Numerous studies on tourism forecasting have now been published over the past five decades. However,
no consensus has been reached in terms of which types of forecasting models tend to be more accurate
and in which circumstances. This study uses meta-analysis to examine the relationships between the
accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65
studies published during the period 1980e2011, the meta-regression analysis shows that the origins of
tourists, destination, time period, modeling method, data frequency, number of variables and their
measures and sample size all significantly influence the accuracy of forecasting models. This study is the
first attempt to pair forecasting models with the data characteristics and the tourism forecasting context.
The results provide suggestions for the choice of appropriate forecasting methods in different forecasting
settings.