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Tracking the US Business Cycle With a Singular Spectrum Analysis

Author

Listed:
  • Miguel de Carvalho
  • Paulo C. Rodrigues
  • António Rua

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

  • Miguel de Carvalho & Paulo C. Rodrigues & António Rua, 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. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    2. Yogo, Motohiro, 2008. "Measuring business cycles: A wavelet analysis of economic time series," Economics Letters, Elsevier, vol. 100(2), pages 208-212, August.
    3. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    4. Andrew C. Harvey & Thomas M. Trimbur, 2003. "General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 244-255, May.
    5. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    6. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    7. Stock, James H. & Watson, Mark W., 1999. "Business cycle fluctuations in us macroeconomic time series," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 1, pages 3-64 Elsevier.
    8. Valle e Azevedo, Joao & Koopman, Siem Jan & Rua, Antonio, 2006. "Tracking the Business Cycle of the Euro Area: A Multivariate Model-Based Bandpass Filter," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 278-290, July.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0003-4 is not listed on IDEAS
    6. repec:eee:reensy:v:114:y:2013:i:c:p:126-136 is not listed on IDEAS
    7. 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.

    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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