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Forecasting Using Principal Components From a Large Number of Predictors
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.
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Cited by
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Fan, J., Han, F., & Liu, H. (2014) · National Science Review · doi:10.1093/nsr/nwt032 · Cited by 1,482
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Kelly, B. T., Pruitt, S., & Su, Y. (2019) · Journal of Financial Economics · doi:10.1016/j.jfineco.2019.05.001 · Cited by 809
A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series
Marcellino, M., Stock, J. H., & Watson, M. W. (2006) · Journal of Econometrics · doi:10.1016/j.jeconom.2005.07.020 · Cited by 789
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Metadata
- Authors
- Stock, J. H., & Watson, M. W.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2002-12
- Volume
- 97
- Issue
- 460
- Pages
- 1167-1179
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2079563517
- Cited by
- 2,985
- References
- 0
- FWCI
- 30.32
- Type
- journal article
- Language
- en
- Open access
- closed
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