Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61987
Title: Forecasting tourism demand with composite search index
Authors: Li, X
Pan, B
Law, R 
Huang, X
Keywords: Big data analytics
Composite search index
Generalized dynamic factor model
Search query data
Tourism demand forecast
Issue Date: 2017
Publisher: Pergamon Press
Source: Tourism management, 2017, v. 59, p. 57-66 How to cite?
Journal: Tourism management 
Abstract: Researchers have adopted online data such as search engine query volumes to forecast tourism demand for a destination, including tourist numbers and hotel occupancy. 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.
URI: http://hdl.handle.net/10397/61987
ISSN: 0261-5177
EISSN: 1879-3193
DOI: 10.1016/j.tourman.2016.07.005
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