Bayes model averaging of cyclical decompositions in economic time series
AbstractA flexible decomposition of a time series into stochastic cycles under possible non-stationarity is specified, providing both a useful data analysis tool and a very wide model class. A Bayes procedure using Markov Chain Monte Carlo (MCMC) is introduced with a model averaging approach which explicitly deals with the uncertainty on the appropriate number of cycles. The convergence of the MCMC method is substantially accelerated through a convenient reparametrization based on a hierarchical structure of variances in a state space model. The model and corresponding inferential procedure are applied to simulated data and to economic time series like industrial production, unemployment and real exchange rates. We derive the implied posterior distributions of model parameters and some relevant functions thereof, shedding light on a wide range of key features of each economic time series.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2003-48.
Date of creation: 07 Aug 2003
Date of revision:
Contact details of provider:
Web page: http://www.few.eur.nl/few
model averaging; Markov Chain Monte Carlo; state space models; Fourier analysis; time series decomposition;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Cheung, Yin-Wong & Lai, Kon S., 2000. "On the purchasing power parity puzzle," Journal of International Economics, Elsevier, vol. 52(2), pages 321-330, December.
- Schotman, Peter & van Dijk, Herman K., 1991. "A Bayesian analysis of the unit root in real exchange rates," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 195-238.
- Gary Koop & Herman K. van Dijk & Henk Hoek, 1997.
"Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach,"
Tinbergen Institute Discussion Papers
97-078/4, Tinbergen Institute.
- Koop, Gary & Dijk, Herman K. Van, 2000. "Testing for integration using evolving trend and seasonals models: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 97(2), pages 261-291, August.
- Koop, G. & Dijk, H.K. van, 1999. "Testing for integration using evolving trend and seasonal models: A Bayesian approach," Econometric Institute Report EI 9934/A, Erasmus University Rotterdam, Econometric Institute.
- Gary Koop & Herman K. van Dijk, 1999. "Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach," Tinbergen Institute Discussion Papers 99-072/4, Tinbergen Institute.
- Harvey, A. & TTrimbur, T. & van Dijk, H., 2003.
"Cyclical Components in Economic Time Series: a Bayesian Approach,"
Cambridge Working Papers in Economics
0302, Faculty of Economics, University of Cambridge.
- Herman K. van Dijk & Andrew Harvey & Thomas Trimbur, 2004. "Cyclical components in economic time series: A Bayesian approach," Econometric Society 2004 Australasian Meetings 105, Econometric Society.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anneke Kop).
If references are entirely missing, you can add them using this form.