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

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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 indicatorsf 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.

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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-101.

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Length: 34 pages
Date of creation: Jan 2001
Date of revision:
Handle: RePEc:tky:fseres:2001cf101

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  1. Francis X. Diebold & Glenn D. Rudebusch, 1994. "Measuring Business Cycles: A Modern Perspective," NBER Working Papers 4643, National Bureau of Economic Research, Inc.
  2. Taizo Motonishi & Hirshi Yoshikawa, 1999. "Causes of the Long Stagnation of Japan During the 1990s: Financial or Real?," NBER Working Papers 7351, National Bureau of Economic Research, Inc.
  3. 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, vol. 23(3), pages 385-400, December.
  4. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
  5. Kanoh, Satoru, 1990. "Statistical reconsideration of the epa diffusion index," Journal of the Japanese and International Economies, Elsevier, vol. 4(2), pages 139-156, June.
  6. 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.
  7. 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 357-84, March.
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Cited by:
  1. Claudia Godbout & Marco J. Lombardi, 2012. "Short-Term Forecasting of the Japanese Economy Using Factor Models," Working Papers 12-7, Bank of Canada.
  2. Jean-Michel Sahut & Medhi Mili & Frédéric Teulon, 2014. "What is the linkage between real growth in the Euro area and global financial market conditions ?," Working Papers 2014-324, Department of Research, Ipag Business School.
  3. Mototsugu Shintani, 2010. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Levine's Working Paper Archive 506439000000000168, David K. Levine.
  4. Avanzini, Diego B., 2009. "Designing Composite Entrepreneurship Indicators: An Application Using Consensus PCA," Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).
  5. 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.

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