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Forecasting Monthly Tourist Departures from Australia

Author

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  • Jae H. Kim

    (Department of Economics, School of Business, James Cook University, Townsville, Queensland 4811, Australia)

Abstract

This paper builds time series forecasting models for monthly international tourist departures from Australia, categorized by the major destinations and by the main purposes of travel. Descriptive and statistical analyses reveal that strong trend and seasonality are the major features of all time series considered. The seasonal ARIMA model is used as a forecasting tool and its trend and seasonal components are specified based on inferential outcomes of the HEGY test for seasonal unit roots. Other alternative forecasting methods used include Holt-Winters' exponential smoothing method and the regression models that involve deterministic trend or seasonal dummy variables. It is found that forecasts generated from seasonal ARIMA models are more accurate than other alternative forecasts in most cases.

Suggested Citation

  • Jae H. Kim, 1999. "Forecasting Monthly Tourist Departures from Australia," Tourism Economics, , vol. 5(3), pages 277-291, September.
    • Handle: RePEc:sae:toueco:v:5:y:1999:i:3:p:277-291
    DOI: 10.1177/135481669900500304
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    Cited by:

    1. Christine Lim & Michael McAleer, 2001. "Time Series Forecasts of International Tourism Demand for Australia," ISER Discussion Paper 0533, Institute of Social and Economic Research, Osaka University.
    2. Anappattath Muhammed Salim & Thomas Shiby M., 2023. "Demand for Kerala’s International Tourism by the Top Three Source Markets: A Comparative Analysis," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 11(1), pages 208-226, October.
    3. Paz Rico & Bernardí Cabrer-Borrás & Francisco Morillas-Jurado, 2021. "Seasonality in Tourism: Do Senior Programs Mitigate It?," Mathematics, MDPI, vol. 9(16), pages 1-27, August.
    4. Ms. Evridiki Tsounta, 2008. "What Attracts Tourists to Paradise?," IMF Working Papers 2008/277, International Monetary Fund.
    5. Nicholas Apergis & Andrea Mervar & James E. Payne, 2017. "Forecasting disaggregated tourist arrivals in Croatia," Tourism Economics, , vol. 23(1), pages 78-98, February.
    6. Luis Alberiko Gil-Alaña, 2010. "Tourism in South Africa. Time series persistence and the nature of shocks. Are they transitory or permament?," NCID Working Papers 06/2011, Navarra Center for International Development, University of Navarra.
    7. Juncal Cuñado & Alberiko Gil-Alana, Luis & Perez De Gracia, Fernando, 2011. "Modelling International Monthly Tourist in Spain/Modelización de llegadas mensuales de turistas a España," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 29, pages 723-736, Diciembre.
    8. 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.
    9. Duro Moreno, Juan Antonio & Turrión Prats, Judith, 2018. "Tourism seasonality worldwide," Working Papers 2072/351586, Universitat Rovira i Virgili, Department of Economics.
    10. Tihomir Stučka, 2002. "A Comparison of Two Econometric Models (OLS and SUR) for Forecasting Croatian Tourism Arrivals," Working Papers 8, The Croatian National Bank, Croatia.
    11. Saroja Selvanathan, 2007. "The effect of war and other factors on Sri Lankan tourism," Applied Economics Letters, Taylor & Francis Journals, vol. 14(1), pages 35-38.

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