Forecasting Unemployment with Google Searches

Tuhkuri Joonas

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.

Publication info

Part of researches
ETLAnow
Work and Wealth in the Era of Digital Platforms
Part of programs
Macroeconomy, international economy and cycles
Knowledge and technology
Series
ETLA Working Papers 35
Date
02.03.2016
Keywords
Big Data, Google, Internet, Nowcasting, Forecasting, Unemployment
ISSN
2323-2420, 2323-2439
JEL
C22, C53, C55, C82, E27
Pages
34
Price
15 €
Availability of print version
Available
Language
English