Measuring Business Cycle Turning Points in Japan with a Dynamic Markov Switching Factor Model
AbstractIn 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.
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Bibliographic InfoArticle provided by Institute for Monetary and Economic Studies, Bank of Japan in its journal Monetary and Economic Studies.
Volume (Year): 21 (2003)
Issue (Month): 1 (February)
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- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
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- Ippei Fujiwara, 2004.
"Evaluating Monetary Policy When Nominal Interest Rates Are Almost Zero,"
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- 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.
- Konstantin A. Kholodilin, 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 225(6), pages 653-674, November.
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