Inference via kernel smoothing of bootstrap P values
Resampling methods such as the bootstrap are routinely used to estimate the finite-sample null distributions of a range of test statistics. We present a simple and tractable way to perform classical hypothesis tests based upon a kernel estimate of the CDF of the bootstrap statistics. This approach has a number of appealing features: i) it can perform well when the number of bootstraps is extremely small, ii) it is approximately exact, and iii) it can yield substantial power gains relative to the conventional approach. The proposed approach is likely to be useful when the statistic being bootstrapped is computationally expensive.
|Date of creation:||Mar 2006|
|Date of revision:|
|Contact details of provider:|| Postal: Kingston, Ontario, K7L 3N6|
Phone: (613) 533-2250
Fax: (613) 533-6668
Web page: http://qed.econ.queensu.ca/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jeff Racine & James G. MacKinnon, 2004. "Simulation-based Tests that Can Use Any Number of Simulations," Working Papers 1027, Queen's University, Department of Economics.
When requesting a correction, please mention this item's handle: RePEc:qed:wpaper:1054. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Babcock)
If references are entirely missing, you can add them using this form.