Bayesian bootstrap aggregation for tourism demand forecasting. (20th April 2021)

• Record Type: Journal Article
• Authors: Song, Haiyan; Liu, Anyu; Li, Gang; Liu, Xinyang
• Is Part Of: International journal of tourism research. Volume 23:Number 5(2021)
• Page Start: 914
• Page End: 927
• Publication Date: 2021-04-20
• Subjects: bagging -- Bayesian -- forecasting -- general‐to‐specific -- tourism demand; Tourism -- Periodicals; 338.4791
• DOI: 10.1002/jtr.2453
• Languages: English
• ISSNs: 1099-2340

Abstract

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.