![]() These caveats call for specific attention and statistical pre processing. High-frequency and big data have limitations as scientific analysis is usually not the original purpose of their collection. searches for food banks).1 Using multiple variables reduces the risk related to structural breaks in specific series, which was highlighted by the failure of the ‘Google Flu’ experiment.3 ![]() searches for recession) and poverty (e.g. searches for exports or freight) as well as economic sentiment (e.g. searches for maritime transport or agricultural equipment), trade (e.g. ![]() searches for ‘venture capital or bankruptcy), industrial activity (e.g. searches for real estate agencies or mortgages), business services (e.g. searches for unemployment benefits), housing (e.g. from searches for vehicles or households appliances), labour markets (e.g. The algorithm extracts and compiles information about consumption (e.g. Signals about multiple facets of the economy are aggregated to infer a timely picture of the macro economy. Cross country differences related to Google Search’s market penetration or institutional settings are flexibly captured as the neural network allows for all possible interactions between Google Trends variables and country dummies. Google Trends ‘big’ data make it possible to use algorithms that are powerful but require large samples. The algorithm captures non-linearities that are likely to be key in extreme situations, but which are difficult to estimate with more conventional econometric approaches. The relationship between Google Trends variables and GDP growth is fitted using a machine learning algorithm (‘neural network’). The OECD Weekly Tracker can therefore be interpreted as an estimate of the year on year growth rate of ‘weekly GDP’ (the same week compared to the previous year). ![]() 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. A model of GDP growth based on Google Trends and machine learning The OECD Economic Outlook (OECD 2020), and a recent OECD paper (Woloszko 2020), discuss one such indicator based on Google Trends, which are used to construct a ‘Weekly Tracker’ that provides real-time estimates of GDP growth in 46 economies covering G20, OECD, and OECD partner countries. The OECD Weekly Tracker data are updated on a regular basis and made available on this webpage. Given that GDP figures are usually only available on a quarterly basis, and that monthly survey-based indicators (such as the Purchasing Managers’ Indices) can become unreliable when changes in economic activity are abrupt and massive, the current crisis has prompted a search for alternative high frequency indicators of economic activity. ![]() A pre-requisite for good macroeconomic policymaking is timely information on the current state of the economy, particularly when economic activity is changing rapidly. ![]()
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