Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Ulrich Gunter, Irem Önder and Egon Smeral
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Ulrich Gunter: Department of Tourism and Service Management, MODUL University Vienna, 1190 Vienna, Austria
Irem Önder: Department of Hospitality and Tourism Management, University of Massachusetts Amherst, Amherst, MA 01003, USA
Egon Smeral: Department of Tourism and Service Management, MODUL University Vienna, 1190 Vienna, Austria

Forecasting, 2020, vol. 2, issue 3, 1-19

Abstract: 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.

Keywords: Bates–Granger weights; uniform weights; (REG) ARIMA; ETS; Hodrick–Prescott trend; Google Trends indices (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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