Modeling and Forecasting Inbound Tourism Demand for Long-Haul Markets of Beijing

北京入境旅游需求建模与预测分析:以长线市 场为例

Bibliographic Data

ID12974652
AuthorsEddy K Tukamushaba (0000-0002-8110-6581, Pwani University, corresponding author), Vera Shanshan Lin (0000-0003-4434-3729, Zhejiang University, corresponding author), Thomas Bwire (corresponding author)
Year2013
Volume9
Issue4
Pages489-506
Publication date2013-09-13
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of China Tourism Research (JOURNAL)
Journal identifiersISSN: 1938-8160 • E-ISSN: 1938-8179
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/19388160.2013.841505
OpenAlexW1854066877
LanguageEN
References cited21

This paper aims to identify the most influencing factors of Beijing's inbound tourism demand using the autoregressive distributed lag model (ADLM) and then generates forecasts of international tourist arrivals from the United States, the United Kingdom, and Canada for the period of 2010Q3–2015Q4. The general-to-specific modeling approach was adopted to achieve final models while the exponential smoothing method was used to produce forecasts for independent variables. Results show that factors such as “word of mouth” effect, income level of the origin source markets, the costs of tourism in Beijing, and the cost of tourism in the competing destinations are crucial determinants of the tourism flows from three long-haul international markets. A group of error measures, such as the mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), mean absolute error (MAE), root mean square error (RMSE), and Theil's U statistic, were used to evaluate the forecasting accuracy. The results suggest that all three models have good forecasting abilities with the MAPEs ranging from 5.73% to 14.89%. Implications are discussed and recommendations as well as future research directions are provided

Beijing · Distributed lag · Econometrics · Economics · Exponential smoothing · Forecast error · Geography · Mean absolute percentage error · Mean squared error · Statistic · Statistics · Tourism · Diverse Aspects of Tourism Research · Mathematics · Sport and Mega-Event Impacts · Wine Industry and Tourism

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