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Selecting a Boosted HP Filter for Growth Cycle Analysis Based on Maximising Sharpness

Author

Listed:
  • Viv B. Hall

    (Victoria University of Wellington
    ANU)

  • Peter Thomson

    (Victoria University of Wellington
    Statistics Research Associates Ltd)

Abstract

The boosted HP (bHP) trend filter iterates the standard HP filter until the resulting trend deviation is free of any stochastic trend with the latter determined by suitable stopping rules. Here the performance and properties of the bHP trend filter for growth cycle analysis are considered based on the time-invariant moving-average representation of the bHP filter in the body of the series. We propose alternative trend selection criteria based on a constant cut-off frequency and maximising sharpness. We find there is a strong case for selecting a bHP trend filter with an 8-year rather than a 10-year cut-off period, and that using a bHP filter with 2 iterations (twicing) or a sharpened HP filter is preferable to using the standard HP filter.

Suggested Citation

  • Viv B. Hall & Peter Thomson, 2024. "Selecting a Boosted HP Filter for Growth Cycle Analysis Based on Maximising Sharpness," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(2), pages 193-217, July.
  • Handle: RePEc:spr:jbuscr:v:20:y:2024:i:2:d:10.1007_s41549-024-00093-9
    DOI: 10.1007/s41549-024-00093-9
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    References listed on IDEAS

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    1. Christian J. Murray, 2003. "Cyclical Properties of Baxter-King Filtered Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 472-476, May.
    2. Paul Beaudry & Dana Galizia & Franck Portier, 2020. "Putting the Cycle Back into Business Cycle Analysis," American Economic Review, American Economic Association, vol. 110(1), pages 1-47, January.
    3. Regina Kaiser & Agustín Maravall, 1999. "Estimation of the business cycle: A modified Hodrick-Prescott filter," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 175-206.
    4. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    5. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    6. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    7. Peter C. B. Phillips & Sainan Jin, 2021. "Business Cycles, Trend Elimination, And The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 469-520, May.
    8. Frederick R. Macaulay, 1931. "The Smoothing of Economic Time Series, Curve Fitting and Graduation," NBER Chapters, in: The Smoothing of Time Series, pages 31-42, National Bureau of Economic Research, Inc.
    9. Frederick R. Macaulay, 1931. "The Smoothing of Time Series," NBER Books, National Bureau of Economic Research, Inc, number maca31-1, March.
    10. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    11. 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.
    12. Viv B. Hall & Peter Thomson, 2022. "A Boosted HP Filter for Business Cycle Analysis: Evidence from New Zealand's Small Open Economy," CAMA Working Papers 2022-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    14. Frederick R. Macaulay, 1931. "Appendices to "The Smoothing of Time Series"," NBER Chapters, in: The Smoothing of Time Series, pages 118-169, National Bureau of Economic Research, Inc.
    15. Frederick R. Macaulay, 1931. "Introduction to "The Smoothing of Time Series"," NBER Chapters, in: The Smoothing of Time Series, pages 17-30, National Bureau of Economic Research, Inc.
    16. 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.
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    More about this item

    Keywords

    Boosted HP filter; Hodrick–Prescott filter; Growth cycles; Transfer function; Sharpness; Cut-off frequency;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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