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State Space Model with Mixtures of Normals: Specifications and Applications to International Data

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Author Info

  • Tatsuma Wada

    ()
    (Department of Economics, Boston University\par)

  • Pierre Perron

    ()
    (Department of Economics, Boston University)

Abstract

This paper first generalizes the trend-cycle decomposition framework of Perron and Wada (2005) based on an unobserved components model with innovations having a mixture of Normal distribution, which is able to handle sudden level and slope changes to the trend function as well as outliers. We investigate how important are the differences in the implied trend and cycle compared to the popular decomposition based on the Hodrick and Prescott (HP) (1997) filter. Our results show important qualitative and quantitative differences in the implied cycles for both real GDP and consumption series for the G7 countries. Most of the differences can be ascribed to the fact that the HP filter does not handle well slope changes, level shifts and outliers, while our method does so. Third, we assess how such different cycles affect some so-called “stylized facts†about the relative variability of consumption and output across countries. Our results show again important differences. In particular, the cross-country consumption correlations are generally higher than the output correlations, except for the period from 1975 to 1985, provided Canada is excluded. Our results therefore provide a partial solution to this puzzle. The evidence is particularly strong for the most recent period.

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Bibliographic Info

Paper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number WP2006-029.

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Length: 42 pages
Date of creation: Sep 2006
Date of revision:
Handle: RePEc:bos:wpaper:wp2006-029

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Related research

Keywords: Trend-Cycle Decomposition; Unobserved Components Model; International Business Cycle; Non Gaussian Filter.;

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References

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Citations

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Cited by:
  1. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
  2. Pierre Perron & Rasmus T. Varneskov, 2011. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2011-050, Boston University - Department of Economics.
  3. Sinchan Mitra & Tara M. Sinclair, . "Output Fluctuations in the G-7: An Unobserved Components Approach," MRG Discussion Paper Series 2509, School of Economics, University of Queensland, Australia.
  4. Pierre Perron & Tatsuma Wada, 2005. "Let’s Take a Break: Trends and Cycles in US Real GDP," Boston University - Department of Economics - Working Papers Series wp2009-006, Boston University - Department of Economics, revised Feb 2009.

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