Approximately normal tests for equal predictive accuracy in nested models

Todd Clark and Kenneth West ()

No RWP 05-05, Research Working Paper from Federal Reserve Bank of Kansas City

Abstract: 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.

Date: 2005
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (6)

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Related works:
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 (2006)
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