Abstract
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 language | English |
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Article number | 103277 |
Journal | Annals of Tourism Research |
Volume | 90 |
DOIs | |
Publication status | Published - Sept 2021 |
Externally published | Yes |
Keywords
- GNST model
- Panel data
- SAC model
- Spatiotemporal model
- Tourism demand forecasting
ASJC Scopus subject areas
- Development
- Tourism, Leisure and Hospitality Management