Forecasting and Analysis of Australian Tourist Visits to Bali Using Bayesian Vector Autoregression

Abstract

Information about tourist visits, especially foreign tourist visits, plays important role in tourism planning. One of the main tourist markets for Bali is Australia. The aims of this research are twofold. First, we forecast the number of Australian tourist visits and we also forecast exchange rate and inflation. Second, we study the dynamic relationship between the number of tourist visits, exchange rate, and inflation for the next twelve months, for instance, 2017 and 2018. We model the visits, exchange rate and inflation using Bayesian vector autoregression. We compare several different priors such as Minnesota, normal-Wishart and normal diffuse independent normal-Wishart. Among these priors, the normal-Wishart prior produces the smallest root mean square error. Hence, the normal-Wishart prior was chosen as the prior of choice for our Bayesian Vector Autoregressive model. The forecast shows that there is a decline in the number of tourist visits, but inflation and exchange rates tend to reach a certain level, for instance, stabilized. The impulse response function shows that there were shocks in the beginning period before reaching zero.