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Diffusion Indexes

  • James H. Stock
  • Mark W. Watson

This paper considers forecasting a single time series using more predictors than there are time series observations. The approach is to construct a relatively few indexes, akin to diffusion indexes, which are weighted averages of the predictors, using an approximate dynamic factor model. Estimation is discussed for balanced and unbalanced panels. The estimated dynamic factors are (uniformly) consistent, even in the presence of time varying parameters and/or data contamination, and forecasts based on the estimated factors are efficient. In an application to forecasting U.S. inflation and industrial production using 224 monthly time series, these forecasts outperform various state-of-the-art benchmark models.

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File URL: http://www.nber.org/papers/w6702.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 6702.

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Date of creation: Aug 1998
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Publication status: published as Stock, James H. and Mark W. Watson. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business and Economic Statistics, 2002, v20(2,Apr), 147-162.
Handle: RePEc:nbr:nberwo:6702
Note: EFG
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  1. Bekker, Paul & Dobbelstein, Pascal & Wansbeek, Tom, 1996. "The APT Model as Reduced-Rank Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 199-202, April.
  2. Danny Quah & Thomas J. Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 285-310 National Bureau of Economic Research, Inc.
  3. Forni, Mario & Reichlin, Lucrezia, 1995. "Dynamic Common Factors in Large Cross-Sections," CEPR Discussion Papers 1285, C.E.P.R. Discussion Papers.
  4. John Geweke & Guofu Zhou, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," CEMA Working Papers 276, China Economics and Management Academy, Central University of Finance and Economics.
  5. Chamberlain, Gary & Rothschild, Michael, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Scholarly Articles 3230355, Harvard University Department of Economics.
  6. Gordon, Robert J, 1996. "The Time-varying NAIRU and its Implications for Economic Policy," CEPR Discussion Papers 1492, C.E.P.R. Discussion Papers.
  7. Forni, Mario & Reichlin, Lucrezia, 1995. "Let's Get Real: A Dynamic Factor Analytical Approach to Disaggregated Business Cycle," CEPR Discussion Papers 1244, C.E.P.R. Discussion Papers.
  8. Jeffrey C. Fuhrer, 1995. "The Phillips curve is alive and well," New England Economic Review, Federal Reserve Bank of Boston, issue Mar, pages 41-56.
  9. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-87, April.
  10. repec:cup:cbooks:9780521599214 is not listed on IDEAS
  11. Douglas Staiger & James H. Stock & Mark W. Watson, 1997. "The NAIRU, Unemployment and Monetary Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 33-49, Winter.
  12. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
  13. Forni, Mario & Reichlin, Lucrezia, 1997. "National Policies and Local Economies: Europe and the United States," CEPR Discussion Papers 1632, C.E.P.R. Discussion Papers.
  14. Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-91, September.
  15. Schneeweiss, H. & Mathes, H., 1995. "Factor Analysis and Principal Components," Journal of Multivariate Analysis, Elsevier, vol. 55(1), pages 105-124, October.
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