Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators

Bo E. Honore and Luojia Hu

No WP-2015-7, Working Paper Series from Federal Reserve Bank of Chicago

Abstract: The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honor and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation of one-dimensional estimators. The applicability of that approach is quite general. In this paper, we propose an alternative method which is specific to extremum estimators based on U-statistics. The contribution here is that rather than repeated re-calculating the U-statistic-based estimator, we can recalculate a related estimator based on single-sums. A simulation study suggests that the approach leads to a good approximation to the standard bootstrap, and that if this is the goal, then our approach is superior to numerical derivative methods.

Keywords: U-statistics; bootstrap; inference; numerical derivatives (search for similar items in EconPapers)
JEL-codes: C10 C18 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2015-09-15
New Economics Papers: this item is included in nep-ecm and nep-ore
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Journal Article: Simpler bootstrap estimation of the asymptotic variance of U‐statistic‐based estimators (2018)
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