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A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance?

Author

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  • Shin-ichi Fukuda

    (Faculty of Economics, Universtiy of Tokyo)

  • Takashi Onodera

    (Nihon Keizai Shimbun, Inc.)

Abstract

The purpose of this paper is to construct a new composite index of coincident economic indicators in Japan and to demonstrate their usefulness in forecasting recent short-run economic fluctuations. The method of construction is based on the single-index dynamic factor model. Our two types of indexes are highly correlated with the traditional composite index compiled by the EPA over business-cycle horizons. However, standard leading indicators, which failed to forecast the traditional composite index, make a satisfactory performance in forecasting our indexes in the 1990s. In addition, lagged values of our indexes help to improve the leading indicators' performance in forecasting the traditional composite index in the 1990s. The result is noteworthy because a large number of research institutes made serious errors in forecasting recent recessions in Japan.

Suggested Citation

  • Shin-ichi Fukuda & Takashi Onodera, 2001. "A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance? ," CIRJE F-Series CIRJE-F-101, CIRJE, Faculty of Economics, University of Tokyo.
  • Handle: RePEc:tky:fseres:2001cf101
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    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2001/2001cf101.pdf
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    References listed on IDEAS

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    1. Motonishi, Taizo & Yoshikawa, Hiroshi, 1999. "Causes of the Long Stagnation of Japan during the 1990s: Financial or Real?," Journal of the Japanese and International Economies, Elsevier, pages 181-200.
    2. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, pages 67-77.
    3. 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.
    4. 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, pages 608-616.
    5. Kanoh, Satoru, 1990. "Statistical reconsideration of the epa diffusion index," Journal of the Japanese and International Economies, Elsevier, pages 139-156.
    6. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, pages 385-400.
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    Cited by:

    1. Patrick M. Crowley & Tony Schildt, 2012. "An Analysis of the Embedded Frequency Content of Macroeconomic Indicators and their Counterparts using the Hilbert-Huang Transform," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, pages 1-31.
    2. Patrick M. Crowley & Tony Schildt, 2012. "An Analysis of the Embedded Frequency Content of Macroeconomic Indicators and their Counterparts using the Hilbert-Huang Transform," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, pages 1-31.
    3. 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.
    4. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, pages 390-402.
    5. Lombardi, Marco J. & Godbout, Claudia, 2012. "Short-term forecasting of the Japanese economy using factor models," Working Paper Series 1428, European Central Bank.
    6. repec:eee:jjieco:v:45:y:2017:i:c:p:27-36 is not listed on IDEAS
    7. Avanzini, Diego B., 2009. "Designing Composite Entrepreneurship Indicators: An Application Using Consensus PCA," WIDER Working Paper Series 041, World Institute for Development Economic Research (UNU-WIDER).
    8. Mapa, Dennis S. & Simbulan, Maria Christina, 2014. "Analyzing and Forecasting Movements of the Philippine Economy using the Dynamic Factor Models (DFM)," MPRA Paper 54478, University Library of Munich, Germany.
    9. Maximo Camacho, 2004. "Vector smooth transition regression models for US GDP and the composite index of leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 173-196.
    10. Jean-michel Sahut & Medhi Mili & Frédéric Teulon, 2012. "What is the linkage between real growth in the Euro area and global financial market conditions?," Economics Bulletin, AccessEcon, vol. 32(3), pages 2464-2480.
    11. Urasawa, Satoshi, 2014. "Real-time GDP forecasting for Japan: A dynamic factor model approach," Journal of the Japanese and International Economies, Elsevier, pages 116-134.

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