Experts, firms, consumers or even hard data? Forecasting employment in Germany

ABSTRACT

In this article, we forecast employment growth for Germany with data for the period from November 2008 to November 2015. Hutter and Weber (2015) introduced an innovative unemployment indicator and evaluated the performance of several leading indicators, including the Ifo Employment Barometer (IEB), to predict unemployment changes. Since the IEB focuses on employment growth instead of unemployment developments, we mirror the study by Hutter and Weber (2015). It turns out that in our case, and in contrast to their article, the IEB outperforms their newly developed indicator. Additionally, consumers’ unemployment expectations and hard data such as new orders exhibit a high forecasting accuracy.

KEYWORDS:

• Survey data
• employment forecasts
• model confidence set

JEL CLASSIFICATION:

• E24
• E27
• J00

Acknowledgement

We thank Lisa Giani Contini for editing this text.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Studies that evaluate unemployment forecasts include Claveria, Pons, and Ramos (2007) and Martinsen, Ravazzolo, and Wulfsberg (2014).

² In contrast to these studies, Weber and Zika (2013) evaluate whether sectoral disaggregated employment forecasts improve the accuracy of the aggregate of total employment. For the short-term forecasts, they do indeed find a significant improvement via disaggregation.

³ For more details on the indicator, see Delfs et al. (2013) or Hutter and Weber (2015). The data can be retrieved from http://www.iab.de/en/daten/arbeitsmarktbarometer.aspx

⁴ Hutter and Weber (2015) consider even more models. In our application, however, these models do not perform well so that we skip these below.