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Forecasting extreme seasonal tourism demand

Author

Listed:
  • Niematallah Elamin

    (Graduate School of Economics, Osaka University)

  • Mototsugu Fukushige

    (Graduate School of Economics, Osaka University)

Abstract

When we face an extreme seasonal demand such that off-season monthly demands go down almost to zero, it is important to forecast the high-season demand only. In forecasting tourism demand for a place like a cold-water island, we meet such an extreme seasonality. However, in such places, tourism is one of the most important industries, and to forecast tourism demand in the high season is important. Rishiri Island in Japan is a typical example. We construct some forecasting models and evaluate their forecasting performance using several criteria. Forecasts from the SARIMA model with full-season data and without a trend term perform best in postsample evaluation. This shows that off-season data also provide useful information for forecasting the high-season tourism demand.

Suggested Citation

  • Niematallah Elamin & Mototsugu Fukushige, 2016. "Forecasting extreme seasonal tourism demand," Discussion Papers in Economics and Business 16-23, Osaka University, Graduate School of Economics.
    • Handle: RePEc:osk:wpaper:1623
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    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    2. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    3. 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.
    4. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    5. Lindsay W. Turner & Stephen F. Witt, 2001. "Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models," Tourism Economics, , vol. 7(2), pages 135-147, June.
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    More about this item

    Keywords

    Tourism forecasting; Seasonal time series; SARIMA model; State-space modeling;
    All these keywords.

    JEL classification:

    • Z33 - Other Special Topics - - Tourism Economics - - - Marketing and Finance
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • R29 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other

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