Many prominent forecasters publish their projections at an annual frequency. However, for applied work, an estimate of the underlying quarterly forecasts is often indispensable. We demonstrate that a simple state-space model can be used to obtain good estimates of the quarterly forecasts underlying annual projections. We validate the methodology by aggregating professional forecasts for quarterly GDP growth in the United States to the annual frequency and then applying our imputation methodology. The imputed forecasts perform as well as the original quarterly forecasts. Applying the imputation methodology to Consensus forecasts for other advanced economies provides further evidence of the good performance of our proposed methodology.
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