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Tests for Forecast Encompassing
Abstract
We consider the situation in which two forecasts of the same variable are available. The possibility exists of forming a combined forecast as a weighted average of the individual ones and estimating the weights that should be optimally attached to each forecast. If the entire weight should optimally be associated with one forecast, that forecast is said to encompass the other. A natural test for forecast encompassing is based on least squares regression. We find, however, that the null distribution of this test statistic is not robust to nonnormality in the forecast errors. We discuss several alternative tests that are robust.
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Cites
Bates, J. M., & Granger, C. W. J. (1969) · Journal of the Operational Research Society · doi:10.1057/jors.1969.103 · Cited by 3,201
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A Test for Superior Predictive Ability
Hansen, P. R. (2005) · Journal of Business & Economic Statistics · doi:10.1198/073500105000000063 · Cited by 1,230
Investor Sentiment Aligned: A Powerful Predictor of Stock Returns
Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015) · Review of Financial Studies · doi:10.1093/rfs/hhu080 · Cited by 1,123
Forecasting the equity risk premium: the role of technical indicators
Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014) · Management Science · doi:10.1287/mnsc.2013.1838 · Cited by 1,003
Hendry, D. F., & Clements, M. P. (2004) · The Econometrics Journal · doi:10.1111/j.1368-423x.2004.00119.x · Cited by 466
Metadata
- Authors
- Harvey, D. I., Leybourne, S. J., & Newbold, P.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 1998-04
- Volume
- 16
- Issue
- 2
- Pages
- 254-259
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W1967883775
- Cited by
- 775
- References
- 1
- FWCI
- 11.95
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
