Investigating the Time-varying Effect of Search Index in Predicting Tourism Volume Using Dynamic Model Averaging

Authors

Yuan Chen, School of Information Management, Wuhan University, Wuhan, 430072, China；Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, China
Yiping Ma, School of Information Management, Wuhan University, Wuhan, 430072, China；Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, China
Zhongyi Hu, School of Information Management, Wuhan University, Wuhan, 430072, China；Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, ChinaFollow
Tao Xiong, College of Economics and Management, Huazhong Agricultural University, Wuhan, 430070, China
Jiang Wu, School of Information Management, Wuhan University, Wuhan, 430072, China；Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, China
Tingshun Xiong, School of Information Management, Wuhan University, Wuhan, 430072, China

Abstract

Search index has been gradually used for tourism volume forecasting. While most literature aimed to improve the accuracy of a tourism prediction model, rarely considered the influencing characteristics of the search index in the prediction model. In this study, a dynamic model averaging (DMA) approach is applied to build tourism volume model and to investigate the time-varying effect of search indexes in predicting tourism volume. The tourism volume of Jiuzhai Valley and related search indexes from Baidu Index are considered for experimental purpose. According to the results, search index present time-varying characteristic, specifically, the search indexes of Jiuzhai Valley hotel, Jiuzhai Valley, Jiuzhai Valley map and Jiuzhai Valley guide have higher probabilities in supporting the prediction of tourism volume.

Recommended Citation

Chen, Yuan; Ma, Yiping; Hu, Zhongyi; Xiong, Tao; Wu, Jiang; and Xiong, Tingshun, "Investigating the Time-varying Effect of Search Index in Predicting Tourism Volume Using Dynamic Model Averaging" (2021). WHICEB 2021 Proceedings. 44.
https://aisel.aisnet.org/whiceb2021/44