The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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)
Citations
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
Robustness in the Strategy of Scientific Model Building
Box, G. E. P. (1979) · Elsevier eBooks · doi:10.1016/b978-0-12-438150-6.50018-2 · Cited by 1,698
Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy
Rapach, D. E., Strauss, J., & Zhou, G. (2009) · Review of Financial Studies · doi:10.1093/rfs/hhp063 · Cited by 1,621
Tests of Conditional Predictive Ability
Giacomini, R., & White, H. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00718.x · Cited by 1,598
Asymptotic Inference about Predictive Ability
West, K. D. (1996) · Econometrica · doi:10.2307/2171956 · Cited by 1,365
Handbook of Economic Forecasting
(2013) · Handbook of economic forecasting · doi:10.1016/b978-0-444-53683-9.00023-2 · Cited by 1,290
Tests of equal forecast accuracy and encompassing for nested models
Clark, T. E., & McCracken, M. W. (2001) · Journal of Econometrics · doi:10.1016/s0304-4076(01)00071-9 · Cited by 1,126
Why is it so difficult to beat the random walk forecast of exchange rates?
Kilian, L., & Taylor, M. P. (2003) · Journal of International Economics · doi:10.1016/s0022-1996(02)00060-0 · Cited by 797
In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?
Inoue, A., & Kilian, L. (2005) · Econometric Reviews · doi:10.1081/etc-200040785 · Cited by 731
Asymptotics for out of sample tests of Granger causality
McCracken, M. W. (2006) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.07.020 · Cited by 671
Yang, Y. (2005) · Biometrika · doi:10.1093/biomet/92.4.937 · Cited by 608
Efficient market hypothesis and forecasting
Timmermann, A., & Granger, C. W. J. (2004) · International Journal of Forecasting · doi:10.1016/s0169-2070(03)00012-8 · Cited by 517
Forecast comparisons in unstable environments
Giacomini, R., & Rossi, B. (2010) · Journal of Applied Econometrics · doi:10.1002/jae.1177 · Cited by 449
Out-of-Sample Forecast Tests Robust to the Choice of Window Size
Rossi, B., & Inoue, A. (2012) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2012.693850 · Cited by 417
Model selection tests for nonlinear dynamic models
Rivers, D., & Vuong, Q. (2002) · Econometrics Journal · doi:10.1111/1368-423x.t01-1-00071 · Cited by 366
Geweke, J. F., & Amisano, G. (2011) · Journal of Econometrics · doi:10.1016/j.jeconom.2011.02.017 · Cited by 351
On Predictive Least Squares Principles
Wei, C.-Z. (1992) · The Annals of Statistics · doi:10.1214/aos/1176348511 · Cited by 199
On the selection of forecasting models
Inoue, A., & Kilian, L. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2005.03.003 · Cited by 170
Chapter 17 Forecasting with Real-Time Macroeconomic Data
Croushore, D. (2006) · Handbook of economic forecasting · doi:10.1016/s1574-0706(05)01017-7 · Cited by 163
Choice of Sample Split in Out-of-Sample Forecast EvaluationPreprint
Hansen, P. R., & Timmermann, A. (2012) · RePEc: Research Papers in Economics · Cited by 135
Complete and Incomplete Econometric Models
Geweke, J. F. (2010) · Princeton University Press eBooks · doi:10.1515/9781400835249 · Cited by 112
Tests of Equal Predictive Ability With Real-Time Data
Clark, T. E., & McCracken, M. W. (2009) · Journal of Business and Economic Statistics · doi:10.1198/jbes.2009.07204 · Cited by 100
Can the Strengths of AIC and BIC Be SharedPreprint
Yang, Y. (2003) · Cited by 76
Advances in Forecast Evaluation
McCracken, M. W., & Clark, T. E. (2011) · doi:10.20955/wp.2011.025 · Cited by 63
A Note on Bayesian Forecast Combination Procedures
Diebold, F. X. (1991) · Springer eBooks · doi:10.1007/978-3-662-06824-3_15 · Cited by 28
Self-citationNested Forecast Model Comparisons: A New Approach to Testing Equal Accuracy
McCracken, M. W., & Clark, T. E. (2009) · doi:10.20955/wp.2009.050 · Cited by 23
Comparing Predictive Accuracy I: An Asymptotic TestPreprint
Diebold, F. X., & Mariano, R. S. (1991) · doi:10.21034/dp.52 · Cited by 20
Self-citationImproving Asset Price Prediction When All Models are False
Durham, G. B., & Geweke, J. F. (2013) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbt001 · Cited by 19
Ghysels, É., & Hall, A. R. (2002) · Journal of Business and Economic Statistics · doi:10.1198/073500102753410345 · Cited by 15
Simulation based selection of competing structural econometric models
Li, T. (2008) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.10.001 · Cited by 15
To Hold Out or Not to Hold Out
Schorfheide, F., & Wolpin, K. I. (2013) · National Bureau of Economic Research · doi:10.3386/w19565 · Cited by 4
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
Cited by
Sorted by citations
Empirical Asset Pricing via Machine Learning
Gu, S., Kelly, B., & Xiu, D. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhaa009 · Cited by 2,545
Electricity price forecasting: A review of the state-of-the-art with a look into the future
Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
Forecasting: theory and practice
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babaï, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., et al. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.11.001 · Cited by 1,187
Recent advances in electricity price forecasting: A review of probabilistic forecasting
Nowotarski, J., & Weron, R. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.234 · Cited by 563
Yang, D., Kleissl, J. P., Gueymard, C. A., Pedro, H. T. C., & Coimbra, C. F. M. (2018) · Solar Energy · doi:10.1016/j.solener.2017.11.023 · Cited by 584
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
