Approximately Normal Tests for Equal Predictive Accuracy in Nested Models
Kenneth West () and
No 326, NBER Technical Working Papers from National Bureau of Economic Research, Inc
Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for this noise. We propose applying standard methods (West (1996)) to test whether the adjusted mean squared error difference is zero. We refer to nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size. Simulation evidence supports our recommended procedure.
JEL-codes: C22 C53 E17 F37 (search for similar items in EconPapers)
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Journal Article: Approximately normal tests for equal predictive accuracy in nested models (2007)
Working Paper: Approximately normal tests for equal predictive accuracy in nested models (2005)
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