Spatial-temporal forecasting of tourism demandYang Yang and Honglei Zhang Annals of Tourism Research, 2019, vol. 75, issue C, 106-119 Abstract: This study conducts spatial-temporal forecasting to predict inbound tourism demand in 29 Chinese provincial regions. Eight models are estimated among a-spatial models (autoregressive integrated moving average [ARIMA] model and unobserved component model [UCM]) and spatial-temporal models (dynamic spatial panel models and space-time autoregressive moving average [STARMA] models with different specifications of spatial weighting matrices). An ex-ante forecasting exercise is conducted with these models to compare their one-/two-step-ahead predictions. The results indicate that spatial-temporal forecasting outperforms the a-spatial counterpart in terms of average forecasting error. Auxiliary regression finds the relative error of spatial-temporal forecasting to be lower in regions characterized by a stronger level of local spatial association. Lastly, theoretical and practical implications are provided. Keywords: Spatial-temporal forecasting; Tourism forecasting; Dynamic spatial panel model; Space-time autoregressive moving average model; Local indicators of spatial association (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:75:y:2019:i:c:p:106-119 DOI: 10.1016/j.annals.2018.12.024 Access Statistics for this article Annals of Tourism Research is currently edited by John Tribe More articles in Annals of Tourism Research from Elsevier |
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