Abstract

http://ssrn.com/abstract=1659302
 
 

Citations (35)



 
 

Footnotes (6)



 


 



Predicting the Present with Google Trends


Hal R. Varian


University of California, Berkeley - School of Information; University of California, Berkeley - Operations and Information Technology Management Group; National Bureau of Economic Research (NBER)

Hyunyoung Choi


Triggit, Inc

April 2, 2009


Abstract:     
Can Google queries help predict economic activity?

The answer depends on what you mean by "predict." Google Trends and Google Insights for Search provide a real time report on query volume, while economic data is typically released several days after the close of the month. Given this time lag, it is not implausible that Google queries in a category like "Automotive/Vehicle Shopping" during the first few weeks of March may help predict what actual March automotive sales will be like when the official data is released halfway through April.

That famous economist Yogi Berra once said "It's tough to make predictions, especially about the future." This inspired our approach: let us lower the bar and just try to predict the present.

Our work to date is summarized in a paper called Predicting the Present with Google Trends. We find that Google Trends data can help improve forecasts of the current level of activity for a number of different economic time series, including automobile sales, home sales, retail sales, and travel behavior.

Even predicting the present is useful, since it may help identify "turning points" in economic time series. If people start doing significantly more searches for "Real Estate Agents" in a certain location, it is tempting to think that house sales might increase in that area in the near future.

Our paper outlines one approach to short-term economic prediction, but we expect that there are several other interesting ideas out there. So we suggest that forecasting wannabes download some Google Trends data and try to relate it to other economic time series. If you find an interesting pattern, post your findings on a website and send a link to econ-forecast@google.com. We'll report on the most interesting results in a later blog post.

It has been said that if you put a million monkeys in front of a million computers, you would eventually produce an accurate economic forecast. Let's see how well that theory works.

Number of Pages in PDF File: 23

Keywords: Google Trends, Nowcasting

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Date posted: August 17, 2010  

Suggested Citation

Varian, Hal R. and Choi, Hyunyoung, Predicting the Present with Google Trends (April 2, 2009). Google Research Blog http://googleresearch.blogspot.com/2009/04/predicting-present-with-google-trends.html. Available at SSRN: http://ssrn.com/abstract=1659302 or http://dx.doi.org/10.2139/ssrn.1659302

Contact Information

Hal R. Varian
University of California, Berkeley - School of Information ( email )
102 South Hall
Berkeley, CA 94720-4600
United States
510-642-9980 (Phone)
510-642-5814 (Fax)
HOME PAGE: http://www.sims.berkeley.edu/~hal/people/hal/biography.html
University of California, Berkeley - Operations and Information Technology Management Group ( email )
545 Student Services Building
Berkeley, CA 94720
United States
510-643-6388 (Phone)
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Hyunyoung Choi (Contact Author)
Triggit, Inc ( email )
1535 Mission Street
San Francisco, CA 94134
United States
Feedback to SSRN


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