In this paper we introduce a bootstrap procedure to test parameter restrictions in vector autoregressive models which … is robust in cases of conditionally heteroskedastic error terms. The adopted wild bootstrap method does not require any … Monte Carlo investigation empirical size and power properties of the new method are illustrated. We compare the bootstrap …

bootstrap for estimating the variance of the index estimator and a variant of bagging for numerically stabilizing its variance …

observations. Accordingly, we propose an i.i.d.-type bootstrap to determine the critical value for the test. …

conditional heteroskedasticity. We show that the wild bootstrap provides convenient critical values for the considered OLS …-based statistics under both homoskedastic and conditionally heteroskedastic model errors. The wild bootstrap is easy to implement and … approximations. We prove further that the wild bootstrap retains its validity for inference within a system of pooled equations …

asymptotic distribution is derived and shown to be approximated by a bootstrap procedure. …

different methods of estimations are used. The first method is the SIMEX algorithm which attempts to estimate the bias, and …

asymptotically for the bootstrap to be valid. However, certain model-based bootstrap methods remain valid for some interesting … bootstrap schemes remain valid for supremum-type functionals as long as they mimic the corresponding finite-dimensional joint … distributions consistently. As an example, we investigate a finite order Markov chain bootstrap in the context of a general …

A bootstrap methodology for the periodogram of a stationary process is proposed which is based on a combination of a … time domain parametric and a frequency domain nonparametric bootstrap. The parametric fit is used to generate periodogram … theory developed shows validity of the proposed bootstrap procedure for a large class of periodogram statistics. For …