Assessing the Accuracy of Google Trends for Predicting Presidential Elections: The Case of Chile, 2006–2021

Francisco Vergara-Perucich ()
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Francisco Vergara-Perucich: Núcleo Centro Producción del Espacio, Facultad de Arquitectura, Animación, Diseño y Construcción, Universidad de Las Américas, Providencia 7500000, Chile

Data, 2022, vol. 7, issue 11, 1-12

Abstract: This article presents the results of reviewing the predictive capacity of Google Trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn Matthei in 2013, Sebastián Piñera and Alejandro Guillier in 2017, and Gabriel Boric and José Antonio Kast in 2021 were reviewed. The time series analyzed were organized on the basis of relative searches between the candidacies, assisted by R software, mainly with the gtrendsR and forecast libraries. With the series constructed, forecasts were made using the Auto Regressive Integrated Moving Average (ARIMA) technique to check the weight of one presidential option over the other. The ARIMA analyses were performed on 3 ways of organizing the data: the linear series, the series transformed by moving average, and the series transformed by Hodrick–Prescott. The results indicate that the method offers the optimal predictive ability.

Keywords: ARIMA; elections; time series; forecasting; Chile (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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