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The Use of Composite National Indicators for Tourism Forecasting

Author

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  • L. Turner
  • N. Kulendran
  • H. Fernando

Abstract

This paper identifies the usefulness of available national indicators in forecasting tourist arrivals to Australia from the USA, Japan, the UK and New Zealand. Income, unemployment, forward exchange rate, money supply, price ratio, industrial production, imports and exports of the tourist's country of origin are potential leading indicators of the tourist arrival series. A composite indicator is developed to forecast tourist arrivals. Further, disaggregated data are analysed to determine whether there is likely to be any increase in accuracy if the travel data is divided by travel type. The main travel types analysed are ‘holiday’, ‘visiting friends and relatives’ (VFR) and ‘business’ as well as total flow. To establish the causality between the indicator and international tourist arrivals to Australia, the performance of the transfer function model incorporating the national indicator is compared with ARIMA forecasts.

Suggested Citation

  • L. Turner & N. Kulendran & H. Fernando, 1997. "The Use of Composite National Indicators for Tourism Forecasting," Tourism Economics, , vol. 3(4), pages 309-317, December.
    • Handle: RePEc:sae:toueco:v:3:y:1997:i:4:p:309-317
    DOI: 10.1177/135481669700300401
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    Cited by:

    1. Ketenci, Natalya, 2009. "The ARDL Approach to Cointegration Analysis of Tourism Demand in Turkey: with Greece as the substitution destination," MPRA Paper 86602, University Library of Munich, Germany.
    2. Yair Eilat & Liran Einav, 2004. "Determinants of international tourism: a three-dimensional panel data analysis," Applied Economics, Taylor & Francis Journals, vol. 36(12), pages 1315-1327.
    3. Jaume Rosselló Nadal & Antoni Riera Font & Vivian Cardenas, 2008. "The impact of weather variability on British outbound flows," CRE Working Papers (Documents de treball del CRE) 2008/3, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    4. Tiago Neves Sequeira & Paulo Maçãs Nunes, 2008. "Does Country Risk Influence International Tourism? A Dynamic Panel Data Analysis," The Economic Record, The Economic Society of Australia, vol. 84(265), pages 223-236, June.
    5. Egon Smeral & Michael Wüger, 2004. "Does Complexity Matter? Methods for Improving Forecasting Accuracy in Tourism," WIFO Working Papers 225, WIFO.
    6. -, 2011. "An assessment of the economic impact of climate change on the tourism sector in the Bahamas," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38601, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    7. -, 2011. "An assessment of the economic impact of climate change on the water sector in the Turks and Caicos Islands," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38581, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    8. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).
    9. Silva, Emmanuel Sirimal & Ghodsi, Zara & Ghodsi, Mansi & Heravi, Saeed & Hassani, Hossein, 2017. "Cross country relations in European tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 151-168.
    10. KETENCI, Natalya, 2010. "Cointegration Analysis Of Tourism Demand For Turkey," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 10(1).
    11. Ferda Halicioglu, 2005. "An Ardl Model Of Aggregate Tourism Demand For Turkey," International Trade 0503005, University Library of Munich, Germany.

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