Australasian Meetings from Econometric Society

Abstract: 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 mode

Keywords: Band pass filter; Markov Chain Monte Carlo; State Space Model (search for similar items in EconPapers)
JEL-codes: C11 C32 E32 (search for similar items in EconPapers)
Date: 2004-08-11
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Working Paper: Cyclical Components in Economic Time Series: a Bayesian Approach (2003)
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Persistent link: http://EconPapers.repec.org/RePEc:ecm:ausm04:105

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