Just Google It: Can Internet Search Terms Help Explain Movements in Retail Sales?

1. Just Google It: Can Internet Search Terms Help Explain Movements in Retail Sales? Daniel Ayoubkhani (ONS) & Matthew Swannell (ONS)

2. Contents • Introduction to Google Trends • Existing Literature • Aims of Current ONS Research • Data • Methods • Results • Conclusions • Considerations

3. 1. Introduction to Google Trends • Google provide information on search query share for a given week • Data are available in 25 top level categories and hundreds of lower level categories • Reported as how share of search queries has grown since 1st week of January 2004

4. 1. Introduction to Google Trends Search Query: Football Transfers Source: Google Insights for Search

5. 1. Introduction to Google Trends Search Query: Football Transfers Summer Transfer Deadline Reached January Transfer Deadline Reached Summer Transfer Window January Transfer Window

6. 2. Existing Literature Choi, H and Varian, H (2009) Predicting the Present with Google Trends: • Paper pioneered use of Google Trends (GT) data as a nowcasting tool • Applied log–linear “nowcast” to US retail sales • Performance of models increased when Google Trends data were included

7. 2. Existing Literature Chamberlin, G (2010) Googling the Present, Economic and Labour Market Review (Dec 2010): • Modelled 11 UK Retail Sales Index (RSI) time series • Relatively simple benchmark models • Alternative models included GT category data as predictors • GT terms significant in eight models

8. 3. Aims of Current ONS Research Focus of this investigation: quality assurance of the UK RSI • Fit benchmark models that are representative of current ONS practice • Fit alternative models that include appropriate GT terms as predictors • Compare models using empirical measures • Draw conclusions to inform ONS strategy

9. 4. Data – Retail Sales Index • All Retail Sales • Non-Specialised Food Stores • Non-Specialised Non-Food Stores • Textiles, Clothing and Footwear • Furniture and Lighting • Home Appliances • Hardware, Paints and Glass • Audio and Video Equipment and Recordings • Books, Newspapers and Stationary • Computers and Telecommunications • Non-Store Retailing

10. 4. Data – Retail Sales Index All extracted RSI time series: • represent monthly GB retail sales • start in January 1988 • end in June 2011 • are not seasonally adjusted • are chained volume indices

11. 4. Data – Retail Sales Index Source: ONS

12. 4. Data – Google Trends • All extracted GT time series: • represent weekly UK search activity • start in January 2004 • end in July 2011 • Each RSI series matched with: • at least one GT search category • top five search queries with each category

13. 4. Data – Google Trends

14. 4. Data – Google Trends • Raw data are weekly growth rates in query shares • Indices constructed by setting first full week in January 2004 to 100 and applying growth rates • Monthly data formed by taking weighted averages of weekly data

15. 5. Methods – Benchmark Models • Each RSI “month” is 4- or 5-week long period (SRP) • Disparity between survey and Gregorian months evolves by one or two days each year (“phase shift”) • One-week long survey break every five or six years • Example – September SRP:

16. 5. Methods – Benchmark Models Therefore SRPs not comparable with each other due to: • their compositions • moving holidays

17. 5. Methods – Benchmark Models • Regression models used to estimate phase shift effects • Example – Spring bank holiday variable:

18. 5. Methods – Benchmark Models Differenced (regular and seasonal) Log transformed Follows an ARMA process

19. 5. Methods – Alternative Models Benchmark models extended with (log transformed, differenced) GT variables • Static relationships estimated for all series • Lagged relationships modelled where identified • Relationships identified at more than one lag modelled both individually and together • Multiple regression models estimated for RSI series matched with more than one GT search category

20. 5. Methods – Alternative Models Lagged relationships identified from cross-correlation plots of pre-whitened series • ARIMA models fit to all RSI and GT series • used the (0,1,1)(0,1,1) model for all series • Each RSI residual series correlated with each of its corresponding GT residual series • series exhibit common trends and seasonality, so correlate the shocks

21. 5. Methods – Alternative Models • Example – Furniture and Lighting vs “garden”

22. 5. Methods – Alternative Models • Example – Furniture and Lighting vs “garden” No significant phase shift effects so models are:

23. 6. Results

24. 6. Results – Furniture and Lighting Top three alternative models in terms of AICC

25. 6. Results – Furniture and Lighting Top three alternative models in terms of MAPE • Out-of-sample, one-step-ahead predictions • 12 periods: July 2010 – June 2011

26. 7. Conclusions • Promising results for some RSI components... • Furniture and Lighting • Hardware, Paints and Glass • Audio Equipment and Recordings • ...but less so for others • All Retail Sales • Non-Specialised Food Stores • Non-Specialised Non-Food Stores Additional information is only useful when the RSI series is not dominated by trend and seasonality

27. 8. Considerations • GT variable selection • Transitory nature of search queries • Changes to GT category taxonomy • Future cost and accessibility of GT data? • Wider applicability to ONS outputs?

28. Questions?