![]() 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).
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