Forecasting Unemployment with Google Searches

Joonas Tuhkuri

No 35, ETLA Working Papers from The Research Institute of the Finnish Economy

Abstract: Data on Google searches help predict the unemployment rate in the U.S. But the predictive power of Google searches is limited to short-term predictions, the value of Google data for forecasting purposes is episodic, and the improvements in forecasting accuracy are only modest. The results, obtained by (pseudo) out-of-sample forecast comparison, are robust to a state-level fixed effects model and to different search terms. Joint analysis by cross-correlation function and Granger non-causality tests verifies that Google searches anticipate the unemployment rate. The results illustrate both the potentials and limitations of using big data to predict economic indicators.

Keywords: Big Data; Google; Internet; Nowcasting; Forecasting; Unemployment (search for similar items in EconPapers)
JEL-codes: C22 C53 C55 C82 E27 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2016-03-02
New Economics Papers: this item is included in nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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