using google trends data for stats paper

#Using google trends data for stats paper series

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).

#Using google trends data for stats paper series

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.