Looking into the black box of boosting: the case of Germany

ABSTRACT

This article looks into the ‘fine print’ of boosting for economic forecasting. By using German industrial production for the period from 1996 to 2014 and a data set consisting of 175 monthly indicators, we evaluate which indicators get selected by the boosting algorithm over time and four different forecasting horizons. It turns out that a number of hard indicators like turnovers, as well as a small number of survey results, get selected frequently by the algorithm and are therefore important to forecasting the performance of the German economy. However, there are indicators such as money supply that never get chosen by the boosting approach at all.

KEYWORDS:

• Boosting
• economic forecasting
• industrial production
• Germany

JEL CLASSIFICATION:

• C53
• E17
• E37

Acknowledgement

We thank Lisa Giani Contini for editing this text.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ A complete list of all indicators and their relative frequency can be found in the latest working paper version (MPRA Paper No. 67608, https://ideas.repec.org/p/pra/mprapa/67608.html).