Premium
Bayesian bootstrap aggregation for tourism demand forecasting
Author(s) -
Song Haiyan,
Liu Anyu,
Li Gang,
Liu Xinyang
Publication year - 2021
Publication title -
international journal of tourism research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.155
H-Index - 58
eISSN - 1522-1970
pISSN - 1099-2340
DOI - 10.1002/jtr.2453
Subject(s) - tourism , bayesian probability , econometrics , econometric model , demand forecasting , economics , bayesian inference , computer science , geography , artificial intelligence , operations management , archaeology
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.