Research output: Working paper › Research

- Rp10 76
Submitted manuscript, 354 KB, PDF-document

- Peter Reinhard Hansen, Denmark
- Asger Lunde James M. Nason, Federal Reserve Bank of Philadelphia, United States

- School of Economics and Management
- Centre for Research in Econometric Analysis of Time Series (CREATES)
- Department of Marketing and Statistics

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 theMCS 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.

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.

Original language | English |
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Place of publication | Aarhus |

Publisher | CREATES, Institut for Økonomi, Aarhus Universitet |

Number of pages | 38 |

Publication status | Published - 2010 |

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ID: 34269365