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 …

with Generalised AutoRegressive Conditional Heteroskedasticity are characterised by the ability to estimate and forecast …

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

functionals of kernel-type estimators (1 < p < É) 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 …

tails and time dependence in the return data. In particular, we will promote a studentized time series bootstrap procedure …

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 …