Model Confidence Sets for Forecasting Models
نویسندگان
چکیده
The paper introduces the model confidence set (MCS) and applies it to the selection of forecasting models. A MCS is a set of models that is constructed such that it will contain the ’best’ forecasting model, given a level of confidence. Thus, a MCS is 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. We revisit the empirical application in Stock and Watson (1999) and apply the MCS procedure to their set of inflation forecasts. Although the MCS contains only a few models in the first subsample, there is little information in the second post-1984 subsample, which results in a large MCS. Yet, the random walk forecast is not contained in the MCS for either of the samples. This shows that the random walk forecast is inferior to principal component-based inflation forecasts. JEL Classification: C12, C19, C44, C52, and C53.
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