We use a subsample bootstrap method to get a consistent estimate of the asymptotically optimal choice of the sample …

between nominal and actual finite sample significance levels. The use of the bootstrap overcomes this problem for general … approaches that lead to asymptotically pivotal test statistics. Power comparisons are made for bootstrap tests and modified …

This paper uses Monte Carlo simulation analysis to study the finite-sample behavior of bootstrap estimators and tests … tailored to handle heteroskedasticity. Our results show that weighted bootstrap methods can be successfully used to estimate … test and of Bartlett and Edgeworth-corrected tests. The bootstrap test was found to be robust against unfavorable …

The main contribution of this paper is a proof of the asymptotic validity of the application of the bootstrap to AR … establishing that a suitably constructed bootstrap estimator will have the same limit distribution as the least-squares estimator … robust standard errors or the bootstrap approximation of the distribution of autoregressive parameters. A simulation study …

confidence intervals are approximated out-of sample using the bootstrap algorithm and the percentile method. The main conclusion …

variance of the process. The latter is derived through bootstrap exercises using the models alone or pooled together. The …, the bootstrap exercises point to confidence bands close to 1 per cent around the estimated value. JEL Classification: C11 …

shows how a Monte Carlo study of a bootstrap test procedure may be done in parallel. Using a cluster of 20 nodes, the …

addition to point forecasts, prediction intervals (calculated by using the recently proposed bootstrap-after-bootstrap) are …