Abstract:
This paper evaluates the use of several parametric and nonparametric forecasting techniques
for predicting tourism demand in selected European countries. We find that no single
model can provide the best forecasts for any of the countries in the short-, medium- and
long-run. The results, which are tested for statistical significance, enable forecasters to
choose the most suitable model (from those evaluated here) based on the country and horizon
for forecasting tourism demand. Should a single model be of interest, then, across all
selected countries and horizons the Recurrent Singular Spectrum Analysis model is found
to be the most efficient based on lowest overall forecasting error. Neural Networks and
ARFIMA are found to be the worst performing models.
