Forecasting international tourism demand: a local spatiotemporal model
Gang Li and
Jason Li Chen
Annals of Tourism Research, 2020, vol. 83, issue C
This study investigates whether tourism forecasting accuracy is improved by incorporating spatial dependence and spatial heterogeneity. One- to three-step-ahead forecasts of tourist arrivals were generated using global and local spatiotemporal autoregressive models for 37 European countries and the forecasting performance was compared with that of benchmark models including autoregressive moving average, exponential smoothing and Naïve 1 models. For all forecasting horizons, the two spatial models outperformed the non-spatial models. The superior forecasting performance of the local model suggests that the full reflection of spatial heterogeneity can improve the accuracy of tourism forecasting.
Keywords: Tourism demand; Spatial spillover; Spatial heterogeneity; Panel; Forecasting; Local estimation (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300815
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