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Tracking the US Business Cycle With a Singular Spectrum Analysis

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  • António Rua
  • Miguel de Carvalho

Abstract

The monitoring of economic developments is an exercise of considerable importance forpolicy makers, namely, central banks and fiscal authorities as well as for other economic agents such as financial intermediaries, firms and households. However, the assessment of the business cycle is not an easy endeavor as the cyclical component is not an observable variable. In this paper we resort to singular spectrum analysis in order to disentangle the US GDP into several underlying components of interest. The business cycle indicator yielded through this method is shown to bear a resemblance with band-pass filtered output. As the end-of-sample behavior is typically a thorny issue in business cycle assessment, a real-time estimation exercise is here conducted to assess the reliability of the several filters. The obtained results suggest that the business cycle indicator proposed herein possesses a better revision performance than other filters commonly applied in the literature.

Suggested Citation

  • António Rua & Miguel de Carvalho, 2010. "Tracking the US Business Cycle With a Singular Spectrum Analysis," Working Papers w201009, Banco de Portugal, Economics and Research Department.
    • Handle: RePEc:ptu:wpaper:w201009
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    References listed on IDEAS

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    1. Miguel de Carvalho & Gabriel Martos, 2022. "Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 167-180, January.
    2. Svatopluk KAPOUNEK & Jitka POMĚNKOVÁ, 2013. "The endogeneity of optimum currency area criteria in the context of financial crisis: Evidence from the time-frequency domain analysis," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(9), pages 389-395.
    3. Andreas Groth & Michael Ghil & Stéphane Hallegatte & Patrice Dumas, 2015. "The role of oscillatory modes in US business cycles," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(1), pages 63-81.
    4. Bógalo, Juan & Poncela, Pilar & Senra, Eva, 2017. "Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA," MPRA Paper 76023, University Library of Munich, Germany.
    5. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    6. Lisa Sella & Gianna Vivaldo & Andreas Groth & Michael Ghil, 2016. "Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 25-48, September.
    7. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    8. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    9. Lisa Sella & Gianna Vivaldo & Andreas Groth & Michael Ghil, 2016. "Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries," Post-Print hal-01701122, HAL.
    10. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    11. Hua, Jia-Chen & Roy, Sukesh & McCauley, Joseph L. & Gunaratne, Gemunu H., 2016. "Using dynamic mode decomposition to extract cyclic behavior in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 172-180.
    12. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    13. Roman Marsalek & Jitka Pomenkova & Svatopluk Kapounek, 2014. "A Wavelet-Based Approach to Filter Out Symmetric Macroeconomic Shocks," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 477-488, December.
    14. Rocco S, Claudio M., 2013. "Singular spectrum analysis and forecasting of failure time series," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 126-136.
    15. Hicham M. Hachem, 2017. "How Moderate was the Great Moderation and how Destabilizing is Secular Stagnation? Fiscal and monetary policy implications based on åvidence from US macro data," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 226-236, June.
    16. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.
    17. Coussin, Maximilien, 2022. "Singular spectrum analysis for real-time financial cycles measurement," Journal of International Money and Finance, Elsevier, vol. 120(C).

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    More about this item

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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