New approaches for data collection and analyses

Per Nymand-AndersenEuropean Central Bank, Directorate General Statistics

CCSA session on International StatisticsAnkara, 5 SEPTEMBER 2013

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Exploring statistics from the internet

Characteristics of the statistics

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Lessons learned and way forward

Exploring the statistics for analytical purposes

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4 Preliminary results

Agenda

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1 Exploring statistics from the internet

Using Google Trends data - http://www.google.com/trends

Increasing use of internet data for conducting consumer analysis and as predictor for selective macro-economics indicators

The majority of literature is based on Google search; a database storing the terms used in Google search (Search, YouTube, Images)

Could be useful for now casting and short term forecasting of consumer trends mainly where statistics is not available or to gauge directions prior to official statistics is released

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Using Google Trends data - http://www.google.com/trends

Free public available dataset; search per country, category, period

Google taxonomy of 256 categories (“jobs” including “job listings” “career resources and planning”, “resumes & portfolios”, “developing jobs)

Overview of increases and decreases in the use of search category in real time (normalised within search categories)

Exploring statistics from the internet

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Using Google Trends Data - http://www.google.com/trends

Characteristics of the statistics

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2 Characteristics of the statistics

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3 Exploring the statistics for analytical purposes

Using Google Trends data - http://www.google.com/trends

“Nowcasting unemployment rate in Turkey: let’s ask Google” Meltem Gülenay Chadwick & Gönül Sengül (June 2012) Central Bank of the Republic of Turkey.

Linear regression models and Bayesian Model Averaging to nowcast non agriculture unemployment rate in Turkey.

Finds that using the Google trends perform statistically better than using a benchmark model both in-sample and out of sample results (RMSE)

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New and increasing field for experimental nowcasting for mainly consumption and selective macro-indicators

→Since 2008, research institutions and universities are using

Google trends data: Ginsberg (2008) → influenza epidemics),

Hal Varian and Choi (2009) → retail sales, home sales, travel.

Vosen & Schmidt (2011) → private consumption in Germany

Carriere-Swallow (2011) → car purchases in Chile

Lynn Wu & Erik Brynjolfsson) → UK housing prices & sales

Hal Varian and Choi → unemployment rate in US

Hyunyoung Choi, Rob ON, Hal Varian (2011) – CPI !

Exploring the statistics for analytical purposes

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ECB’s on-going research: “Nowcasting European Unemployment Using Internet Search Data” (Morgan, Muzikarova & Onorante, 2013)

Data: individual Google Trends internet searches for DE, FR, IT, ES, and NL starting in 2004; weekly & monthly frequency

Deliverable: euro area aggregate (using German, French, Italian, Spanish & Dutch search terms) as an early diagnostic tool for euro area unemployment

Empirical method to assess each search term’s (or their combination) explanatory power for unemployment: Bayesian Model Averaging averaging models by their in-sample RMSE (hedging against misspecification)

Tentative conclusions: Google appear informative, can substantially improve on autoregressive models. The reduction in RMSFE in nowcasting varies across countries but can reach 80% compared to the naïve model

3 Exploring the statistics for analytical purposes

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4 Preliminary results

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4 Preliminary results

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5 Lessons learned and way forward

large potential for exploring new causality in understanding consumer behaviour, retail market and certain macroeconomic statistics, and ability to build new consumer indicators, indexes of certain product classes and new economic consumer theories

Predominate results are tested for unemployment, tourism, private consumption and housing markets

increasing use and developing literature

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5 Lessons learned and way forward

applying data and statistics from the internet is subject to obtaining sufficient information on the methodology applied (new private data sources may consider this as an intellectual competitive advantage)

new ideas for statistics input are always meet with a degree of scepticism

simple, cheap and easy to put into statistics production

challenges the statistics communication function

creates dependencies though always free in the start up phase

Statisticians may need to explore private sources in meeting increasing user demands for statistics