An Investigation of the Time Series Behaviour of International Tourist Arrivals

Most tourism econometric models are based on conventional least squares estimation, which assumes stationarity in their data generating mechanism. However, they fail to recognize the implications of the integrated properties of the historical time series of tourism data. Such time series properties may have important consequences with regard to the theoretical implication and interpretation of these tourism models. In this paper, historical data on international tourist arrivals from six major regions and seventeen individual countries are analysed to determine whether the series is better characterized by a stationary or non-stationary type process. Based on unit root tests, the results in most cases indicate that international tourist arrivals exhibit a non-stationary stochastic process that has the tendency to fluctuate away from a given initial state as time passes. These findings imply that studies which conveniently draw standard inferences from ordinary least squares estimated tourism models based on the levels of international tourist arrivals can be very misleading since non-stationarity in the data will produce inconsistent parameter estimators and unreliable test statistics. Furthermore, model misspecification that arises from unrelated integrated series can seriously bias conventional significance tests towards the acceptance of an apparently significant relationship. In this preliminary investigation, we conclude that econometric tourism models that focus on the levels of international tourist arrivals may not be reliable since the series is non-stationary and is integrated of order one, I(1).