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Cycle Extraction: A Comparison of the Phase-Average Trend Method, the Hodrick-Prescott and Christiano-Fitzgerald Filters

Author

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  • Ronny Nilsson

    (OECD)

  • Gyorgy Gyomai

    (OECD)

Abstract
This paper reports on revision properties of different de-trending and smoothing methods (cycle estimation methods), including PAT with MCD smoothing, a double Hodrick-Prescott (HP) filter and the Christiano-Fitzgerald (CF) filter. The different cycle estimation methods are rated on their revision performance in a simulated real time experiment. Our goal is to find a robust method that gives early turning point signals and steady turning point signals. The revision performance of the methods has been evaluated according to bias, overall revision size and signal stability measures. In a second phase, we investigate if revision performance is improved using stabilizing forecasts or by changing the cycle estimation window from the baseline 6 and 96 months (i.e. filtering out high frequency noise with a cycle length shorter than 6 months and removing trend components with cycle length longer than 96 months) to 12 and 120 months. The results show that, for all tested time series, the PAT de-trending method is outperformed by both the HP or CF filter. In addition, the results indicate that the HP filter outperforms the CF filter in turning point signal stability but has a weaker performance in absolute numerical precision. Short horizon stabilizing forecasts tend to improve revision characteristics of both methods and the changed filter window also delivers more robust turning point estimates. Ce document présente l’impact des révisions dû à différentes méthodes de lissage et de correction de la tendance (méthodes d'estimation du cycle), comme la méthode PAT avec lissage en utilisant le mois de dominance cyclique (MCD), le double filtre de Hodrick-Prescott (HP) et le filtre Christiano-Fitzgerald (CF). Les différentes méthodes d'estimation du cycle sont évaluées sur leur performance de révision faite à partir d’une simulation en temps réel. Notre objectif est de trouver une méthode robuste qui donne des signaux de point de retournement tôt et stable á la fois. La performance de révisions de ces méthodes a été évaluée en fonction du biais, de la grandeur de la révision et de la stabilité du signal. Nous examinerons ensuite si la performance de la révision peut être améliorée en utilisant des prévisions de stabilisation ou en changeant la fenêtre d'estimation du cycle de base de 6 et 96 mois à une fenêtre de 12 et 120 mois. La fenêtre d’estimation de base correspond à un filtre pour éliminer le bruit (hautes fréquences) avec une longueur de cycle de moins de 6 mois et supprimer la tendance avec une longueur de cycle supérieure à 96 mois. Les résultats montrent que, pour toutes les séries testées, la méthode PAT est moins performante que les deux filtres HP ou CF. En outre, les résultats indiquent que le filtre HP surpasse le filtre CF du point de vue de la stabilité du signal du point de retournement mais sa performance est plus faible quant à la précision numérique absolue. Des prévisions à court terme ont la tendance à améliorer les caractéristiques des révisions des deux méthodes et la modification de la fenêtre de base offre aussi des estimations plus robustes des points de retournement.

Suggested Citation

  • Ronny Nilsson & Gyorgy Gyomai, 2011. "Cycle Extraction: A Comparison of the Phase-Average Trend Method, the Hodrick-Prescott and Christiano-Fitzgerald Filters," OECD Statistics Working Papers 2011/4, OECD Publishing.
    • Handle: RePEc:oec:stdaaa:2011/4-en
    DOI: 10.1787/5kg9srt7f8g0-en
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    Cited by:

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    5. Saša Obradović & Vladimir Mihajlović, 2013. "Synchronization of Business Cycles in the Selected European Countries," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 60(6), pages 759-773, December.
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    7. Cavallaro, Eleonora & Villani, Ilaria, 2022. "Beyond financial deepening: Rethinking the finance-growth relationship in an uneven world," Economic Modelling, Elsevier, vol. 116(C).
    8. Agnieszka Gehringer & Jörg König, 2021. "Recent Patterns of Economic Alignment in the European (Monetary) Union," JRFM, MDPI, vol. 14(8), pages 1-23, August.
    9. Albers, Thilo Nils Hendrik, 2018. "The prelude and global impact of the Great Depression: Evidence from a new macroeconomic dataset," Explorations in Economic History, Elsevier, vol. 70(C), pages 150-163.
    10. Mustapha Olalekan Ojo & Luís Aguiar‐Conraria & Maria Joana Soares, 2024. "The performance of OECD's composite leading indicator," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2265-2277, April.
    11. Zafar Hayat & Muhammad Nadim Hanif, 2020. "Assessing the Role of Money versus Interest Rate in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 59(1), pages 101-114.
    12. Antonio José Orozco-Gallo & Pavel Vidal-Alejandro & Johana Sanabria-Domínguez & Jaime Andrés Collazos-Rodríguez, 2021. "Indicador coincidente de actividad económica en la recesión pandémica: el caso del Caribe colombiano," Documentos de Trabajo Sobre Economía Regional y Urbana 19285, Banco de la República, Economía Regional.
    13. Anastasiou, Dimitrios, 2017. "Is ex-post credit risk affected by the cycles? The case of Italian banks," Research in International Business and Finance, Elsevier, vol. 42(C), pages 242-248.
    14. Anastasiou, Dimitrios, 2017. "The Interplay between Ex-post Credit Risk and the Cycles: Evidence from the Italian banks," MPRA Paper 79470, University Library of Munich, Germany.
    15. Kollar, Miroslav & Schmieder, Christian, 2019. "Macro-based asset allocation: An empirical analysis," EIB Working Papers 2019/11, European Investment Bank (EIB).
    16. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    17. repec:agr:journl:v:10(587):y:2013:i:10(587):p:97-110 is not listed on IDEAS
    18. Brunhart, Andreas & Kellermann, Kersten & Schlag, Carsten-Henning, 2012. "Drei Phasen des Potentialwachstums in Liechtenstein," KOFL Working Papers 11, Konjunkturforschungsstelle Liechtenstein (KOFL), Vaduz.
    19. Li, Xiao-Lin & Yan, Jing & Wei, Xiaohui, 2021. "Dynamic connectedness among monetary policy cycle, financial cycle and business cycle in China," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 640-652.
    20. Inna S. Lola, 2017. "The Statistical Measurement of Business Conditions for Small Entrepreneurs," HSE Working papers WP BRP 71/STI/2017, National Research University Higher School of Economics.
    21. Dilip Nachane & Aditi Chaubal, 2022. "A Comparative Evaluation of Some DSP Filters vis-à-vis Commonly Used Economic Filters," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 161-190, September.

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