In forecasting, bootstrapping typically refers to judgmental bootstrapping. Bootstrapping is also a term used by statisticians to describe estimation methods that reuse a sample of data. It calls for taking random samples from the data with replacement, such that the resampled data have similar properties to the original sample. Applying these ideas to time-series data is difficult because of the natural ordering of the data. Statistical bootstrapping methods are computationally intensive and are used when theoretical results are not available. To date, statistical bootstrapping has been of little use to forecasters, although it might help in assessing prediction intervals for cross-sectional data.