The Size Problem of Bootstrap Tests when the Null is Non- or Semiparametric

In non- and semiparametric testing, the wild bootstrap is a standard method for determining the critical values of tests. If the null hypothesis is also semi- or nonparametric, then we know that at least asymptotically oversmoothing is necessary in the pre-estimation of the null model for generating the bootstrap samples. See Hardle & Marron (1990, 1991). However, in practice this knowledge is of little help. In this note we highlight that this bandwidth choice problem can become quite serious. As an alternative, we briegly discuss the possibility of subsampling.(1)