Bootstrap confidence bands for shrinkage estimators

Abstract

Empirical application of shrinkage estimators has been limited by the inability to estimate confidence intervals. We investigate confidence band estimators based on Edgeworth expansions and bootstrapping. Our Monte Carlo results suggest that both Efron's bias correction method with acceleration and the simple percentile bootstrap methods generate reasonable confidence bands. Approximations based on Edgeworth expansions performed poorly. We then use the percentile method with a single bootstrap to generate bands for predictions of GNP growth rates from a leading-indicators model. Our study shows that simple percentile bootstrap confidence bands perform well enough to support empirical applications of shrinkage estimators.