Cyclical components in economic time series: A Bayesian approach

Herman van Dijk, Andrew 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
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.few.eur.nl/few/research/pubs/ei/2002/reports.htm main text (text/html)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Working Paper: Cyclical Components in Economic Time Series: a Bayesian Approach (2003)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecm:ausm04:105

Access Statistics for this paper

More papers in Econometric Society 2004 Australasian Meetings from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().