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Bayes model averaging of cyclical decompositions in economic time series

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Author Info
Richard Kleijn (Econometric Institute, Erasmus University Rotterdam, The Netherlands)
Herman K. van Dijk (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

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Abstract

A 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 cyclical economic time series like US industrial production and unemployment. We derive the implied posterior distributions of model parameters and some relevant functions thereof, shedding light on several key features of economic time series. Copyright © 2006 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/jae.823
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 21 (2006)
Issue (Month): 2 ()
Pages: 191-212
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Handle: RePEc:jae:japmet:v:21:y:2006:i:2:p:191-212

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  1. Philip Hans Franses & Yoshinori Kawasaki, 2004. "Do seasonal unit roots matter for forecasting monthly industrial production?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 77-88. [Downloadable!]
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  3. Van Dijk, H.K. & Koop, G., 1999. "Testing for Integration Using Evolving Trend and Seasonals Models : A Bayesian Approach," Papers 9934/a, Erasmus University of Rotterdam - Econometric Institute.
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  4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
  5. 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. [Downloadable!]
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  6. G. Huerta & M. West, 1999. "Priors and component structures in autoregressive time series models," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 881-899. [Downloadable!] (restricted)
  7. repec:cup:etheor:v:10:y:1994:i:3-4:p:552-78 is not listed on IDEAS
  8. Zivot, Eric, 1994. "A Bayesian Analysis Of The Unit Root Hypothesis Within An Unobserved Components Model," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 552-578, August. [Downloadable!]
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  10. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March. [Downloadable!] (restricted)
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  11. 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. [Downloadable!] (restricted)
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  12. A.C. Harvey & T.M. Trimbur & H.K. Van Dijk, 2002. "Cyclical components in economic time series," Econometric Institute Report 293, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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  13. Charles Nelson & Eric Zivot, 2000. "Why are Beveridge-Nelson and Unobserved-Component Decompositions of GDP so Different?," Econometric Society World Congress 2000 Contributed Papers 0692, Econometric Society. [Downloadable!]
  14. Zivot, E., 1993. "A Bayesian Analysis of the Unit Root Hypothesis Within an Unobserved Components Model," Discussion Papers in Economics at the University of Washington 93-15, Department of Economics at the University of Washington.
  15. Paap, Richard & van Dijk, Herman K, 2003. "Bayes Estimates of Markov Trends in Possibly Cointegrated Series: An Application to U.S. Consumption and Income," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 547-63, October.
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