On selecting policy analysis models by forecast accuracy

Abstract

The value of selecting the best forecasting model as the basis for empirical economic policy analysis is questioned. When no model coincides with the data generation process, non-causal statistical devices may provide the best available forecasts: examples from recent work include intercept corrections and differenced-data VARs. However, the resulting models need have no policy implications. A ‘paradox’ may result if their forecasts induce policy changes which can be used to improve the statistical forecast. This suggests correcting statistical forecasts by using the econometric model’s estimate of the ‘scenario’ change. An application to UK consumers expenditure illustrates the analysis.

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