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Foreign arrivals nowcasting in Italy with Google Trends data

Author

Listed:
  • F. Antolini

    (University of Teramo)

  • L. Grassini

    (University of Florence)

Abstract

The development of the ICT has deeply transformed the tourism industry. ICT has become a key determinant for competitiveness that deeply impacts on marketing and communication strategies. Online Travel Agency is accumulating a huge mass of valuable information. Web Data (Big Data) can actually represent an up-to-date information, which can be used as a support to improve statistical information, especially for monitoring current phenomena, as arrivals, spent nights, or the average length of stay. In this respect, an interesting issue is the assessment of the contribution of Web data for forecasting tourism flows. Specifically, nowcasting is a special case of forecasting as it deals with the knowledge of the present, immediate past and very near future. The aim of the paper is to assess the effective advantage of Google Trends (GT) data in forecasting tourist arrivals in Italy. The analysis is related to monthly foreign arrivals in tourist accommodations facilities. Google Trends data are used to predict the monthly number of foreign arrivals released by the Italian national statistical office, which is the dependent variable. Specifically, we have assessed the contribution of lagged GT variables in a standard ARIMA model and in a time series regression model with seasonal dummies and autoregressive components.

Suggested Citation

  • F. Antolini & L. Grassini, 2019. "Foreign arrivals nowcasting in Italy with Google Trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2385-2401, September.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:5:d:10.1007_s11135-018-0748-z
    DOI: 10.1007/s11135-018-0748-z
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    Cited by:

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    2. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    3. Azmat Gani, 2022. "Using a consumer choice model to explain the effect of the newly developed oxford COVID-19 government stringency measure on hotel occupancy rates," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4313-4333, December.
    4. Massimiliano Giacalone & Raffaele Mattera & Eugenia Nissi, 2020. "Economic indicators forecasting in presence of seasonal patterns: time series revision and prediction accuracy," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 67-84, February.
    5. 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.
    6. 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.
    7. Fernando Delbianco & Andrés Fioriti & Fernando Tohmé & Federico Contiggiani, 2022. "A Tale of two narratives: assessing the sociological hypothesis of the appeal of the US dollar in Argentina," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3519-3537, October.

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