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 …

powerful to detect changes in the conditional model parameters, whether or not the variance of the regression error is included …

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 …

requirements that are not always obvious to the non-expert user. Bootstrap DEA is a significant development of the past decade …, which could be extended to test almost any hypothesis in bootstrap DEA. Moreover, it enhances the intuition behind bootstrap …

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

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

Applied researchers often test for the difference of the variance of two investment strategies;in particular, when the … investment strategies under consideration aim to implementthe global minimum variance portfolio. A popular tool to this end is … particular, we suggest to construct a studentizedtime series bootstrap confidence interval for the ratio of the two variances and …