scrambling bootstrap method to give a non-parametric estimate of the variance of our test statistic under the null hypothesis …

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 … simulation study shows that our method is competitive to standard block bootstrap methods in finite samples. …

disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can … dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the … same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and …

In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and … parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods … the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with …

disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can … dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the … same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and …

disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can … dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the … same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and …

bootstrap methods also fail to produce consistent estimators. To overcome these problems we develop percentile–t, subsample … bootstrap approximations to estimator distributions. Studentizing is employed to approximate scale, and the subsample bootstrap …

disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can … dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the … same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and …

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 … simulation study shows that our method is competitive to standard block bootstrap methods in finite samples. …