Tourism demand modelling and forecasting: how should demand be measured?

被引:170
|
作者
Song, Haiyan [1 ]
Li, Gang [2 ]
Witt, Stephen F. [1 ]
Fei, Baogang [1 ]
机构
[1] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China
[2] Univ Surrey, Fac Management & Law, Guildford GU2 7XH, Surrey, England
关键词
tourist arrivals; tourist expenditure; forecasting accuracy; Hong Kong;
D O I
10.5367/000000010790872213
中图分类号
F [经济];
学科分类号
02 ;
摘要
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-month'/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.
引用
收藏
页码:63 / 81
页数:19
相关论文
共 50 条
  • [31] Forecasting tourism demand using search query data: A hybrid modelling approach
    Wen, Long
    Liu, Chang
    Song, Haiyan
    [J]. TOURISM ECONOMICS, 2019, 25 (03) : 309 - 329
  • [32] System dynamics modelling and forecasting health tourism demand: the case of Russian resorts
    Vetitnev, Alexandr
    Kopyirin, Andrey
    Kiseleva, Anna
    [J]. CURRENT ISSUES IN TOURISM, 2016, 19 (07) : 618 - 623
  • [33] Modelling international tourism demand for the Caribbean
    Onafowora, Olugbenga A.
    Owoye, Oluwole
    [J]. TOURISM ECONOMICS, 2012, 18 (01) : 159 - 180
  • [34] Modelling the Inbound Tourism Demand in Vietnam
    Selvanathan, Eliyathamby A.
    Selvanathan, Saroja
    [J]. PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON FINANCE AND ECONOMICS (ICFE 2017), 2017, : 617 - 632
  • [35] ARMAX Modelling of International Tourism Demand
    Lim, C.
    Min, J. C. H.
    McAleer, M.
    [J]. MODSIM 2007: INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION: LAND, WATER AND ENVIRONMENTAL MANAGEMENT: INTEGRATED SYSTEMS FOR SUSTAINABILITY, 2007, : 1885 - 1891
  • [36] ARMAX modelling of international tourism demand
    Lim, Christine
    McAleer, Michael
    Mine, Jennifer C. H.
    [J]. MATHEMATICS AND COMPUTERS IN SIMULATION, 2009, 79 (09) : 2879 - 2888
  • [37] Modelling and forecasting the demand for coal in China
    Chan, HL
    Lee, SK
    [J]. ENERGY ECONOMICS, 1997, 19 (03) : 271 - 287
  • [38] Forecasting optimal tourism product demand and price
    Tsoi, MY
    [J]. Korus 2004, Vol 3, Proceedings, 2004, : 287 - 289
  • [39] Forecasting daily tourism demand with multiple factors
    Xu, Shilin
    Liu, Yang
    Jin, Chun
    [J]. ANNALS OF TOURISM RESEARCH, 2023, 103
  • [40] Tourism demand forecasting - a review on the variables and models
    Khaidi, Sarah Mohd
    Abu, Noratikah
    Muhammad, Noryanti
    [J]. 2ND INTERNATIONAL CONFERENCE ON APPLIED & INDUSTRIAL MATHEMATICS AND STATISTICS, 2019, 1366