Open AccessApplication of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan
Author(s) -
Chien-jung Ting,
Yi-Long Hsiao,
Ruijun Su
Publication year - 2022
Language(s) - English
DOI - 10.47260/jafb/1244
Subject(s) - nowcasting , consumption (sociology) , tourism , service (business) , visitor pattern , business , database , computer science , geography , marketing , social science , archaeology , sociology , meteorology , programming language
In this paper, we examined the relationship between tourism and serviceconsumption in Taiwan. The service consumption in Taiwan is nowcasted with thereal-time tourism data in Google Trends database. We used the high-frequencyinternet-searching tourism data to predict the low-frequency service consumptiondata, for the real-time data with rich information could enhance prediction accuracy.Applying the Principal Components Analysis (PCA), we used the internet-searchingtourism 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. Wefocused on the reciprocal reactions between those diffusion indices with serviceconsumption to conclude which component has higher influence on serviceconsumption in Taiwan. Our empirical results indicated that the keywords in“Recreational areas, and Travel-related” group have significant effects on serviceconsumption in Taiwan via nowcasting. Among the components of those diffusionindices, “Farm, Travel insurance, and Visitor center” are important variables withhigher weights in common.JEL classification numbers: C60, C80, E01, E2, E60.Keywords: Nowcasting, the Principal Components Analysis (PCA), ServiceConsumption, Tourism.