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Re: st: Modeling proportion with endogenous treatment indicator


From   Guy Grossman <guygrossman1@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Modeling proportion with endogenous treatment indicator
Date   Sat, 11 Sep 2010 09:53:48 -0400

Thanks for the recommendation. I just took a look. the beta
coefficient from ssm and cmp are very different for the dnogenous
treatment indicator (about half for cmp).



On Sat, Sep 11, 2010 at 9:36 AM, Martin Weiss <martin.weiss1@gmx.de> wrote:
>
> <>
>
>
> Have you looked at
>
> *************
> ssc d cmp
> *************
>
> ?
>
> HTH
> Martin
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Guy Grossman
> Sent: Samstag, 11. September 2010 15:24
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Modeling proportion with endogenous treatment indicator
>
> Thanks - this is helpful!
>
> May I follow up with another question - how will the response change
> when Yij is binary? treatreg and iverg2 assume continuous dependent
> variable and ivprobit assumes continuous endogenous regressor - so
> none would be appropriate.
>
> By contrast ssm (wrapper for gllamm which is a user-written program)
> can fit an IV model with endogenous binary treatment and binary
> response. however it does not seem to allow clustering SEs at the
> group level.
>
> Advice would be highly appreciated!
> regards,
> Guy
>
> On Fri, Sep 10, 2010 at 5:33 PM, Austin Nichols <austinnichols@gmail.com>
> wrote:
>> Guy Grossman <guygrossman1@gmail.com>:
>> I would start with -ivreg2- (on SSC) and use robust SEs (or
>> cluster-robust); the
>> coefficients will certainly be easy to interpret as dp/dX.  But see
>> http://www.stata.com/meeting/snasug08/abstracts.html#wooldridge
>> for panel models with fractional outcomes and instruments.
>>
>> On Fri, Sep 10, 2010 at 5:25 PM, Guy Grossman <guygrossman1@gmail.com>
> wrote:
>>> Dear Stata list -
>>>
>>> I am using Stata 10.1 on Mac and am seeking advice about the best way
>>> to fit the following model:
>>>
>>> Yij = bo + b1 * Tj  + b2 * Ci + b3 * Cj + eij + ej
>>>
>>> Tj = v0 + v1 * Zj + epsilon j, where...
>>>
>>>
>>> Yij = the dependent variable is a proportion (0<Yij <1), for person i
>>> from group j.
>>>
>>> Zj = encouragement to take up one of two types of treatment - applied
>>> at the group level j . Zj is binary: groups are either encouraged to
>>> take up Zj=0 or Zj=1.
>>>
>>> Tj = treatment take up - takes place at the group level j. Tj is
>>> binary (Tj=0 or Tj =1). Take up rates are about 80% for both
>>> treatments.
>>>
>>> Ci = control variables at the individual level i
>>>
>>> Cj = control variables at the group level j
>>>
>>> The idea is to apply an encouragement research design, using Zj as an
>>> instrumental variable for Tj.
>>>
>>> Given the nature of the dependent variable (proportion), my question
>>> is how is it best to fit the model in Stata. In the past I have used
>>> the glm command with link(logic) and family (bin) to fit a model with
>>> a dependent variable that was a proportion, but the independent
>>> variables were all exogenous. Is there a way to fit a glm with an
>>> endogenous independent variable? Is it possible to use treatreg with
>>> DV which is a proportion?
>>>
>>> I look forward for your astute recommendations.
>>> Thanks!
>>> Guy
>>
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>>
>
>
>
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>



-- 
Guy Grossman
Department of Political Science
Columbia University

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