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
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
Items 201–300 of 493
A note on the stability of the Swedish Phillips curve
Karlsson, S., & Österholm, P. (2019) · Empirical Economics · doi:10.1007/s00181-019-01746-w · Cited by 10
Hansen, P. R., & Timmermann, A. (2015) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2014.983601 · Cited by 10
Chevalier, G., Coqueret, G., & Raffinot, T. (2022) · Quantitative Finance · doi:10.1080/14697688.2022.2122543 · Cited by 10
Lokanan, M. E. (2023) · Applied Economics Letters · doi:10.1080/13504851.2023.2176435 · Cited by 9
He, L.-Y., Li, H., Chen, X.-Z., & Yu, L. (2024) · Tourism Management · doi:10.1016/j.tourman.2024.104882 · Cited by 9
Mubarik, S., Hu, Y., & Yu, C. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01187-5 · Cited by 9
A NOVEL APPROACH TO PREDICTIVE ACCURACY TESTING IN NESTED ENVIRONMENTS
Pitarakis, J. (2023) · Econometric Theory · doi:10.1017/s0266466623000154 · Cited by 8
Bai, S., Jung, J., & Li, S. (2024) · Journal of risk and financial management · doi:10.3390/jrfm17120569 · Cited by 8
Cui, X., Yu, X., Niu, H., Niu, D., & Liu, D. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126348 · Cited by 8
A secondary decomposition–ensemble approach to interval predicting China’s railway container volume
Yuan, S., Jia, P., & Wang, S. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110374 · Cited by 8
Multivariate models of commodity futures markets: a dynamic copula approach
Chen, S., Li, Q., Wang, Q., & Zhang, Y. Y. (2023) · Empirical Economics · doi:10.1007/s00181-023-02373-2 · Cited by 8
A bottom-up approach for forecasting GDP in a data-rich environment
Dias, F., Pinheiro, M., & Rua, A. (2017) · Applied Economics Letters · doi:10.1080/13504851.2017.1361000 · Cited by 8
Is euro area lowflation here to stay? Insights from a time‐varying parameter model with survey data
Stevens, A., & Wauters, J. (2021) · Journal of Applied Econometrics · doi:10.1002/jae.2813 · Cited by 8
Evaluating Government Budget Forecasts
Ericsson, N. R., & Martinez, A. B. (2019) · Palgrave studies in public debt, spending, and revenue · doi:10.1007/978-3-030-18195-6_3 · Cited by 8
Evaluating Ex Ante Counterfactual Predictions Using Ex Post Causal InferencePreprint
Gechter, M., Samii, C., Dehejia, R., & Pop-Eleches, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07016 · Cited by 8
Mutinda, J. K., Langat, A. K., & Mwalili, S. M. (2025) · International Journal of Mathematics and Mathematical Sciences · doi:10.1155/ijmm/2710277 · Cited by 8
Quantum Decision Theory, Bounded Rationality and the Ellsberg Paradox
Wei, M., al‐Nowaihi, A., & Dhami, S. (2019) · Studies in Microeconomics · doi:10.1177/2321022219845568 · Cited by 7
How to Dominate the Historical Average
Li, K., Li, Y., Lyu, C. F., & Yu, J. (2025) · Review of Financial Studies · doi:10.1093/rfs/hhaf010 · Cited by 7
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models
Hubin, A. (2019) · International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC) · doi:10.1145/3371425.3371641 · Cited by 7
Procasky, W. J., & Yin, A. (2022) · Journal of Futures Markets · doi:10.1002/fut.22342 · Cited by 7
Procasky, W. J., & Yin, A. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102926 · Cited by 7
Alternative HAC Covariance Matrix Estimators with Improved Finite Sample Properties
Hartigan, L. (2016) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2017.09.007 · Cited by 7
A Top-Down Approach to the Stress-Testing of BanksPreprint
Kapinos, P. S., & Mitnik, O. A. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2469425 · Cited by 7
Commodity Futures Return Predictability and Intertemporal Asset PricingPreprint
Cotter, J., Eyiah-Donkor, E., & Potì, V. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4095741 · Cited by 7
Forecasting Inflation Using Economic NarrativesPreprint
Hong, Y., Jiang, F., Meng, L., & Xue, B. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4175749 · Cited by 7
Yao, Z., Wang, Z., Xu, N., Wu, J., & Cui, X. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.134084 · Cited by 6
Evaluating probabilistic forecasts for maritime engineering operations
Astfalck, L., Bertolacci, M., & Cripps, E. J. (2023) · Data-Centric Engineering · doi:10.1017/dce.2023.11 · Cited by 6
A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts
Şener, U., & Terregrossa, S. J. (2024) · SAGE Open · doi:10.1177/21582440241262695 · Cited by 7
Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
Μaragoudakis, M. (2019) · Algorithms · doi:10.3390/a12010027 · Cited by 6
Time series prediction: A combination of Long Short-Term Memory and structural time series models
Luu, Q., Nguyễn, S., & Pham, U. H. (2020) · Science & Technology Development Journal - Economics - Law and Management · doi:10.32508/stdjelm.v4i1.593 · Cited by 7
Song, W., Chen, D., & Zio, E. (2023) · Fractal and Fractional · doi:10.3390/fractalfract8010007 · Cited by 6
Quasi-Real-Time Data of the Economic Tendency Survey
Billstam, M., Frändén, K., Samuelsson, J., & Österholm, P. (2017) · Journal of Business Cycle Research · doi:10.1007/s41549-017-0016-7 · Cited by 6
Lai, Y. (2018) · Journal of Futures Markets · doi:10.1002/fut.21937 · Cited by 6
Forecasting sovereign CDS VOLATILITY: A comparison of univariate GARCH-class models
Sabkha, S., de Peretti, C., & Mallek, S. (2020) · Vie & sciences de l entreprise · doi:10.3917/vse.209.0027 · Cited by 6
Multilevel and Tail Risk Management
Khalaf, L., Leccadito, A., & Urga, G. (2020) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbaa044 · Cited by 6
Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews
Symitsi, E., Stamolampros, P., & Karatzas, A. (2021) · Public Opinion Quarterly · doi:10.1093/poq/nfab017 · Cited by 6
Disentangling and assessing uncertainties in multiperiod corporate default risk predictions
Yuan, M., Tang, C. Y., Hong, Y., & Yang, J. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1170 · Cited by 6
Simple robust tests for the specification of high-frequency predictors of a low-frequency series
Miller, J. I. (2016) · Econometrics and Statistics · doi:10.1016/j.ecosta.2016.09.001 · Cited by 6
The role of temporal dependence in factor selection and forecasting oil prices
Binder, K. E., Pourahmadi, M., & Mjelde, J. W. (2018) · Empirical Economics · doi:10.1007/s00181-018-1574-9 · Cited by 6
Dynamics of Global Business Cycles InterdependencePreprint
Ductor, L., & Leiva‐León, D. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2691698 · Cited by 6
Conditionally Optimal Weights and Forward-Looking Approaches to Combining ForecastsPreprint
Gibbs, C. G., & Vasnev, A. L. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2947395 · Cited by 6
Modeling Renewable Power Purchase Agreements PricesPreprint
Peña, J. I., Rodriguez, R. M., & Mayoral, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4153205 · Cited by 6
Hybrid neural network wind speed prediction based on two-level decomposition and weighted averaging
Bi, Q., Bai, Y., Hou, Z.-H., & Wang, R. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01388-2 · Cited by 5
Cui, X., Yin, S., Chen, H., & Niu, D. (2025) · Energy · doi:10.1016/j.energy.2025.138516 · Cited by 5
Tang, W., Huang, K., Qian, T., Li, W., & Xie, X. (2024) · Frontiers in Energy Research · doi:10.3389/fenrg.2024.1343220 · Cited by 5
Putting VAR forecasts of the real price of crude oil to the test
Ellwanger, R., & Snudden, S. (2025) · Finance research letters · doi:10.1016/j.frl.2025.106940 · Cited by 5
Improving empirical models and forecasts with saturation-based machine learning
Martinez, A. B., & Ericsson, N. R. (2025) · Annals of Operations Research · doi:10.1007/s10479-024-06373-y · Cited by 5
River Stage Forecasting using Enhanced Partial Correlation Graph
Venna, S. R., Katragadda, S., Raghavan, V. V., & Gottumukkala, R. N. (2021) · Water Resources Management · doi:10.1007/s11269-021-02933-0 · Cited by 5
Reducing Overestimating and Underestimating Volatility via the Augmented Blending-ARCH Model
Lu, J., & Yi, S. (2022) · Applied Economics and Finance · doi:10.11114/aef.v9i2.5507 · Cited by 5
Net Load Forecasting Based on Residual Attention CNN-BiLSTM Model
Shen, S., He, D. J., & Yu, W. (2023) · International Conference on Smart Grids and Energy Systems (SGES) · doi:10.1109/sges59720.2023.10366974 · Cited by 6
Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?
Benmoussa, A. A., Ellwanger, R., & Snudden, S. (2025) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2025.02.009 · Cited by 5
German forecasters’ narratives: How informative are German business cycle forecast reports?
Müller, K. (2021) · Empirical Economics · doi:10.1007/s00181-021-02100-9 · Cited by 5
Gaussian Estimation and Forecasting of the UK Yield Curve with Multi-Factor Continuous Time Models
Tunaru, D. E. (2015) · International Review of Financial Analysis · doi:10.1016/j.irfa.2017.05.003 · Cited by 5
Foreign exchange interventions under a minimum exchange rate regime and the Swiss franc
Hertrich, M. (2021) · Review of International Economics · doi:10.1111/roie.12571 · Cited by 5
Conditionally Optimal Weights and Forward-Looking Approaches to Combining ForecastsPreprint
Gibbs, C. G. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2919117 · Cited by 5
Efficient Factor Selection: Explaining Risk and Mean Returns JointlyPreprint
Balvers, R. J., & Stivers, A. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3156699 · Cited by 5
Tests of Conditional Predictive Ability: Some Simulation Evidence
McCracken, M. W. (2019) · doi:10.20955/wp.2019.011 · Cited by 5
Sefene, E. M., Gomez, W., & Chen, C.-C. A. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02696-0 · Cited by 4
Ye, Y., Fan, Z., & Ruan, X. (2025) · Journal of Futures Markets · doi:10.1002/fut.22579 · Cited by 4
Xuan, C., & Kim, C.-J. (2020) · Japan and the World Economy · doi:10.1016/j.japwor.2020.101027 · Cited by 4
Econometric modelling and forecasting of wholesale electricity prices
Sapio, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-814712-2.00015-4 · Cited by 4
A reappraisal of Katona’s adaptive theory of consumer behaviour using U.K. data
Gausden, R., & Hasan, M. S. (2022) · Manchester School · doi:10.1111/manc.12395 · Cited by 4
Maximum-Subsampling Test of Equal Predictive Ability
Lan, W., Lei, B., Feng, L., & Tsai, C. (2024) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2024.2311196 · Cited by 4
Tests for equal forecast accuracy under heteroskedasticity
Harvey, D. I., Leybourne, S. J., & Zu, Y. (2024) · Journal of Applied Econometrics · doi:10.1002/jae.3050 · Cited by 4
Nonejad, N. (2024) · Empirical Economics · doi:10.1007/s00181-024-02599-8 · Cited by 4
The time-varying Multivariate Autoregressive Index model
Cubadda, G., Grassi, S., & Guardabascio, B. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.04.007 · Cited by 4
Factor-Augmented Autoregressive Neural Network to forecast NOx in the city of Madrid
Fernández‐Avilés, G., Mattera, R., & Scepi, G. (2024) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2024.101958 · Cited by 6
Ensemble distributional forecasting for insurance loss reserving
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2024) · Scandinavian Actuarial Journal · doi:10.1080/03461238.2024.2365392 · Cited by 4
Salmabadi, N., Salari, M., & Shadman, A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-10988-y · Cited by 4
Regime-specific exchange rate predictability
Beckmann, J., Kerkemeier, M., & Kruse, R. (2025) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2025.105095 · Cited by 4
In-sample tests of predictability are superior to pseudo-out-of-sample tests, even when data mining
Hunt, I. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.05.006 · Cited by 4
Will the last be the first? Ranking German macroeconomic forecasters based on different criteria
Köhler, T., & Döpke, J. (2022) · Empirical Economics · doi:10.1007/s00181-022-02267-9 · Cited by 4
Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects
Papantonis, I., Rompolis, L. S., Tzavalis, E., & Agapitos, O. (2022) · Studies in Nonlinear Dynamics and Econometrics · doi:10.1515/snde-2020-0131 · Cited by 4
Optimal out‐of‐sample forecast evaluation under stationarity
Staněk, F. (2023) · Journal of Forecasting · doi:10.1002/for.3013 · Cited by 4
Forecasting US Inflation in Real Time
Fulton, C., & Hubrich, K. (2021) · Finance and Economics Discussion Series · doi:10.17016/feds.2021.014 · Cited by 4
Information in the Term Structure: A Forecasting Perspective
Doshi, H., Jacobs, K., & Liu, R. (2020) · Management Science · doi:10.1287/mnsc.2020.3715 · Cited by 4
Narita, F., & Yin, R. (2018) · RePEc: Research Papers in Economics · Cited by 4
Construction of a new multi-layer stacked model for precipitable water vapor prediction
Li, B., Shao, Z., Zhang, K., Li, M., Yang, C., & Zhang, W. (2025) · Theoretical and Applied Climatology · doi:10.1007/s00704-025-05688-5 · Cited by 3
Deep Learning and Econometric Time Series Analysis: An Assessment of Daily Return Forecasts
Berger, T. (2025) · Journal of Forecasting · doi:10.1002/for.70045 · Cited by 9
Wafi, A. S., El-Halaby, S. I., & Ahmed, H. (2026) · Journal of risk and financial management · doi:10.3390/jrfm19030228 · Cited by 4
Statistical testing of DeMark technical indicators on commodity futures
Lissandrin, M., Daly, D., & Sornette, D. (2017) · The Journal of Investment Strategies · doi:10.21314/jois.2017.086 · Cited by 3
Nie, H., & Waelbroeck, H. (2023) · Quantitative Finance · doi:10.1080/14697688.2023.2175715 · Cited by 3
Efficiency of poll-based multi-period forecasting systems for German state elections
Fritsch, M., Haupt, H., & Schnurbus, J. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.04.008 · Cited by 3
Cash Flow Forecasting for Self-employed Workers: Fuzzy Inference Systems or Parametric Models?
Palomero, L., García, V., & Sánchez, J. S. (2024) · Computational Economics · doi:10.1007/s10614-024-10723-0 · Cited by 3
Edvinsson, R., Karlsson, S., & Österholm, P. (2024) · Empirical Economics · doi:10.1007/s00181-024-02684-y · Cited by 3
Zens, G., & Thalheimer, L. (2025) · The Annals of Applied Statistics · doi:10.1214/24-aoas1959 · Cited by 3
Fatriansyah, J. F., Tihara, I. H., Ramadano, M. R., Federico, A., Pradana, A. F., Surip, S. N., & Gascoin, N. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107350 · Cited by 4
Comparing Predictive Accuracy under Long Memory, With an Application to Volatility Forecasting*
Kruse, R., Leschinski, C., & Will, M. (2018) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nby011 · Cited by 3
Kling, C. L., Arritt, R. W., Calhoun, G., Keiser, D. A., Antle, J. M., Arnold, J. G., Carriquiry, M., Chaubey, I., Christensen, P., Ganapathysubramanian, B., et al. (2016) · Iowa State University Digital Repository (Iowa State University) · Cited by 3
Probabilistic Forecasting of Reserve Power Prices in Germany using Quantile Regression
Jahns, C., & Weber, C. D. (2019) · International Conference on the European Energy Market (EEM) · doi:10.1109/eem.2019.8916228 · Cited by 3
Finite sample weighting of recursive forecast errors
Brooks, C., Burke, S. P., & Stanescu, S. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2015.05.003 · Cited by 3
Asset Price Prediction via Machine-Learning Method: A Review
Yang, Y., Chen, B., & Zhang, L. (2021) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis54983.2021.00043 · Cited by 3
Investors’ attention and network spillover for commodity market forecasting
Cerqueti, R., Ficcadenti, V., & Mattera, R. (2024) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2024.102023 · Cited by 3
“Go Wild for a While!”: A New Test for Forecast Evaluation in Nested Models
Pincheira, P., Hardy, N., & Muñoz, F. (2021) · Mathematics · doi:10.3390/math9182254 · Cited by 3
Kruger, C., Schutte, W. D., & Verster, T. (2021) · Risks · doi:10.3390/risks9110204 · Cited by 3
Forecasting Bond Risk Premia with Unspanned Macroeconomic Information
Liu, R. (2018) · Quarterly Journal of Finance · doi:10.1142/s2010139219400019 · Cited by 3
Hybrid machine intelligent wind speed forecasting models
Dhiman, H. S., Deb, D., & Bălaş, V. E. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821353-7.00017-x · Cited by 3
Realized Volatility and Change of RegimesPreprint
Gallo, G. M., & Otranto, E. (2012) · RePEc: Research Papers in Economics · Cited by 3
Fixed-b Inference in the Presence of Time-Varying VolatilityPreprint
Demetrescu, M., Hanck, C., & Kruse, R. (2016) · RePEc: Research Papers in Economics · Cited by 3
Discussion of “Elicitability and backtesting: Perspectives for banking regulation”
Holzmann, H., & Klar, B. (2017) · The Annals of Applied Statistics · doi:10.1214/17-aoas1041a · Cited by 3
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