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Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data

Author

Listed:
  • Silvia Emili

    (9296University of Bologna, Italy)

  • Paolo Figini

    (9296University of Bologna, Italy; North-West University, South Africa)

  • Andrea Guizzardi

    (9296University of Bologna, Italy)

Abstract

We investigate if and how climate indicators and web-traffic data may improve the estimates of demand functions’ parameters, considering specific origins and destinations. Overall, augmented demand functions show better fit and more reliable price and income elasticities whether the demand is measured with arrivals or with overnights. However, heterogeneity stemming from the main type of tourism (business vs. cultural vs. sea and sun) affects both the web-based and the climate indicators better describing tourists demand as well as their optimal lags. Our findings highlight the utility of such prompt and territorial detailed information for local policymakers, showing, however, how sensitive different demand segments are to policy intervention.

Suggested Citation

  • Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.
    • Handle: RePEc:sae:toueco:v:26:y:2020:i:7:p:1129-1151
    DOI: 10.1177/1354816619867804
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    References listed on IDEAS

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    1. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
    2. 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.
    3. Andergassen, Rainer & Candela, Guido & Figini, Paolo, 2013. "An economic model for tourism destinations: Product sophistication and price coordination," Tourism Management, Elsevier, vol. 37(C), pages 86-98.
    4. Cuccia, Tiziana & Rizzo, Ilde, 2011. "Tourism seasonality in cultural destinations: Empirical evidence from Sicily," Tourism Management, Elsevier, vol. 32(3), pages 589-595.
    5. Rainer Andergassen & Guido Candela & Paolo Figini, 2017. "The management of tourism destinations," Tourism Economics, , vol. 23(1), pages 49-65, February.
    6. Martins, Luís Filipe & Gan, Yi & Ferreira-Lopes, Alexandra, 2017. "An empirical analysis of the influence of macroeconomic determinants on World tourism demand," Tourism Management, Elsevier, vol. 61(C), pages 248-260.
    7. Seetaram, Neelu & Forsyth, Peter & Dwyer, Larry, 2016. "Measuring price elasticities of demand for outbound tourism using competitiveness indices," Annals of Tourism Research, Elsevier, vol. 56(C), pages 65-79.
    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. Dogru, Tarik & Sirakaya-Turk, Ercan & Crouch, Geoffrey I., 2017. "Remodeling international tourism demand: Old theory and new evidence," Tourism Management, Elsevier, vol. 60(C), pages 47-55.
    10. John Trevor Coshall, 2005. "A Selection Strategy for Modelling UK Tourism Flows by Air to European Destinations," Tourism Economics, , vol. 11(2), pages 141-158, June.
    11. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    12. Haiyan Song & Gang Li & Stephen F. Witt & Baogang Fei, 2010. "Tourism Demand Modelling and Forecasting: How Should Demand Be Measured?," Tourism Economics, , vol. 16(1), pages 63-81, March.
    13. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    14. Peter Fuleky & Carl S. Bonham & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii Tourism," Working Papers 2013-2R, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Aug 2013.
    15. Sainaghi, Ruggero & Baggio, Rodolfo, 2017. "Complexity traits and dynamics of tourism destinations," Tourism Management, Elsevier, vol. 63(C), pages 368-382.
    16. Crouch, Geoffrey I., 1996. "Demand elasticities in international marketing : A meta-analytical application to tourism," Journal of Business Research, Elsevier, vol. 36(2), pages 117-136, June.
    17. Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.
    18. 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.
    19. Christine Lim, 1997. "An Econometric Classification and Review of International Tourism Demand Models," Tourism Economics, , vol. 3(1), pages 69-81, March.
    20. Paul Smith, 2016. "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 263-284, April.
    21. 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.
    22. Schiff, Aaron & Becken, Susanne, 2011. "Demand elasticity estimates for New Zealand tourism," Tourism Management, Elsevier, vol. 32(3), pages 564-575.
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