Bayesian bootstrap aggregation for tourism demand forecasting

Haiyan Song, Anyu Liu, Gang Li*, Xinyang Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (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
Pages (from-to)914-927
Number of pages14
JournalInternational Journal of Tourism Research
Volume23
Issue number5
Early online date20 Apr 2021
DOIs
Publication statusPublished - 1 Sept 2021
Externally publishedYes

Cite this