![]() 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. First, a quarterly model of GDP growth is estimated based on Google Trends search intensities at a quarterly frequency. The Weekly Tracker uses a two-step model to nowcast weekly GDP growth based on Google Trends. Using many variables reduces the risk related to structural breaks in specific series, which was highlighted by the failure of the “Google Flu” experiment. ![]() ![]() “maritime transport”, “agricultural equipment”), trade (e.g., “exports”, “freight”) as well as economic sentiment (e.g. “venture capital”, “bankruptcy”), industrial activity (e.g. “real estate agency”, “mortgage”), business services (e.g. from searches for “vehicles”, “households appliances”), labour markets (e.g. The algorithm extracts and compiles information about consumption (e.g. 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. Ĭontact: Questions on the tracker can be sent to with Google Trends (2020), “Tracking activity in real time with Google Trends”, OECD Economics Department Working Papers, No. Scientific publications using the OECD Weekly Tracker may cite the following paper: Woloszko, N. 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. Please note these are not official OECD forecasts, which are most recently published in the OECD Economic Outlook. It covers the period from early 2004 to today. Its methodology is described in this note.Įach series has its own 95% confidence intervals (lower and higher bands). The GDP level Tracker provides estimates of the level of weekly GDP relative to 2019 Q4.It covers the period from early 2020 to today. The GDP growth Tracker (yoy) provides estimates of weekly GDP relative to the same week in the previous year.There are two series of the Weekly Tracker: 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 ). 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 has a wide country coverage of OECD and G20 countries. The OECD Weekly Tracker of GDP growth provides a real-time high-frequency indicator of economic activity using machine learning and Google Trends data. Green growth and sustainable development.
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