Tracking activity in real time with Google Trends

Nicolas Woloszko

No 1634, OECD Economics Department Working Papers from OECD Publishing

Abstract: 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.

Keywords: COVID-19; Google Trends; high-frequency; interpretability; machine learning; nowcasting (search for similar items in EconPapers)
JEL-codes: C45 C53 C55 E37 (search for similar items in EconPapers)
Date: 2020-12-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (36)

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Persistent link: https://EconPapers.repec.org/RePEc:oec:ecoaaa:1634-en

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