In search of a job: Forecasting employment growth using Google Trends
Daniel Borup () and
Erik Christian Montes Schütte ()
Additional contact information
Daniel Borup: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Aarhus University, Fuglesangs Allé 4, 8210 Aarhus V, DK
Erik Christian Montes Schütte: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Aarhus University, Fuglesangs Allé 4, 8210 Aarhus V, DK
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the US over the period 2004-2018 at both short and long horizons. Using a subset of ten keywords associated with “jobs”, we construct a large panel of 173 variables using Google’s own algorithms to find related search queries. We find that the best Google Trends model achieves an out-of-sample R2 between 26% and 59% at horizons spanning from one month to a year ahead, strongly outperforming benchmarks based on a large set of macroeconomic and financial predictors. This strong predictability extends to US state-level employment growth, using state-level specific Google search activity. Encompassing tests indicate that when the Google Trends panel is exploited using a non-linear model it fully encompasses the macroeconomic forecasts and provides significant information in excess of those.
Keywords: Google Trends; Forecast comparison; US employment growth; Targeting predictors; Random forests; Keyword search. (search for similar items in EconPapers)
JEL-codes: C22 C53 C55 E17 E24 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-for, nep-lab and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2019-13
Access Statistics for this paper
More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().