Density tourism demand forecasting revisited

Haiyan Song, Long Wen, Chang Liu

Research output: Journal PublicationArticlepeer-review

11 Citations (Scopus)

Abstract

This study used scoring rules to evaluate density forecasts generated by different time-series models. Based on quarterly tourist arrivals to Hong Kong from ten source markets, the empirical results suggest that density forecasts perform better than point forecasts. The seasonal autoregressive integrated moving average (SARIMA) model was found to perform best among the competing models. The innovation state space models for exponential smoothing and the structural time-series models were significantly outperformed by the SARIMA model. Bootstrapping improved the density forecasts, but only over short time horizons. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.

Original languageEnglish
Pages (from-to)379-392
Number of pages14
JournalAnnals of Tourism Research
Volume75
DOIs
Publication statusPublished - Mar 2019

Keywords

  • Bootstrap
  • Density forecasts
  • Scoring rules
  • Tourism demand

ASJC Scopus subject areas

  • Development
  • Tourism, Leisure and Hospitality Management

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