Forecasting tourism demand with composite search index

Xin Li, Bing Pan, Rob Law and Xiankai Huang

Tourism Management, 2017, vol. 59, issue C, 57-66

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.

Keywords: Tourism demand forecast; Big data analytics; Search query data; Generalized dynamic factor model; Composite search index (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (63)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:touman:v:59:y:2017:i:c:p:57-66

DOI: 10.1016/j.tourman.2016.07.005

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