Forecasting using principal components from a large number of predictors

James H. Stock, Mark W. Watson

Research output: Contribution to journalArticlepeer-review

1063 Scopus citations

Abstract

This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large. The estimated factors are shown to be consistent, even in the presence of time variation in the factor model.

Original languageEnglish (US)
Pages (from-to)1167-1179
Number of pages13
JournalJournal of the American Statistical Association
Volume97
Issue number460
DOIs
StatePublished - Dec 1 2002

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Factor models
  • Forecasting
  • Principal components

Fingerprint Dive into the research topics of 'Forecasting using principal components from a large number of predictors'. Together they form a unique fingerprint.

Cite this