Tracking activity in real time with Google Trends

Nicolas Woloszko

Organisation for Economic Co-operation and Development

关键词: Google Trends;    machine learning;    COVID-19;    high-frequency;    interpretability;    nowcasting;   

DOI  :  https://doi.org/10.1787/6b9c7518-en

学科分类：社会科学、人文和艺术（综合）

来源: OECD iLibrary

【 摘 要 】

This paper introduces the OECD Weekly Tracker of economic activity for 46 OECD and G20 countries using Google Trends search data. The Tracker performs well in pseudo-real time simulations including around the COVID-19 crisis. The underlying model adds to the previous Google Trends literature in two respects: (1) the data are adjusted for common long-term bias and (2) the data include variables based on both Google Search categories and topics (the latter being a collection of related keywords), thus further exploiting the potential of Google Trends. The paper highlights the predictive power of specific topics, including "bankruptcies", "economic crisis", "investment", "luggage" and "mortgage". Calibration is performed using a neural network that captures non-linear patterns, which are shown to be consistent with economic intuition using machine learning interpretability tools ("Shapley values"). The tracker sheds light on the recent downturn and the dynamics of the rebound, and provides evidence about lasting shifts in consumption patterns.