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 …

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

one input and one output, and previous bootstrap methods proposed for inference have not been proven consistent, making … result is then used to prove that two different bootstrap procedures (one based on sub-sampling, the other based on smoothing …) provide consistent inference. The smooth bootstrap requires smoothing the irregularly-bounded density of inputs and outputs as …

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 …

properties of several bootstrap-based inference procedures associated with a kernel-based estimator of density-weighted average … derivatives proposed by Powell, Stock, and Stoker (1989). In many cases, the validity of bootstrap-based inference procedures is … exception to this rule occurs for inference procedures involving a studentized estimator that employs a 'robust' variance …

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 …