Forecasting tourism demand with composite search index
Researchers have adopted online data such as search query volumes to forecast tourism demand for a destination, including tourist volumes 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) to forecast tourist demand in a destination. This research empirically tests the framework in predicting monthly Beijing tourist volumes. Findings suggest that the proposed method improves the forecast accuracy of monthly Beijing tourist volumes compared with two benchmark models: a time series model and a model with an index created by principal component analysis. The method demonstrates the combination of composite search index and a GDFM in accurately forecasting tourism demand.