Application of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan

Abstract

Abstract In this paper, we examined the relationship between tourism and service consumption in Taiwan. The service consumption in Taiwan is nowcasted with the real-time tourism data in Google Trends database. We used the high-frequency internet-searching tourism data to predict the low-frequency service consumption data, for the real-time data with rich information could enhance prediction accuracy. Applying the Principal Components Analysis (PCA), we used the internet-searching tourism keywords in Google Trends database to construct the diffusion indices. Following the classification of the tourism keywords in Matsumoto et al. (2013), we classified those keywords into five groups and twenty-nine classifications. We focused on the reciprocal reactions between those diffusion indices with service consumption to conclude which component has higher influence on service consumption in Taiwan. Our empirical results indicated that the keywords in “Recreational areas, and Travel-related” group have significant effects on service consumption in Taiwan via nowcasting. Among the components of those diffusion indices, “Farm, Travel insurance, and Visitor center” are important variables with higher weights in common. JEL classification numbers: C60, C80, E01, E2, E60. Keywords: Nowcasting, the Principal Components Analysis (PCA), Service Consumption, Tourism.