normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an … enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap …

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 …

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 …

disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can … dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the … same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and …

normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an … enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap …

The bootstrap is a statistical technique used more and more widely in econometrics. While it is capable of yielding …, if observed, help to obtain the best the bootstrap can offer. Bootstrapping always involves setting up a bootstrap data …-generating process (DGP). The main types of bootstrap DGP in current use are discussed, with examples of their use in econometrics. The …

impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are …

The fast double bootstrap can improve considerably on the single bootstrap when the bootstrapped statistic is … approximately independent of the bootstrap DGP. This is because, among the approximations that underlie the fast double bootstrap … bootstrap counterpart, rather than the joint distribution of the statistic and the full distribution of its bootstrap …