estimating the memory parameter can avoid bias incurred by misspecification of the short memory component. We introduce a broad … to a smooth interpolation of asymptotic variances. The bias of these two estimates also differs to higher order, and we … also show how bias, and asymptotic mean square error, can be reduced, across the class of estimates studied, by means of a …

show that a bootstrap distribution achieves a valid Edgeworth correction in case of density-weighted averaged derivative … estimates of semiparametric index models. Approaches to bias-reduction are discussed. We also develop a higher order expansion …, to show that the bootstrap achieves a further reduction in size distortion in case of two-sided testing. The finite …

data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/Bootstrap …

Carlo simulations, these tests, and bootstrap tests, generally significantly outperform χ2-based tests. …

selecting the two bandwidths for either estimator. We also develop a new bootstrap test for the symmetry of conditional density …

time–series of which we are interested in obtaining a biologically meaningful grouping. Here, we propose a bootstrap … that for finite sample size, bootstrap provides a better approximation than classical asymptotic theory.We then apply the …

asymptotic distribution that is free of nuisance parameters. Secondly, we propose a bootstrap analogue of the transformation and …

This paper studies robustness of bootstrap inference methods for instrumental variable (IV)regression models. We …) estimator introduced by Cížek (2008, 2009),and compare the pairs and implied probability bootstrap approximations for these … statistics byapplying the finite sample breakdown point theory. In particular, we study limiting behaviors ofthe bootstrap …