Google Econometrics and Unemployment Forecasting

By N. Askitas, Klaus F. Zimmermann. Social Science Research Network, 2009

597 citations recorded by the source corpus.

The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword searches and unemployment rates using monthly German data and exhibits a strong potential for the method used.

Subfields: Economics and Econometrics, Epidemiology, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/d19369b1195b4b7e410bd59a687e5d484aa0bdcd

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Citations in the corpus

Can Google Data Help Predict French Youth Unemployment (2012, background): “Askitas and Zimmermann (2009) use four groups consisting of one to eight keywords and the boolean operator “OR”.”
Predicting Initial Claims for Unemployment Benefits (2009, methodology): “We applied standard ARIMA model selection procedures and selected AR(1) model as a baseline.”
Internet search behavior as an economic forecasting tool: The case of inflation expectations (2011, unknown): “4 basis.(3) The topic in this case is inflation, as measured by changes in the number of searches performed on the keyword “inflation” in the US.”
Predicting consumer behavior with Web search (2010, background): “Finally, Choi and Varian (3, 4) have compared search volume to economic activity, including auto and home sales, international visitor statistics, and US unemployment claims; and similar work has been reported for German unemployment claims (11).”
The Predictive Power of Google Searches in Forecasting Unemployment (2012, methodology): “Billari et al. (2012) use web-search data related to fertility as a leading indicator of the US birth rate.”
Forecasting tourist arrivals with machine learning and internet search index (2017, background): “9], economic indicators [10], unemployment rate [11] and stock returns [12].”
Forecasting Chinese tourist volume with search engine data (2015, background): “…disease outbreaks (Carneiro & Mylonakis, 2009), consumer consumption (Vosen & Schmidt, 2011), popularity of songs and movies (Goel, Hofman, Lahaie, Pennock, & Watts, 2010), unemployment rates (Askitas & Zimmermann, 2009), and hotel room demand (Pan, Wu, & Song, 2012; Yang, Pan, & Song, 2014).”
Predicting the Present with Google Trends (2009, unknown): “This work was widely publicized and stimulated several further findings in epidemiology, including Brownstein et al. [2009], Corley et al.”
Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends (2009, background): “Carroll et al. (1994) and Ludvigson (2004) find in insample regressions that consumer sentiment indicators nevertheless have explanatory power for US consumption additional to that contained in other macroeconomic variables. Other studies, including Croushore (2005) who uses real-time data for out-o”