Abstract
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.