In recent years, many exciting developments have taken place in bootstrapping time series.
Advances have proceeded along a number of distinct paths. Some authors have focused on …

This paper surveys recent development in bootstrap methods and the modifications needed
for their applicability in time series models. The paper discusses some guidelines for …

This article introduces a resampling procedure called the stationary bootstrap as a means of
calculating standard errors of estimators and constructing confidence regions for parameters …

The jackknife and bootstrap are the most popular data-resampling meth ods used in
statistical analysis. The resampling methods replace theoreti cal derivations required in …

In this chapter, a general theory for the construction of confidence intervals or regions is
presented. Much of what is presented is extracted from Politis and Romano (1992c, 1994b) …

In econometric applications, often several hypothesis tests are carried out at once. The
problem then becomes how to decide which hypotheses to reject, accounting for the …

This is a book on bootstrap and related resampling methods for temporal and spatial data
exhibiting various forms of dependence. Like the resam pling methods for independent data …

The bootstrap is a method for estimating the distribution of an estimator or test statistic by
resampling one's data or a model estimated from the data. Under conditions that hold in a …

We address the issue of optimal block choice in applications of the block bootstrap to
dependent data. It is shown that optimal block size depends significantly on context, being …

Fixed effects estimators of panel models can be severely biased because of the well‐known
incidental parameters problem. We show that this bias can be reduced by using a panel …