Big Data: Google Searches Predict Unemployment in Finland

Tuhkuri Joonas

Abstract

There are over 3 billion searches globally on Google every day. This report examines whether Google search queries can be used to predict the present and the near future unemployment rate in Finland. Predicting the present and the near future is of interest, as the official records of the state of the economy are published with a delay. To assess the information contained in Google search queries, the report compares a simple predictive model of unemployment to a model that contains a variable, Google Index, formed from Google data. In addition, cross-correlation analysis and Granger-causality tests are performed. Compared to a simple benchmark, Google search queries improve the prediction of the present by 10 % measured by mean absolute error. Moreover, predictions using search terms perform 39 % better over the benchmark for near future unemployment 3 months ahead. Google search queries also tend to improve the prediction accuracy around turning points. The results suggest that Google searches contain useful information of the present and the near future unemployment rate in Finland.

Information om publikationen

Serie
ETLA Raportit - Reports 31
Datum
14.08.2014
Nyckelord
Big Data, Google, Internet, Nowcasting, Forecasting, Unemployment, Time-series analysis
ISSN
2323-2447, 2323-2455 (Pdf)
JEL
C1, C22, C43, C53, C82, E27
Sidor
36
Språk
Finska
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