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New approaches for data collection and analyses

New approaches for data collection and analyses. Per Nymand-Andersen European Central Bank, Directorate General Statistics CCSA session on International Statistics Ankara, 5 September 2013. Agenda. 1. Exploring statistics from the internet. 2. Characteristics of the statistics. 3.

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New approaches for data collection and analyses

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  1. New approaches for data collection and analyses Per Nymand-AndersenEuropean Central Bank, Directorate General StatisticsCCSA session on International StatisticsAnkara, 5 September 2013

  2. Agenda 1 Exploring statistics from the internet 2 Characteristics of the statistics 3 Exploring the statistics for analytical purposes 4 Preliminary results 5 Lessons learned and way forward

  3. Exploring statistics from the internet 1 • 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

  4. Exploring statistics from the internet 1 • 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)

  5. Characteristics of the statistics 2 Using Google Trends Data - http://www.google.com/trends

  6. Characteristics of the statistics 2

  7. Exploring the statistics for analytical purposes 3 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)

  8. Exploring the statistics for analytical purposes 3 • 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 !

  9. Exploring the statistics for analytical purposes 3 • 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

  10. Preliminary results 4

  11. Preliminary results 4

  12. Lessons learned and way forward 5 • 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

  13. Lessons learned and way forward 5 • 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

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