Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries

Dean Fantazzini

Applied Econometrics, 2020, vol. 59, 33-54

Abstract: The ability of Google Trends data to forecast the number of new daily cases and deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the computations of lag correlations between confirmed cases and Google data, Granger causality tests, and an out-of-sample forecasting exercise with 18 competing models with a forecast horizon of 14 days ahead. This evidence shows that Google-augmented models outperform the competing models for most of the countries. This is significant because Google data can complement epidemiological models during difficult times like the ongoing COVID-19 pandemic, when official statistics maybe not fully reliable and/or published with a delay. Moreover, real-time tracking with online-data is one of the instruments that can be used to keep the situation under control when national lockdowns are lifted and economies gradually reopen.

Keywords: Covid-19; Google Trends; VAR; ARIMA; ARIMA-X; ETS; LASSO; SIR model. (search for similar items in EconPapers)
JEL-codes: C22 C32 C51 C53 G17 I18 I19 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Working Paper: Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries (2020)
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