Model-free Bootstrap for a General Class of Stationary Time Series
Statistics Theory
2020-01-01 v1 Statistics Theory
Abstract
A model-free bootstrap procedure for a general class of stationary time series is introduced. The theoretical framework is established, showing asymptotic validity of bootstrap confidence intervals for many statistics of interest. In addition, asymptotic validity of one-step ahead bootstrap prediction intervals is also demonstrated. Finite-sample experiments are conducted to empirically confirm the performance of the new method, and to compare with popular methods such as the block bootstrap and the autoregressive (AR)-sieve bootstrap.
Keywords
Cite
@article{arxiv.1912.13185,
title = {Model-free Bootstrap for a General Class of Stationary Time Series},
author = {Yiren Wang and Dimitris N. Politis},
journal= {arXiv preprint arXiv:1912.13185},
year = {2020}
}