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

Contents 1.Introduction to Google Trends 2.Existing Literature 3.Aims of Current ONS Research 4.Data 5.Methods 6.Results 7.Conclusions 8.Considerations

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 1 st week of January 2004

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

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

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 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 2. Existing Literature

Focus of this investigation: quality assurance of the UK RSI 1.Fit benchmark models that are representative of current ONS practice 2.Fit alternative models that include appropriate GT terms as predictors 3.Compare models using empirical measures 4.Draw conclusions to inform ONS strategy 3. Aims of Current ONS Research

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 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 4. Data – Retail Sales Index

Source: ONS

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

4. Data – Google Trends RSI Series: Furniture and Lighting Google Trends CategoryGoogle Trends Queries Lightinglighting, light, lights, lamp, lamps Home and Gardenfurniture, ikea, garden, b&q, homebase Homemaking and Interior Decor blinds, curtains, curtains curtains curtains, bedroom, ikea Home Furnishings furniture, ikea, beds, lighting, table table

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

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:

5. Methods – Benchmark Models Therefore SRPs not comparable with each other due to: their compositions moving holidays HolidayPositionSRP Easter Good Friday and Easter Monday Mar or Apr Spring (Late May)Last Monday in MayMay or Jun Summer (Late August) Last Monday in August Aug or Sep

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

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

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

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

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

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

6. Results Component of the RSI (and no. alternative models fitted) % Alternative models with AICC lower than benchmark % GT terms significant at 5% level All Retail Sales (8) Non–Specialised Food Stores (6)0.0 Non-Specialised Non-Food Stores (6) Textiles, Clothing and Footwear (23) Furniture and Lighting (31) Home Appliances (7) Hardware, Paints and Glass (6) Audio Equipment and Recordings (44) Books, Newspapers and Stationary (6) Computers and Telecoms (31) Non-Store Retailing (7)42.9

6. Results – Furniture and Lighting Top three alternative models in terms of AICC GT Term in ModelLag(s)GT CategoryAICC lighting0Home Furnishings curtains curtains curtains 0 & 1 Homemaking & Interior Decor lights0Lighting Benchmark432.29

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 GT Term in ModelLag(s)GT CategoryMAPE lighting0Home Furnishings2.38 lighting0Lighting2.49 Home Furnishings0N/A2.51 Benchmark3.87

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

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?

Questions?