The use of Google Search data for macro-economic nowcasting

1. The use of Google Search data for macro-economic nowcasting Per Nymand-Andersen, European Central Bank CCSA Special session on showcasing big data ESCAP Headquarters, Bangkok, Thailand

2. Agenda 1 Reflections on “big data” for policy purposes 2 Show casing “big data” for macro-economic purposes 3 Preliminary lessons and way forward

3. Reflections on “big data” for policy purposes 1 “Big data are a source of information and intelligence that have been gathered from a recorded action or from a combination of records” • For example: • records of supermarket purchases (Walmart tracts > 1 mil. transactions/hour) • robot and sensor information in production processes • road tolls, train, ship, aeroplane, mobile tracking devices, navigation systems • telephone operators and satellite sensors, Electronic images, • behaviour, event-driven and opinion-gathering from search engines, such as social media (Twitter, blogs, text messages, Facebook, LinkedIn), • speech and word recognition • credit and debit payments, trading and settlement platforms, • The list seem endless as more and more information becomes public and digital

4. Reflections on “big data” for policy purposes 1 The term “big data” – a large variety of interpretation* • While some institutions may consider single sourced data, such as granular “administrative data” (business registers) or micro information data” (security-by-security datasets) as “big data”; others may take a more holistic approach of complexity of combining size, formats and sources mainly focussed on non public/private sources • Big data is not just about large data sets. • The 4 Vs (IBM) relates to Volume, Velocity, Variety and Veracity. Volume Scale of data Velocity Analysis of streaming data Variety Different forms of data Veracity Uncertainty of data “Big data – The hunt for timely insights and decision certainty. Central banking reflections on the use of big data for policy purposes” P. Nymand- Andersen, IFC publication, (2015).

5. Reflections on “big data” for policy purposes 1

6. Show casing “big data” for macro-economic purposes 2 • Since 2008, new and increasing field for experimental nowcasting of mainly consumption and selective macro-economic indicators “Predicting the euro area unemployment rate using Google data: central banks’ interest in and use of big data. ”Nymand- Andersen, P & Koivupalo H, forthcoming publication(2015).

7. Show casing “big data” for macro-economic purposes 2

8. Show casing “big data” for macro-economic purposes 2 • How to use google search data to nowcasteuro area unemployment • Eurostat’s euro area 13 and 19 unemployment rates • testing using two periods; 2011–2012 & 2012–2014 • Dataset: Google search data (google search machines) • using Google’s taxonomy of categorising search terms, includes 26 main categories and 269 sub-categories. (Finance and Banking) • Google search data is an index of weekly volume changes • The volumes are normalised starting at 1.00 and next week value shows the relative change of Google searches within the category (no absolute volumes) • Data from 14 countries: Austria, Belgium, Denmark, France, Germany, Ireland, Italy, Netherlands, Portugal, Spain, Sweden, Slovenia, United Kingdom, USA

9. Show casing “big data” for macro-economic purposes 2 • Two autoregressive models are used to nowcasteuro area unemployment rate • log(yt) = a + b* log(yt-1) + c*log(yt-y12) + et, • log(yt) = a + b* log(yt-1) + c*log(yt-y12) + G + et, • Where Y(t) is the unemployment rate at month(t) • And G is the google search index

10. Show casing “big data” for macro-economic purposes 2

11. Show casing “big data” for macro-economic purposes 2 • Applying the mean absolute error (MAE) • Preliminary indications suggest that the naïve model including the Google data seems to perform better over the two periods • The improvement (reduction in the errors) range from 18.1% to 28,7%

12. Preliminary lessons and way forward 3

13. Preliminary lessons and way forward 3

14. Preliminary lessons and way forward 3 • new ideas for statistical input are always meet with a degree of scepticism • simple, cheap and easy to put into statistics production • creates dependencies though always free in the start up phase • challenges the statistics communication function • Statisticians may need to explore private sources in meeting increasing user demands for statistics

15. Preliminary lessons and way forward 3 Central banks are interested in cooperating in a structural approach • establishing a big data road map • identify joint pilot projects • sharing experience • Relevant pilot projects within the field of using • 1) administrative dataset (e.g. corporate balance sheet data) • 2) web search data set (e.g. Google type search info) • 3) commercial dataset (e.g. credit card operators) • 4) financial market data (e.g. high frequency trading)

16. Outlet for statistical papers including big data 3 • ECB Statistics Paper Series (big data) • “Nowcasting GDP with electronic payments data” by Galbraith J & Tkacz G. • Electronic payment transactions can be used in nowcasting current gross domestic product growth • finds that debit card transactions contribute most to forecast accuracy • “Social media sentiment and consumer confidence” by Daas P & Puts M • Relationships between the changes in consumer confidence and Dutch public social media? • Could be used as an indicator for changes in consumer confidence and as an early indicator • “Quantifying the effects of online bullishness on international financial markets” by Mao H & Counts S, Bollen J. • Develops a measure of investor sentiment based on Twitter and Google search queries • Twitter and Google bullishness are positively correlated to investor sentiment