IDEAS home Printed from https://ideas.repec.org/a/spr/svcbiz/v2y2008i3p219-232.html
   My bibliography  Save this article

Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan

Author

Listed:
  • Luiz Moutinho
  • K.-H. Huarng
  • Tiffany Yu
  • C.-Y. Chen

Abstract

The study of tourism demand is attracting more and more attention. Hence, it is important to understand the variables that affect tourism demand and to forecast the demand. Many studies have been conducted to analyze the demands in various countries. Recently, China has been expected to become one of the largest originators of outbound tourists in the world. Hence, it is interesting to explore what the variables are that affect the Mainland Chinese arrivals to Taiwan and to forecast its corresponding tourism demand. This study applies neural networks to select proper models, and then to forecast the demand. Copyright Springer-Verlag 2008

Suggested Citation

  • Luiz Moutinho & K.-H. Huarng & Tiffany Yu & C.-Y. Chen, 2008. "Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan," Service Business, Springer;Pan-Pacific Business Association, vol. 2(3), pages 219-232, September.
  • Handle: RePEc:spr:svcbiz:v:2:y:2008:i:3:p:219-232
    DOI: 10.1007/s11628-008-0037-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11628-008-0037-3
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11628-008-0037-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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.
    2. Sven W. Arndt & J. David Richardson, 1987. "Real-Financial Linkages Among Open Economies," NBER Working Papers 2230, National Bureau of Economic Research, Inc.
    3. Egon Smeral, 2003. "A Structural View of Tourism Growth," Tourism Economics, , vol. 9(1), pages 77-93, March.
    4. 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.
    5. V. Kerry Smith & Raymond J. Kopp, 1980. "The Spatial Limits of the Travel Cost Recreational Demand Model," Land Economics, University of Wisconsin Press, vol. 56(1), pages 64-72.
    6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    7. Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), pages 373-380, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Keating, Byron W. & Kriz, Anton, 2008. "Outbound tourism from China: literature review and research agenda," MPRA Paper 40509, University Library of Munich, Germany.
    2. Kun-Huang Huarng & Tiffany Hui-Kuang Yu & Francesc Solé Parellada, 2010. "An innovative regime switching model to forecast Taiwan tourism demand," The Service Industries Journal, Taylor & Francis Journals, vol. 31(10), pages 1603-1612, March.
    3. Hwa-Kyung Kim & Timothy J. Lee, 2018. "Brand Equity of a Tourist Destination," Sustainability, MDPI, vol. 10(2), pages 1-21, February.
    4. Phillips, Paul & Zigan, Krystin & Santos Silva, Maria Manuela & Schegg, Roland, 2015. "The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis," Tourism Management, Elsevier, vol. 50(C), pages 130-141.
    5. Chih-Yuan Lin & Mateus Lee, 2020. "Taiwan’s opening policy to Chinese tourists and cross-strait relations: The impacts on inbound tourism into Taiwan," Tourism Economics, , vol. 26(1), pages 27-44, February.

    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. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    2. Huarng, Kunhuang & Yu, Tiffany Hui-Kuang, 2006. "The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 481-491.
    3. Li, Gang & Song, Haiyan & Witt, Stephen F., 2006. "Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 22(1), pages 57-71.
    4. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    5. 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.
    6. Apergis Nicholas, 2021. "Forecasting US overseas travelling with univariate and multivariate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 963-976, September.
    7. Dirk Steffen & Ingo Pitterle, 2004. "Spillover Effects of Fiscal Policy Under Flexible Exchange Rates," Econometric Society 2004 Australasian Meetings 286, Econometric Society.
    8. Andrew B. Bernard & J. Bradford Jensen & Stephen J. Redding & Peter K. Schott, 2018. "Global Firms," Journal of Economic Literature, American Economic Association, vol. 56(2), pages 565-619, June.
    9. Bhattacharya, Prasad S. & Thomakos, Dimitrios D., 2008. "Forecasting industry-level CPI and PPI inflation: Does exchange rate pass-through matter?," International Journal of Forecasting, Elsevier, vol. 24(1), pages 134-150.
    10. Peter Rowland & Hugo OLiveros C., 2003. "Colombian Purchasing Power Parity Analysed Using a Framework of Multivariate Cointegration," Borradores de Economia 252, Banco de la Republica de Colombia.
    11. Adolfson, Malin, 2001. "Export price responses to exogenous exchange rate movements," Economics Letters, Elsevier, vol. 71(1), pages 91-96, April.
    12. Levent, Korap, 2007. "Impact of Exchange Rate Changes on Domestic Inflation: he Turkish Experience," MPRA Paper 19589, University Library of Munich, Germany.
    13. Saghaian, Sayed H. & Reed, Michael R., 2004. "Integrating Marginal Cost into Pricing-to-market Models for U.S. Agricultural Products," CAFRI: Current Agriculture, Food and Resource Issues, Canadian Agricultural Economics Society, issue 5, pages 1-17, July.
    14. George Alessandria & Joseph P. Kaboski, 2011. "Pricing-to-Market and the Failure of Absolute PPP," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(1), pages 91-127, January.
    15. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    16. Livengood, Kerry R., 1981. "Bias in Recreation Benefit Estimates: Further Evidence," 1981 Annual Meeting, July 26-29, Clemson, South Carolina 279406, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Fedoseeva, Svetlana, 2015. "Same Currency, Different Strategies? The Role of the Exchange Rate in Shaping European Agri-Food Exports," 2015 Conference, August 9-14, 2015, Milan, Italy 211630, International Association of Agricultural Economists.
    18. Yunus Aksoy & Hanno Lustig, 2007. "Exchange Rates, Prices And International Trade In A Model Of Endogenous Market Structure," Manchester School, University of Manchester, vol. 75(2), pages 160-192, March.
    19. 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.
    20. Kristin Forbes & Ida Hjortsoe & Tsvetelina Nenova, 2020. "International Evidence on Shock-Dependent Exchange Rate Pass-Through," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 68(4), pages 721-763, December.

    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:spr:svcbiz:v:2:y:2008:i:3:p:219-232. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.