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Diffusion Indexes

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  • James H. Stock
  • Mark W. Watson

Abstract

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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Bibliographic Info

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

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  7. Robert J. Gordon, 1997. "The Time-Varying NAIRU and Its Implications for Economic Policy," Journal of Economic Perspectives, American Economic Association, American Economic Association, vol. 11(1), pages 11-32, Winter.
  8. Forni, Mario & Reichlin, Lucrezia, 1995. "Dynamic Common Factors in Large Cross-Sections," CEPR Discussion Papers, C.E.P.R. Discussion Papers 1285, C.E.P.R. Discussion Papers.
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  11. Snower,Dennis J. & Dehesa,Guillermo de la (ed.), 1997. "Unemployment Policy," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521599214.
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  14. Douglas Staiger & James H. Stock & Mark W. Watson, 1997. "The NAIRU, Unemployment and Monetary Policy," Journal of Economic Perspectives, American Economic Association, American Economic Association, vol. 11(1), pages 33-49, Winter.
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