- International Journal of Health Management and Tourism
- Volume:7 Issue:1
- TURKEY`S HEALTH TOURISM DEMAND FORECAST: THE ARIMA MODEL APPROACH
TURKEY`S HEALTH TOURISM DEMAND FORECAST: THE ARIMA MODEL APPROACH
Authors : Necla YILMAZ
Pages : 47-63
View : 10 | Download : 10
Publication Date : 2022-03-23
Article Type : Research Paper
Abstract :Aim: A large number of people around the world travel abroad to get health services at more affordable prices. In terms of travel, Turkey is among the countries with a high potential to attract foreign patients. The development of health tourism has accelerated due to many advantages such as the work quality of the services provided in Turkey, the affordable price policy, the presence of specialist physicians, and the geographical location. The actualization of future plans by making health tourism demand forecasting depends on the decisions taken today. From this aspect, it is of great importance to forecast the demand for health tourism. This study aims to predict the future status of patients who come to Turkey to receive health services and to examine them within the scope of health tourism. Methods: In the study, the data obtained within the scope of `Visitors Leaving by Reason of Arrival` in TUIK Tourism Statistics were used. Data refers for quarters period of 2003q1-2019q4. ARIMA models were used to predict the future of health tourism. Analysis and estimation equations were obtained using Eviews 10.0 package software. Findings: ARIMA insert ignore into journalissuearticles values(3,0,1); was obtained as the most suitable model for the study. It is predicted that the number of health tourists arriving in Turkey will be 734,860 in 2022 and 780,754 in 2023. Conclusion: In the next years, Turkey has high growth potential in terms of health tourism. Considering the expected increase in the demand for health tourism, it will be seen that Turkey has a rising trend in terms of attracting foreign patients. The results of the study will make it easier for policymakers to make decisions on critical issues.Keywords : health tourism, time series analysis, tourism demand forecasting, arima model