Inference via kernel smoothing of bootstrap P values
AbstractResampling 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 51 (2007)
Issue (Month): 12 (August)
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Web page: http://www.elsevier.com/locate/csda
Other versions of this item:
- Jeff Racine & James G. MacKinnon, 2006. "Inference via kernel smoothing of bootstrap P values," Working Papers 1054, Queen's University, Department of Economics.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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.
- Lechner, Michael & Downward, Paul, 2013.
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