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

Abstract

This paper aims to identify themost influencing factors ofBeijing’s inbound tourismdemand 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 longhaul 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 theMAPEsranging from 5.73%to 14.89%. Implications are discussed and recommendations as well as future research directions are provided.