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Forecasting tourism demand: Developing a general nesting spatiotemporal model

Xiaoying Jiao, Jason Li Chen and Gang Li

Annals of Tourism Research, 2021, vol. 90, issue C

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.

Keywords: Tourism demand forecasting; Spatiotemporal model; SAC model; GNST model; Panel data (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.annals.2021.103277

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