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Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan

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  • Mototsugu Shintani

    ()
    (Department of Economics, Vanderbilt University)

Abstract

This paper extends the diffusion index (DI) forecast approach of Stock and Watson (1998, 2002) to the case of possibly nonlinear dynamic factor models. When the number of series is large, a two-step procedure based on the principal components method is useful since it allows the wide variety of the nonlinearity in the factors. The factors extracted from a large Japanese data suggest some evidence of nonlinear structure. Furthermore, both the linear and nonlinear DI forecasts in Japan outperform traditional time series forecasts, while the linear DI forecast, in most cases, performs as well as the nonlinear DI forecast.

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File URL: http://www.accessecon.com/pubs/VUECON/vu03-w22R.pdf
File Function: Revised version, 2004
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Bibliographic Info

Paper provided by Vanderbilt University Department of Economics in its series Vanderbilt University Department of Economics Working Papers with number 0322.

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Date of creation: Oct 2003
Date of revision: Apr 2004
Handle: RePEc:van:wpaper:0322

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Web page: http://www.vanderbilt.edu/econ/wparchive/index.html

Related research

Keywords: Diffusion Index; Dynamic Factor Model; Nonlinearity; Prediction;

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References

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Citations

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Cited by:
  1. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research.
  2. Boriss Siliverstovs & Kinstantin Kholodilim, 2009. "On selection of components for a diffusion index model: it's not the size, it's how you use it," Applied Economics Letters, Taylor & Francis Journals, vol. 16(12), pages 1249-1254.
  3. Todd E. Clark & Michael W. McCracken, 2007. "Combining forecasts from nested models," Finance and Economics Discussion Series 2007-43, Board of Governors of the Federal Reserve System (U.S.).
  4. Mototsugu Shintani, 2004. "A Dynamic Factor Approach to Nonlinear Stability Analysis," Econometric Society 2004 Far Eastern Meetings 538, Econometric Society.
  5. Shibamoto, Masahiko, 2008. "The estimation of monetary policy reaction function in a data-rich environment: The case of Japan," Japan and the World Economy, Elsevier, vol. 20(4), pages 497-520, December.
  6. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
  7. Alessandro Giovannelli, 2012. "Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies," CEIS Research Paper 255, Tor Vergata University, CEIS, revised 08 Nov 2012.
  8. Heij, C., 2007. "Improved forecasting with leading indicators: the principal covariate index," Econometric Institute Research Papers EI 2007-23, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  9. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
  10. Godbout, Claudia & Lombardi, Marco J., 2012. "Short-term forecasting of the Japanese economy using factor models," Working Paper Series 1428, European Central Bank.
  11. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.

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