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The Model Confidence Set

  • Peter R. Hansen

    ()

    (Stanford University, Department of Economics and CREATES)

  • Asger Lunde

    ()

    (Aarhus University, School of Economics and Management and CREATES)

  • James M. Nason

    ()

    (Federal Reserve Bank of Philadelphia)

The 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 yields a MCS with many models, whereas informative data yields 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 comparemore 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 in terms of in-sample likelihood criteria.

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File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_76.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-76.

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Length: 38
Date of creation: 01 Mar 2010
Date of revision:
Handle: RePEc:aah:create:2010-76
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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