Forecasting tourism demand with composite search index

Xin Li, Bing Pan, Chun Hung Roberts Law, Xiankai Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

264 Citations (Scopus)


However, the massive yet highly correlated query data pose challenges when researchers attempt to include them in the forecasting model. We propose a framework and procedure for creating a composite search index adopted in a generalized dynamic factor model (GDFM). This research empirically tests the framework in predicting tourist volumes to Beijing. Findings suggest that the proposed method improves the forecast accuracy better than two benchmark models: a traditional time series model and a model with an index created by principal component analysis. The method demonstrates the validity of the combination of composite search index and a GDFM.
Original languageEnglish
Pages (from-to)57-66
Number of pages10
JournalTourism Management
Publication statusPublished - 1 Apr 2017


  • Big data analytics
  • Composite search index
  • Generalized dynamic factor model
  • Search query data
  • Tourism demand forecast

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management


Dive into the research topics of 'Forecasting tourism demand with composite search index'. Together they form a unique fingerprint.

Cite this