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Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests
Abstract
The Diebold–Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Much of the large ensuing literature, however, uses DM-type tests for comparing models, in pseudo-out-of-sample environments. In that case, simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that pseudo-out-of-sample analysis is somehow the “only,” or “best,” or even necessarily a “good” way to provide insurance against in-sample overfitting in model comparisons proves largely false. On the other hand, pseudo-out-of-sample analysis remains useful for certain tasks, perhaps most notably for providing information about comparative predictive performance during particular historical episodes.
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“As emphasized byDiebold (2015), the model-free nature of the Diebold-Mariano test means that it should be interpreted as a comparison of forecasts, not as a comparison of”
From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as Diebold (2015)
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
Hamilton, J. D. (1989) · Econometrica · doi:10.2307/1912559 · Cited by 9,696
Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
Vuong, Q. H. (1989) · Econometrica · doi:10.2307/1912557 · Cited by 5,929
Diebold, F. X., & Mariano, R. S. (1994) · National Bureau of Economic Research · doi:10.3386/t0169 · Cited by 5,498
Self-citationDiebold, F. X., & Mariano, R. S. (1995) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1995.10524599 · Cited by 5,240
Self-citationHansen, P. R., Lunde, A., & Nason, J. M. (2011) · Econometrica · doi:10.3982/ecta5771 · Cited by 2,229
A Reality Check for Data Snooping
White, H. (2000) · Econometrica · doi:10.1111/1468-0262.00152 · Cited by 1,851
Flagged first, then by citations
Research works printed but not linked: 2
- Durham G., 2013, Journal of Financial Econometrics, 11
- Elliott G., 2006, Handbook of Economic Forecasting, 33, 100
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Costantini, M., & Kunst, R. M. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.010 · Cited by 27
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Otieno, G., Mutemi, J. N., Opijah, F. J., Ogallo, L. A., & Omondi, M. H. (2019) · Pure and Applied Geophysics · doi:10.1007/s00024-019-02293-2 · Cited by 26
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Jin, S., Corradi, V., & Swanson, N. R. (2015) · Econometric Theory · doi:10.1017/s0266466616000426 · Cited by 23
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Metadata
- Authors
- Diebold, F. X.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Diebold, Francis X.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2015-01-02
- Volume
- 33
- Issue
- 1
- Pages
- 1-1
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2007694684
- Cited by
- 496
- References
- 41
- Citations by year
- 2026: 66 · 2025: 60 · 2024: 48 · 2023: 46 · 2022: 39 · 2021: 46 · 2020: 35 · 2019: 34 · 2018: 36 · 2017: 33 · 2016: 20 · 2015: 22
- FWCI
- 46.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Business and Economic Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Monetary Policy and Economic Impact
- Keywords
- Diebold-Mariano test, forecast evaluation, model comparison, prediction accuracy, forecasting model comparison
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