Improving Coverage Accuracy of Block Bootstrap Confidence Intervals

TL;DR

This paper introduces two novel methods to enhance the coverage accuracy of block bootstrap confidence intervals for dependent data, reducing the need for complex Studentization and improving practical applicability.

Contribution

It proposes two approaches—iterative calibration and direct Studentization—that achieve high-order accuracy with simple tuning, applicable under broad conditions.

Findings

Both methods improve coverage accuracy in simulations.

The approaches require only nested block bootstrap resampling.

Empirical results support theoretical high-order accuracy.

Abstract

The block bootstrap confidence interval based on dependent data can outperform the computationally more convenient normal approximation only with non-trivial Studentization which, in the case of complicated statistics, calls for highly specialist treatment. We propose two different approaches to improving the accuracy of the block bootstrap confidence interval under very general conditions. The first calibrates the coverage level by iterating the block bootstrap. The second calculates Studentizing factors directly from block bootstrap series and requires no non-trivial analytic treatment. Both approaches involve two nested levels of block bootstrap resampling and yield high-order accuracy with simple tuning of block lengths at the two resampling levels. A simulation study is reported to provide empirical support for our theory.