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Testing Alternative Dynamic Systems for Modelling Tourism Demand

Author

Listed:
  • Maria M. De Mello

    (CETE, Faculdade de Economia, Universidade do Porto)

  • Natércia Fortuna

    (CEMPRE, Faculdade de Economia, Universidade do Porto)

Abstract
The goals in this paper are to contribute an empirical study of tourism demand dynamics, and to point out areas where the scrutiny of relationships between theoretical and empirical considerations are likely to produce new insights in this area of research. A flexible general form of a Dynamic Almost Ideal Demand System (DAIDS) is derived to analyse the UK tourism demand for its geographically proximate neighbours Portugal, Spain and France, in the period 1969-1997. 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 to conduct tourism demand analysis in a temporal context. Moreover, the dynamic model offers statistically strong evidence on the inadequacy of the orthodox static AIDS and the other restricted models to reconcile consistently 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 long and short run sensitivity of the 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," CEF.UP Working Papers 0501, Universidade do Porto, Faculdade de Economia do Porto.
    • Handle: RePEc:por:cetedp:0501
    as

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    File URL: http://www.fep.up.pt/investigacao/cete/papers/dp0501.pdf
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    References listed on IDEAS

    as
    1. Maria De Mello & Alan Pack & M. Thea Sinclair, 2002. "A system of equations model of UK tourism demand in neighbouring countries," Applied Economics, Taylor & Francis Journals, vol. 34(4), pages 509-521.
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    3. HENDRY, David F. & RICHARD, Jean-François, 1983. "The econometric analysis of economic time series," LIDAM Reprints CORE 531, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. 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.
    5. 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.
    6. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, September.
    7. Banerjee, Anindya & Hendry, David F & Mizon, Grayham E, 1996. "The Econometric Analysis of Economic Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 573-600, November.
    8. Anderson, Gordon & Blundell, Richard, 1984. "Consumer Non-Durables in the U.K. A Dynamic Demand System," Economic Journal, Royal Economic Society, vol. 94(376a), pages 35-44, Supplemen.
    9. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    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. 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.
    4. Farai Jena & Barry Reilly, 2013. "The determinants of United Kingdom student visa demand from developing countries," IZA Journal of Labor & Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-22, December.
    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. Wani, M.H. & Paul, Ranjit Kumar & Bazaz, Naseer H. & Manzoor, M., 2015. "Market integration and Price Forecasting of Apple in India," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 70(2), pages 1-13.
    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. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Andrea Saayman & Melville Saayman, 2008. "Determinants of Inbound Tourism to South Africa," Tourism Economics, , vol. 14(1), pages 81-96, March.

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    More about this item

    Keywords

    Tourism Demand; dynamic almost ideal system; partial adjustment system; autoregressive distributed lag system.;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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