Munich Personal RePEc Archive

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

Fantazzini, Dean (2020): Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries. Forthcoming in: Applied Econometrics (2020): 1 -20.

[img]
Preview
PDF
MPRA_paper_102315.pdf

Download (569kB) | Preview

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.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.