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Description:
Including disaggregate variables or using information extracted from the disaggregate variables into a forecasting model for an eco- nomic aggregate may improve the forecasting accuracy. In this paper we suggest to use boosting as a method to select the disaggregate variables which are most helpful in predicting an aggregate of interest. We compare this method with the direct forecast of the aggregate, a forecast which aggregates the disaggregate forecasts and a direct forecast which additionally uses information from factors obtained from the disaggregate components. A recursive pseudo-out-of-sample forecasting experiment for key Euro area macroeconomic variables is conducted. The results suggest that using boosting to select relevant predictors is a viable and competitive approach in forecasting an aggregate.