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Testing Alternative Dynamic Systems for Modelling Tourism Demand

Author

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  • Maria M. De Mello

    (Centro de Estudos de Economia Industrial, do Trabalho e da Empresa (CETE), Universidade do Porto, Faculdade de Economia do Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal)

  • Natércia Fortuna

    (Centro de Estudos Macroeconómicos e Previsão (CEMPRE), Universidade do Porto)

Abstract

This paper presents an empirical study of tourism demand dynamics and identifies areas in which the scrutiny of relationships between theoretical and empirical considerations is likely to produce new insights. A flexible general form of a Dynamic Almost Ideal Demand System (DAIDS) is derived to analyse UK tourism demand for the neighbouring destinations of Portugal, Spain and France during 1969–97. Nested within the general dynamic structure are Deaton and Muellbauer's static AIDS model itself, the partial adjustment model and the auto-regressive distributed lag model, which are tested against the general dynamic alternative. The empirical results obtained show that DAIDS is a data-coherent and theoretically consistent model, providing evidence of the robustness of this methodology for tourism demand analysis in a temporal context. Moreover, the dynamic model offers statistically strong evidence of the inadequacy of the orthodox static AIDS and other restricted models for the consistent reconciliation of data and theory within their formulations. Estimates for tourism price and expenditure elasticities are obtained, permitting a comparative analysis of the relative magnitudes and statistical relevance of the long-run and short-run sensitivity of UK tourism demand to changes in its determinants.

Suggested Citation

  • Maria M. De Mello & Natércia Fortuna, 2005. "Testing Alternative Dynamic Systems for Modelling Tourism Demand," Tourism Economics, , vol. 11(4), pages 517-537, December.
    • Handle: RePEc:sae:toueco:v:11:y:2005:i:4:p:517-537
    DOI: 10.5367/000000005775108719
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    References listed on IDEAS

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    1. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    2. Maria De Mello & Kevin Nell, 2005. "The forecasting ability of a cointegrated VAR system of the UK tourism demand for France, Spain and Portugal," Empirical Economics, Springer, vol. 30(2), pages 277-308, September.
    3. Gordon Anderson & Richard Blundell, 1983. "Testing Restrictions in a Flexible Dynamic Demand System: An Application to Consumers' Expenditure in Canada," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 50(3), pages 397-410.
    4. Andreas Papatheodorou, 1999. "The demand for international tourism in the Mediterranean region," Applied Economics, Taylor & Francis Journals, vol. 31(5), pages 619-630.
    5. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762.
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    Citations

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    Cited by:

    1. 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.
    2. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    3. Jorge M. Andraz & Nélia M. Norte & Hugo S. Gonçalves, 2016. "Do tourism spillovers matter in regional economic analysis? An application to Portugal," Tourism Economics, , vol. 22(5), pages 939-963, October.
    4. İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    5. Mihaela Simionescu, 2017. "The Relationship Between Tourist Arrivals And Accomodation In Romanian Regions. A Panel Data Approach," Revista de turism - studii si cercetari in turism / Journal of tourism - studies and research in tourism, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 23(23), pages 1-2, June.
    6. Saayman, Andrea & Viljoen, Armand & Saayman, Melville, 2018. "Africa’s outbound tourism: An Almost Ideal Demand System perspective," Annals of Tourism Research, Elsevier, vol. 73(C), pages 141-158.
    7. Bonham, Carl & Gangnes, Byron & Zhou, Ting, 2009. "Modeling tourism: A fully identified VECM approach," International Journal of Forecasting, Elsevier, vol. 25(3), pages 531-549, July.
    8. Jian-Wu Bi & Tian-Yu Han & Hui Li, 2022. "International tourism demand forecasting with machine learning models: The power of the number of lagged inputs," Tourism Economics, , vol. 28(3), pages 621-645, May.
    9. Wai Kit Tsang & Dries F. Benoit, 2020. "Gaussian processes for daily demand prediction in tourism planning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 551-568, April.
    10. Athanasopoulos, George & Deng, Minfeng & Li, Gang & Song, Haiyan, 2014. "Modelling substitution between domestic and outbound tourism in Australia: A system-of-equations approach," Tourism Management, Elsevier, vol. 45(C), pages 159-170.
    11. Ogechi Adeola & Nathaniel Boso & Olaniyi Evans, 2018. "Drivers of international tourism demand in Africa," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 53(1), pages 25-36, January.

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