Forecasting the Demand for International Business Tourism

Abstract

Previous research in the area of tourism demand modeling and forecasting has paid little attention to business tourism. This study provides the most comprehensive comparison to date of the accuracy of modern forecasting methods in the context of international business tourism demand forecasting. Seven forecasting models are examined, including the error correction model and various structural time-series and autoregressive integrated moving average (ARIMA) models. The empirical results show that relative forecasting performance is highly dependent on the length of forecasting horizon, that adding explanatory variables to the structural time-series model does not improve forecasting performance, and that testing for unit roots is likely to yield reasonably accurate results under certain conditions.