st: Re: Bootstrapping to get Standard Errors for Regression Discontinuity Estimators

[nshephard <nshephard@gmail.com>]

Jen Zhen wrote:
> 
> Dear listers,
> 
> When bootstrapping Austin Nichol's rd command:
> 
> bs, reps(100): rd outcome assignment, mbw(100) ,
> 
> I find that often the resulting P value tells me the estimate is not
> statistically significant at the conventional levels, even when visual
> inspection and more basic methods like simple OLS regressions on a
> treatment dummy, assignment and assignment squared suggest huge
> statistical significance.
> 
> That makes me wonder whether possibly this boot-strapping method might
> somehow understate the true statistical significance of the effect in
> question? Or can and should I fully trust these results and conclude
> that the estimate is not statistically significant at the conventional
> levels?"

What do you mean by "conventional levels [of significance]"?

You should set your threshold for declaring statistical significance in the
context of your study.  Using p < 0.05 to declare something statistically
significant is often inappropriate.

Often of greater interest is an estimate of the effect size (and associated
CI's), what do these tell you?

see e.g. Gardner & Altman (1986)
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1339793/pdf/bmjcred00225-0036.pdf 


Try more replications for your bootstrapping too, 100 isn't that many
really, try at least 1000.

Neil
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