Better Confidence Intervals: The Double Bootstrap with No Pivot

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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.

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