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A Complete Model-Based Interpretation of the Hodrick-Prescott Filter: Spuriousness Reconsidered

Author

Listed:
  • Regina Kaiser

    () (Universidad Carlos III de Madrid)

  • Agustín Maravall

    () (Banco de España)

Abstract

The Hodrick-Prescott filter applied to seasonally adjusted series has become a paradigm for business-cycle estimation at many economic agencies and institutions. We show that the filter can be obtained from MMSE estimation of the components in an unobserved component model, where the original series is decomposed into (long-term) trend, cyclical, seasonal, and (highlytransitory) irregular components. The component models are sensible and combine desirable “ad-hoc” features with series-dependent features that guarantee consistency with the data. The model-based framework provides improvements having to do with the precision of end-point estimation and the stability of the cyclical signal.

Suggested Citation

  • Regina Kaiser & Agustín Maravall, 2002. "A Complete Model-Based Interpretation of the Hodrick-Prescott Filter: Spuriousness Reconsidered," Working Papers 0208, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:0208
    as

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    File Function: First version, March 2002
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    References listed on IDEAS

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    2. Víctor Gómez & Pilar Bengoechea, 2000. "The quarterly national accounts trend-cycle filter versus model-based filters," Spanish Economic Review, Springer;Spanish Economic Association, vol. 2(1), pages 29-48.
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    5. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
    6. Maravall, Agustin, 1985. "On Structural Time Series Models and the Characterization of Components," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 350-355, October.
    7. 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.
    8. King, Robert G. & Rebelo, Sergio T., 1993. "Low frequency filtering and real business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 207-231.
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    11. Claude Giorno & Pete Richardson & Deborah Roseveare & Paul van den Noord, 1995. "Estimating Potential Output, Output Gaps and Structural Budget Balances," OECD Economics Department Working Papers 152, OECD Publishing.
    12. Pierce, David A., 1980. "Data revisions with moving average seasonal adjustment procedures," Journal of Econometrics, Elsevier, vol. 14(1), pages 95-114, September.
    13. Gomez, Victor, 1999. "Three Equivalent Methods for Filtering Finite Nonstationary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 109-116, January.
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    Citations

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    Cited by:

    1. Martin Fukac & Adrian Pagan, 2009. "Structural Macro-Econometric Modelling in a Policy Environment," NCER Working Paper Series 50, National Centre for Econometric Research.
    2. Artis, Michael & Okubo, Toshihiro, 2009. "Globalization and business cycle transmission," The North American Journal of Economics and Finance, Elsevier, vol. 20(2), pages 91-99, August.
    3. Michael Artis & Toshihiro Okubo, 2008. "The Intranational Business Cycle: Evidence from Japan," Centre for Growth and Business Cycle Research Discussion Paper Series 101, Economics, The Univeristy of Manchester.
    4. Martin Fukac & Adrian Pagan, 2010. "Limited information estimation and evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 55-70.
    5. Michael J. Artis & Toshihiro Okubo, 2012. "Business Cycle, Currency And Trade, Revisited," Pacific Economic Review, Wiley Blackwell, vol. 17(1), pages 160-180, February.

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