This research is dedicated to the study of periodic characteristics of periodically correlated time series such as seasonal means, seasonal variances and autocovariance functions. Two bootstrap methods are used: the extension of the usual Moving Block Bootstrap (EMBB) and the Generalized Seasonal Block Bootstrap (GSBB). The first approach is proposed, because the usual MBB does not preserve the...

A functional law is given for an I(1) sample data version of the continuous-path block bootstrap of Paparoditis and Politis (2001a). The results provide an alternative demonstration that continuous-path block bootstrap unit root tests are consistent under the null. © 2010 Elsevier B.V. All rights reserved.

We compare three methods of constructing confidence intervals for sample autocorrelations of squared returns modeled by models from the GARCH family. We compare the residual bootstrap, block bootstrap and subsampling methods. The residual bootstrap based on the standard GARCH(1,1) model is seen to perform best.

BACKGROUND Statistical inference of signals is key to understand fundamental processes in the neurosciences. It is essential to distinguish true from random effects. To this end, statistical concepts of confidence intervals, significance levels and hypothesis tests are employed. Bootstrap-based approaches complement the analytical approaches, replacing the latter whenever these are not possible...

May 24, 2011 This review of bootstrap methods for time series is most welcome especially coming from two key figures in the development of these methods. We would like to complement their exposition by focusing on some further issues of current interest. Block bootstrap methods for time series data have been most intensively studied under the assumption of stationarity and mixing. An important ...

This paper proposes a block bootstrap method for measuring mortality risk under the Lee-Carter model framework. In order to take account of all sources of risk the process risk, the parameter risk, and the model risk properly, a block bootstrap is needed to cope with the spatial dependence found in the residuals. As a result, the prediction intervals we obtain for life expectancy are more accur...