Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/201250 
Authors: 
Year of Publication: 
2016
Series/Report no.: 
ETLA Working Papers No. 35
Publisher: 
The Research Institute of the Finnish Economy (ETLA), Helsinki
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.
Subjects: 
Big Data
Google
Internet
Nowcasting
Forecasting
Unemployment
JEL: 
C22
C53
C55
C82
E27
Document Type: 
Working Paper

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