Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/201313 
Titel (übersetzt): 
Big Data: Google Searches Predict Unemployment in Finland
Autor:innen: 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
ETLA Report No. 31
Verlag: 
The Research Institute of the Finnish Economy (ETLA), Helsinki
Zusammenfassung (übersetzt): 
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.
Schlagwörter: 
Big Data
Google
Internet
Nowcasting
Forecasting
Unemployment
Time-series analysis
JEL: 
C1
C22
C43
C53
C82
E27
Dokumentart: 
Research Report

Datei(en):
Datei
Größe
404.19 kB





Publikationen in EconStor sind urheberrechtlich geschützt.