A time-dependant parameter approach of demand analysis of tourism in Sri Lanka

Abstract

Traditional tourism demand analysis uses ordinary least squares or maximum likelihood methods to estimate demand models like Box-Jenkins and State-Space, assuming that the parameters of the models remain constant over the sample period. This assumption is too restrictive, as it does not allow for behavioral changes of arrival of tourists over time. This study proposes a new methodology the Generalized autoregressive conditional heterosedasticity model (GARCH) approach to tourism demand modeling. This method relaxes the assumption of parameter constancy, and the behavioral change of tourists over time is traced using a statistical estimator known as a Kalman filter. GARCH models permit time-varying conditional covariances as well as variances, and the former quantity can be of substantial practical use for both modeling and forecasting. The appropriateness of the GARCH approach to tourism demand modeling is tested based on a data set of the tourist demand for Sri Lanka and estimated Mean Percentage Errors(MAPE) are explained 9.7% 6% and 2% respectively