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Macroeconomic Forecasting Using Diffusion Indexes
Abstract
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-monthahead 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.
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Citations
Cited by
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
Pesaran, M. H. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00692.x · Cited by 5,078
Tests of Conditional Predictive Ability
Giacomini, R., & White, H. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00718.x · Cited by 1,646
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
Self-citationPrior Selection for Vector Autoregressions
Giannone, D., Lenza, M., & Primiceri, G. E. (2015) · Review of Economics and Statistics · doi:10.1162/rest_a_00483 · Cited by 710
Has the Business Cycle Changed and Why?
Stock, J., & Watson, M. (2002) · National Bureau of Economic Research · doi:10.3386/w9127 · Cited by 560
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Metadata
- Authors
- Stock, J. H., & Watson, M. W.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2002-04
- Volume
- 20
- Issue
- 2
- Pages
- 147-162
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2038601479
- Cited by
- 2,547
- References
- 0
- FWCI
- 60.09
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
