旅游
贝叶斯概率
计量经济学
分布滞后
索引(排版)
自回归模型
经济
滞后
事前
需求预测
计算机科学
人工智能
运营管理
宏观经济学
地理
考古
计算机网络
万维网
作者
Xinyang Liu,Anyu Liu,Jason Li Chen,Gang Li,Haiyan Song
标识
DOI:10.1177/10963480251313492
摘要
Taking advantage of the merits of both the bootstrap-aggregating method and the Bayesian forecasting approach, this study introduces their combination—the BayesBag method—to the tourism forecasting literature for the first time. In this study, we examine whether the novel BayesBag method can improve the forecasting performance of the traditional Autoregressive-Distributed-Lag (ADL) model in both normal (i.e., pre-COVID-19) and crisis (i.e., during the pandemic) times. This is also the first study to incorporate the global travel sentiment index as a measure of visitors’ behavioral intentions for forecasting tourism demand in a crisis situation. We conduct both ex-post and ex-ante forecasting of European monthly tourism demand, and our empirical results show that the newly proposed BayesBag method outperforms other methods in both periods.
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