Bayesian forecasting of China's tourism demand with VAR models

Tourism plays an important role in hastening the development of economy and society in China. This paper extends the existing forecasting accuracy debated in the tourism literature by examining the forecasting performance of various vector autoregressive models, analyze the statistical structure of VAR models, and design three Bayesian VAR models using the parameters' Minnesota priors, which would lead to an improvement in forecasting performance. The empirical results based on a data set on the demand for China tourism show that the Bayesian VAR models invariably outperform their unrestricted VAR counterparts. It is noteworthy that the univariate BVAR was found to be the best performing model among all the competing models examined.