The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
The Model Confidence Set
Abstract
This paper introduces the model confidence set (MCS) and applies it to the selection of models. A MCS is a set of models that is constructed such that it will contain the best model with a given level of confidence. The MCS is in this sense analogous to a confidence interval for a parameter. The MCS acknowledges the limitations of the data, such that uninformative data yield a MCS with many models, whereas informative data yield a MCS with only a few models. The MCS procedure does not assume that a particular model is the true model; in fact, the MCS procedure can be used to compare more general objects, beyond the comparison of models. We apply the MCS procedure to two empirical problems. First, we revisit the inflation forecasting problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare a number of Taylor rule regressions and determine the MCS of the best regression in terms of in-sample likelihood criteria.
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Citations
Cites
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Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
RETRACTED: Cryptocurrencies as a financial asset: A systematic analysis
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
RetractedDiebold, F. X. (2015) · Journal of Business & Economic Statistics · doi:10.1080/07350015.2014.983236 · Cited by 496
Forecasting carbon futures volatility using GARCH models with energy volatilities
Byun, S. J., & Cho, H. (2013) · Energy Economics · doi:10.1016/j.eneco.2013.06.017 · Cited by 344
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Steel, M. F. J. (2020) · Journal of Economic Literature · doi:10.1257/jel.20191385 · Cited by 321
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Zhu, B., Ye, S., Wang, P., He, K., Zhang, T., & Wei, Y.-M. (2018) · Energy Economics · doi:10.1016/j.eneco.2017.12.030 · Cited by 236
Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say 'Usually Not'
Chang, A. C., & Li, P. (2015) · doi:10.2139/ssrn.2669564 · Cited by 217
Cross-Validation With Confidence
Lei, J. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1672556 · Cited by 93
Automatic Indexation of the Pension Age to Life Expectancy: When Policy Design Matters
Ayuso, M., Bravo, J. M., Holzmann, R., & Palmer, E. (2021) · Risks · doi:10.3390/risks9050096 · Cited by 44
Hasanov, A. S., Burkhanov, A. U., Usmonov, B., Khajimuratov, N. S., & Khurramova, M. M. Q. (2024) · Energy · doi:10.1016/j.energy.2024.130535 · Cited by 43
Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?
Feng, Y., Zhang, Y., & Wang, Y. (2024) · Journal of Forecasting · doi:10.1002/for.3046 · Cited by 22
Alam, M. S., Amendola, A., Candila, V., & Jabarabadi, S. D. (2024) · Econometrics · doi:10.3390/econometrics12010002 · Cited by 15
Oh, G., Kim, S., Park, S., & Kim, B.-H. (2026) · Journal of Medical Internet Research · doi:10.2196/90693 · Cited by 7
Metadata
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2011
- Volume
- 79
- Issue
- 2
- Pages
- 453-497
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2125536334
- Cited by
- 2,096
- References
- 49
- FWCI
- 35.05
- Type
- journal article
- Language
- en
- Open access
- closed
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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.