In general, there will be visible differences between a parametric and a nonparametric curve estimate. It is therefore quite natural to compare these in order to decide whether the parametric model could be justified. An asymptotic quantification is the distribution of the integrated squared difference between these curves. We show that the standard way of bootstrapping this statistic fails. We use and analyse a different form of bootstrapping for this task. We call this method the wild bootstrap and apply it to fitting Engel curves in expenditure data analysis.
"Comparing Nonparametric Versus Parametric Regression Fits." Ann. Statist. 21 (4) 1926 - 1947, December, 1993. https://doi.org/10.1214/aos/1176349403