The Block-Block Bootstrap: Improved Asymptotic Refinements

Abstract

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join-point problem.

In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join-point features that are similar to those of the block bootstrap versions of these statistics. We refer to the application of the block bootstrap to block statistics as the block-block bootstrap. The asymptotic refinements of the block-block bootstrap are shown to be greater than those obtained with the block bootstrap and close to those obtained with the nonparametric iid bootstrap and parametric bootstrap.

Keywords: Asymptotics, Block Bootstrap, Block Statistics, Edgeworth Expansion, Extremum Estimator, Generalized Method of Moments Estimator, Maximum Likelihood Estimator, t Statistic, Test of Over-identifying Restrictions

JEL Classification: C12, C13, C15