Macroeconomic forecasting using diffusion indexes

James H. Stock, Mark W. Watson

Research output: Contribution to journalArticlepeer-review

1314 Scopus citations


This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, and 24-month-ahead forecasts for eight monthly U.S. macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998. During this sample period these new forecasts outperformed univariate autoregressions, small vector autoregressions, and leading indicator models.

Original languageEnglish (US)
Pages (from-to)147-162
Number of pages16
JournalJournal of Business and Economic Statistics
Issue number2
StatePublished - 2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


  • Factor model
  • Forecasting
  • Principal components


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