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 …

method to obtain a uniform confidence band and show that the bootstrap can be used to obtain the required critical values …

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

functionals of kernel-type estimators (1 < p < É) 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 …

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 …

methods for selecting bandwidths that under- or oversmooth. This paper extends the bootstrap method of Hall and Horowitz (2013 …) for conditional mean functions to conditional quantile functions. The paper also shows how the bootstrap method can be … used to obtain uniform confidence bands. The bootstrap method uses only bandwidths that are selected by standard methods …

estimation, especially, when researchers use bootstrap to estimate standard errors, which may be wrong without a global estimator …

the distribution of a suitable estimator theta_n, its bootstrap approximation, and the Bayesian posterior for theta all … distribution of g(theta) and the bootstrap distribution of theta_n coincide asymptotically. One implication is that Bayesians can … interpret bootstrap inference for g(theta) as approximately valid posterior inference in a large sample. Another implication …