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Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data

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  • Havranek, Tomas
  • Zeynalov, Ayaz

Abstract

In this paper, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using Mixed-data sampling (MIDAS). Our results stress the potential of Google Trends to offer more accurate prediction in the context of tourism: we find that Google Trends information, both two months and one week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model that employs weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

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  • Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:90205
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    Cited by:

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    2. Patrik Kajzar & Radim Dolák & Radmila Krkošková, 2021. "Using Google Trends in International Tourism: A Case Study of the Czech and Slovak Republics," Academica Turistica - Tourism and Innovation Journal, University of Primorska Press, vol. 14(2), pages 205-216.
    3. Marta Crispino & Vincenzo Mariani, 2023. "A tool to nowcast tourist overnight stays with payment data and complementary indicators," Questioni di Economia e Finanza (Occasional Papers) 746, Bank of Italy, Economic Research and International Relations Area.
    4. Tianxiang Zheng & Shaopeng Liu & Zini Chen & Yuhan Qiao & Rob Law, 2020. "Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    5. 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.
    6. Wanhai You & Yuming Huang & Chien‐Chiang Lee, 2024. "Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 473-489, March.
    7. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).
    8. Juan Manuel García Sánchez & Xavier Vilasís Cardona & Alexandre Lerma Martín, 2022. "Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting," Forecasting, MDPI, vol. 4(3), pages 1-20, July.
    9. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    10. Nava, Consuelo R. & Osti, Linda & Zoia, Maria Grazia, 2022. "Forecasting Domestic Tourism across Regional Destinations through MIDAS Regressions," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202207, University of Turin.
    11. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    12. 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.
    13. Abay, Kibrom A. & Ibrahim, Hosam, 2020. "Winners and losers from COVID-19: Evidence from Google search data for Egypt," MENA policy notes 8, International Food Policy Research Institute (IFPRI).
    14. 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.

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    More about this item

    Keywords

    Google trends; mixed-frequency data; forecasting; tourism;
    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

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