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Comparing Predictive Accuracy
Abstract
We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss of function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample tests are proposed, evaluated, and illustrated.
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Cited by
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Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.09.003 · Cited by 1,172
RetractedWang, L., Zeng, Y., & Chen, T. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.018 · Cited by 631
Bond Risk Premiums with Machine Learning
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Shahi, T. B., Shrestha, A., Neupane, A., & Guo, W. (2020) · Mathematics · doi:10.3390/math8091441 · Cited by 183
Do Machine Learning Models Need to Be Sector Experts?
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Metadata
- Authors
- Diebold, F. X., & Mariano, R. S.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2002-01
- Volume
- 20
- Issue
- 1
- Pages
- 134-144
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W4292671038
- Cited by
- 1,728
- References
- 0
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- closed
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