The OECD Weekly Tracker of GDP growth provides a real-time high-frequency indicator of economic activity using machine learning and Google Trends data. It has a wide country coverage of OECD and G20 countries. The Tracker is thus particularly well suited to assessing activity when it is changing very rapidly due to the impact of a major shock. It applies a machine learning model to a panel of Google Trends data for 46 countries, and aggregates together information about search behaviour related to consumption, labour markets, housing, trade, industrial activity and economic uncertainty (see Working paper).
There are two series of the Weekly Tracker:
Each series has its own 95% confidence intervals (lower and higher bands).
Please note these are not official OECD forecasts, which are most recently published in the OECD Economic Outlook. However, the Tracker is one of several indicators that feeds into the OECD forecast process, which helps to situate the current state of the economy.
Scientific publications using the OECD Weekly Tracker may cite the following paper: Woloszko, N. (2020), “Tracking activity in real time with Google Trends”, OECD Economics Department Working Papers, No. 1634, OECD Publishing, Paris, https://dx.doi.org/10.1787/6b9c7518-en.
Contact: Questions on the tracker can be sent to OECDWeeklyTracker@oecd.org
Nowcasting with Google Trends
Signals about multiple facets of the economy from Google Trends are extracted and aggregated using machine learning in order to infer a timely picture of the macro economy. The algorithm extracts and compiles information about consumption (e.g. from searches for “vehicles”, “households appliances”), labour markets (e.g. “unemployment benefits”), housing (e.g. “real estate agency”, “mortgage”), business services (e.g. “venture capital”, “bankruptcy”), industrial activity (e.g. “maritime transport”, “agricultural equipment”), trade (e.g., “exports”, “freight”) as well as economic sentiment (e.g. “recession”) and poverty (e.g. “food bank”). Using many variables reduces the risk related to structural breaks in specific series, which was highlighted by the failure of the “Google Flu” experiment.
The Weekly Tracker uses a two-step model to nowcast weekly GDP growth based on Google Trends. First, a quarterly model of GDP growth is estimated based on Google Trends search intensities at a quarterly frequency. Second, the relationship between Google Trends and activity, using the same elasticities estimated from the quarterly model, is applied to the weekly Google Trends series to yield a weekly tracker. The OECD Weekly Tracker can thus be interpreted as an estimate of the year on year growth rate of “weekly GDP” (the same week compared to the previous year).
High-frequency and big data have limitations as scientific analysis is usually not the original purpose of their collection. These caveats call for specific attention and statistical pre processing. Among the many available Google Trends variables, 215 “categories” and “topics” are judged relevant for economic analysis and selected to feature in the model. Selected variables are transformed to year on year growth rates. Finally, as the Google Search user base has increased dramatically since 2004, the relative search intensities of most search categories decrease over time. This long term trend is filtered out using a methodology described in Woloszko (2020).
Woloszko, N. (2020), "Tracking activity in real time with Google Trends", OECD Economics Department Working Papers, No. 1634, OECD Publishing, Paris, https://doi.org/10.1787/6b9c7518-en.
Woloszko, N., Tracking GDP using Google Trends and machine learning: A new OECD model, VoxEU, December 2020
OECD (2020), OECD Economic Outlook, Volume 2020 Issue 2: Preliminary version, OECD Publishing, Paris, https://dx.doi.org/10.1787/39a88ab1-en.
Woloszko, N., Can Google Trends be used to track economic activity in real-time?, Ecoscope post, December 2020.
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