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

Author

Listed:
  • Francisco Vergara-Perucich

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

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.

Suggested Citation

  • Francisco Vergara-Perucich, 2022. "Assessing the Accuracy of Google Trends for Predicting Presidential Elections: The Case of Chile, 2006–2021," Data, MDPI, vol. 7(11), pages 1-12, October.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:143-:d:955366
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    References listed on IDEAS

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    1. Stockton, David J & Glassman, James E, 1987. "An Evaluation of the Forecast Performance of Alternative Models of Inflation," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 108-117, February.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Martín Arias-Loyola, 2021. "Evade neoliberalism’s turnstiles! Lessons from the Chilean Estallido Social," Environment and Planning A, , vol. 53(4), pages 599-606, June.
    4. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    5. Nikos Askitas, 2015. "Calling the Greek Referendum on the nose with Google Trends," RatSWD Working Papers 249, German Data Forum (RatSWD).
    6. Chih‐Yu Chin & Cheng‐Lung Wang, 2021. "A new insight into combining forecasts for elections: The role of social media," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 132-143, January.
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    1. Alexander J. Smith & Juan Graña & Rowalt Alibudbud & Antonio Ventriglio & Anna Buadze & Michael Liebrenz, 2024. "Mental health and the ballot box: A correlational analysis of Google searches for mental health and national election periods in the United States and the United Kingdom from 2008 to 2020," International Journal of Social Psychiatry, , vol. 70(6), pages 1155-1163, September.

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