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Measuring Business Cycle Turning Points in Japan with a Dynamic Markov Switching Factor Model

Author

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  • Watanabe, Toshiaki

    (Tokyo Metropolitan U)

Abstract

In the dynamic factor model, a single unobserved factor common to some macroeconomic variables is defined as a composite index to measure business cycles. This model has recently been developed by combining with the regime switching model so that the mean growth of the index may shift depending on whether the economy is in a boom regime or in a recession regime. An advantage of this dynamic Markov switching factor model is that estimating the model by a Bayesian method produces the posterior probabilities that the economy is in the recession regime, which can be used to date the business cycle turning points. This article estimates the dynamic Markov switching factor model using some macroeconomic variables in Japan. The model comparison using the Bayes factor does not provide strong evidence that the mean growth of the index shifts, but the dynamic Markov switching factor model is found to produce the estimates of turning points close to the reference dates of the Economic and Social Research Institute in the Cabinet Office, unless only weakly correlated variables are used.

Suggested Citation

  • Watanabe, Toshiaki, 2003. "Measuring Business Cycle Turning Points in Japan with a Dynamic Markov Switching Factor Model," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 21(1), pages 35-68, February.
  • Handle: RePEc:ime:imemes:v:21:y:2003:i:1:p:35-68
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    Citations

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    Cited by:

    1. Fujiwara, Ippei, 2006. "Evaluating monetary policy when nominal interest rates are almost zero," Journal of the Japanese and International Economies, Elsevier, vol. 20(3), pages 434-453, September.
    2. Shintani, Mototsugu, 2005. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 517-538, June.
    3. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    4. van Os, Bram & van Dijk, Dick, 2024. "Accelerating peak dating in a dynamic factor Markov-switching model," International Journal of Forecasting, Elsevier, vol. 40(1), pages 313-323.
    5. Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
    6. Konstantin A. Kholodilin, 2005. "Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching," Discussion Papers of DIW Berlin 494, DIW Berlin, German Institute for Economic Research.
    7. Howard J. Wall, 2007. "Regional business cycle phases in Japan," Review, Federal Reserve Bank of St. Louis, vol. 89(Jan), pages 61-80.
    8. Chen, Shyh-Wei, 2007. "Measuring business cycle turning points in Japan with the Markov Switching Panel model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 76(4), pages 263-270.
    9. Yoshihiro Ohtsuka, 2018. "Large Shocks and the Business Cycle: The Effect of Outlier Adjustments," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 143-178, April.
    10. Koki Kyo & Hideo Noda & Genshiro Kitagawa, 2022. "Co-movement of Cyclical Components Approach to Construct a Coincident Index of Business Cycles," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 101-127, March.

    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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