In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the … possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the … sequence of bootstrap P values obtained by iterating the bootstrap. The main idea of the paper is that, if this sequence …

bootstrap algorithm to implement the proposed test and show its asymptotic validity. The proposed test procedure can apply to …

This article generalizes and extends the kernel block bootstrap (KBB) method of Parente and Smith (2018, 2021) to … moment conditions. KBB procedures that employ bootstrap distributions with generalised empirical likelihood implied … bootstrap distributions in the extant literature. Simulation experiments reveal that critical values arising from the empirical …

functionals of kernel-type estimators (1 < p < ∞) and is easy to implement in general, mainly due to its recourse to the bootstrap … method. The bootstrap procedure is based on nonparametric bootstrap applied to kernel-based test statistics, with estimated … "contact sets". We provide regularity conditions under which the bootstrap test is asymptotically valid uniformly over a large …

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of …" bootstrap. We investigate the first order asymptotic properties of the kernel block bootstrap method for quasi …-maximum likelihood demonstrating, in particular, its consistency and the first-order asymptotic validity of the bootstrap approximation …

(g) can be carried out by using by using the bootstrap. An empirical application illustrates the usefulness of shape …

using the bootstrap. An empirical application illustrates the usefulness of shape restrictions for carrying out …

This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in …