Forecasting Aggregates with Disaggregate Variables: Does boosting help to select the most informative predictors?

Jing Zeng

VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy from Verein für Socialpolitik / German Economic Association

Abstract: 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.

JEL-codes: C22 C43 C53 (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-for
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc14:100310

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