Google Econometrics and Unemployment Forecasting

Nikos Askitas () and Klaus Zimmermann ()

Applied Economics Quarterly (formerly: Konjunkturpolitik), 2009, vol. 55, issue 2, 107-120

Abstract: The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword searches and unemployment rates using monthly German data and exhibits a strong potential for the method used.

Keywords: Google; internet; keyword search; search engine; unemployment; predictions; time-series analysis (search for similar items in EconPapers)
JEL-codes: C22 C82 E17 E24 E37 (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (4)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: Google Econometrics and Unemployment Forecasting (2009)
Working Paper: Google Econometrics and Unemployment Forecasting (2009)
Working Paper: Google Econometrics and Unemployment Forecasting (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aeq:aeqaeq:v55_y2009_i2_q2_p107-120

Ordering information: This journal article can be ordered from
http://www.duncker-h ... nomicsquarterly.html

Access Statistics for this article

Applied Economics Quarterly (formerly: Konjunkturpolitik) is currently edited by Ansgar Belke, Uwe Sunde and Winfried Koeniger

More articles in Applied Economics Quarterly (formerly: Konjunkturpolitik) from Duncker & Humblot, Berlin
Bibliographic data for series maintained by Deborah Anne Bowen ( this e-mail address is bad, please contact ).