Bayesian bootstrap aggregation for tourism demand forecasting

被引:17
作者
Song, Haiyan [1 ]
Liu, Anyu [2 ]
Li, Gang [2 ]
Liu, Xinyang [2 ]
机构
[1] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Hospitality & Tourism Res Ctr, Hong Kong, Peoples R China
[2] Univ Surrey, Sch Hospitality & Tourism Management, Guildford GU2 7XH, Surrey, England
关键词
bagging; Bayesian; forecasting; general‐ to‐ specific; tourism demand; ECONOMIC-CRISIS; TIME-SERIES; COMBINATION; IMPACTS; PRICE;
D O I
10.1002/jtr.2453
中图分类号
F [经济];
学科分类号
02 ;
摘要
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.
引用
收藏
页码:914 / 927
页数:14
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