In this paper, we propose a model-free bootstrap method for the empirical process under absolute regularity. More precisely, consistency of an adapted version of the so-called dependent wild bootstrap, that was introduced by Shao (2010) and is very easy to implement, is proved under minimal...

In this paper we investigate bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of the co-integration rank tests of Johansen (1996). In order to do so we adapt the framework of Kilian (1998) which estimates...

The concept of the autoregressive (AR) sieve bootstrap is investigated for the case of spatial processes in Z2. This procedure fits AR models of increasing order to the given data and, via resampling of the residuals, generates bootstrap replicates of the sample. The paper explores the range of...