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Bayesian Analysis of Business Cycles in Japan by Extending the Markov Switching Model

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  • WATANABE, Toshiaki

Abstract

This paper analyzes business cycles in Japan by applying Markov switching (MS) models to monthly data on the coincident indicator of composite index (CI) during the period of 1985/01-2025/05 calculated by Economic and Social Research Institute (ESRI), Cabinet Office, the Government of Japan. During the latter half of the sample period, the Japanese economy experienced major shocks such as the global financial crisis in 2008, the Great East Japan Earthquake in 2011 and the COVID-19 pandemic in 2020. CI fell sharply during these periods, which make it difficult to estimate business cycle turning points using the simple MS model. In this paper, the MS model is extended by incorporating Student's t-error and stochastic volatility (SV). Since it is difficult to evaluate the likelihood once SV is introduced, a Bayesian method via Markov chain Monte Carlo (MCMC) is employed. The MS model with t-error or SV is shown to provide the estimates of the business cycle turning points close to those published by ESRI. A new method for evaluating marginal likelihood is evaluated. Bayesian model comparison based on marginal likelihood provides evidence that t-error is not needed once SV is introduced. Using the MS model with normal error and SV, structural changes in CI's mean growth rates during booms and recessions are also analyezed and two break points are found in the both mean growth rates. One is 2008/10 and the other is 2010/02, during which the mean growth rate during recession falls and that during boom rises due to the global financial crisis.

Suggested Citation

  • WATANABE, Toshiaki, 2025. "Bayesian Analysis of Business Cycles in Japan by Extending the Markov Switching Model," Discussion paper series HIAS-E-148, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    • Handle: RePEc:hit:hiasdp:hias-e-148
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    References listed on IDEAS

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    1. Ishihara, Tsunehiro & Watanabe, Toshiaki, 2015. "Econometric Analysis of Business Cycles: A Survey with the Application to the Composite Index in Japan," Economic Review, Hitotsubashi University, vol. 66(2), pages 145-168, April.
    2. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    3. Watanabe, Toshiaki, 2014. "Bayesian Analysis of Business Cycle in Japan Using Markov Switching Model with Stochastic Volatility and Fat-tail Distribution," Economic Review, Hitotsubashi University, vol. 65(2), pages 156-167, April.
    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. Jouchi Nakajima & Toshiaki Watanabe, 2011. "Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model with the Ordering of Variables for the Japanese Economy and Monetary Policy," Global COE Hi-Stat Discussion Paper Series gd11-196, Institute of Economic Research, Hitotsubashi University.
    6. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    7. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    8. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    9. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    10. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    11. Chang-Jin Kim & Charles R. Nelson, 1999. "Has The U.S. Economy Become More Stable? A Bayesian Approach Based On A Markov-Switching Model Of The Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 608-616, November.
    12. Nakajima, Jouchi & Kasuya, Munehisa & Watanabe, Toshiaki, 2011. "Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy," Journal of the Japanese and International Economies, Elsevier, vol. 25(3), pages 225-245, September.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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