In this paper, we examined the relationship between tourism and GDP in Taiwan. The GDP 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 GDP 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 GDP to conclude which component has higher influence on GDP in Taiwan. Our empirical results indicated that the keywords in “Recreational areas, Grand tour, and Travel-related” group have significant effects on various concepts of national income in Taiwan via nowcasting. Among the components of those diffusion indices, “Amusement park, Hot spring, Farm, Working holiday, and Travel insurance” are important variables with higher weights in common.
JEL classification numbers: C60, C80, E01, E60.
Keywords: Nowcasting, the Principal Components Analysis (PCA), Internet-searching Keywords, GDP, Tourism.
ISSN: 1792-7552 (Online)