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Some Reflections on Trend-Cycle Decompositions with Correlated Components


  • Tommaso PROIETTI


This paper discusses a few interpretative issues arising from trend- cycle decompositions with correlated components. We determine the conditions under which correlated components may originate from: underestimation of the cyclical component; a cycle in growth rates, rather than in the levels; the hysteresis phenomenon; permanent- transitory decompositions, where the permanent component has richer dynamics than a pure random walk. Moreover, the consequences for smoothing and signal extraction are discussed: in particular, we establish that a negative correlation implies that future observations carry most of the information needed to assess cyclical stance. As a result, the components will be subject to high revisions. The overall conclusion is that the characterisation of economic fluctuations in macroeconomic time series largely remains an open issue.
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  • Tommaso PROIETTI, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Economics Working Papers ECO2002/23, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2002/23

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    References listed on IDEAS

    1. Harvey, Andrew, 2001. "Testing in Unobserved Components Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 1-19, January.
    2. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    3. Lippi, Marco & Reichlin, Lucrezia, 1992. "On persistence of shocks to economic variables : A common misconception," Journal of Monetary Economics, Elsevier, vol. 29(1), pages 87-93, February.
    4. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, June.
    5. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
    6. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    7. Proietti, Tommaso & Harvey, Andrew, 2000. "A Beveridge-Nelson smoother," Economics Letters, Elsevier, vol. 67(2), pages 139-146, May.
    8. 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.
    9. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July.
    10. Peter K. Clark, 1987. "The Cyclical Component of U. S. Economic Activity," The Quarterly Journal of Economics, Oxford University Press, vol. 102(4), pages 797-814.
    11. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    12. Jaeger, Albert & Parkinson, Martin, 1994. "Some evidence on hysteresis in unemployment rates," European Economic Review, Elsevier, vol. 38(2), pages 329-342, February.
    13. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
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    Cited by:

    1. de Silva, Ashton & Hyndman, Rob J. & Snyder, Ralph, 2009. "A multivariate innovations state space Beveridge-Nelson decomposition," Economic Modelling, Elsevier, vol. 26(5), pages 1067-1074, September.
    2. Riccardo Corradini, 2005. "An Empirical Analysis of Permanent Income Hypothesis Applied to Italy using State Space Models with non zero correlation between trend and cycle," Computing in Economics and Finance 2005 28, Society for Computational Economics.
    3. de Silva, Ashton, 2008. "Forecasting macroeconomic variables using a structural state space model," MPRA Paper 11060, University Library of Munich, Germany.
    4. Cliff L.F. Attfield & Jonathan R.W. Temple, 2003. "Measuring trend output: how useful are the Great Ratios?," Bristol Economics Discussion Papers 03/555, Department of Economics, University of Bristol, UK.
    5. Julien Garnier, 2004. "UK in or UK Out? A Common Cycle Analysis Between the UK and the Euro Zone," Working Papers 2004-17, CEPII research center.
    6. Jonathan Temple & Cliff Attfield, 2004. "Measuring trend growth: how useful are the great ratios?," Money Macro and Finance (MMF) Research Group Conference 2003 101, Money Macro and Finance Research Group.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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