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Business Cycle Dating after the Great Moderation: A Consistent Two – Stage Maximum Likelihood Method

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  • Gilbert Mbara

    (University of Warsaw)

Abstract

The two-state Markov switching model of dating recessions breaks down when confronted with the low volatility macroeconomic time series of the post 1984 Great Moderation era. In this paper, I present a new model specification and a two--stage maximum likelihood estimation procedure that can account for the lower volatility and persistence of macroeconomic times series after 1984, while preserving the economically interpretable two--state boom--bust business cycle switching. I first demonstrate the poor finite sample properties (bias and inconsistency) of standard models then suggest a new specification and estimation procedure that resolves these issues. The suggested likelihood profiling method achieves consistent estimation of unconditional variances across volatility regimes while resolving the poor performance of models with multiple lag structures in dating business cycle turning points. Based on this novel model specification and estimation, I find that the nature of US business cycles has changed: economic growth has permanently become lower while booms last longer than before. The length and size of recessions however remain unchanged.

Suggested Citation

  • Gilbert Mbara, 2017. "Business Cycle Dating after the Great Moderation: A Consistent Two – Stage Maximum Likelihood Method," Working Papers 2017-13, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2017-13
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/3603/
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    References listed on IDEAS

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    1. James Bullard & Aarti Singh, 2012. "Learning And The Great Moderation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(2), pages 375-397, May.
    2. Boldin Michael D., 1996. "A Check on the Robustness of Hamilton's Markov Switching Model Approach to the Economic Analysis of the Business Cycle," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(1), pages 1-14, April.
    3. Alejandro Justiniano & Giorgio E. Primiceri, 2008. "The Time-Varying Volatility of Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 98(3), pages 604-641, June.
    4. Hamilton, J.D., 2016. "Macroeconomic Regimes and Regime Shifts," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 163-201, Elsevier.
    5. Leena Kalliovirta & Mika Meitz & Pentti Saikkonen, 2015. "A Gaussian Mixture Autoregressive Model for Univariate Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 247-266, March.
    6. Chang, Yoosoon & Choi, Yongok & Park, Joon Y., 2017. "A new approach to model regime switching," Journal of Econometrics, Elsevier, vol. 196(1), pages 127-143.
    7. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    8. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2016. "Real-time nowcasting of nominal GDP with structural breaks," Journal of Econometrics, Elsevier, vol. 191(2), pages 312-324.
    9. Zacharias Psaradakis & Nicola Spagnolo, 2006. "Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 753-766, September.
    10. Chen, Shyh-Wei, 2006. "Simultaneously modeling the volatility of the growth rate of real GDP and determining business cycle turning points: Evidence from the U.S., Canada and the UK," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 71(2), pages 87-102.
    11. Jushan Bai & Peng Wang, 2011. "Conditional Markov chain and its application in economic time series analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 715-734, August.
    12. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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    More about this item

    Keywords

    Regime Switching; Hidden Markov Models; Great Moderation; Maximum Likelihood Estimation;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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