  We study an AMOC time series model with an abrupt change in the mean and
dependent errors that fulfill certain mixing conditions. We obtain confidence
intervals for the unknown change-point via bootstrapping methods.
  Precisely we use a block bootstrap of the estimated centered error sequence.
Then we reconstruct a sequence with a change in the mean using the same
estimators as before. The difference between the change-point estimator of the
resampled sequence and the one for the original sequence can be use as an
approximation of the difference between the real change-point and its
estimator. This enables us to construct confidence intervals using the
empirical distribution of the resampled time series.
  A simulation study shows that the resampled confidence intervals are usually
closer to their target levels and at the same time smaller than the asymptotic
intervals.
