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Re: st: Re: comparing regression discontinuity treatment effects for different subsamples

From   Austin Nichols <[email protected]>
To   [email protected]
Subject   Re: st: Re: comparing regression discontinuity treatment effects for different subsamples
Date   Tue, 12 Oct 2010 16:10:24 -0400

In that case, I strongly disagree with your advice--you are
constraining the slope of pretest to be the same on both sides of the
discontinuity, and not using any concept of a bandwidth around the
cutoff; local linear regression is the standard approach, not linear
regression.  Further, if you mean to subtract the mean of the
assignment variable when you say "pretest is mean-centered" then the
coefficient on group does not measure the jump in outcomes at the
cutoff unless the cutoff happens to be right at the mean of pretest.

On Tue, Oct 12, 2010 at 8:50 AM, John Antonakis <[email protected]> wrote:
> Hi Austin:
> Using the "classical" RDD design, "group" is the treatment indicator;
> pretest is the "cutoff" measure for assignment to group.
> Best,

> On 12.10.2010 03:34, Austin Nichols wrote:
>> John --
>> I don't understand your advice here at all--is group supposed to be a
>> treatment indicator?  Is pretest an assignment variable or a control
>> variable?
>> Prashant --
>> One can of course write a wrapper -program- containing several
>> estimators and -bootstrap- the whole thing, which then allows testing
>> across estimators--the -rd- package on SSC is no exception to that
>> general rule, but make sure you set the bandwidth exogenously if you
>> are using local linear regression as -rd- does.  Also -findit ivqte-
>> for one approach to quantile TE, and note that RD can be seen as a
>> version of IV; see refs cited in -help rd-.
>> On Mon, Oct 11, 2010 at 3:04 AM, John Antonakis<[email protected]>
>>  wrote:
>>> Hi:
>>> You could use -suest-.  For example, suppose you have the following basic
>>> specification (where pretest is mean-centered, to set the intercept to
>>> the
>>> cut-off value):
>>> y = b0 + b1*pretest + b2*group + e
>>> Estimate the model for each group, e.g.,
>>> reg y pretest group if boys==1
>>> est store boys
>>> reg y pretest group if boys==0
>>> est store girls
>>> suest boys girls
>>> Now you can do cross-equation tests, e.g.,
>>> test [boys_mean]group = [girls_mean]group
>>> Hope this helps.
>>> John.

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