IDEAS home Printed from https://ideas.repec.org/a/col/000174/019606.html
   My bibliography  Save this article

Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends

Author

Listed:
  • Correa, Alexander

Abstract

Resumen: Este artículo examina si los criterios de búsqueda de Google Trends son útiles para predecir la llegada mensual de turistas a Colombia. Para este fin, se compara un modelo base que utiliza como predictor los rezagos propios de la llegada de turistas con dos especificaciones alternativas: (i) el modelo base aumentado con la inclusión de datos mensuales de Google Trends; y (ii) el modelo base, pero modificado con la inclusión de datos semanales de Google Trends. Los resultados obtenidos presentan evidencia estadísticamente significativa de que los datos de Google Trends aportan beneficios a la evaluación y predicción de llegadas de turistas a Colombia. En particular, se encuentra que datos de alta frecuencia (semanales) agregan alto valor predictivo en comparación con los modelos que usan datos de la misma frecuencia (mensuales). De este modo, la industria del turismo y los encargados de la política pública de turismo pueden apoyarse de la capacidad predictiva de los datos de Google Trends para mejorar sus procesos de planeación en el corto y mediano plazo. Abstract: This study examines whether the Google Trends search criteria are useful in forecasting the monthly arrival of tourists to Colombia. To this end, a baseline model that employs as a predictor the lags values of tourist arrivals is compared with two alternative specifications: (i) the baseline model augmented with monthly data from Google Trends; and (ii) the baseline model but modified with the inclusion of weekly data from Google Trends. The results show statistically significant evidence that Google Trends data provide benefits for the evaluation and prediction of tourist arrivals to Colombia. High-frequency (weekly) data adds high predictive value compared to models that use data of the same fre quency (monthly). In this way, the tourism industry and those in charge of tourism public policy can rely on the predictive capacity of Google Trends data to improve their planning processes in the short and medium run. Résumé: Cet article cherche a savoir si les criteres de recherche de Google Trends sont utiles pour prévoir les arrivées touristiques mensuelles en Colombie. Pour ce faire, nous proposons un modele de base qui utilise comme prédicteur les décalages inhérents a l’arrivée des touristes. Ce modele est ensuite comparé avec deux spécifications alternatives : (i) le modele de base augmenté par l’inclusion des données mensuelles issues de Google Trends ; et (ii) le modele de base augmenté par l’inclusion des données hebdomadaires issues également de Google Trends. Nous montrons que les données de Google Trends apportent des avantages statistiquement significatifs a l’évaluation et la prévision des arrivées touristiques en Colombie. Tout particulierement, les modeles qui utilisent des données a haute fréquence (hebdomadaire), ajoutent une valeur prédictive plus élevée par rapport aux modeles utilisant des données de la meme fréquence (mensuelle). Ainsi, l’industrie du tourisme et les responsables de sa politique publique peuvent s’appuyer sur la capacité prédictive des données Google Trends, afin d’améliorer leurs processus de planification a court et moyen terme.

Suggested Citation

  • Correa, Alexander, 2021. "Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 95, pages 105-134, July.
    • Handle: RePEc:col:000174:019606
    as

    Download full text from publisher

    File URL: https://revistas.udea.edu.co/index.php/lecturasdeeconomia/article/view/343462/20805369
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    2. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    3. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    4. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, July.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
    7. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    8. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    9. Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George, 2011. "Forecasting tourist arrivals using time-varying parameter structural time series models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 855-869.
    10. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    11. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
    12. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    13. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    14. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    15. Gonzalez, Pilar & Moral, Paz, 1995. "An analysis of the international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 11(2), pages 233-251, June.
    16. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    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. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    2. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    3. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    4. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    5. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.
    6. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    7. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    8. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
    9. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    10. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    11. Ahmed Shoukry Rashad, 2022. "The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai," Forecasting, MDPI, vol. 4(3), pages 1-11, July.
    12. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    13. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    14. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    15. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    16. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
    17. Katerina Volchek & Anyu Liu & Haiyan Song & Dimitrios Buhalis, 2019. "Forecasting tourist arrivals at attractions: Search engine empowered methodologies," Tourism Economics, , vol. 25(3), pages 425-447, May.
    18. Serhan Cevik, 2022. "Where should we go? Internet searches and tourist arrivals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4048-4057, October.
    19. Hatice Öncel Çekim & Ahmet Koyuncu, 2022. "The Impact of Google Trends on the Tourist Arrivals: A Case of Antalya Tourism," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(1), pages 1-14, June.
    20. Fabo, B., 2017. "Towards an understanding of job matching using web data," Other publications TiSEM b8b877f2-ae6a-495f-b6cc-9, Tilburg University, School of Economics and Management.

    More about this item

    Keywords

    demanda de turismo; Google Trend; proyecciones; mixed data sampling; llegada de turistas;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • Z32 - Other Special Topics - - Tourism Economics - - - Tourism and Development

    Statistics

    Access and download statistics

    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:col:000174:019606. 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: Universidad de Antioquia. Facultad de Ciencias Economicas. (Laura Maria Posada Arboleda) (email available below). General contact details of provider: https://edirc.repec.org/data/ciantco.html .

    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.