IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i11p143-d955366.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/11/143/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/7/11/143/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    4. Askitas, Nikos, 2015. "Calling the Greek Referendum on the Nose with Google Trends," IZA Discussion Papers 9569, Institute of Labor Economics (IZA).
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fofana, Abdulai & Toma, Luiza & Moran, Dominic & Gunn, George J. & Stott, Alistair W., 2009. "Measuring the economic benefits and costs of Bluetongue virus outbreak and control strategies in Scotland," 83rd Annual Conference, March 30 - April 1, 2009, Dublin, Ireland 51052, Agricultural Economics Society.
    2. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2015. "Forecasting the price of gold," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4141-4152, August.
    3. Ntebogang Dinah Moroke, 2014. "The robustness and accuracy of Box-Jenkins ARIMA in modeling and forecasting household debt in South Africa," Journal of Economics and Behavioral Studies, AMH International, vol. 6(9), pages 748-759.
    4. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
    5. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.
    6. repec:ipg:wpaper:2014-480 is not listed on IDEAS
    7. Rizwan Raheem AHMED & Dalia STREIMIKIENE & Saghir Pervaiz GHAURI & Muhammad AQIL, 2021. "Forecasting Inflation by Using the Sub-Groups of both CPI and WPI: Evidence from Auto Regression (AR) and ARIMA Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 144-161, June.
    8. Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.
    9. Javed Farhan & Ghim Ping Ong, 2018. "Forecasting seasonal container throughput at international ports using SARIMA models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 20(1), pages 131-148, March.
    10. Nyoni, Thabani, 2018. "Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach," MPRA Paper 88622, University Library of Munich, Germany, revised 19 Aug 2018.
    11. Meyler, Aidan & Kenny, Geoff & Quinn, Terry, 1998. "Forecasting irish inflation using ARIMA models," MPRA Paper 11359, University Library of Munich, Germany.
    12. Gatt, William, 2013. "Forecasting inflation at the Central Bank of Malta�," MPRA Paper 56876, University Library of Munich, Germany.
    13. Caruso, Alberto & Reichlin, Lucrezia & Ricco, Giovanni, 2019. "Financial and fiscal interaction in the Euro Area crisis: This time was different," European Economic Review, Elsevier, vol. 119(C), pages 333-355.
    14. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    15. Stefan Laséen & Andrea Pescatori, 2020. "Financial stability and interest‐rate policy: A quantitative assessment of costs and benefit," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(3), pages 1246-1273, August.
    16. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    17. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    18. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    19. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2001. "Comparing dynamic equilibrium economies to data," FRB Atlanta Working Paper 2001-23, Federal Reserve Bank of Atlanta.
    20. Gossé, Jean-Baptiste & Guillaumin, Cyriac, 2013. "L’apport de la représentation VAR de Christopher A. Sims à la science économique," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 309-319, Décembre.
    21. Neely, Christopher J. & Weller, Paul, 2000. "Predictability in International Asset Returns: A Reexamination," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(4), pages 601-620, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:143-:d:955366. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.