The bootstrap is a method for estimating the distribution of an estimator or test statistic by
resampling one's data or a model estimated from the data. The methods that are available for
implementing the bootstrap and the accuracy of bootstrap estimates depend on whether the data are an
independent random sample or a time series. This paper is concerned with the application of the bootstrap
to time-series data when one does not have a finite-dimensional parametric model that reduces the data
generation process to independent random sampling. We review the methods that have been proposed for
implementing the bootstrap in this situation and discuss the accuracy of these methods relative to that of
first-order asymptotic approximations. We argue that methods for implementing the bootstrap with time-series
data are not as well understood as methods for data that are independent random samples. Although
promising bootstrap methods for time series are available, there is a considerable need for further research
in the application of the bootstrap to time series. We describe some of the important unsolved problems.