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Modelling and forecasting alpine skier visits

Author

Listed:
  • Iveta Malasevska

    (Lillehammer University College, Norway)

  • Erik Haugom

    (Lillehammer University College, Norway)

  • Gudbrand Lien

    (Lillehammer University College, Norway)

Abstract

This study is the first to examine formally what drives variations in skier attendance at Norwegian ski resorts. The authors use a unique data set of the number of daily visitors at a specific ski resort from 2007/2008 to 2013/2014. The main findings suggest that weather conditions, day of the week and holidays significantly affect the number of daily visitors. The time series regression analysis highlights the demand pattern and specific non-linear relationships between visitors and wind chill temperature. The study finds that, if the wind chill temperature is below −9.5°C, a temperature increase has a positive effect on the number of daily visitors. Similarly, if the wind chill temperature is above −9.5°C, a higher temperature leads to a lower number of skier visits, on average. Tourism providers can use these results for decision-making, planning and managing ski resort operations. In addition, the findings could serve as an incentive to implement innovative pricing tactics.

Suggested Citation

  • Iveta Malasevska & Erik Haugom & Gudbrand Lien, 2017. "Modelling and forecasting alpine skier visits," Tourism Economics, , vol. 23(3), pages 669-679, May.
    • Handle: RePEc:sae:toueco:v:23:y:2017:i:3:p:669-679
    DOI: 10.5367/te.2015.0524
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    References listed on IDEAS

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    1. Martin Falk, 2015. "The Demand for Winter Sports: Empirical Evidence for the Largest French Ski-Lift Operator," Tourism Economics, , vol. 21(3), pages 561-580, June.
    2. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
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    4. Morey, Edward R., 1981. "The demand for site-specific recreational activities: A characteristics approach," Journal of Environmental Economics and Management, Elsevier, vol. 8(4), pages 345-371, December.
    5. Falk, Martin, 2010. "A dynamic panel data analysis of snow depth and winter tourism," Tourism Management, Elsevier, vol. 31(6), pages 912-924.
    6. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Citations

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    Cited by:

    1. Marius Mayer & O. Cenk Demiroglu & Oguzhan Ozcelebi, 2018. "Microclimatic Volatility and Elasticity of Glacier Skiing Demand," Sustainability, MDPI, vol. 10(10), pages 1-14, October.
    2. Andrea Martínez Salgueiro & Maria-Antonia Tarrazon-Rodon, 2021. "Weather derivatives to mitigate meteorological risks in tourism management: An empirical application to celebrations of Comunidad Valenciana (Spain)," Tourism Economics, , vol. 27(4), pages 591-613, June.
    3. Martin Falk & Robert Steiger, 2020. "Size facilitates profitable ski lift operations," Tourism Economics, , vol. 26(7), pages 1197-1211, November.
    4. Martin Falk & Xiang Lin, 2018. "The declining dependence of ski lift operators on natural snow conditions," Tourism Economics, , vol. 24(6), pages 662-676, September.
    5. Troxler, Pascal, 2022. "Weather Forecasts and their Relation to Ski Demand," VfS Annual Conference 2022 (Basel): Big Data in Economics 264121, Verein für Socialpolitik / German Economic Association.

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