Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

It is widely recognized that the tourism industry is susceptible to crisis or natural disaster. Although some literature has studied the consequences of the crisis and disaster, there remains a lack of study on the effect of COVID‐19. Against this background, this paper investigates the tourist flow forecasting by adopting an advanced nonparametric mixed‐frequency vector autoregressions model using Bayesian additive regression trees. This is particularly suitable for forecasting the presence of extreme observations, for example, the COVID‐19 pandemic. We investigate tourism demand forecasting using a large number of predictors, including industrial production index, CPI, exchange rate, economic policy uncertainty, Google trends index, and COVID‐19 infection rate. The data used for this study relate to tourist flows in Chinese Hong Kong, Japan, and South Korea. Empirical study demonstrates that this novel model significantly outperforms the traditional mixed‐frequency vector autoregressions model to quarterly tourist flow forecasting. Therefore, this model can significantly enhance tourism forecast accuracy in the face of extreme events. This study contributes to the literature on tourism forecasting and provides policymakers with policy implications.