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Forecasting Aggregates by Disaggregates

  • Kirstin Hubrich
  • David F. Hendry


    (Research Department European Central Bank)

We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over forecasting the disaggregates and aggregating those forecasts, or using only aggregate information in forecasting the aggregate. An implication of a general theory of prediction is that the first should outperform the alternative methods to forecasting the aggregate in population. However, forecast models are based on sample information. The data generation process and the forecast model selected might differ. We show how changes in collinearity between regressors affect the bias-variance trade-off in model selection and how the criterion used to select variables in the forecasting model affects forecast accuracy. We investigate why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of Euro area inflation in some situations, but not in others.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 270.

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Date of creation: 11 Nov 2005
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Handle: RePEc:sce:scecf5:270
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