@article{Davidson:273505,
      recid = {273505},
      author = {Davidson, Russell and MacKinnon, James},
      title = {The Power of Bootstrap and Asymptotic Tests},
      address = {2004-07},
      number = {2110-2018-4154},
      series = {Working Paper No.1035},
      pages = {24},
      year = {2004},
      abstract = {We introduce the concept of the bootstrap discrepancy,  which measures the difference in rejection probabilities  between a bootstrap test based on a given test statistic  and that of a (usually infeasible) test based on the true  distribution of the statistic. We show that the bootstrap  discrepancy is of the same order of magnitude under the  null hypothesis and under non-null processes described by a  Pitman drift. However, complications arise in the  measurement of power. If the test statistic is not an exact  pivot, critical values depend on which data-generating  process (DGP) is used to determine the distribution under  the null hypothesis. We propose as the proper choice the  DGP which minimizes the bootstrap discrepancy. We also show  that, under an asymptotic independence condition, the power  of both bootstrap and asymptotic tests can be estimated  cheaply by simulation. The theory of the paper and the  proposed simulation method are illustrated by Monte Carlo  experiments using the logit model. This research was  supported, in part, by grants from the Social Sciences and  Humanities Research Council of Canada. We are grateful to  Don Andrews, Joel Horowitz, two referees, and numerous  seminar participants for comments on earlier versions.},
      url = {http://ageconsearch.umn.edu/record/273505},
      doi = {https://doi.org/10.22004/ag.econ.273505},
}