Better Confidence Intervals: The Double Bootstrap with No Pivot
介绍一种无需枢轴的双重自助法，用于生成置信区间，其收敛速度比单重自助法更快。通过Waugh数据的蒙特卡洛分析，发现该方法能达到名义覆盖率，而单重自助法不能。文中还提出一种技巧，大幅降低了计算时间。
Abstract The double bootstrap is an important advance in confidence interval generation because it converges faster than the already popular single bootstrap. Yet the usual double bootstrap requires a stable pivot that is not always available, e.g., when estimating flexibilities or substitution elasticities. A recently developed double bootstrap does not require a pivot. A Monte Carlo analysis with the Waugh data finds the double bootstrap achieves nominal coverage whereas the single bootstrap does not. A useful artifice dramatically decreases the computational time of the double bootstrap.