  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.
