Forecasting international tourism demand: a local spatiotemporal model

Xiaoying Jiao, Gang Li, Jason Li Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number102937
JournalAnnals of Tourism Research
Volume83
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes

Keywords

  • Forecasting
  • Local estimation
  • Panel
  • Spatial heterogeneity
  • Spatial spillover
  • Tourism demand

ASJC Scopus subject areas

  • Development
  • Tourism, Leisure and Hospitality Management

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