Re: st: RD and binary outcomes

[D-Ta <altruist81@gmx.de>]

I am totally on your side. I just used the flexible parametric model as 
a comparison. And, as mentioned, the standard errors of the -rd- 
estimator are oftentimes even smaller when the outcome is continous. 
Also, the point estimates are largely in line.
What I do not understand, however, is why -rd- point estimates differ 
considerable from the parametric counterparts and standard errors are so 
much larger when the outcome is binary.

This is not about publications (at least not at this stage), I just want 
to understand the properties of the -rd- procedure when the outcome of 
interest is binary. Is -rd- adequate in such (binary) settings or 
shouldnt one rather use a local logit version of -rd- ?

Darjusch

Am 18.10.2011 17:23, schrieb Austin Nichols:
> D-Ta<altruist81@gmx.de>:
> It is true that -rd- uses only linear models, but this is by design.
> I have seen many papers using a parametric model, but it is not always
> clear that the parametric model is justified.  The strength of the RD
> design is that it relies on so few assumptions; using a parametric
> model adds many more assumptions, and it is not always clear what they
> are.  Think about what IV model you would run, using assignment to
> treatment (one side or the other of the cutoff) as an instrument for
> treatment received (for a linear model this is easy, otherwise not so
> much); how would you weight (i.e. what kernel and bandwidth choice
> would you make)?  I would want some simulation evidence for any
> application of a parametric model to an RD design, using data built to
> look like the problem at hand, before I trusted the results.  Smaller
> standard errors are often desirable not because they are closer to the
> truth, but because they lead to higher publication probabilities!  In
> the case of RD, the large standard errors often reflect true
> uncertainty or variability of estimates, not simply the sample loss
> that accompanies focusing on the discontinuity.  But I am sympathetic;
> looking only near the discontinuity increases internal validity at the
> cost of sample and leads many apparently strong effects to look
> statistically insignificant.  Start with a good simulation, and make
> sure you put in some wiggles in the conditional mean functions (under
> treatment and no treatment).
>
> On Tue, Oct 18, 2011 at 9:41 AM, D-Ta<altruist81@gmx.de>  wrote:
> > Dear Stata-Users,
> >
> > I am currently working with the RD command provided by  Austin Nichols. I
> > investigate continous as well as binary outcome variables. I use cubic and
> > quartic parametric models as benchmarks to compare the RD results against.
> >
> > In the case of continous variables the RD command does fine (i.e. very close
> > to the parametric model with even smaller standard errors). However, when
> > looking at binary outcomes, the RD procedure produces much wider standard
> > errors and the point estimates differ a lot from the parametric (probit)
> > specifications.
> >
> > I assume that this is due to the underlying local linear model used by RD.
> > (Perhaps it would be better to use a local logit(?))
> >
> > Am I right that -in its current format- the RD command is not the right tool
> > to use in case of binary outcome variables?
> >
> > Many thanks
> >
> > Darjusch
>
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