Closed ccepeda10 closed 2 years ago
The nonparametric bootstrap is infeasible when the number of treated units is small -- they simply would not appear in some of the bootstrapped samples.
On Fri, Feb 4, 2022 at 9:44 AM ccepeda10 @.***> wrote:
Hello there, I was wondering about the criteria for preferring one inference method over another (parametric/nonparametric bootstrap or jackknife). The package recommends using parametric bootstraps or jackknife for small samples, but there isn't much guidance for bigger samples. I also read the paper, but I couldn't find much information about this either, except from a brief comment about the validity of parametric bootstrapping under some conditions (which are not specified). I know this is a niche subject, but is there any reference about this matter? Thanks
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-- Yiqing Xu
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Thanks for the answer! What about bigger samples, though? Which one would be preferable? Or it's relatively indifferent?
Inference using the nonparametric bootstraps is unconditional on factors and loadings and thus is preferable in my view. It is also easier to interpret.
On Fri, Feb 4, 2022 at 9:50 AM ccepeda10 @.***> wrote:
Thanks for the answer! What about bigger samples, though? Which one would be preferable? Or it's relatively indifferent?
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-- Yiqing Xu
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Thanks a lot for the help and the prompt answer! I'm going to use nonparametric bootstraps then. Best regards.
Hello there, I was wondering about the criteria for preferring one inference method over another (parametric/nonparametric bootstrap or jackknife). The package recommends using parametric bootstraps or jackknife for small samples, but there isn't much guidance for bigger samples. I also read the paper, but I couldn't find much information about this either, except from a brief comment about the validity of parametric bootstrapping under some conditions (which are not specified). I know this is a niche subject, but is there any reference about this matter? Thanks