We analyze daily lead-time distributions for two Airbnb demand metrics, Nights Booked (volume) and Gross Booking Value (revenue), treating each day's allocation across 0-365 days as a compositional vector. The data span 2,557 days from January 2019 through December 2025 in a large North American region. Three findings emerge. First, GBV concentrates more heavily in mid-range horizons: beyond 90 days, GBV tail mass typically exceeds Nights by 20-50%, with ratios reaching 75% at the 180-day threshold during peak seasons. Second, Gamma and Weibull distributions fit comparably well under interval-censored cross-entropy. Gamma wins on 61% of days for Nights and 52% for GBV, with Weibull close behind at 38% and 45%. Lognormal rarely wins (<3%). Nonparametric GAMs achieve 18-80x lower CRPS but sacrifice interpretability. Third, generalized Pareto fits suggest bounded tails for both metrics at thresholds below 150 days, though this may partly reflect right-truncation at 365 days; above 150 days, estimates destabilize. Bai-Perron tests with HAC standard errors identify five structural breaks in the Wasserstein distance series, with early breaks coinciding with COVID-19 disruptions. The results show that volume and revenue lead-time shapes diverge systematically, that simple two-parameter distributions capture daily pmfs adequately, and that tail inference requires care near truncation boundaries.

翻译：本研究分析了Airbnb平台上两种需求指标——预订夜数（数量指标）与总预订价值（收入指标）的每日提前期分布，将每日在0-365天范围内的分配视为一个成分向量。数据涵盖北美某大型区域2019年1月至2025年12月共2,557天的观测记录。主要发现如下：第一，总预订价值更集中分布于中期提前期区间：超过90天后，总预订价值的尾部质量通常比预订夜数高出20-50%，在旺季时段，180天阈值处的比例可达75%。第二，在区间删失交叉熵准则下，Gamma分布与Weibull分布展现出相当的拟合优度。在61%的日期中Gamma分布对预订夜数拟合最优，对总预订价值的最优拟合比例为52%；Weibull分布紧随其后，比例分别为38%和45%。对数正态分布极少表现最优（<3%）。非参数广义可加模型虽能降低连续分级概率评分18-80倍，但牺牲了模型可解释性。第三，广义帕累托拟合表明两种指标在低于150天的阈值处均呈现有界尾部特征，但这可能部分反映了365天处的右截断效应；超过150天后参数估计出现不稳定现象。采用异方差自相关稳健标准误的Bai-Perron检验在Wasserstein距离序列中识别出五个结构断点，其中早期断点与新冠疫情冲击时段吻合。研究结果表明：数量与收入指标的提前期分布形态存在系统性差异；简单的双参数分布足以捕捉每日概率质量函数的核心特征；在截断边界附近进行尾部推断需格外审慎。