Forecasting Tourism Demand for Medical Services

Medical tourism is considered nowadays as a multi-billion industry which can promote a country's economic growth. Therefore, forecasting the scheduled tourism demand for medical services is of great importance for policy makers. Doing so, however, is not an easy task due to the following reasons: Data on medical tourism are (i) not easily accessible; (ii) not typically distinguished from tourists' non-scheduled (unintentional) use of a country's medical services; and (iii) usually not publicly available for long time periods. In this paper, we present a novel way to forecast tourism demand (intentional and unintentional) for medical services — a rough but informative proxy of medical tourism — using limited data. To perform the analysis, we use data on the percentage of hospital discharges of non-residents for 17 European countries over the period 2008-2019 retrieved from Eurostat. Our methodological approach is based on a forecasting technique recently developed by Kyriazi, Thomakos and Guerard; the adaptive learning forecasting. According to this method, MSE (Mean Squared Error)-performance enhancements can be achieved using any forecast as input — as long as that input is not a 'perfect' forecast — by learning from past forecast errors. Within this context, even the most basic forecast, the no-change or naïve forecast, can be used as input to the adaptive learning procedure. Kyriazi, Thomakos and Guerard approach is very well suited to our research question because (i) the no-change forecast is a natural candidate in a short time series where models cannot be estimated with sufficient accuracy, (ii) the no-change forecast is obviously far from being the 'perfect' forecast, and (iii) the adaptive learning process can be initialized by the no-change forecast and then learn by its own past forecast errors. Our results show that adaptive learning forecasting leads to performance enhancements that range from 5% to more than 20% relative to the no-change benchmark. This finding indicates the efficiency of the adaptive learning method in forecasting medical tourism demand; an important subcategory of tourism demand for which data are not easily accessible and freely available historical data exist for short time periods.