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Cyclical components in economic time series: A Bayesian approach

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  • Herman K. van Dijk
  • Andrew Harvey
  • Thomas Trimbur

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

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 105.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:105

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Keywords: Band pass filter; Markov Chain Monte Carlo; State Space Model;

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Cited by:
  1. Harm Bandholz & Gebhard Flaig & Johannes Mayr, 2005. "Wachstum und Konjunktur in OECD-Ländern: Eine langfristige Perspektive," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 58(04), pages 28-36, 02.
  2. Gonzalo Llosa/Shirley Miller, 2004. "Using additional information in estimating output gap in Peru: a multivariate unobserved component approach," Econometric Society 2004 Latin American Meetings 243, Econometric Society.
  3. Kleijn, R.H. & van Dijk, H.K., 2003. "Bayes model averaging of cyclical decompositions in economic time series," Econometric Institute Research Papers EI 2003-48, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. Gonzalo Llosa & Shirley Miller, 2005. "Using additional information in estimating the output gap in Peru: a multivariate unobserved component approach," Working Papers 2005-004, Banco Central de Reserva del Perú.
  5. Klaus Abberger & Gebhard Flaig & Wolfgang Nierhaus, 2007. "ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst," ifo Forschungsberichte, Ifo Institute for Economic Research at the University of Munich, number 33.

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