= Wild bootstrap
{title2=$y_i^*=x_i^\top\widehat\beta+\widehat e_iW_i$}
For <independent> mean-zero errors in a fixed-design regression, form $y_i^*=x_i^\top\widehat\beta+\widehat e_i W_i$, where the <independent> multipliers have <mean> zero and <variance> one, and refit. Rademacher multipliers taking the values $-1$ and $1$ with equal <probability> are a simple choice. Conditional on the data, the <bootstrap> errors have <mean> zero and preserve observation-specific residual <variances>, supporting <heteroscedasticity> rather than imposing a common residual distribution. Appropriate leverage corrections and regularity assumptions are required for accurate studentized inference.
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