Tourism demand forecasting is methodologically mature, but it typically treats accommodation supply as fixed or exogenous. In platform-mediated short-term rentals, supply is elastic, decision-driven, and co-evolves with demand through pricing, information design, and interventions. I reframe the core issue as endogenous stock-out censoring: realized booked nights satisfy B_{k,t} <= min(D_{k,t}, S_{k,t}), so booking models that ignore supply learn a regime-specific ceiling and become fragile under policy changes and supply shocks. This narrated review synthesizes work from tourism forecasting, revenue management, two-sided market economics, and Bayesian time-series methods; develops a three-part coupling framework (behavioral, informational, intervention); and illustrates the identification failure with a toy simulation. I conclude with a focused research agenda for jointly forecasting supply, demand, and their compositions.

翻译：旅游需求预测在方法论上已较为成熟，但通常将住宿供给视为固定或外生变量。在平台中介的短租市场中，供给具有弹性、受决策驱动，并通过定价、信息设计与干预手段与需求共同演化。本文将核心问题重新定义为内生性库存截断：实际已预订过夜数满足B_{k,t} <= min(D_{k,t}, S_{k,t})，因此忽略供给的预订模型所学习的是特定制度环境下的上限，在政策变化与供给冲击下将变得脆弱。本述评综合了旅游预测、收益管理、双边市场经济学与贝叶斯时间序列方法的研究成果；构建了三部分耦合框架（行为耦合、信息耦合、干预耦合）；并通过玩具模拟说明了识别失效问题。最后，本文提出了一项针对供给、需求及其构成联合预测的聚焦研究议程。