Forecasting tourism demand: Developing a general nesting spatiotemporal model

Xiaoying Jiao, Jason Li Chen, Gang Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)


This study proposes a general nesting spatiotemporal (GNST) model in an effort to improve the accuracy of tourism demand forecasts. The proposed GNST model extends the general nesting spatial (GNS) model into a spatiotemporal form to account for the spatial and temporal effects of endogenous and exogenous variables as well as unobserved factors. As a general specification of spatiotemporal models, the proposed model provides high flexibility in modelling tourism demand. Based on a panel dataset containing quarterly inbound visitor arrivals to 26 European destinations, this empirical study demonstrates that the GNST model outperforms both its non-spatial counterparts and spatiotemporal benchmark models. This finding confirms that spatial and temporal exogenous interaction effects contribute to improved forecasting performance.

Original languageEnglish
Article number103277
JournalAnnals of Tourism Research
Publication statusPublished - Sept 2021
Externally publishedYes


  • GNST model
  • Panel data
  • SAC model
  • Spatiotemporal model
  • Tourism demand forecasting

ASJC Scopus subject areas

  • Development
  • Tourism, Leisure and Hospitality Management


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