Inference via kernel smoothing of bootstrap P values

Jeffrey Scott Racine () and James MacKinnon ()

No 1054, Working Papers from Queen's University, Department of Economics

Abstract: 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.

Keywords: resampling; Monte Carlo test; bootstrap test; percentiles; kernel; smoothing (search for similar items in EconPapers)
JEL-codes: C12 C14 C15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2006-03
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Journal Article: Inference via kernel smoothing of bootstrap P values (2007)
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Persistent link: http://EconPapers.repec.org/RePEc:qed:wpaper:1054

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