Econometrica: Mar 2011, Volume 79, Issue 2

The Model Confidence Set

https://doi.org/10.3982/ECTA5771
p. 453-497

Peter R. Hansen, Asger Lunde, James M. Nason

This paper introduces the (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 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 inā€sample likelihood criteria.

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Supplemental Material

Supplement to "The Model Confidence Set"

PDF file containing tables and four parts: Bootstrap Procedure, Inflation Forecasting, Regression Simulation, and Taylor Rules.

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Supplement to "The Model Confidence Set"

A zip file containing replication files for the manuscript.

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