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Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it!

Author

Listed:
  • Mahalia Jackman

    (Antilles Economics, #3 Bulkeley Close, St George, Barbados)

  • Simon Naitram

    (Central Bank of Barbados, St Michael, Barbados)

Abstract

This paper uses support vector regressions (SVRs) and Google search data to test whether observing Internet habits can provide insights into trends in tourist arrivals in Barbados. The empirical evidence suggests that Google Trends data may be used to pick up changing patterns and trends in tourist arrivals from the UK and Canada. In the case of the USA, the authors find no evidence to suggest that Google data add any significant information to what can be ‘learned’ from an autoregressive SVR.

Suggested Citation

  • Mahalia Jackman & Simon Naitram, 2015. "Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it!," Tourism Economics, , vol. 21(6), pages 1309-1313, December.
    • Handle: RePEc:sae:toueco:v:21:y:2015:i:6:p:1309-1313
    DOI: 10.5367/te.2014.0402
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    References listed on IDEAS

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    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    3. Jackman M.M., 2012. "Revisiting The Tourism-Led Growth Hypothesis For Barbados: A Disaggregated Market Approach," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 12(2).
    4. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    5. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1.
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    Citations

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    Cited by:

    1. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    2. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    3. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    4. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    5. Maximo Camacho & Matías José Pacce, 2018. "Forecasting travellers in Spain with Google’s search volume indices," Tourism Economics, , vol. 24(4), pages 434-448, June.
    6. 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.
    7. 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.

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