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

-sided. In such problems, power gains can be obtained for bootstrap multiple testing procedures in scenarios where some of the …

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 …

manner, we propose a model-based (semiparametric)bootstrap method to approximate critical values of the test and verify its …

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

rejection regions can be calculated by a simple parametric bootstrap procedure, when the sample size is small. The size and …

-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are …

iteration. We then extend the NPL estimators to develop one-step NPL bootstrap procedures for discrete Markov decision models … and provide some Monte Carlo evidence based on a machine replacement model of Rust (1987). The proposed one-step bootstrap … expansion ; k-step bootstrap ; maximum pseudo-likelihood estimators ; nested fixed point algorithm ; Newton-Raphson method …