Presently, conditions ensuring the validity of bootstrap methods for the sample mean of (possibly heterogeneous) near epoch dependent (NED) functions of mixing processes are unknown. Here we establish the validity of the bootstrap in this context, extending the applicability of bootstrap methods to a class of processes broadly relevant for applications in economics and Þnance. Our results apply...

the statistical analysis of spatial data is usually done under gaussian assumption for the underlying random field model. when this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. one of the crucial problems in this setting is specifying the block sizes. in this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...

The chapter gives a review of the literature on bootstrap methods for time series data. It describes various possibilities on how the bootstrap method, initially introduced for independent random variables, can be extended to a wide range of dependent variables in discrete time, including parametric or nonparametric time series models, autoregressive and Markov processes, long range dependent t...

In this paper we investigate the applicability of reliable and fast bootstrap methods on two non-smoothing, consistent specification tests (Bierens, 1982; Escanciano, 2006). Through Monte Carlo experiments we compare the performance of these tests under the null when the null distribution is simulated by the bootstrap, the double bootstrap (Beran, 1988) and the fast double bootstrap (FDB, David...

Abstract: The article proposes a computationally efficient procedure for bias adjustment in the iterated bootstrap. The new technique replaces the need for successive levels of bootstrap resampling by proposing an approximation for the double bootstrap “calibrating coefficient” using only one draw from the second level probability distribution. Extensive Monte Carlo evidence suggest that the pr...

This paper investigates the effects of smoothed bootstrap iterations on coverage probabilities of smoothed bootstrap and bootstrap-t confidence intervals for population quantiles, and establishes the optimal kernel bandwidths at various stages of the smoothing procedures. The conventional smoothed bootstrap and bootstrap-t methods have been known to yield one-sided coverage errors of orders O(n...

Ever since its introduction, the bootstrap has provided both a powerful set of solutions for practical statisticians, and a rich source of theoretical and methodological problems for statistics. In this article, some recent developments in bootstrap methodology are reviewed and discussed. After a brief introduction to the bootstrap, we consider the following topics at varying levels of detail: ...

Two procedures are proposed for estimating the rejection probabilities of bootstrap tests in Monte Carlo experiments without actually computing a bootstrap test for each replication. These procedures are only about twice as expensive (per replication) as estimating rejection probabilities for asymptotic tests. Then a new procedure is proposed for computing bootstrap P values that will often be ...

This vignette illustrates the application of variance estimation procedures to indicators on social exclusion and poverty using the R package laeken. To be more precise, it describes a general framework for estimating variance and confidence intervals of indicators under complex sampling designs. Currently, the package is focused on bootstrap approaches. While the naive bootstrap does not modif...