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In search of a job: Forecasting employment growth using Google Trends

Daniel Borup () and Erik Christian Montes Schütte ()
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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

Abstract: 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
Date: 2019-08-22
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