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Data source combination for tourism demand forecasting

Author

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  • Mingming Hu

    (12664Guangxi University; The Hong Kong Polytechnic University, China)

  • Haiyan Song

    (26680The Hong Kong Polytechnic University, China)

Abstract

Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models.

Suggested Citation

  • Mingming Hu & Haiyan Song, 2020. "Data source combination for tourism demand forecasting," Tourism Economics, , vol. 26(7), pages 1248-1265, November.
    • Handle: RePEc:sae:toueco:v:26:y:2020:i:7:p:1248-1265
    DOI: 10.1177/1354816619872592
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    References listed on IDEAS

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    1. Li, Gang & Song, Haiyan & Witt, Stephen F., 2006. "Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 22(1), pages 57-71.
    2. Pesaran, M. H. & Shin, Y. & Smith, R. J., 1996. "Testing for the 'Existence of a Long-run Relationship'," Cambridge Working Papers in Economics 9622, Faculty of Economics, University of Cambridge.
    3. Goldfarb, Avi & Greenstein, Shane M. & Tucker, Catherine E. (ed.), 2015. "Economic Analysis of the Digital Economy," National Bureau of Economic Research Books, University of Chicago Press, number 9780226206981.
    4. 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.
    5. Marie-Louise Mangion & Ramesh Durbarry & M. Thea Sinclair, 2005. "Tourism Competitiveness: Price and Quality," Tourism Economics, , vol. 11(1), pages 45-68, March.
    6. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    7. Avi Goldfarb & Shane M. Greenstein & Catherine E. Tucker, 2015. "Economic Analysis of the Digital Economy," NBER Books, National Bureau of Economic Research, Inc, number gree13-1.
    8. Tsui, Wai Hong Kan & Ozer Balli, Hatice & Gilbey, Andrew & Gow, Hamish, 2014. "Forecasting of Hong Kong airport's passenger throughput," Tourism Management, Elsevier, vol. 42(C), pages 62-76.
    9. Lynn Wu & Erik Brynjolfsson, 2015. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 89-118, National Bureau of Economic Research, Inc.
    10. 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.
    11. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    12. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    13. Haiyan Song & Stephen F. Witt & Gang Li, 2003. "Modelling and Forecasting the Demand for Thai Tourism," Tourism Economics, , vol. 9(4), pages 363-387, December.
    14. 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.
    15. Clive L. Morley, 2009. "Dynamics in the Specification of Tourism Demand Models," Tourism Economics, , vol. 15(1), pages 23-39, March.
    16. Nada Kulendran & Kenneth Wilson, 2000. "Modelling Business Travel," Tourism Economics, , vol. 6(1), pages 47-59, March.
    17. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    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. 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.
    20. Olugbenga A. Onafowora & Oluwole Owoye, 2012. "Modelling International Tourism Demand for the Caribbean," Tourism Economics, , vol. 18(1), pages 159-180, February.
    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.
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    5. Park, Eunhye & Park, Jinah & Hu, Mingming, 2021. "Tourism demand forecasting with online news data mining," Annals of Tourism Research, Elsevier, vol. 90(C).

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