ratio and the variance). Unfortunately, many of the inference procedures that have been suggested previously in the applied … tails and time dependence in the return data. In particular, we will promote a studentized time series bootstrap procedure …

We introduce a new, factor based bootstrap approach which is robust under heteroskedastic error terms for inference in … functional coefficient models. Modeling the functional coefficient parametrically, the bootstrap approximation of an F statistic …, factor based bootstrap inference outperforms the wild bootstrap and pairs bootstrap approach according to its size features …

the test statistics or using bootstrap methods to obtain critical values. Our simulation results indicate that our tests …

coverage for small sample sizes, a simple bootstrap procedure is designed based on the leading term of the Bahadur … demonstrated that the bootstrap procedure considerably outperforms the asymptotic bands in terms of coverage accuracy. Finally, the … bootstrap confidence corridors are used to study the efficacy of the National Supported Work Demonstration, which is a …

functionals of kernel-type estimators (1 < p < ∞) and is easy to implement in general, mainly due to its recourse to the bootstrap … method. The bootstrap procedure is based on nonparametric bootstrap applied to kernel-based test statistics, with estimated … "contact sets". We provide regularity conditions under which the bootstrap test is asymptotically valid uniformly over a large …

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 …

different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and …

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of …" bootstrap. We investigate the first order asymptotic properties of the kernel block bootstrap method for quasi …-maximum likelihood demonstrating, in particular, its consistency and the first-order asymptotic validity of the bootstrap approximation …