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A Selection Strategy for Modelling UK Tourism Flows by Air to European Destinations

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  • John Trevor Coshall

    (Department of Accounting, Banking and Financial Systems, North Campus, London Metropolitan University, Holloway Road, London N7 8HN, UK)

Abstract

Given the importance of tourism demand forecasting as a research topic, the search for more accurate modelling processes continues. A model selection strategy is presented for UK outbound tourism by air to a set of the most popular European destinations. The first stage in this process presents an objective method for assessing whether seasonality should be modelled by additive or multiplicative means. The strategy moves on to the derivation of appropriate difference filters for ARIMA models of these tourism flows, a process which inherently considers alternatives for modelling increasing seasonal variation. Unlike studies involving longer-haul tourism, it is found at this spatial scale that no generalizations can be made about the stochastic or deterministic nature of trends or seasonality in the tourism flows over the destinations examined. The results also suggest that the commonly applied logarithmic transformation is not the most appropriate way to model increasing seasonal variation.

Suggested Citation

  • John Trevor Coshall, 2005. "A Selection Strategy for Modelling UK Tourism Flows by Air to European Destinations," Tourism Economics, , vol. 11(2), pages 141-158, June.
    • Handle: RePEc:sae:toueco:v:11:y:2005:i:2:p:141-158
    DOI: 10.5367/0000000054183487
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    References listed on IDEAS

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    Cited by:

    1. António Rua & Carlos Melo Gouveia & Nuno Lourenço, 2020. "Forecasting tourism with targeted predictors in a data-rich environment," Working Papers w202005, Banco de Portugal, Economics and Research Department.
    2. Cem Işık & Ercan Sirakaya-Turk & Serdar Ongan, 2020. "Testing the efficacy of the economic policy uncertainty index on tourism demand in USMCA: Theory and evidence," Tourism Economics, , vol. 26(8), pages 1344-1357, December.
    3. Massidda, Carla & Piras, Romano & Seetaram, Neelu, 2020. "A Microeconomics Analysis of the Per Diem Expenditure of British Travellers," Annals of Tourism Research, Elsevier, vol. 82(C).
    4. Seetaram, Neelu & Forsyth, Peter & Dwyer, Larry, 2016. "Measuring price elasticities of demand for outbound tourism using competitiveness indices," Annals of Tourism Research, Elsevier, vol. 56(C), pages 65-79.
    5. Coshall, John T. & Charlesworth, Richard, 2011. "A management orientated approach to combination forecasting of tourism demand," Tourism Management, Elsevier, vol. 32(4), pages 759-769.
    6. Chia-Lin Chang & Thanchanok Khamkaew & Roengchai Tansuchat & Michael McAleer, 2011. "Interdependence of International Tourism Demand and Volatility in Leading ASEAN Destinations," Tourism Economics, , vol. 17(3), pages 481-507, June.
    7. Narangajavana, Yeamduan & Garrigos-Simon, Fernando J. & García, Javier Sanchez & Forgas-Coll, Santiago, 2014. "Prices, prices and prices: A study in the airline sector," Tourism Management, Elsevier, vol. 41(C), pages 28-42.
    8. 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").
    9. Omkar Joshi & Neelam C. Poudyal & Lincoln R. Larson, 2017. "The influence of sociopolitical, natural, and cultural factors on international tourism growth: a cross-country panel analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(3), pages 825-838, June.
    10. Lourenço, Nuno & Gouveia, Carlos Melo & Rua, António, 2021. "Forecasting tourism with targeted predictors in a data-rich environment," Economic Modelling, Elsevier, vol. 96(C), pages 445-454.
    11. Petrevska, Biljana, 2012. "Forecasting International Tourism Demand: The Evidence Of Macedonia," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 3(1), pages 45-55.
    12. Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.
    13. Ki-Hong Choi & Insin Kim, 2021. "Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data," Sustainability, MDPI, vol. 13(3), pages 1-13, January.

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