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Arrivals of Tourists in Cyprus: Mind the Web Search Intensity

Author

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  • Theologos Dergiades
  • Eleni Mavragani
  • Bing Pan

Abstract

This paper validates the raison d’être of the effortlessly recovered web Search Intensity Indices (SII) for predicting the arrivals of tourists in Cyprus. By using monthly data (2004-2015) and two causality testing procedures we find, for properly selected key-phrases, that web search intensity (adjusted for different languages and different search engines) turns out to convey a useful predictive content for the arrivals of tourists in Cyprus. Additionally, we show that whenever the prevailing shares of visitors come from countries in different languages, then the identification of the aggregate SII becomes complex. Hence, we argue that blindly using key-phrases to identify an aggregate SII is like an immersion into the unknown, since two sources of bias (the language bias and the search engine bias) are fully neglected. Given the importance of the tourism sector in the total economy activity of Cyprus, our findings might prove to be quite useful to governmental agencies, policy makers and other stakeholders of the sector when their purpose is to allocate effectively the existing limited resources, and to plan short- and long-run promotion and investment strategies.

Suggested Citation

  • Theologos Dergiades & Eleni Mavragani & Bing Pan, 2017. "Arrivals of Tourists in Cyprus: Mind the Web Search Intensity," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 107, Hellenic Observatory, LSE.
  • Handle: RePEc:hel:greese:107
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    File URL: http://www.lse.ac.uk/europeanInstitute/research/hellenicObservatory/CMS%20pdf/Publications/GreeSE/GreeSE-No.107.pdf
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    References listed on IDEAS

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    1. Sofronis Clerides & Nicoletta Pashourtidou, 2007. "Tourism in Cyprus: Recent Trends and Lessons from the Tourist Satisfaction Survey," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 1(2), pages 51-72, December.
    2. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    3. Adamos Adamou & Sofronis Clerides, 2010. "Prospects and Limits of Tourism-Led Growth: The International Evidence," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 2(3), pages 287-303, September.
    4. 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.
    5. 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.
    6. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
    7. Martin, Christine A. & Witt, Stephen F., 1989. "Forecasting tourism demand: A comparison of the accuracy of several quantitative methods," International Journal of Forecasting, Elsevier, vol. 5(1), pages 7-19.
    8. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    9. 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.
    10. Ferda Halicioglu, 2010. "An Econometric Analysis of the Aggregate Outbound Tourism Demand of Turkey," Tourism Economics, , vol. 16(1), pages 83-97, March.
    11. 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.
    12. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    13. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
    14. 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.
    15. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    16. Xiang, Zheng & Pan, Bing, 2011. "Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations," Tourism Management, Elsevier, vol. 32(1), pages 88-97.
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    Cited by:

    1. Hatgioannides, John & Karanassou, Marika & Sala, Hector & Karanasos, Menelaos G. & Koutroumpis, Panagiotis, 2017. "The legacy of a fractured Eurozone: the Greek Dra(ch)ma," LSE Research Online Documents on Economics 84542, London School of Economics and Political Science, LSE Library.

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