Many simulation experiments have shown that, in a variety of circumstances, bootstrap tests perform better than current … asymptotic theory predicts. Specifically, the discrepancy between the actual rejection probability of a bootstrap test under the … which bootstrap procedures can be improved so as to yield more accurate inference. …

the others are based on inverting t statistics or the bootstrap P values associated with them. We propose a new method for … constructing bootstrap confidence sets based on t statistics. In large samples, the procedures that generally work best are CLR … confidence sets using asymptotic critical values and bootstrap confidence sets based on LIML estimates. …

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 …

bootstrapping the three non-exact test statistics and also a new conditional bootstrap version of the LR test. These use more … procedures is used, both the K and conditional bootstrap LR tests have excellent performance under the null. However, power …

the driving stationary series. The situation is analysed from the point of view of bootstrap testing, and an exact … quantitative account is given of the error in rejection probability of a bootstrap test. A particular method of estimating the MA … parameter is recommended, as it leads to very little distortion even when the MA parameter is close to -1. A new bootstrap …

underlying distribution is heavy. Bootstrap methods are presented which alleviate this problem except in cases in which the …

can be either asymptotic or bootstrap-based. In principle, the bootstrap is an ideal tool, since in this paper I ignore … issues of complex sampling schemes, and suppose that observations are IID. However both bootstrap inference, and, to a …

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 …