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Forecasting New Zealand Tourism Demand with Disaggregated Data

Author

Listed:
  • Lindsay W. Turner

    (Department of Applied Economics, Victoria University of Technology, Melbourne, Australia)

  • N. Kulendran

    (Department of Applied Economics, Victoria University of Technology, Melbourne, Australia)

  • V. Pergat

    (Tangentyre Council, Alice Springs, NT, Australia)

Abstract

This paper compares the forecasting performance of the ARIMA model and the Winters Exponential Smoothing method against each other and the naive No Change process. The models are fitted to quarterly international tourist flow data to New Zealand, from June 1978 to September 1992. Forecasting performance is compared between 11 different countries and world regions with the travel flow divided by type of tourism into categories including Holiday travel, VFR travel and Business travel. It is concluded that the Winters and ARIMA methods outperform the No Change process. In all cases, the relative performance between ARIMA and Winters is affected by whether tourism is disaggregated by types of tourist travel, or analysed only as total flow.

Suggested Citation

  • Lindsay W. Turner & N. Kulendran & V. Pergat, 1995. "Forecasting New Zealand Tourism Demand with Disaggregated Data," Tourism Economics, , vol. 1(1), pages 51-69, March.
    • Handle: RePEc:sae:toueco:v:1:y:1995:i:1:p:51-69
    DOI: 10.1177/135481669500100105
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    Cited by:

    1. Richa Dhariwal, 2005. "Tourist Arrivals in India: How Important are Domestic Disorders?," Tourism Economics, , vol. 11(2), pages 185-205, June.
    2. 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.
    3. J. Cunado & L.A. Gil-Alana & F. Pérez de Gracia, 2004. "Modelling Monthly Spanish Tourism: A Seasonal Fractionally Integrated Approach," Tourism Economics, , vol. 10(1), pages 79-94, March.
    4. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
    5. 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.
    6. J. Cunado & L.A. Gil-Alana & F. Péarez de Gracia, 2005. "The Nature of Seasonality in Spanish Tourism Time Series," Tourism Economics, , vol. 11(4), pages 483-499, December.
    7. Liang Zhu & Christine Lim & Wenjun Xie & Yuan Wu, 2017. "Analysis of tourism demand serial dependence structure for forecasting," Tourism Economics, , vol. 23(7), pages 1419-1436, November.
    8. Nicholas Apergis & Andrea Mervar & James E. Payne, 2017. "Forecasting disaggregated tourist arrivals in Croatia," Tourism Economics, , vol. 23(1), pages 78-98, February.

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