Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189334 
Year of Publication: 
2006
Series/Report no.: 
Queen's Economics Department Working Paper No. 1054
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
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.
Subjects: 
resampling
Monte Carlo test
bootstrap test
percentiles
kernel
smoothing
JEL: 
C12
C14
C15
Document Type: 
Working Paper

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