This paper investigates the research question of whether principle parsimony carries over into interval forecasting, and proposes new semiparametric prediction intervals that apply block bootstrap to first-order autoregression. The AR(1) model is parsimonious in which error term may be serially correlated. Then, utilized resample blocks consecutive observations account for serial correlation. M...

A new resampling procedure, the continuous-path block bootstrap, is proposed in the context of testing for integrated (unit root) time series. The continuous-path block bootstrap (CBB) is a nonparametric procedure that successfully generates unit root integrated pseudo time series retaining the important characteristics of the data, e.g., the dependence structure of the stationary process drivi...

This review of bootstrap methods for time series is most welcome especially coming from two key figures in the development of thesemethods.Wewould 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 example is the s...

Abstract. In this paper we show that the linear process bootstrap (LPB) and the autoregressive sieve bootstrap (AR sieve) fail in general for statistics whose large-sample distribution depends on higher order features of the dependence structure rather than just on autocovariances. We discuss why this is still the case under linearity if it does not come along with causality and invertibility w...

The chapter gives a review of the literature on bootstrap methods for time series data. It describes various possibilities on how the bootstrap method, initially introduced for independent random variables, can be extended to a wide range of dependent variables in discrete time, including parametric or nonparametric time series models, autoregressive and Markov processes, long range dependent t...

The chapter gives a review of the literature on bootstrap methods for time series data. It describes various possibilities on how the bootstrap method, initially introduced for independent random variables, can be extended to a wide range of dependent variables in discrete time, including parametric or nonparametric time series models, autoregressive and Markov processes, long range dependent t...