Bayesian bootstrap aggregation for tourism demand forecasting

Haiyan Song, Anyu Liu, Gang Li, Xinyang Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Limited historical data are the primary cause of the failure of tourism forecasts. Bayesian bootstrap aggregation (BBagging) may offer a solution to this problem. This study is the first to apply BBagging to tourism demand forecasting. An analysis of annual and quarterly tourism demand for Hong Kong shows that BBagging can, in general, improve the forecasting accuracy of the econometric models obtained using the general-to-specific (GETS) approach by reducing, relative to the ordinary bagging method, the variability in the posterior distributions of the forecasts it generates.

Original languageEnglish
JournalInternational Journal of Tourism Research
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • bagging
  • Bayesian
  • forecasting
  • general-to-specific
  • tourism demand

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Nature and Landscape Conservation

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