Spatial-temporal forecasting of tourism demand
Yang Yang and
Annals of Tourism Research, 2019, vol. 75, issue C, 106-119
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)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:75:y:2019:i:c:p:106-119
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