Bootstrap schemes for time series (in Russian)

Abstract

We review and compare block, sieve and local bootstraps for time series and thereby illuminate theoretical aspects of the procedures as well as their performance on finite-sample data. Our view is selective with the intention of providing a new and fair picture of some particular aspects of bootstrapping time series. The generality of the block bootstrap is contrasted with the sieve bootstrap. We discuss implementational advantages and disadvantages, and argue that the sieve often outperforms the block method. Local bootstraps, designed for nonparametric smoothing problems, are easy to use and implement but exhibit in some cases low performance.