Pooling in Tourism Demand Forecasting

Wen Long, Chang Liu, Haiyan Song

Research output: Journal PublicationArticlepeer-review

43 Citations (Scopus)


This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

Original languageEnglish
Pages (from-to)1161-1174
Number of pages14
JournalJournal of Travel Research
Issue number7
Publication statusPublished - 1 Sept 2019


  • forecasting
  • panel
  • pooling
  • spillover effects
  • tourism demand

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management


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