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Cited by 2,096

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

·2011·Econometrica
DOI 10.3982/ecta5771
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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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Metadata

Venue
Econometrica
Publisher
The Econometric Society
Published
2011
Volume
79
Issue
2
Pages
453-497
ISSN
0012-9682 (ISSN-L)
DOI
10.3982/ecta5771
OpenAlex
W2125536334
Cited by
2,096
References
49
FWCI
35.05
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.