Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

• Main Authors: Ulrich Gunter, Irem Önder, Egon Smeral
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Forecasting
• Subjects: Bates–Granger weights; uniform weights; (REG)ARIMA; ETS; Hodrick–Prescott trend; Google Trends indices
• Online Access: https://www.mdpi.com/2571-9394/2/3/12

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.