On the Estimation of Western Countries’ Tourism Demand for Thailand Taking into Account of Possible Structural Changes Leading to a Better Prediction

Abstract

Forecasting tourist arrivals is an essential feature in tourism demand prediction. This paper applies Self Exciting Threshold Autoregressive (SETAR) models. The SETAR takes into account of possible structural changes leading to a better prediction of western tourist arrivals to Thailand. The finding reveals that although the forecasting method such as SARIMA GARCH is the state of art model in econometrics, forecasting tourism demand for some specific destinations without consideration of the potential structural changes means ignoring the long persistence of some shocks to volatility and the conditional mean values leading to less efficient forecast results than SETAR model. The findings show that SETAR model outperforms SARIMA GARCH model. Then this study based on the SETAR model uses the Bayesian analysis of Threshold Autoregressive (BAYSTAR) method to make one step ahead forecasting. This study contributes that SETAR overtakes SARIMA GARCH as it takes into account of the nonlinear features of the data via structural changes resulting in the better forecasting of Western Countries tourism demand for Thailand.