tagged · 2 articles

bootstrap

2026

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 every residual exactly where it is. Just flip its sign at random.
Jul 6, 2026
2026

Your bootstrap is lying to you
The ordinary bootstrap assumes your observations are independent. Feed it a time series and it quietly shuffles away the autocorrelation, then hands you a confidence interval too narrow to be true: a nominal 90% interval that covers the truth about half the time. Here is the failure on real numbers, and the block bootstrap that keeps the dependence and tells you most of the truth.
Jul 6, 2026