Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?

Jing Zeng ()
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Jing Zeng: Department of Economics, University of Konstanz, Germany

No 2014-20, Working Paper Series of the Department of Economics, University of Konstanz from Department of Economics, University of Konstanz

Abstract: Including disaggregate variables or using information extracted from the disaggregate variables into a forecasting model for an economic aggregate may improve the forecasting accuracy. In this paper we suggest to use the boosting method to select the disaggregate variables which are most helpful in predicting an aggregate of interest. We conduct a simulation study to investigate the variable selection ability of this method. To assess the forecasting performance a recursive pseudo-out-of-sample forecasting experiment for six key Euro area macroeconomic variables is conducted. The results suggest that using boosting to select relevant predictors is a feasible and competitive approach in forecasting an aggregate.

Keywords: aggregation; macroeconomic forecasting; componentwise boosting; factor analysis (search for similar items in EconPapers)
JEL-codes: C22 C43 C52 C53 C82 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2014-09-23
New Economics Papers: this item is included in nep-ets and nep-for
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Citations: View citations in EconPapers (1)

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