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

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2

Reflections on “big data” for policy purposes

Show casing “big data” for macro-economic purposes

3 Preliminary lessons and way forward

Agenda

1 Reflections on “big data” for policy purposes

“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

1 Reflections on “big data” for policy purposes

The term “big data” – a large variety of interpretation*

“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).

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.

Velocity Analysis of streaming

data

VolumeScale of data

VarietyDifferent

forms of data

Veracity Uncertainty

of data

1 Reflections on “big data” for policy purposes

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

UnemploymentStock market

House marketPredict sales/consumption

TravelConsumer sentiment

InflationState of economy

Detect influenza

0 2 4 6 8 10 12

Macro-economic topic and number of releases

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).

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

Authors Area of macro-economic topic

Hal Varian & Choi (2009, 2011, 2013) unemployment rate, retail sales, home sales, travel/tourism, car sales, consumer confidence,

Zimmermann K & Askitas N (2009) DE unemployment rate

D’Amuri F, & Marcucci J (2010, 2013) US unemployment rate

McLaren N & Shanbhogue R (2011) UK unemployment rate & housing market trends

Vosen & Schmidt (2011) DE private consumption

Carriere-Swallow (2011) Car purchases in Chile

Guzmán G (2011) Inflations

Fantazzini D & Toktamysova Z (2014) German car sales

Morgan J, e all (2015) DE, FR, IT, ES NL unemployment rates

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

How to use google search data to nowcast euro 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

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

Two autoregressive models are used to nowcast euro 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

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

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

Unemployment rate – EA13 Unemployment rate – EA18

MAE/Forecast period

Jan2011–Dec2012

Nov2012–Oct2014

Jan2011–Dec2012

Nov2012–Oct2014

Base model 1,97 2,23 1,97 2,02base model Google data

1,61 1,73 1,41 1,57

Errors reduced 18,1% 22,6% 28,7% 22,2%

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%

3 Preliminary lessons and way forwardRobustness

•stability of search terms

•volatility in analytical results

•based on one search engine

Methodology

•coverage, weights, normalisations

•aggregation methods

•price information

•short time series

Quality

•differ across regions

•no quality measurements

•No unit tracking

•rebasing and time lag

•home and host concept

3 Preliminary lessons and way forwardUsability

• public and free, easy to use

• one system for all countries

• comparability & timeliness

• large taxonomy of searches

Availability

• nowcasting of retail consumption and selective macro-economic indicators

• conjunctural analysis

• consumer behaviour

• price indexes

Innovation

• trends in communications

•product loyalty

•advertisement

•social patterns in retail markets

•households & business surveys

3 Preliminary lessons and way forward

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

3 Preliminary lessons and way forward

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)

3 Outlet for statistical papers including big data

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