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Analysis of the US Business Cycle with a Vector-Markov-Switching Model

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  • Kontolemis, Zenon G

Abstract

This paper identifies turning points for the US "business cycle" using information from different time series. The model, a multivariate Markov-switching model, assumes that each series is characterized by a mixture of two normal distributions (a high and low mean) with the switching from one to the other determined by a common Markov process. The procedure is applied to the series composing the composite coincident indicator in the USA 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 some encouraging results. Copyright © 2001 by John Wiley & Sons, Ltd.

Suggested Citation

  • Kontolemis, Zenon G, 2001. "Analysis of the US Business Cycle with a Vector-Markov-Switching Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 47-61, January.
    • Handle: RePEc:jof:jforec:v:20:y:2001:i:1:p:47-61
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    Cited by:

    1. Moolman, Elna, 2004. "A Markov switching regime model of the South African business cycle," Economic Modelling, Elsevier, vol. 21(4), pages 631-646, July.
    2. 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.
    3. repec:rza:wpaper:267 is not listed on IDEAS
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. Stéphane Goutte & Benteng Zou, 2012. "Continuous time regime switching model applied to foreign exchange rate," Working Papers hal-00643900, HAL.
    12. 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.
    13. 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.
    14. 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.

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