We introduce two new variance estimation procedures by using non-overlapping and overlapping blocks, respectively. The non-overlapping block (NOB) estimator can be viewed as the limit of the thinned block bootstrap (TBB) estimator recently proposed in Guan and Loh (2007), by letting the number of thinned processes and bootstrap samples therein both increase to infinity. Compared to the latter, ...

In this paper a new block bootstrap method for periodic times series called Generalized Seasonal Tapered Block Bootstrap (GSTBB) is introduced. Consistency of the GSTBB for parameters associated with periodically correlated time series is shown; these are the overall mean, seasonal means and Fourier coefficients of the autocovariance function. Consequently, the construction of bootstrap pointwi...

The block bootstrap for time series consists in randomly resampling blocks of consecutive values of the given data and aligning these blocks into a bootstrap sample The matched block bootstrap Carlstein et al samples blocks dependently attempting to follow each block with one that might realistically follow it in the underlying process to better match the dependence structure of the data Blocks...

The situation where the available data arise from a general linear process with a unit root is discussed. We propose a modi cation of the Block Bootstrap which generates replicates of the original data and which correctly imitates the unit root behavior and the weak dependence structure of the observed series. Validity of the proposed method for estimating the unit root distribution is shown. R...

This paper considers the issue of bootstrap resampling in panel datasets. The availability of datasets with large temporal and cross sectional dimensions suggests the possibility of new resampling schemes. We suggest one possibility which has not been widely explored in the literature. It amounts to constructing bootstrap samples by resampling whole cross sectional units with replacement. In ca...

SUMMARY. The block bootstrap for time series consists in randomly resampling blocks of consecutive values of the given data and aligning these blocks into a bootstrap sample. Here we suggest improving the performance of this method by aligning with higher likelihood those blocks which match at their ends. This is achieved by resampling the blocks according to a Markov chain whose transitions de...