distribution depend on unknown nuisance parameters. A bootstrap method is used to obtain more reliable inference. The regularized …

theta_n, its bootstrap approximation, and the Bayesian posterior for all agree asymptotically. It is shown that whenever g … is Lipschitz, though not necessarily differentiable, the posterior distribution of g(theta) and the bootstrap … distribution of g(theta_n) coincide asymptotically. One implication is that Bayesians can interpret bootstrap inference for g …

In this paper we investigate bootstrap-based methods for bias-correcting the first-stage parameter estimates used in … some recently developed bootstrap implementations of the co-integration rank tests of Johansen (1996). In order to do so we … the corresponding parameter esti- mates taken across a large number of auxiliary bootstrap replications. A number of …

manner, we propose a model-based (semiparametric)bootstrap method to approximate critical values of the test and verify its …

shown that a bootstrap approximation to the sampling distribution of the weighted least squares estimate is valid, which …-sample properties of this new estimator as well as the improvements in performance realized by bootstrap confidence intervals. …

our approach bootstrap fails in practice and theory. Instead, we propose a subsampling procedure with automatic parameter …