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Analysis of the U.S. Business Cycle with a Vector-Markov-Switching Model

Author

Listed:
  • Mr. Zenon Kontolemis

Abstract

This paper identifies turning points for the U.S. business cycle using different time series. The model, a multivariate Markov-Swiching model, assumes that each series is characterized by a mixture of two normal distributions (a high and low mean) with switching determined by a common Markov process. The procedure is applied to the series that make up the composite U.S. coincident indicator to obtain business cycle turning points. The business cycle chronology is closer to the NBER reference cycle than the turning points obtained from the individual series using a univariate model. The model is also used to forecast the series, with encouraging results.

Suggested Citation

  • Mr. Zenon Kontolemis, 1999. "Analysis of the U.S. Business Cycle with a Vector-Markov-Switching Model," IMF Working Papers 1999/107, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:1999/107
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    Cited by:

    1. Vasyl Golosnoy & Jens Hogrefe, 2013. "Signaling NBER turning points: a sequential approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 438-448, February.
    2. Robert A Buckle & David Haugh & Peter Thomson, 2002. "Growth and volatility regime switching models for New Zealand GDP data," Treasury Working Paper Series 02/08, New Zealand Treasury.
    3. Stéphane Goutte & Benteng Zou, 2012. "Continuous time regime switching model applied to foreign exchange rate," Working Papers hal-00643900, HAL.
    4. Nikolaos Papanikolaou, 2020. "Markov-Switching Model of Family Income Quintile Shares," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 48(2), pages 207-222, June.
    5. Moolman, Elna, 2004. "A Markov switching regime model of the South African business cycle," Economic Modelling, Elsevier, vol. 21(4), pages 631-646, July.
    6. Stéphane GOUTTE & Benteng Zou, 2011. "Foreign exchange rates under Markov Regime switching model," DEM Discussion Paper Series 11-16, Department of Economics at the University of Luxembourg.
    7. repec:rza:wpaper:267 is not listed on IDEAS
    8. Nan Li & Simon S. Kwok, 2021. "Jointly determining the state dimension and lag order for Markov‐switching vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 471-491, July.
    9. Giampaoli, Noemi & Cucculelli, Marco & Sullo, Valerio, 2024. "Business and financial cycle across regimes: Does financial stress matter?," International Review of Economics & Finance, Elsevier, vol. 96(PB).
    10. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
    11. Maddalena Cavicchioli, 2025. "Forecasting Markov switching vector autoregressions: Evidence from simulation and application," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 136-152, January.
    12. David Bock & Eva Andersson & Marianne Frisén, 2005. "Statistical surveillance of cyclical processes with application to turns in business cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 465-490.
    13. E. Andersson & D. Bock & M. Frisen, 2006. "Some statistical aspects of methods for detection of turning points in business cycles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(3), pages 257-278.
    14. Adél Bosch & Franz Ruch, 2013. "An Alternative Business Cycle Dating Procedure for South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 81(4), pages 491-516, December.

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