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Arrivals of Tourists in Cyprus: Mind the Web Search Intensity

Listed author(s):
  • Theologos Dergiades
  • Eleni Mavragani
  • Bing Pan

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.

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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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Paper provided by Hellenic Observatory, LSE in its series GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe with number 107.

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Date of creation: Feb 2017
Handle: RePEc:hel:greese:107
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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. 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.
  4. Adamos Adamou & Sofronis Clerides, 2010. "Prospects and Limits of Tourism-Led Growth: The International Evidence," Review of Economic Analysis, Rimini Centre for Economic Analysis, vol. 2(3), pages 287-303, September.
  5. 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.
  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. 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, 06.
  10. 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.
  11. 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.
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