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.
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- Russell Davidson & James MacKinnon, 2000.
"Bootstrap tests: how many bootstraps?,"
Taylor & Francis Journals, vol. 19(1), pages 55-68.
- Russell Davidson & James G. MacKinnon, 2001. "Bootstrap Tests: How Many Bootstraps?," Working Papers 1036, Queen's University, Department of Economics.
- 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. Full references (including those not matched with items on IDEAS)