IDEAS home Printed from https://ideas.repec.org/a/taf/servic/v31y2010i10p1603-1612.html
   My bibliography  Save this article

An innovative regime switching model to forecast Taiwan tourism demand

Author

Listed:
  • Kun-Huang Huarng
  • Tiffany Hui-Kuang Yu
  • Francesc Solé Parellada

Abstract

The tourism industry has become a major part of economic development for many countries. These countries have greatly invested in tourism to attract more tourist arrivals. Hence, the need for more accurate forecasts of tourism demand is important. Various approaches have been applied to forecast tourism demand of different countries. However, tourism demands tend to be imprecise and their trends nonlinear. In addition, there may be drastic changes in the tourism demand time series. To properly handle these problems, this study proposes an innovative forecasting model to detect the regime switching properly and to apply fuzzy time-series model to forecast. The monthly tourist arrivals to Taiwan will be used as forecasting target. The analysis by the proposed model will be validated by the major events as well as previous studies.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:servic:v:31:y:2010:i:10:p:1603-1612
    DOI: 10.1080/02642069.2010.485637
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02642069.2010.485637
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02642069.2010.485637?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. 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.
    2. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    3. 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.
    4. Jane Bryan & Calvin Jones & Max Munday, 2006. "The contribution of tourism to the UK economy: Satellite account perspectives," The Service Industries Journal, Taylor & Francis Journals, vol. 26(5), pages 493-511, July.
    5. Clem Tisdell (ed.), 2000. "The Economics of Tourism," Books, Edward Elgar Publishing, volume 0, number 1004.
    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. Wai Soe Zin & Aya Suzuki & Kelvin S.-H. Peh & Alexandros Gasparatos, 2019. "Economic Value of Cultural Ecosystem Services from Recreation in Popa Mountain National Park, Myanmar: A Comparison of Two Rapid Valuation Techniques," Land, MDPI, vol. 8(12), pages 1-20, December.
    2. Nuno Carlos LEITÃO & Muhammad SHAHBAZ, 2012. "Migration and Tourism Demand," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(567)), pages 39-48, February.
    3. Peter Fuleky & Carl S. Bonham & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii's Tourism Industry," Working Papers 201314, University of Hawaii at Manoa, Department of Economics.
    4. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    5. Hsiao, Chiu-Ming & Zhang, Wei-Fang & Chiu, Chi-Chang & Huang, Jung-Chang & Huang, Yu-Ling, 2017. "The Enterprise Risk Management of Foreign Exchange Exposures: Evidence from Taiwanese Hospitality Industry," Asian Journal of Economics and Empirical Research, Asian Online Journal Publishing Group, vol. 4(1), pages 32-48.
    6. Tisdell, Clement A., 2012. "Ecosystem Services: A Re-examination of Some Procedures for Determining their Economic Value," Economics, Ecology and Environment Working Papers 140550, University of Queensland, School of Economics.
    7. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    8. Lin, Tun & De Guzman, Franklin, 2007. "Tourism for pro-poor and sustainable growth: economic analysis of tourism projects," MPRA Paper 24994, University Library of Munich, Germany.
    9. Chen, Tai-Liang & Cheng, Ching-Hsue & Jong Teoh, Hia, 2007. "Fuzzy time-series based on Fibonacci sequence for stock price forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 377-390.
    10. Agiomirgianakis, George & Serenis, Dimitrios & Tsounis, Nicholas, 2017. "Effective timing of tourism policy: The case of Singapore," Economic Modelling, Elsevier, vol. 60(C), pages 29-38.
    11. Niematallah Elamin & Mototsugu Fukushige, 2016. "Forecasting extreme seasonal tourism demand," Discussion Papers in Economics and Business 16-23, Osaka University, Graduate School of Economics.
    12. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    13. Darboe, sarjo, 2024. "The Dynamic Impact of biodiversity on Tourism: empirical evidence from Gambia," MPRA Paper 120063, University Library of Munich, Germany.
    14. Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," IREA Working Papers 201321, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
    15. Seetaram, Neelu, 2010. "Computing airfare elasticities or opening Pandora's box," Research in Transportation Economics, Elsevier, vol. 26(1), pages 27-36.
    16. Allison Zhou & Carl Bonham & Byron Gangnes, 2007. "Modeling the supply and demand for tourism: a fully identified VECM approach," Working Papers 200717, University of Hawaii at Manoa, Department of Economics.
    17. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2018. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming," Forecasting, MDPI, vol. 1(1), pages 1-17, September.
    18. Thanh-Lam Nguyen & Jui-Chan Huang & Chuang-Chi Chiu & Ming-Hung Shu & Wen-Ru Tsai, 2013. "Forecasting Model for the International Tourism Demand in Taiwan," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    19. Mihail N. Diakomihalis, 2012. "Maritime Tourism Tax Revenues in Greece: A New Framework for Collection," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 5(1), pages 109-127, April.
    20. Bahodirhon Safarov & Hisham Mohammad Al-Smadi & Makhina Buzrukova & Bekzot Janzakov & Alexandru Ilieş & Vasile Grama & Dorina Camelia Ilieș & Katalin Csobán Vargáné & Lóránt Dénes Dávid, 2022. "Forecasting the Volume of Tourism Services in Uzbekistan," Sustainability, MDPI, vol. 14(13), pages 1-18, June.

    More about this item

    Statistics

    Access and download statistics

    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:taf:servic:v:31:y:2010:i:10:p:1603-1612. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/FSIJ20 .

    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.