We propose a generalization of the wild bootstrap of Wu (1986) and Liu (1988) based upon perturbing the scores of M …-estimators. This "score bootstrap" procedure avoids recomputing the estimator in each bootstrap iteration, making it substantially less … costly to compute than the conventional nonparametric bootstrap, particularly in complex nonlinear models. Despite this …

total variation distance between a bootstrap distribution and the normal distribution parameterized by the point estimate … sample of recent empirical articles that use a bootstrap for inference, we find that the conventional normal approximation is …

bootstrap procedure for conducting inference about the conditional pricing errors as well as the shapes of the factor loading …

implement a new testing procedure based on bootstrap methodology. Using this approach, we generate thousands of new exchange …

bootstrap for inference. Because the bounds may carry an index, the approach covers many canonical examples in the partial …

The bootstrap, introduced by Efron (1982), has become a very popular method for estimating variances and constructing … bootstrap procedure that accounts for this uncertainty, and compare its properties to that of the classical bootstrap …

We examine the higher order properties of the wild bootstrap (Wu, 1986) in a linear regression model with stochastic … regressors. We find that the ability of the wild bootstrap to provide a higher order refinement is contingent upon whether the … errors are mean independent of the regressors or merely uncorrelated. In the latter case, the wild bootstrap may fail to …

models simultaneously rather than sequentially, and do not require reestimation of models as part of a bootstrap procedure …