IDEAS home Printed from https://ideas.repec.org/a/sae/toueco/v16y2010i1p63-81.html
   My bibliography  Save this article

Tourism Demand Modelling and Forecasting: How Should Demand Be Measured?

Author

Listed:
  • Haiyan Song

    (School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, PR China)

  • Gang Li

    (Faculty of Management and Law, University of Surrey, Guildford GU2 7XH, UK)

  • Stephen F. Witt
  • Baogang Fei

Abstract

Tourist arrivals and tourist expenditure, in both aggregate and per capita forms, are commonly used measures of tourism demand in empirical research. This study compares these two measures in the context of econometric modelling and the forecasting of tourism demand. The empirical study focuses on demand for Hong Kong tourism by residents of Australia, the UK and the USA. Using the general-to-specific modelling approach, key determinants of tourism demand are identified based on different demand measures. In addition, the forecasting accuracy of these demand measures is examined. It is found that tourist arrivals in Hong Kong are influenced mainly by tourists' income and ‘word-of-mouth’/habit persistence effects, while the tourism price in Hong Kong relative to that of the tourist origin country is the most important determinant of tourist expenditure in Hong Kong. Moreover, the aggregate tourism demand models outperform the per capita models, with aggregate expenditure models being the most accurate. The implications of these findings for tourism decision making are that the choice of demand measure for forecasting models should depend on whether the objective of the decision maker is to maximize tourist arrivals or expenditure (receipts), and also that the models should be specified in aggregate form.

Suggested Citation

  • Haiyan Song & Gang Li & Stephen F. Witt & Baogang Fei, 2010. "Tourism Demand Modelling and Forecasting: How Should Demand Be Measured?," Tourism Economics, , vol. 16(1), pages 63-81, March.
    • Handle: RePEc:sae:toueco:v:16:y:2010:i:1:p:63-81
    DOI: 10.5367/000000010790872213
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5367/000000010790872213
    Download Restriction: no
    File URL: https://libkey.io/10.5367/000000010790872213?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Davidson, James E H, et al, 1978. "Econometric Modelling of the Aggregate Time-Series Relationship between Consumers' Expenditure and Income in the United Kingdom," Economic Journal, Royal Economic Society, vol. 88(352), pages 661-692, December.
    2. Stephan Schulmeister, 1979. "Tourism and the Business Cycle. Econometric Models for the Purpose of Analysis and Forecasting of Short-Term Changes in the Demand for Tourism," WIFO Studies, WIFO, number 2878.
    3. M. Thea Sinclair, 1997. "Tourism and Economic Development: A Survey," Studies in Economics 9703, School of Economics, University of Kent.
    4. Haiyan Song & Stephen F. Witt & Gang Li, 2003. "Modelling and Forecasting the Demand for Thai Tourism," Tourism Economics, , vol. 9(4), pages 363-387, December.
    5. Mizon, Grayham E & Richard, Jean-Francois, 1986. "The Encompassing Principle and Its Application to Testing Non-nested Hypotheses," Econometrica, Econometric Society, vol. 54(3), pages 657-678, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adrian C. Darnell, 1994. "A Dictionary Of Econometrics," Books, Edward Elgar Publishing, number 118.
    2. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    3. Julia Campos & Neil R. Ericsson & David F. Hendry, 2005. "General-to-specific modeling: an overview and selected bibliography," International Finance Discussion Papers 838, Board of Governors of the Federal Reserve System (U.S.).
    4. David F. Hendry & Neil R. Ericsson, 1999. "Encompassing and rational expectations: How sequential corroboration can imply refutation," Empirical Economics, Springer, vol. 24(1), pages 1-21.
    5. D. R. Cox, 2013. "A return to an old paper: ‘Tests of separate families of hypotheses’," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 207-215, March.
    6. Neil R. Ericsson, 1987. "Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses," International Finance Discussion Papers 317, Board of Governors of the Federal Reserve System (U.S.).
    7. Hendry, David F. & Ericsson, Neil R., 1991. "Modeling the demand for narrow money in the United Kingdom and the United States," European Economic Review, Elsevier, vol. 35(4), pages 833-881, May.
    8. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    9. Steve Cook, 2008. "Cross‐data‐vintage Encompassing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 849-865, December.
    10. David F. Hendry, 2013. "Retrospective on ‘Econometric Modelling: The Consumption Function in Retrospect’, Scottish Journal of Political Economy, 30 (1983), 193–220'," Scottish Journal of Political Economy, Scottish Economic Society, vol. 60(5), pages 523-525, November.
    11. David F. Hendry & Peter C.B. Phillips, 2017. "John Denis Sargan at the London School of Economics," Cowles Foundation Discussion Papers 2082, Cowles Foundation for Research in Economics, Yale University.
    12. Ericsson, Neil R., 2017. "Interpreting estimates of forecast bias," International Journal of Forecasting, Elsevier, vol. 33(2), pages 563-568.
    13. de Brouwer, Gordon & Ericsson, Neil R, 1998. "Modeling Inflation in Australia," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 433-449, October.
    14. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
    15. McAleer, Michael, 1994. "Sherlock Holmes and the Search for Truth: A Diagnostic Tale," Journal of Economic Surveys, Wiley Blackwell, vol. 8(4), pages 317-370, December.
    16. Neil R. Ericsson & David F. Hendry, 1985. "Conditional econometric modelling : an application to new house prices in the United Kingdom," International Finance Discussion Papers 254, Board of Governors of the Federal Reserve System (U.S.).
    17. Palumbo, Michael & Rudd, Jeremy & Whelan, Karl, 2006. "On the Relationships Between Real Consumption, Income, and Wealth," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 1-11, January.
    18. Tsang, Shu-ki & Ma, Yue, 1997. "Simulating the impact of foreign capital in an open-economy macroeconomic model of China," Economic Modelling, Elsevier, vol. 14(3), pages 435-478, July.
    19. Thomas Habanabakize & Lerato Mothibi, 2024. "The Implication of Political Risk and Specific Macroeconomic Variables on Total Revenue in Tourism Industry," International Journal of Economics and Financial Issues, Econjournals, vol. 14(3), pages 170-177, May.
    20. Franses, P.H. & McAleer, M., 1995. "Testing Nested and Non-Nested Periodically Integrated Autoregressive Models," Papers 9510, Tilburg - Center for Economic Research.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:toueco:v:16:y:2010:i:1:p:63-81. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.