Forecasting China's monthly inbound travel demand
Kulendran, Nada and Shan, Jordan (2002) Forecasting China's monthly inbound travel demand. Journal of travel and tourism marketing, 13 (1/2). pp. 5-19. ISSN 10548408Full text for this resource is not available from the Research Repository.
China is currently expecting a growth in inbound travel demand as the result of China's “open door policy,” participation in World Trade Organization (WTO), success in hosting the Olympics in Beijing in the year 2008 and political stability. This paper focused on two issues: (1) forecasting China's monthly inbound travel demand and (2) seasonality and seasonal ARIMA model selection for monthly tourism time-series. In this paper following seasonal ARIMA models were considered: the seasonal ARIMA model with first differences and 11 seasonal dummy variables, the conventional seasonal ARIMA model with first and the fourth differences. In order to select the best forecasting model, finally both seasonal ARIMA models were compared with the AR model with fourth differences, the basic structural model (BSM) and the naïve “No Change” model. In the one-step ahead forecasting comparison, the conventional seasonal ARIMA model with first and the fourth differences becomes the best forecasting model for both inbound foreign visitor demand and total visitor demand. This may be due to the nature of monthly seasonal variations in visitor arrivals, which is less marked. Our forecasts indicate that China foreign visitor arrivals and total visitor arrivals are expected to grow by 14% and 27% respectively from 2002 to 2005.
|Uncontrolled Keywords:||seasonal ARIMA modelling, BSM modelling, forecasting China tourism|
|Subjects:||RFCD Classification > 350000 Commerce, Management, Tourism and Services
RFCD Classification > 340000 Economics
Faculty/School/Research Centre/Department > School of Economics and Finance
|Depositing User:||Ms Phung T Tran|
|Date Deposited:||23 Dec 2008 05:36|
|Last Modified:||18 Jul 2011 05:50|
|ePrint Statistics:||View download statistics for this item|
Repository staff only