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Approximately Normal Tests for Equal Predictive Accuracy in Nested Models

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Author Info
Kenneth D. West
Todd Clark

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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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0326.

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Date of creation: Aug 2006
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Handle: RePEc:nbr:nberte:0326

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation
F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation

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