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- W2907651231 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. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field." @default.
- W2907651231 created "2019-01-11" @default.
- W2907651231 creator A5035296788 @default.
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- W2907651231 date "2019-03-01" @default.
- W2907651231 modified "2023-10-14" @default.
- W2907651231 title "Spatial-temporal forecasting of tourism demand" @default.
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- W2907651231 doi "https://doi.org/10.1016/j.annals.2018.12.024" @default.
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