When your errors aren’t equal

The residual bootstrap assumes your model’s errors all come from the same distribution. Feed it a series whose noise grows over time and it quietly shuffles the loud errors in among the quiet ones, averaging the variance away, and hands back a standard error at three-quarters of the truth. Here is the failure on real numbers, and the wild bootstrap: a fix so simple it sounds like a joke. Keep…