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

21 Pages Posted: 21 Aug 2015

See all articles by Bo E. Honoré

Bo E. Honoré

Princeton University - Department of Economics

Luojia Hu

Federal Reserve Bank of Chicago

Multiple version iconThere are 2 versions of this paper

Date Written: August 4, 2015

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 this paper, we propose a method which is specific to extremum estimators based on U-statistics. The contribution here is that rather than repeated re-calculation of 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

JEL Classification: C10, C18

Suggested Citation

Honore, Bo E. and Hu, Luojia, Simpler Bootstrap Estimation of the Asymptotic Variance of U-Statistic Based Estimators (August 4, 2015). Available at SSRN: https://ssrn.com/abstract=2647612 or http://dx.doi.org/10.2139/ssrn.2647612

Bo E. Honore

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States

Luojia Hu (Contact Author)

Federal Reserve Bank of Chicago ( email )

230 South LaSalle Street
Chicago, IL 60604
United States

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